%Converted with wcci2008.awk $Revision: 1.00 $ http://bioinformatics.essex.ac.uk/users/wlangdon/rnanet @inproceedings(Chen:2008:ijcnn, author = "S. Chen and X. Hong and C. J. Harris", title = "Sparse Kernel Density Estimator Using Orthogonal Regression Based on D-Optimality Experimental Design", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0003.pdf}, url = {}, size = {}, abstract = {A novel sparse kernel density estimator is derived based on a regression approach, which selects a very small subset of significant kernels by means of the D-optimality experimental design criterion using an orthogonal forward selection procedure. The weights of the resulting sparse kernel model are calculated using the multiplicative nonnegative quadratic programming algorithm. The proposed method is computationally attractive, in comparison with many existing kernel density estimation algorithms. Our numerical results also show that the proposed method compares favourably with other existing methods, in terms of both test accuracy and model sparsity, for constructing kernel density estimates. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Chen2:2008:ijcnn, author = "S. Chen and X. Hong and C. J. Harris", title = "Fully Complex-Valued Radial Basis Function Networks for Orthogonal Least Squares Regression", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0004.pdf}, url = {}, size = {}, abstract = {We consider a fully complex-valued radial basis function (RBF) network for regression application. The locally regularised orthogonal least squares (LROLS) algorithm with the D-optimality experimental design, originally derived for constructing parsimonious real-valued RBF network models, is extended to the fully complex-valued RBF network. Like its real-valued counterpart, the proposed algorithm aims to achieve maximised model robustness and sparsity by combining two effective and complementary approaches. The LROLS algorithm alone is capable of producing a very parsimonious model with excellent generalisation performance while the D-optimality design criterion further enhances the model efficiency and robustness. By specifying an appropriate weighting for the D-optimality cost in the combined model selecting criterion, the entire model construction procedure becomes automatic. An example of identifying a complex-valued nonlinear channel is used to illustrate the regression application of the proposed fully complex-valued RBF network. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Chen3:2008:ijcnn, author = "S. Chen and C. J. Harris and L. Hanzo", title = "Complex-Valued Symmetric Radial Basis Function Classifier for Quadrature Phase Shift Keying Beamforming Systems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0005.pdf}, url = {}, size = {}, abstract = {We propose a complex-valued symmetric radial basis function (CV-SRBF) network for nonlinear beam forming in multiple-antenna aided communication systems that employ the complex-valued quadrature phase shift keying modulation scheme. The proposed CV-SRBF classifier explicitly exploits the inherent symmetry property of the underlying data generating mechanism, and this significantly enhances the detection accuracy. An orthogonal forward selection (OFS) algorithm based on the multi-class (four-class) Fisher ratio of class separability measure (FRCSM) is derived for constructing parsimonious CV-SRBF classifiers from noisy training data. Effectiveness of the proposed approach is illustrated using simulation, and the results obtained demonstrate that the sparse CV-SRBF classifier constructed by the multi-class FRCSM-based OFS achieves excellent beamforming detection bit error rate performance. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Zhang:2008:ijcnn, author = "Yunong Zhang and Zhiguo Tan and Zhi Yang and Xuanjiao Lv and Ke Chen ", title = "A Simplified LVI-Based Primal-Dual Neural Network for Repetitive Motion Planning of PA10 Robot Manipulator Starting from Different Initial States", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0031.pdf}, url = {}, size = {}, abstract = {This paper presents a simplified primal-dual neural network based on linear variational inequalities (LVI) for online repetitive motion planning of PA10 robot manipulator. To do this, a drift-free criterion is exploited in the form of a quadratic function. In addition, the repetitive-motion-planning scheme could incorporate the joint limits and joint velocity limits simultaneously. Such a scheme is finally reformulated as a time-varying quadratic program (QP). As a QP real-time solver, the simplified LVI-based primal-dual neural network (LVI-PDNN) is designed based on the QP-LVI conversion and Karush-Kuhn-Tucker (KKT) conditions. It has a simple piecewise-linear dynamics and could globally exponentially converge to the optimal solution of strictly-convex quadratic programs. The simplified LVI-PDNN model is simulated based on PA10 robot arm, and simulation results show the effective remedy of the joint angle drift problem of PA10 robot. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Guo:2008:ijcnn, author = "Qinglin Guo and Ming Zhang", title = "A Novel Approach for Fault Diagnosis of Steam Turbine Based on Neural Network and Genetic Algorithm", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0033.pdf}, url = {}, size = {}, abstract = {A novel approach for data mining of steam turbine based on neural network and genetic algorithm is brought forward, aimed at overcoming shortages of some current knowledge attaining methods. The historical fault data of steam turbine is processed with fuzzy and discrete method firstly, a multiplayer backpropagation neural network is structured secondly, the neural network is trained via teacher's guidance thirdly, and the neural network is optimised by genetic algorithm lastly. Based on the ontology of neural network, the data mining algorithm for classified fault diagnosis rules about steam turbine is brought forward; its realisation process is as follows: (1) computing the measurement matrix of effect; (2) extracting rules; (3) computing the importance of rules; (4) shearing the rules by genetic algorithm. An experimental system for data mining and fault diagnosis of steam turbine based on neural network and genetic algorithm is implemented. Its diagnosis precision is 84percent. And experiments do prove that it is feasible to use the method to develop a system for fault diagnosis of steam turbine, which is valuable for further study in more depth. }, keywords = { Neural network, Genetic algorithm, Data mining, Fault diagnosis, Ontology, Steam turbine}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Neville:2008:ijcnn, author = "R. Neville and S. Holland ", title = "Generating Weights and Generating Vectors to Map Complex Functions with Artificial Neural Networks", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0034.pdf}, url = {}, size = {}, abstract = {The generation of weights is an alternative method of loading a set of weights into an artificial neural network. It is a process that transforms a trained base net by multiplying its weights by symmetric matrices [1]. These weights are then assigned to a derived net. The derived nets map symmetrically related functions. At present, the process is limited because it cannot be applied to one-to-many functions. In this paper, this limitation is overcome by generating a set of vectors from the transformed derived nets that are then used to train an ANN to map one-to-many tasks. The associated rotational symmetries performed are also specified. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Knauf:2008:ijcnn, author = "Rainer Knauf and Yoshitaka Sakurai and Setsuo Tsuruta", title = "Applying Knowledge Engineering Methods to Didactic Knowledge First Steps Towards an Ultimate Goal", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0038.pdf}, url = {}, size = {}, abstract = {Generally, learning systems suffer from a lack of an explicit and adaptable didactic design. Since E-Learning systems are digital by their very nature, their introduction rises the issue of modelling the didactic design in a way that implies the chance to apply Knowledge Engineering Techniques (like Machine Learning and Data Mining). A modeling approach called storyboarding, is outlined here. Storyboarding is setting the stage to apply Knowledge Engineering Technologies to verify and validate the didactics behind a learning process. Moreover, didactics can be refined according to revealed weaknesses and proven excellence and successful didactic patterns can be inductively inferred by analysing the particular knowledge processing and its alleged contribution to learning success. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Igarashi:2008:ijcnn, author = "H. Igarashi and K. Nakamura and S. Ishihara", title = "Learning of Soccer Player Agents Using a Policy Gradient Method: Coordination Between Kicker and Receiver During Free Kicks", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0040.pdf}, url = {}, size = {}, abstract = {The RoboCup Simulation League is recognised as a test bed for research on multi-agent learning. As an example of multi-agent learning in a soccer game, we dealt with a learning problem between a kicker and a receiver when a direct free kick is awarded just outside the opponent's penalty area. In such a situation, to which point should the kicker kick the ball? We propose a function that expresses heuristics to evaluate an advantageous target point for safely sending/receiving a pass and scoring. The heuristics includes an interaction term between a kicker and a receiver to intensify their coordination. To calculate the interaction term, we let kicker/receiver agents have a receiver/kicker action decision model to predict his teammate's action. The evaluation function makes it possible to handle a large space of states consisting of the positions of a kicker, a receiver, and their opponents. The target point of the free kick is selected by the kicker using Boltzmann selection with an evaluation function. Parameters in the function can be learnt by a kind of reinforcement learning called the policy gradient method. The point to which a receiver should run to receive the ball is simultaneously learnt in the same manner. The effectiveness of our solution was shown by experiments. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Gorji:2008:ijcnn, author = "Ali A. Gorji and Mohammad B. Menhaj ", title = "Identification of Nonlinear State Space Models Using an MLP Network Trained by the EM Algorithm", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0041.pdf}, url = {}, size = {}, abstract = {Identification of nonlinear state space models when no information is available from the state transition or output model has played an important role in the recent research. In this paper, we propose a new approach for modelling a discrete time nonlinear state space system with a multilayer perceptron (MLP) neural network. The expectation maximisation (EM) algorithm is used for joint parameter and state estimation of the proposed structure where the particle smoothing algorithm will be applied for estimating hidden states. Because of the non-affine structure of MLP networks compared with some other models such as radial basis functions, the gradient method is used at the M phase of the EM algorithm for parameter and noise estimation. Simulation studies show the superiority and fast convergence of our proposed structure in identification of nonlinear state space models. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Qiu:2008:ijcnn, author = "Xiao-Yu Qiu and Kai Kang and Hua-Xiang Zhang", title = "Selection of Kernel Parameters for KNN", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0049.pdf}, url = {}, size = {}, abstract = {How to choose the optimal parameter is crucial for the kernel method, because kernel parameters perform significantly on the kernel method. In this paper, a novel approach is proposed to choose the kernel parameter for the kernel nearest-neighbour classifier (KNN). The values of the kernel parameter are computed through optimising an object function designed for measuring the classification reliability of KNN. We test our approach on both artificial and real-word data sets, and the preliminary results demonstrate that our approach provides a practical solution. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Efe:2008:ijcnn, author = "Mehmet Önder Efe and Cosku Kasnakoglu", title = "A Comparison of Architectural Varieties in Radial Basis Function Neural Networks", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0050.pdf}, url = {}, size = {}, abstract = {Representation of knowledge within a neural model is an active field of research involved with the development of alternative structures, training algorithms, learning modes and applications. Radial Basis Function Neural Networks (RBFNNs) constitute an important part of the neural networks research as the operating principle is to discover and exploit similarities between an input vector and a feature vector. In this paper, we consider nine architectures comparatively in terms of learning performances. Levenberg- Marquardt (LM) technique is coded for every individual configuration and it is seen that the model with a linear part augmentation performs better in terms of the final least mean squared error level in almost all experiments. Furthermore, according to the results, this model hardly gets trapped to the local minima. Overall, this paper presents clear and concise figures of comparison among 9 architectures and this constitutes its major contribution. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Li:2008:ijcnn, author = "Minhua Li and Chunheng Wang", title = "An Adaptive Text Detection Approach in Images and Video Frames", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0052.pdf}, url = {}, size = {}, abstract = {In this paper, an adaptive edge-based text detection approach in images and video frames is proposed. The proposed approach can adopt different edge detection methods according to the image background complexity. It mainly consists of four stages: Firstly, images are classified into different background complexities. Secondly, different edge detectors are applied on the images according to their background complexities. Thirdly, connected component analysis is adopted on the edge image to obtain text candidates. Finally, the text candidates undergo the refinement algorithm to find the exact position. Experimental results demonstrate that the proposed approach is robust to text size and could effectively detect text lines in images and video frames in both simple background and complex background. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Bian:2008:ijcnn, author = "Yong Bian and Yinqing Zhou and Chunsheng Li", title = "Some Discrete Fourier Kind Transforms Based on FLOS", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0054.pdf}, url = {}, size = {}, abstract = {Some discrete Fourier kind transforms (DFKT) which include the discrete Fourier transform (DFT), discrete chirp Fourier transform (DCFT), three-order discrete chirp Fourier transform (TDCFT), discrete matched transform based on phase compensation (DMTPC) are studied in the symmetric alpha-stable (SaS) noise environment. Some DFKTs based on the fractional lower order statistics (FLOS) (DFKT_FLOS) are studied or presented. The DFT based on FLOS (DFT_FLOS) is studied. The DCFT based on FLOS (DCFT_FLOS), TDCFT based on FLOS (TDCFT_FLOS), DMTPC based on FLOS (DMTPC_FLOS) are presented. The simulation shows that in the impulsive noise environment, DFKT_FLOS outperforms their respective DFKT counterpart. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Liu:2008:ijcnn, author = "Qingshan Liu and Jun Wang", title = "A One-Layer Recurrent Neural Network for Convex Programming", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0064.pdf}, url = {}, size = {}, abstract = {This paper presents a one-layer recurrent neural network for solving convex programming problems subject to linear equality and nonnegativity constraints. The number of neurons in the neural network is equal to that of decision variables in the optimisation problem. Compared with the existing neural networks for optimization, the proposed neural network has lower model complexity. Moreover, the proposed neural network is proved to be globally convergent to the optimal solution(s) under some mild conditions. Simulation results show the effectiveness and performance of the proposed neural network. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Horng:2008:ijcnn, author = "Ming-Huwi Horng ", title = "Texture Classification of the Ultrasonic Images of Rotator Cuff Diseases Based on Radial Basis Function Network", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0065.pdf}, url = {}, size = {}, abstract = {This article studies the usages of texture analysis methods to classify ultrasonic rotator cuff images into the different disease groups that are normal, tendon inflammation, calcific tendonitis and tendon tear. The adopted texture analysis methods include the texture feature coding method, gray-level co-occurrence matrix, fractal dimension and texture spectrum. The texture features of the four methods are used to analyse the tissue characteristic of supraspinatus tendon. The mutual information feature selection and F-scoring feature ranking method are independently used to select powerful features from the four texture analysis methods. Furthermore, the trained radial basis function network is used to classify the test images into the ones of four disease group. Experimental results tested on 85 images reveal that the classification accuracy of proposed system can achieves 84percent. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Ling:2008:ijcnn, author = "S. H. Ling and H. H. C. Iu and F. H. F. Leung and K.Y. Chan", title = "Modelling the Development of Fluid Dispensing for Electronic Packaging: Hybrid Particle Swarm Optimization Based-Wavelet Neural Network Approach", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0066.pdf}, url = {}, size = {}, abstract = {An hybrid Particle Swarm Optimisation PSO-based wavelet neural network for modelling the development of fluid dispensing for electronic packaging is presented in this paper. In modelling the fluid dispensing process, it is important to understand the process behaviour as well as determine optimum operating conditions of the process for a high-yield, low cost and robust operation. Modelling the fluid dispensing process is a complex non-linear problem. This kind of problem is suitable to be solved by neural network. Among different kinds of neural networks, the wavelet neural network is a good choice to solve the problem. In the proposed wavelet neural network, the translation parameters are variables depending on the network inputs. Thanks to the variable translation parameters, the network becomes an adaptive one. Thus, the proposed network provides better performance and increased learning ability than conventional wavelet neural networks. An improved hybrid PSO [1] is applied to train the parameters of the proposed wavelet neural network. A case study of modelling the fluid dispensing process on electronic packaging is employed to demonstrate the effectiveness of the proposed method. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Cheng:2008:ijcnn, author = "Long Cheng and Zeng-Guang Hou and Min Tan and Xiuqing Wang", title = "A Simplified Recurrent Neural Network for Solving Nonlinear Variational Inequalities", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0067.pdf}, url = {}, size = {}, abstract = {A recurrent neural network is proposed to deal with the nonlinear variational inequalities with linear equality and nonlinear inequality constraints. By exploiting the equality constraints, the original variational inequality problem can be transformed into a simplified one with only inequality constraints. Therefore, by solving this simplified problem, the neural network architecture complexity is reduced dramatically. In addition, the proposed neural network can also be applied to the constrained optimisation problems, and it is proved that the convex condition on the objective function of the optimization problem can be relaxed. Finally, the satisfactory performance of the proposed approach is demonstrated by simulation examples. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Yang:2008:ijcnn, author = "Liying Yang and Jianda Han and Chendong Wu", title = "A Solution of Mixed Integer Linear Programming for Obstacle-Avoided Pursuit Problem", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0070.pdf}, url = {}, size = {}, abstract = {In this paper the path planning for obstacle-avoided pursuit problem (OAP) is studied. The OAP models based on the mixed integer linear programming (MILP) is presented. In the OAP models, the dynamic equation of mass point with linear damping is taken as the state equation of vehicle's motion. Integer variables are used to describe the relative position of vehicle and obstacles. ``Expansible Target Size'' is proposed to describe the pursuit process for target step-by-step. ``Pursuit Direction'' of vehicle is defined. The Isometric Plane Method selected integer variables is used to solve MILP pursuit problem. How to select the integer variables of inner point is also given. Finally, simulations are given to show the efficiency of the method. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wai:2008:ijcnn, author = "Rong-Jong Wai and Chia-Ming Liu", title = "Design of Dynamic Petri Recurrent-Fuzzy-Neural- Network Scheme for Mobile Robot Tracking Control", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0072.pdf}, url = {}, size = {}, abstract = {This study focuses on the design of a dynamic Petri recurrent-fuzzy-neural-network (DPRFNN) control for the path tracking of a nonholonomic mobile robot. In the DPRFNN, the concept of a Petri net (PN) and the recurrent frame of internal feedback loops are incorporated into a traditional fuzzy neural network (FNN) to alleviate the computation burden of parameter learning and to enhance the dynamic mapping of network ability. Moreover, the supervised gradient descent method is used to develop the online training algorithm for the DPRFNN control. In order to guarantee the convergence of path tracking errors, analytical methods based on a discrete-type Lyapunov function are proposed to determine varied learning rates for DPRFNN. In addition, the effectiveness of the proposed DPRFNN control scheme under different moving paths is verified by numerical simulations, and its superiority is indicated in comparison with FNN, recurrent FNN (RFNN) and Petri FNN (PFNN) control systems. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Biazus:2008:ijcnn, author = "Cladio J. Biazus and Mauro Roisenberg ", title = "The Development of a Hybrid, Distributed Architecture for Multiagent Systems and its Application in Robot Soccer", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0073.pdf}, url = {}, size = {}, abstract = {Several issues still need to be unravelled in the development of multiagent systems equipped with global vision, as in robot soccer leagues. Here, we underscore three of them (1) real-time constraints on recognition of scene objects; (2) acquisition of environment knowledge; and (3) distribution and allocation of control competencies shared between the repertoire of the agent's reactive behaviour, and the central control entity's strategic and deliberative behaviour. The objective of this article is to describe the implementation of a distributed and hybrid reactive-deliberative control architecture for a multiagent system, equipped with global vision camera and agent local sensor and cameras. This multiple agent system was developed for application in robot soccer. We present the digital image processing techniques applied, as well as the proposed control architecture aimed at satisfying the constraints of this kind of application. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Yin-Jie:2008:ijcnn, author = "Lei Yin-Jie and Chen Cun-Jian and Lang Fang-Nian", title = "Quaternion Singular Value Decomposition Approach to Color Image De-Noising", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0075.pdf}, url = {}, size = {}, abstract = {Based on quaternion, an algorithm for colour image de-noising has been proposed in this paper. According to the quaternion singular value decomposition theory, in a colour image, the singular values on the diagonal matrix, which were obtained through QSVD, represent the color images in different channels. The additive noise of a color image can be eliminated effectively by keeping the proper singular values, that represent normal image signal, and discarding the noise values. Through the color image energy model, we can reconstruct the image singular values and selectively eliminate the singular values which represent the noise. As the result, the proposed algorithm can de-noise color image rapidly, and also it can be implemented easily in practice. The experiment results prove that the algorithm is correct and the energy model is effective. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Tian:2008:ijcnn, author = "Yingjie Tian and Yunchuan Sun and Chuan-Liang Chen and Zhan Zhang", title = "Unconstrained Transductive Support Vector Machines and Its Application", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0078.pdf}, url = {}, size = {}, abstract = {Support Vector Machines have been extensively used in machine learning because of its efficiency and its theoretical background. This paper focuses on v-Transductive Support Vector Machines for classification (v-TSVC) and construct a new algorithm - Unconstrained v-Transductive Support Vector Machines (Uv-TSVM). After researching on the special construction of primal problem in v-TSVM, we transform it to an unconstrained problem and then smooth the derived problem in order to apply usual optimisation methods. Numerical experiments prove its successful application in real life credit card dataset. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Zhang2:2008:ijcnn, author = "Yunong Zhang and Zenghai Chen and Ke Chen and Binghuang Cai", title = "Zhang Neural Network without Using Time-Derivative Information for Constant and Time-Varying Matrix Inversion", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0079.pdf}, url = {}, size = {}, abstract = {To obtain the inverses of time-varying matrices in real time, a special kind of recurrent neural networks has recently been proposed by Zhang et al. It is proved that such a Zhang neural network (ZNN) could globally exponentially converge to the exact inverse of a given time-varying matrix. To find out the effect of time-derivative term on global convergence as well as for easier hardware-implementation purposes, the ZNN model without exploiting time-derivative information is investigated in this paper for inverting online matrices. Theoretical results of both constant matrix inversion case and time-varying matrix inversion case are presented for comparative and illustrative purposes. In order to substantiate the presented theoretical results, computer-simulation results are shown, which demonstrate the importance of time derivative term of given matrices on the exact convergence of ZNN model to time-varying matrix inverses. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Gorji2:2008:ijcnn, author = "Ali A. Gorji and Mohammad B. Menhaj ", title = "Artificial Neural Networks for Stochastic Control of Nonliner State Space Systems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0080.pdf}, url = {}, size = {}, abstract = {In this paper, stochastic control of nonlinear state space models is discussed. After a brief review on nonlinear state space models, a multi layer perceptron (MLP) neural network is considered to represent the general structure of the controller. Then, an expectation maximisation (EM) algorithm joint with the particle smoothing framework are proposed for updating parameters of the MLP network. The suggested structure is also applied to the trajectory tracking of a nonlinear/nonstationary system. Simulation results show the superiority of our method in the control of nonlinear and stochastic state space models. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Huo:2008:ijcnn, author = "Juan Huo and Alan Murray and Leslie Smith and Zhijun Yang", title = "Adaptation of Barn Owl Localization System with Spike Timing Dependent Plasticity", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0081.pdf}, url = {}, size = {}, abstract = {To localise a seen object, the superior colliculus of the barn owl integrates the visual and auditory localisation cues which are accessed from the sensory system of the brain. These cues are formed as visual and auditory maps, thus the alignment between visual and auditory maps is very important for accurate localisation in prey behaviour. Blindness or prism wearing may disturb this alignment. The juvenile barn owl could adapt its auditory map to this mismatch after several weeks training. Here we investigate this process by building a computational model of auditory and visual integration with map adjustment in the deep superior colliculus. The adaptation is based on activity dependent axon developing which is instructed by an inhibitory network. In the inhibitory network, the strength of the inhibition is adjusted by spike timing dependent plasticity (STDP). The simulation results are in line with the biological experiment and support the idea that the STDP is involved in the alignment of sensory maps. The system of the model provides a new mechanism capable of eliminating the disparity in visual and auditory map integration. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Liu2:2008:ijcnn, author = "Feng Liu and Hu-cheng An and Jia-ming Li and Lin-dong Ge ", title = "Blind Equalization Using v- Support Vector Regressor for Constant Modulus Signals", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0084.pdf}, url = {}, size = {}, abstract = {The support vector machine has been recently developed for blind equalisation of constant modulus signals. In this paper we propose to use a v-support vector regressor (v-SVR) for blindly equalising multipath channels because of the high generalisation ability of the SVR for short burst sequences. A weighted least square procedure is presented for solving the blind v-SVR equaliser. The performance of the proposed algorithm is analysed by means of computer simulations. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(El-Alfy:2008:ijcnn, author = "El-Sayed M. El-Alfy and Radwan E. Abdel-Aal", title = "Spam Filtering with Abductive Networks", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0085.pdf}, url = {}, size = {}, abstract = {Spam messages pose a major threat to the usability of electronic mail. Spam wastes time and money for network users and administrators, consumes network bandwidth and storage space, and slows down email servers. In addition, it provides a medium to distribute harmful code and/or offensive content. In this paper, we investigate the application of abductive learning in filtering out spam messages. We study the performance for various network models on the spambase dataset. Results reveal that classification accuracies of 91.7percent can be achieved using only 10 out of the available 57 content attributes. The attributes are selected automatically by the abductive learning algorithm as the most effective feature subset, thus achieving approximately 6:1 data reduction. Comparison with other techniques such as multi-layer perceptrons and naïve Bayesian classifiers show that the abductive learning approach can provide better spam detection accuracies, e.g. false positive rates as low as 5.9percent while requiring much shorter training times. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Debnath:2008:ijcnn, author = "Jayanta Kumar Debnath", title = "A Modified Vector Quantization Based Image Compression Technique Using Wavelet Transform", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0087.pdf}, url = {}, size = {}, abstract = {An image compression method combining discrete wavelet transform (DWT) and vector quantisation (VQ) is presented. First, a three-level DWT is performed on the original image resulting in ten separate subbands (ten code books are generated using the Self Organising Feature Map algorithm, which are then used in Vector Quantisation, of the wavelet transformed subband images, i.e. one codebook for one subband). These subbands are then vector quantised. VQ indices are Huffman coded to increase the compression ratio. A novel iterative error correction scheme is proposed to continuously check the image quality after sending the Huffman coded bit stream of the error codebook indices through the channel so as to improve the peak signal to noise ratio (PSNR) of the reconstructed image. Ten error code books (each for each subband of the wavelet transformed image) are also generated for the error correction scheme using the difference between the original and the reconstructed images in the wavelet domain. The proposed method shows better image quality in terms of PSNR at the same compression ratio as compared to other DWT and VQ based image compression techniques found in the literature. The proposed method of image compression is useful for various applications in which high quality (i.e. high precision) are critical (like criminal investigation, medical imaging, etc). }, keywords = {Vector Quantisation, Wavelet Transform, Compression Ratio.}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Refaat:2008:ijcnn, author = "Khaled S. Refaat and Wael N. Helmy and AbdelRahman H. Ali and Amir F. Atiya", title = "A New Approach for Context-Independent Handwritten Offline Diagram Recognition Using Support Vector Machines", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0089.pdf}, url = {}, size = {}, abstract = {Structured diagrams are very prevalent in many document types. Most people who need to create such diagrams use structured graphics editors such as Microsoft Visio [1]. Structured graphics editors are extremely powerful and expressive but they can be cumbersome to use [2]. We have shown through extensive timing experiments that structured diagrams drawn by hand will take only about 10percent of the time it takes to draw one using a tool like Visio. This indicates the value of automated recognition of hand-written diagrams. Recently, applications have been developed that use online systems running on pen-input PCs that allow users to create structured diagrams by drawing the diagram on the PC tablet. The progress of offline diagram recognition is still minimal. The objective of this paper is to propose a context-independent off-line diagram recognition system. Our approach uses support vector machines [3] for recognition and Line Primitive Extraction by Interpretation of Line Continuation for segmentation [4]. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Xu:2008:ijcnn, author = "Rui Xu and Steven Damelin and Donald C. Wunsch II", title = "Clustering of Cancer Tissues Using Diffusion Maps and Fuzzy ART with Gene Expression Data", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0090.pdf}, url = {}, size = {}, abstract = {Early detection of a tumour's site of origin is particularly important for cancer diagnosis and treatment. The employment of gene expression profiles for different cancer types or subtypes has already shown significant advantages over traditional cancer classification methods. Here, we apply a neural network clustering theory, Fuzzy ART, to generate the division of cancer samples, which is useful in investigating unknown cancer types or subtypes. On the other hand, we use diffusion maps, which interpret the eigenfunctions of Markov matrices as a system of coordinates on the original data set in order to obtain efficient representation of data geometric descriptions, for dimensionality reduction. The curse of dimensionality is a major problem in cancer type recognition-oriented gene expression data analysis due to the overwhelming number of measures of gene expression levels versus the small number of samples. Experimental results on the small round blue-cell tumour (SRBCT) data set, compared with other widely used clustering algorithms, demonstrate the effectiveness of our proposed method in addressing multidimensional gene expression data. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Swiderski:2008:ijcnn, author = "B. Swiderski and S. Osowski and A. Cichocki and A. Rysz", title = "Single-Class SVM Classifier for Localization of Epileptic Focus on the Basis of EEG", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0091.pdf}, url = {}, size = {}, abstract = {The paper presents the application of a single class Support Vector Machine (SVM) for localisation of the focus region at the epileptic seizure on the basis of EEG registration. The diagnostic features used in recognition are derived from the directed transfer function description, determined for different ranges of EEG signals. The results of the performed numerical experiments for the localisation of the seizure focus in the brain have been confirmed by the real surgery of the brain for few patients. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wang:2008:ijcnn, author = "Jiao Wang and Si-wei Luo and Xian-hua Zeng ", title = "A Random Subspace Method for Co-Training", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0092.pdf}, url = {}, size = {}, abstract = {Semi-supervised learning has received much attention recently. Co-training is a kind of semi-supervised learning method which uses unlabelled data to improve the performance of standard supervised learning algorithms. A novel co-training style algorithm, RASCO (for RAndom Subspace CO-training), is proposed which uses stochastic discrimination theory to extend co-training to multi-view situation. The accuracy and generalizability of RASCO are analysed. The influences of the parameters of RASCO are discussed. Experiments on UCI data set demonstrate that RASCO is more effective than other co-training style algorithms. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Ssali:2008:ijcnn, author = "George Ssali and Tshilidzi Marwala", title = "Computational Intelligence and Decision Trees for Missing Data Estimation", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0097.pdf}, url = {}, size = {}, abstract = {This paper introduces a novel paradigm to impute missing data that combines a decision tree with an autoassociative neural network (AANN) based model and a principal component analysis-neural network (PCA-NN) based model. For each model, the decision tree is used to predict search bounds for a genetic algorithm that minimise an error function derived from the respective model. The models' ability to impute missing data is tested and compared using HIV sero-prevalance data. Results indicate an average increase in accuracy of 13percent with the AANN based model's average accuracy increasing from 75.8percent to 86.3percent while that of the PCA-NN based model increasing from 66.1percent to 81.6percent. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Honda:2008:ijcnn, author = "Katsuhiro Honda and Hiromichi Araki and Tomohiro Matsui and Hidetomo Ichihashi", title = "A New Approach to Robust k-Means Clustering Based on Fuzzy Principal Component Analysis", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0102.pdf}, url = {}, size = {}, abstract = {PCA-guided k-Means performs non-hierarchical hard clustering based on PCA-guided subspace learning mechanism in a batch process. Sequential Fuzzy Cluster Extraction (SFCE) is a procedure for analytically extracting fuzzy clusters one by one, and is useful for ignoring noise samples. This paper considers a hybrid concept of the two clustering approaches and proposes a new robust k-Means algorithm that is based on a fuzzy PCA-guided clustering procedure. In the proposed method, a responsibility weight of each sample in k- Means process is estimated based on the noise fuzzy clustering mechanism, and cluster membership indicators in k-Means process are derived as fuzzy principal components considering the responsibility weights in fuzzy PCA. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Aseervatham:2008:ijcnn, author = "Sujeevan Aseervatham ", title = "A Local Latent Semantic Analysis-Based Kernel for Document Similarities", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0104.pdf}, url = {}, size = {}, abstract = {The document similarity measure is a key point in textual data processing. It is the main responsible of the performance of a processing system. Since a decade, kernels are used as similarity functions within inner-product based algorithms such as the SVM for NLP problems and especially for text categorisation. In this paper, we present a semantic space constructed from latent concepts. The concepts are extracted using the Latent Semantic Analysis (LSA). To take into account of the specificity of each document category, we use the local LSA to define the global semantic space. Furthermore, we propose a weighted semantic kernel for the global space. The experimental results of the kernel, on text categorisation tasks, show that this kernel performs better than global LSA kernels and especially for small LSA dimensions. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Botsch:2008:ijcnn, author = "Michael Botsch and Josef A. Nossek", title = "Construction of Interpretable Radial Basis Function Classifiers Based on the Random Forest Kernel", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0105.pdf}, url = {}, size = {}, abstract = {In many practical applications besides a small generalisation error also the interpretability of classification systems is of great importance. There is always a tradeoff among these two properties of classifiers. The similarity measure in the input space as defined by one of the most powerful classifiers, the Random Forest (RF) algorithm, is used in this paper as basis for the construction of Generalised Radial Basis Function (GRBF) classifiers. Hereby, interpretability and a low generalization error can be achieved. The main idea is to approximate the RF kernel by Gaussian functions in a GRBF network. This way the GRBF network can be constructed to approximate the conditional probability of each class given a query input. Since each centre in the GRBF is used for the representation of the distribution of a single target class in a localised area of the classifiers input space, interpretability can be achieved by taking account for the membership of a query input to the different localized areas. Whereas in most algorithms the pruning technique is used only to improve the generalization property, here a method is proposed how pruning can be applied to additionally improve the interpretability. Another benefit that comes along with the resulting GRBF classifier is the possibility to detect outliers and to reject decisions that have a low confidence. Experimental results underline the advantages of the classification system. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(He-Yong:2008:ijcnn, author = "Wang He-Yong ", title = "Combination Approach of SMOTE and Biased-SVM for Imbalanced Datasets", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0107.pdf}, url = {}, size = {}, abstract = {A new approach to construct the classifiers from imbalanced datasets is proposed by combining SMOTE (Synthetic Minority Over-sampling Technique) and Biased-SVM (Biased Support Vector Machine) approaches. A dataset is imbalanced if the classification categories are not approximately equally represented. Often real-world data sets are predominately composed of ``normal'' examples with only a small percentage of ``abnormal'' or ``interesting'' examples. The cost of misclassifying an abnormal (interesting) example into a normal example is often much higher than that of the reverse error. It was known as a means of increasing the sensitivity of a classifier to the minority class using SMOTE over-sampling in minority class. But in this paper, it gives a good means of increasing the sensitivity of a classifier to the minority class by using SMOTE approaches within support vectors. As for support vector over-sampling, this paper proposes two different over-sampling algorithms to deal with the support vectors being over-sampled by its neighbours from the k nearest neighbors, not only within the support vectors but also within the entire minority class. Some experimental results confirms that the proposed combination approach of SMOTE and Biased-SVM can achieve better classifier performance. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Lu:2008:ijcnn, author = "Chi-Jie Lu and Tian-Shyug Lee and Chih-Chou Chiu", title = "Statistical Process Monitoring Using Independent Component Analysis Based Disturbance Separation Scheme", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0109.pdf}, url = {}, size = {}, abstract = {In this paper, an independent component analysis (ICA) based disturbance separation scheme is proposed for statistical process monitoring. ICA is a novel statistical signal processing technique and has been widely applied in medical signal processing, audio signal processing, feature extraction and face recognition. However, there are still few applications of using ICA in process monitoring. In the proposed scheme, firstly, ICA is applied to manufacturing process data to find the independent components containing only the white noise of the process. The traditional control chart is then used to monitor the independent components for process monitoring. In order to evaluate the effectiveness of the proposed scheme, simulated manufacturing process datasets with step-change disturbances are evaluated. The experimental results reveal that the proposed method outperforms the traditional control charts in most instances and thus is effective for statistical process monitoring. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Ping:2008:ijcnn, author = "Ling Ping and Wang Zhe and Wang Xi and Zhou Chun-guang", title = "Derive Local Invariance Transformations from SVM", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0110.pdf}, url = {}, size = {}, abstract = {Invariance transformation (IT) is a rewarding technique to facilitate classification. But it is often difficult to derive its definition. This paper derives a local invariance transformation definition from SVM decision function. The corresponding IT-distance definition is consequently designed in both input space and feature space. And a classification algorithm based on IT and Nearest Neighbour rule is proposed, named as ITNN. ITNN exploits hyper sphere centres as class prototypes and labels data using a weighted voting strategy. ITNN is of computational ease brought by training dataset reduction and hyper parameter self-tuning. We describe experimental evidence of classification performance improved by ITNN on real datasets over state of the arts. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Gutmann:2008:ijcnn, author = "Michael Gutmann and Aapo Hyvärinen", title = "Learning Encoding and Decoding Filters for Data Representation with a Spiking Neuron", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0116.pdf}, url = {}, size = {}, abstract = {Data representation methods related to ICA and sparse coding have successfully been used to model neural representation. However, they are highly abstract methods, and the neural encoding does not correspond to a detailed neuron model. This limits their power to provide deeper insight into the sensory systems on a cellular level. We propose here data representation where the encoding happens with a spiking neuron. The data representation problem is formulated as an optimisation problem: Encode the input so that it can be decoded from the spike train, and optionally, so that energy consumption is minimised. The optimisation leads to a learning rule for the encoder and decoder which features synergistic interaction: The decoder provides feedback affecting the plasticity of the encoder while the encoder provides optimal learning data for the decoder. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Morgan:2008:ijcnn, author = "Ian Morgan and Honghai Liu and George Turnbull and David Brown", title = "Predictive Unsupervised Organisation in Marine Engine Fault Detection", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0122.pdf}, url = {}, size = {}, abstract = {This paper uses topological learners, the Self Organising Map in combination with the K Means algorithm to organise potential engine faults and the respective location of faults, focusing on a 12 cylinder 2 stroke marine diesel engine. This method is applied to reduce the numerosity of the data presented to a user by selecting representative samples from a number of clusters to enable efficient diagnosis. The novelty of the approach centres around the sparsity of the dataset compared to the majority of fault diagnosis techniques, and the potential for improved safety and efficiency within the marine industry compared to existing diagnosis systems. The accuracy of the SOM and K Means, as well as the Neural Gas algorithm is compared to the standard accuracy of the K Means algorithm to validate the algorithm's performance and application to this domain, where it can be seen that topological learners have much potential to be applied to the field of fault diagnosis. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Chen4:2008:ijcnn, author = "Tieming Chen and Samuel H. Huang", title = "Tree Parity Machine-Based One-Time Password Authentication Schemes", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0123.pdf}, url = {}, size = {}, abstract = {One-Time Password (OTP) is always used as the strongest authentication scheme among all password-based solutions. Currently, consumer devices such as smart card have implemented OTP based two-factor authentications for secure access controls. Such solutions are economically sound without support of time stamp mechanisms. Therefore, synchronisation of internal parameters in OTP models, such as moving factor or counter, between the client and server is the key challenge. Recently, a novel phenomenon shows that two interacting neural networks, called Tree Parity Machines (TPM), with common inputs can finally synchronise their weight vectors through finite steps of output-based mutual learning. The improved secure TPM can well be used to synchronize parameters for OTP schemes. In this paper, TPM mutual learning scheme is introduced, then two TPM-based novel OTP solutions are proposed. One is a full implementation model including initialization and rekeying, while the other is light-weight and efficient suitable for resource-constrained embedded environment. Security and performance on the proposed protocols are at final discussed. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Lei:2008:ijcnn, author = "Wu Lei and Sun Feng and Cheng Jianhua ", title = "Fault Diagnosis of FOG SINS Based on Neural Network", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0125.pdf}, url = {}, size = {}, abstract = {Based on nonlinear mapping relationship between fault symptom and fault type in subsystems of FOG SINS (fiber-optic gyroscope strapdown inertial system), BP (back-propagation) and Elman neural network approaches were presented for fault diagnosis. Fault mechanism and failure behavior of FOG SINS was analyzed, then featured fault types were extracted from FOG SINS faults and the extracted features were regarded as fault symptom eigenvector. The process of fault diagnosis principal, fault diagnosis model and fault diagnosis algorithm were given using BP and Elman neural network with enough fault feature information. Trained BP and Elman were used for fault vector recognition and diagnosis to verify the proposed fault diagnosis model effectiveness and rationality. Training and test results of two neural networks were compared. The conclusion was made. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Li2:2008:ijcnn, author = "Bo Li and De-Shuang Huang and Chao Wang", title = "Improving the Robustness of ISOMAP by De-Noising", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0127.pdf}, url = {}, size = {}, abstract = {ISOMAP is a manifold learning based algorithm for dimensionality reduction, which is successfully applied to data visualization. However, there exists such limitation in classical ISOMAP that the algorithm is sensitive to noises, especially outliers. So in this paper an extended ISOMAP algorithm is put forward to solve the problem of sensitivity. The proposed algorithm follows the method of classical ISOMAP except that a preprocessing strategy is introduced to remove the noises and outliers. The likelihood of each point to be a noise or an outlier is quantified by carrying out weighted principal component analysis and box statistics method is adopted to distinguish clear points from noisy ones, then ISOMAP can be performed after de-noising. Experiments on noisy s-curve and noisy Swiss-roll data validate its efficiency for improving robustness. }, keywords = { ISOMAP, robustness, de-noising}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Zhihui:2008:ijcnn, author = "Huang Zhihui and Kan ShuLin and Yuan jing and Ren Yizhou and Wei Yufeng and Dong qiaoying", title = "Intelligent Fuzzy Wavelet System for Electronic Parts Appearance Quality Prediction", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0128.pdf}, url = {}, size = {}, abstract = {With the computer accurate estimation of electronic parts defect detection in quality control play an important role in the manufacturing industry. In order to realization electronic parts product appearance quality detection control, one kind of processor based on the intelligent knowledge automatic extraction and system integration modeling was presented. This paper proposes a method using an adaptive system to establish the relationship between actual electronic parts defect detection and texture features of the surface image. Uses the fuzzy wavelet extraction image feature, and wavelet function is used as fuzzy membership function. The fuzzy inference is realized by neural network and the shape of membership function can be adjusted in real time. It endues the processor with better capability of learning and self adapt. Based on establishment quality-oriented key characteristics index dynamic adaptability analysis control system, an architecture of intelligent fuzzy neural network combined integrated with quality management module of manufacturing execution system (MES) is presented. It formed a detection product appearance quality of intelligent decision support system. The accurate modeling can effectively estimate electronic parts defect detection. The results of experiments demonstrate that the system can detect product appearance quality perfectly, with a high precision and has the practicality. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Jiang:2008:ijcnn, author = "Minghui Jiang and Li Wang and Yi Shen", title = "Asymptotic Behavior of Stochastic Cohen-Grossberg Neural Networks with Variable Delays", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0132.pdf}, url = {}, size = {}, abstract = {Using Chebyshev inequality and nonnegative semimartingale convergence theorem, the paper investigates asymptotic behaviour of stochastic Cohen-Grossberg neural networks with delay by constructing suitable Lyapunov functional. Algebraic criteria are given for stochastic ultimate bounded and almost exponential stability. The result in the paper extend the main conclusion in [9] and [10]. In the end, examples are given to verify the effective of our results. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Ma:2008:ijcnn, author = "Weimu Ma and Yunong Zhang and Jiahai Wang ", title = "MATLAB Simulink Modeling and Simulation of Zhang Neural Networks for Online Time-Varying Sylvester Equation Solving", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0133.pdf}, url = {}, size = {}, abstract = {Recently, a special kind of recurrent neural networks has been proposed by Zhang et al for online solution of Sylvester equation with time-varying coefficients. Their neural dynamics are elegantly introduced by defining a matrix-valued error function rather than the usual scalar-valued norm-based error function, so that the computational error can vanish to zero globally and exponentially. The resultant Zhang neural networks (ZNN), perform much better on solving time-varying problems in comparison with gradient-based neural networks. MATLAB Simulink is a software package for model-based design and multi-domain simulation of dynamic systems. By using click-and-drag mouse operations, it is much easier to model and simulate complex neural systems as compared to MATLAB coding. This paper investigates the MATLAB Simulink modelling and simulative verification of ZNN models for time varying Sylvester equation solving. Computer-simulation results substantiate the ZNN efficacy on solving online the time-varying problems (specifically, the time-varying Sylvester equation). }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Chen5:2008:ijcnn, author = "Tao-wei Chen and Wei-dong Jin", title = "Emitter Number Estimation from Pulse Envelope Using Information Theoretic Criterion", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0134.pdf}, url = {}, size = {}, abstract = {In this paper, An approach for estimating the number of emitters from a set of interleaved pulses trains is proposed. The approach is based on the application of information theoretic criterion, which is formulated by using a new model of eigenvalues from principal component analysis (PCA) of pulse envelope vectors. In this model, the logarithm likelihood function is obtained by clustering the eigenvalues into two groups: signal and noise component group. The experimental results suggest that the present likelihood function can provide a good estimate of the dimension of signal component group from artificial data. When compared with the other information theoretic criteria, the proposed information theoretic criterion does not involve any computationally sophisticated maximum likelihood function. In addition, it is simple, intelligible, and more efficient. Computer simulations are used to show the effectiveness and feasibility of the proposed approach. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Chen6:2008:ijcnn, author = "Tao-wei Chen and Wei-dong Jin and Jie Li", title = "Feature Extraction Using Surrounding-Line Integral Bispectrum for Radar Emitter signal", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0135.pdf}, url = {}, size = {}, abstract = {In ever changing threat emitter environment, specific emitter identification (SEI) technology extracts subtle but persistent features from received pulse signal to create a fingerprint unique to a specific radar. Unlike conventional five parameters deinterleaving algorithm, which can be grossly ambiguous for radar emitter sorting, the SEI technology provides hardware specific identification. In this paper, we propose an approach for extracting unintentional phase modulation features caused by oscillator based on surrounding-line integral bispectrum. The quantitative features, i.e. bispectra entropy, waveform entropy and mean of surrounding-line integrated bispectra, is extracted using entropy-like function to reveal the subtle difference between emitters. Computer simulations show that how the phase-noise-induced signal changes analysis based on bispectrum approach can be used to determine which of emitters transmitted a pulse signal. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Liu3:2008:ijcnn, author = "Yang Liu and Yanwei Zheng and Yuehui Chen", title = "Ensemble Classification Based on Correlation Analysis for Face Recognition", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0136.pdf}, url = {}, size = {}, abstract = {This paper presents a new face recognition approach by using correlation analysis and ensemble classifiers based on Support Vector Machine (SVM). In this approach, image pre-processing techniques such as histogram equalisation, edge detection and geometrical transformation are first used in order to improve the quality of the face images. We further employ correlation analysis method to extract features. At last, ensemble classifiers based on SVM are selected to construct the classification committee using Binary Particle Swarm Optimisation (BPSO). Comparisons with other popular classification methods show that our scheme is very promising in face recognition. }, keywords = { Face recognition, Correlation analysis, Support vector machine, Binary particle swarm optimisation.}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Ouyang:2008:ijcnn, author = "Jianjun Ouyang and Ming Xu and Yunsen Huang", title = "A GMM Based Approach for Real-time Speech Driven 3-D Human Mouth Animation", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0138.pdf}, url = {}, size = {}, abstract = {Compare with cross-modal Hidden Markov Model based and text driven human mouth synthesis schemes, this paper presents a Gaussian Mixture Model (GMM) based approach for speaker-independent real-time speech driven mouth animation. To capture the context information of continuously speaking mouth shapes in acoustic domain, the triseme based modelling technique is employed for acquiring the trisemic GMMs. To obtain the robust model parameters with the limited training data, the states tying procedure is introduced. To avoid the compatibility and ambiguity problems, the visemic questions which assigned in the leaf nodes of decision tree are generated statistically. With the modelled GMM parameters, the viterbi beam searching algorithm is applied to time align the trisemic sequence. Synthesising the recognised trisemes to the corresponding MPEG-4 FAPs represented mouth shapes, the speaking mouth can be finally animated through a smoothing process. In terms of the proposed evaluation criterion, the experimental results demonstrate that the optimising technique is promising and applicable, and also the aligning efficiency is acceptable in human vision. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Mu:2008:ijcnn, author = "Xiaoyan Mu and Paul Watta and Mohamad H. Hassoun", title = "Analysis of a Plurality Voting-Based Combination of Classifiers", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0139.pdf}, url = {}, size = {}, abstract = {In various studies, it has been demonstrated that combining the decisions of multiple classifiers can lead to better recognition results. Plurality voting is one of the most widely used combination strategies. In this paper, we both theoretically and experimentally analyse the performance of a plurality voting-based ensemble classifier. Theoretical expressions for system performance are derived as a function of the model parameters: N (number of classifiers), M (number of classes), and P (probability that a single classifier is correct). Experimental results on the problem of human face recognition show that the voting strategy can successfully achieve high detection and identification rates, and, simultaneously, low false acceptance rates. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Youssef:2008:ijcnn, author = "Khalid Youssef and Peng-Yung Woo", title = "Robotic Position/Orientation Control Using Neural Networks", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0144.pdf}, url = {}, size = {}, abstract = {This paper studies the use of neural networks in robotic position/orientation control. The process is divided into two tasks, i.e., the inverse kinematics solution and the adaptive motor control. Simulation results of a three-link robotic arm in a two-dimensional workspace demonstrate the validity of the design. The hierarchical nature of the design allows it to be applied to more complicated systems that operate in a three dimensional workspace. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Xing:2008:ijcnn, author = "Hong-Jie Xing and Ming-Hu Ha and Da-Zeng Tian and Bao-Gang Hu", title = "A Novel Support Vector Machine with its Features Weighted by Mutual Information", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0145.pdf}, url = {}, size = {}, abstract = {A novel support vector machine (SVM) with weighted features is proposed. To assign appropriate weights for each feature, a mutual information (MI) based approach is presented. Although the calculation of feature weights may add an extra computational cost, the proposed method generally exhibits better generalisation performance over the traditional SVM. The numerical studies on one synthetic and five existing benchmark classification problems confirm the benefits in using the proposed method. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Tian2:2008:ijcnn, author = "Minghui Tian and Shouhong Wan and Yan Ji", title = "Salient Objects Detection in Time Sequenced Images", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0147.pdf}, url = {}, size = {}, abstract = {Salient objects detection in time sequenced images has a very important role in many applications such as surveillance systems, tracking and recognition systems, scene analysis and so on. This paper presents a novel approach for salient objects detection in time sequenced images. The approach in this paper is based on a visual saliency model which is proposed for analysis in time sequenced images. The model in this paper is based on a bottom-up visual saliency model which is presented by Itti in 1998. Multi different features are introduced to describe salient objects globally in time sequenced images. And they are combined into a single saliency map. Salient objects in time sequenced images can be detected by the final saliency map. The detection algorithm is unsupervised and fast. The results of the experiments indicate that our approach is effective and very robust to noise, blur, contrast level and brightness level. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Zhao:2008:ijcnn, author = "Huifang Zhao and Sheng Xu and Changhui Yang", title = "A P-SVM and Chaos Based Model for High-Technology Manufacturing Labor Productivity", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0148.pdf}, url = {}, size = {}, abstract = {Computing high-technology manufacturing (HTM) productivity level and growth rate have gained a renewed interest in both growth economists and trade economists. Measuring productivity performance has become an area of concern for companies and policy makers. A novel way about nonlinear regression modelling of high-technology manufacturing (HTM) productivity with the potential support vector machines (P-SVM) is presented in this paper. Optimisation of labour productivity (LP) is also presented in this paper, which is based on chaos and uses the P-SVM regression model as the objective function. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Hsu:2008:ijcnn, author = "Chun-Fei Hsu and Tsu-Tian Lee and Chih-Min Lin", title = "Design and Simulation of Adaptive Wavelet Neuro Control with UUB Stability", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0150.pdf}, url = {}, size = {}, abstract = {This paper proposes an adaptive wavelet neuro control (AWNC) system, which is composed of a neural controller and a tangent controller. The neural controller uses a wavelet neural network to mimic an ideal controller and the tangent controller is designed to compensate for the approximation error between the ideal controller and the neural controller with using a hyperbolic tangent function. The main advantage of the proposed AWNC is that the weights are tuned on-line, and the uniformly ultimately bounded stability of the system can be guaranteed in the Lyapunov sense. Finally, to show the effectiveness of the proposed AWNC, it is applied to control a chaotic dynamic system. Simulation results verify that the proposed AWNC system can achieve favourable tracking performance. Since the developed AWNC system has no chattering phenomena in the control efforts, it is suitable for practical applications. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Tay:2008:ijcnn, author = "L. P. Tay and J. M. Zurada and L. P. Wong", title = "HieNet Architecture Using the K-Iterations Fast Learning Artificial Neural Networks", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0151.pdf}, url = {}, size = {}, abstract = {This paper proposes a hierarchical architecture, HieNet, that uses the K-Iterations Fast Learning artificial Neural Network (KFLANN). Effective in its clustering capabilities, the KFLANN is capable of providing more stable and consistent clusters that are independent data presentation sequences (DPS). Leveraging on the ability to provide more consistent clusters, the KFLANN is initially used to identify the homogeneous Feature Spaces that prepare large dimensional networks for a hierarchical organization. We illustrate how this hierarchical structure can be constructed through the recurring use of the KFLANN and support our work with experimental results.}, keywords = { Hierarchical Networks, Homogeneous Feature Spaces,Hybrid Networks, Data Presentation Sequence, Curse of Dimensionality. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Prudêncio:2008:ijcnn, author = "Ricardo B. C. Prudêncio and Teresa B. Ludermir", title = "Active Meta-Learning with Uncertainty Sampling and Outlier Detection", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0153.pdf}, url = {}, size = {}, abstract = {Meta-Learning has been used to predict the performance of learning algorithms based on descriptive features of the learning problems. Each training example in this context, i.e. each meta-example, stores the features of a given problem and information about the empirical performance obtained by the candidate algorithms on that problem. The process of constructing a set of meta-examples may be expensive, since for each problem available for meta-example generation, it is necessary to perform an empirical evaluation of the candidate algorithms. Active Meta-Learning has been proposed to overcome this limitation by selecting only the most informative problems in the meta-example generation. In this work, we proposed an Active Meta-Learning method which combines Uncertainty Sampling and Outlier Detection techniques. Experiments were performed in a case study, yielding significant improvement in the Meta- Learning performance. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Huang:2008:ijcnn, author = "Haibin Huang and Guangfu Ma and Yufei Zhuang ", title = "Vehicle License Plate Location Based on Harris Corner Detection", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0154.pdf}, url = {}, size = {}, abstract = {As the characters of license plate have enough corners, a license plate location algorithm of colour plate based on Harris corner detection is proposed. The images are first converted from RGB color model to HSI color model and filtered. Detecting corners of saturation component is then carried out according to hue component. Thirdly, the region of license plate is searched by rough and accurate location based on the characteristic of license plate. Finally, the region is binarized and the plate is corrected based on the information of corners. This algorithm can locate license plate correctly in various conditions and the cost of the system is lower than traditional methods. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Lindgren:2008:ijcnn, author = "Jussi T. Lindgren and Jarmo Hurri and Aapo Hyvärinen", title = "Unsupervised Learning of Dependencies Between Local Luminance and Contrast in Natural Images", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0157.pdf}, url = {}, size = {}, abstract = {Separate processing of local luminance and contrast in biological visual systems has been argued to be due to the independence of these two properties in natural image data. In this paper we examine spatial, retinotopic channels formed by these two quantities and use Independent Component Analysis to study the possible dependencies between the channels. As a result, oriented, localised bandpass filter pairs are learnt, where one filter processes the luminance channel and the other the contrast channel. We study the relationship of the learnt filters and their pairings, and show that these are due to dependencies existing between local luminance and contrast. Subsequently, our results suggest that the separate processing of local luminance and contrast can not be attributed to their independence in natural images. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Soni:2008:ijcnn, author = "Bhuman Soni and Philip Hingston ", title = "Bots Trained to Play Like a Human are More Fun", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0158.pdf}, url = {}, size = {}, abstract = {Computational intelligence methods are well suited for use in computer controlled opponents for video games. In many other applications of these methods, the aim is to simulate near-optimal intelligent behaviour. But in video games, the aim is to provide interesting opponents for human players, not optimal ones. In this study, we trained neural network-based computer controlled opponents to play like a human in a popular first-person shooter. We then had gamers play-test these opponents as well as a hand-coded opponent, and surveyed them to find out which opponents they enjoyed more. Our results show that the neural network-based opponents were clearly preferred }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Noh:2008:ijcnn, author = "Jin Seok Noh and Geun Hyeong Lee and Seul Jung", title = "Position Control of a Mobile Inverted Pendulum System Using Radial Basis Function Network", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0160.pdf}, url = {}, size = {}, abstract = {This article presents the implementation of position control of a mobile inverted pendulum (MIP) system by using the radial basis function network (RBF). The MIP has two wheels to move on the plane and to balance the pendulum. The MIP is known as a nonlinear system whose dynamics is non-holonomic. The goal is to control the MIP to maintain the balance of the pendulum while tracking a desired position of the cart. The reference compensation technique (RCT) scheme is used as a neural network control method to control the MIP. The back-propagation learning algorithm for the RBF network is derived for on-line learning and control. The control algorithm has been embedded on a DSP 2812 board to achieve real-time control. Experimental results are conducted and show successful control performances of both balancing and tracking the position of the MIP. }, keywords = {RBF neural network, mobile inverted pendulum}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Kim:2008:ijcnn, author = "Jeong-seob Kim and Seul Jung", title = "Implementation of the RBF Neural Chip with the On-Line Learning Back-Propagation Algorithm", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0161.pdf}, url = {}, size = {}, abstract = {This article presents the hardware implementation of the Radial Basis Function (RBF) neural network whose internal weights are updated in the real-time fashion by the back-propagation algorithm. The floating-point processor is designed on a field programmable gate array (FPGA) chip to execute nonlinear functions required in the parallel processing calculation of the back-propagation algorithm. The performance of the on-line learning process of the RBF chip is compared numerically with the results of the RBF neural network learning program written in the MATLAB software under the same condition to check the feasibility of the implemented neural chip. The performance of the designed RBF neural chip is tested for the real-time pattern classification of the nonlinear XOR logic. }, keywords = {RBF neural network, back-propagation algorithm, floating point processor, FPGA}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Xu2:2008:ijcnn, author = "J.-X. Xu and B. Ashok and S. K. Panda and V. Bajic", title = "Modeling Transcription Termination of Selected Gene Groups Using Support Vector Machine", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0162.pdf}, url = {}, size = {}, abstract = {In this work we use support vector machine to predict polyadenylation sites (Poly (A) sites) in human DNA and mRNA sequences by analysing features around them. Two models are created. The first model identifies the possible location of the Poly (A) site effectively. The second model distinguishes between true and false Poly (A) sites, hence effectively detect the region where Poly (A) sites and transcription termination occurs. The support vector machine (SVM) approach achieves almost 90percent sensitivity, 83percent accuracy, 80percent precision and 76percent specificity on tests of the chromosomal data such as chromosome 21. The models are able to make on average just about one false prediction every 7000 nucleotides. In most cases, better results can be achieved in comparison with those reported previously on the same data sets. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Yang2:2008:ijcnn, author = "Xiaochun Yang and Weidong Zhao and Li Pan", title = "Graphical Symbol Recognition in Architectural Plans with an Improved Ant-Tree Based Clustering Algorithm", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0165.pdf}, url = {}, size = {}, abstract = {In this paper, an improved clustering algorithm based Ant-Tree is used for recognition of certain kind of architectural symbols with prior knowledge in engineering drawings. Symbols are segmented from an AutoCAD format drawing and a vector of invariants based on pseudo-Zernike moments is calculated to represent the graphical feature of a symbol. A normalisation method is used to make these moments invariant of translation, rotation and scaling. Then the improved Ant-Tree algorithm is applied to cluster the symbols with regard to their features. The class of target symbols can thus be got easily with the guidance of some prior knowledge. For the proposed clustering algorithm, a new initialisation method is presented with regard to the distribution of the data, and centroid approximation is also used to optimise the clustering process. Experiments show the effectiveness of our recognition approach proposed. }, keywords = {architectural symbols recognition, pseudo- Zernike moments, , Ant-Tree algorithm, feature representation}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Cheu:2008:ijcnn, author = "Eng-Yeow Cheu and Hiok-Chai Quek and See-Kiong Ng", title = "TNFIS: Tree-Based Neural Fuzzy Inference System", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0166.pdf}, url = {}, size = {}, abstract = {The restricted structure of fuzzy grid type based partitioning commonly employed in fuzzy model is limiting the fuzzy model on the whole to accurately describe the underlying distribution of data points in feature space. Common solution via the use of more linguistic terms to finely describe the feature space would confute the whole idea of introducing approximate reasoning. This paper proposes the TNFIS (tree-based neural fuzzy inference system) that integrates a decision tree based classification algorithm for identification of weighted rule base. The learning algorithm is fast and highly intuitive. Simulation result of a nonlinear process modelling shows that TNFIS is able to set up reasonable membership functions and generate concise rule base to approximate a desired data set. Comparison with earlier works shows that our model performs better or comparable to other models. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Sheu:2008:ijcnn, author = "Jih-Wen Sheu and Wei-Song Lin", title = "Designing Automatic Train Regulation for MRT System by Adaptive Critic Method", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0167.pdf}, url = {}, size = {}, abstract = {Because of the disturbance of operation environment in Mass Rapid Transit (MRT) system, the robustness against disturbance and the schedule punctuality under control constraint are important issues to be considered in designing Automatic Train Regulation (ATR) for MRT system. In this paper, the study on suitable traffic model for designing ATR system and ATR design based on adaptive critic design (ACD) of approximated dynamic programming, specifically on dual heuristic programming (DHP) are presented. Moreover, the method to deal with control constraint and applying gain scheduling to deal with the time variant environment of MRT system are addressed as well. For comparison, simulations with real operation environment data are done for ATRs designed by both adaptive critic method and LQ optimisation method. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Jiang2:2008:ijcnn, author = "Nan Jiang and Zhaozhi Zhang and Xiaomin Ma and Jian Wang and Yixian Yang", title = "Analysis of Nonseparable Property of Multi-Valued Multi-Threshold Neuron", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0169.pdf}, url = {}, size = {}, abstract = {We consider the multi-valued discrete real training set that can not be separated by one multi-valued multi-threshold neuron. Such training set is defined as linearly non-separable set in this paper. Our objective is to use multi-valued multi-threshold neural networks to learn nonseparable training sets. First we give the method that how to determine a training set is separable or nonseparable (i.e., the necessary and sufficient condition for linearly nonseparable is given). Then we analyse the structures within linearly nonseparable sets: not all the vectors in a linearly nonseparable set are responsible for nonseparability. So the vectors in such set can be partitioned to separable vectors and nonseparable vectors. Finally, we discuss the learning problems for a linearly nonseparable set. Such set can be learnt by a three-layer feedforward neural network with one hidden layer. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Hao:2008:ijcnn, author = "Pei-Yi Hao and Lung-Biao Tsai and Min-Shiu Lin ", title = "A New Support Vector Classification Algorithm with Parametric-Margin Model", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0173.pdf}, url = {}, size = {}, abstract = {In this paper, a new algorithm for Support Vector classification is described. It is shown how to use the parametric margin model with non-constant radius. This is useful in many cases, especially when the noise is heteroscedastic, that is, where it depends on x. Moreover, for a priori chosen v , the proposed new SV classification algorithm has advantage of using the parameter 0 ≤ ν ≤ 1 on controlling the number of support vectors. To be more precise,v is an upper bound on the fraction of margin errors and a lower bound of the fraction of support vectors. Hence, the selection of v is more intuitive. The algorithm is analysed theoretically and experimentally. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Guo2:2008:ijcnn, author = "Zunhua Guo and Weixin Xie and Jingxiong Huang ", title = "Automatic Target Recognition of Aircrafts Using Neural Networks", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0174.pdf}, url = {}, size = {}, abstract = {The multilayered feed-forward neural network was applied to automatic target recognition using the high range resolution (HRR) profiles in this paper. To extract effective features from the HRR profiles, the product spectrum originally proposed for the speech signal processing was introduced to the radar target recognition community. The product spectrum was defined as the product of the power spectrum and the group delay function, which could combine the information contained in the magnitude spectrum and phase spectrum of the HRR profiles and carry more details about the shape of the aircraft. A multilayered feed-forward neural network was selected as classifier. The HRR profiles were obtained using the two-dimensional back scatter distribution data of four different scaled aircraft models. Simulations were presented to evaluate the classification performance with the product spectrum based features. The results demonstrate that the product spectrum based features outperform the original HRR profiles and the multilayered feed-forward neural network is effective for the application of automatic target recognition of aircraft.}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Schemmel:2008:ijcnn, author = "Johannes Schemmel and Johannes Fieres and Karlheinz Meier ", title = "Wafer-Scale Integration of Analog Neural Networks", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0176.pdf}, url = {}, size = {}, abstract = {This paper introduces a novel design of an artificial neural network tailored for wafer-scale integration. The presented VLSI implementation includes continuous-time analog neurons with up to 16k inputs. A novel interconnection and routing scheme allows the mapping of a multitude of network models derived from biology on the VLSI neural network while maintaining a high resource usage. A single 20 cm wafer contains about 60 million synapses. The implemented neurons are highly accelerated compared to biological real time. The power consumption of the dense interconnection network providing the necessary communication bandwidth is a critical aspect of the system integration. A novel asynchronous lowvoltage signaling scheme is presented that makes the wafer-scale approach feasible by limiting the total power consumption while simultaneously providing a flexible, programmable network topology. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Liu4:2008:ijcnn, author = "Yang Liu and Fengqi Yu", title = "Immunity-Based Intrusion Detection for Wireless Sensor Networks", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0178.pdf}, url = {}, size = {}, abstract = {Wireless sensor networks (WSNs) are vulnerable to various attacks since they are distributed in unattended environments and have limited energy, storage and computation abilities. Preventive approaches can be applied to protect WSNs from some kinds of attacks. However, preventive methods are not efficient on specific attacks. So it is necessary to develop some mechanisms for intrusion detection. Intrusion detection system (IDS) not only prevents adversaries from attacking the network, but also provides attacks' features for improving the preventive algorithms. The traditional intrusion detection algorithms can't be applied directly to WSNs due to their constraints of resources. According to the problems in the current intrusion detection systems, based on immunology, we propose a novel IDS which is distributed, robust, and adaptive. The simulation results indicate that the proposed IDS has high accuracy in attack detections. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Lu2:2008:ijcnn, author = "Xin-Jiang Lu and Han-Xiong Li", title = "Sub-Domain Intelligent Modeling Based on Neural Networks", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0179.pdf}, url = {}, size = {}, abstract = {In this paper, a new sub-domain intelligent modeling method based on neural networks is proposed for modeling the nonlinear multivariate process. The new modeling method decomposes the process into several levels sub-models and the low level models are the sub-model of the high level models. Since the modeling method is step by step to build the sub-models from low level models to high level models, it avoids the persistent excitation signal in multi-dimensions space, which is difficult to be produced due to the constraint of industry conditions. The accuracies and efficiencies of the modeling methodology are verified by simulation. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Okun:2008:ijcnn, author = "Oleg Okun and Giorgio Valentini", title = "Dataset Complexity Can Help to Generate Accurate Ensembles of K-Nearest Neighbors", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0180.pdf}, url = {}, size = {}, abstract = {Gene expression based cancer classification using classifier ensembles is the main focus of this work. A new ensemble method is proposed that combines predictions of a small number of k-nearest neighbour (k-NN) classifiers with majority vote. Diversity of predictions is guaranteed by assigning a separate feature subset, randomly sampled from the original set of features, to each classifier. Accuracy of k-NNs is ensured by the statistically confirmed dependence between dataset complexity, determining how difficult is a dataset for classification, and classification error. Experiments carried out on three gene expression datasets containing different types of cancer show that our ensemble method is superior to (1) a single best classifier in the ensemble, (2) the nearest shrunken centroids method originally proposed for gene expression data, and (3) the traditional ensemble construction scheme that does not take into account dataset complexity. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Xiao:2008:ijcnn, author = "Ming Xiao and Shengli Xie and Yuli Fu", title = "Statistically Non-Sparse Decomposition of Two Underdetermined Audio Mixtures", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0181.pdf}, url = {}, size = {}, abstract = {This paper discusses the source recovery step in two-stage blind separation algorithm of underdetermined mixtures. A statistically non-sparse decomposition principle of two mixtures (2d-SNSDP), which is an extension of the SSDP algorithm about two mixtures, is proposed. It overcomes the disadvantage of the SSDP algorithm and sparse representation based on l1-norm. Compared with traditional sparse methods, it is non-sparse method, that is, almost all the recovered sources in any instant t are non-zero. Finally, several stereo audio signals experiments demonstrate its performance and practical. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(M.:2008:ijcnn, author = "Mario I. Chacon M. and Claudia Prieto R. and R. Sandoval R. and Alejandro Rodriguez R.", title = "A Soft Image Edge Detection Approach Based on the Time Matrix of a PCNN", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0184.pdf}, url = {}, size = {}, abstract = {Image segmentation has attracted the attention of researcher for many decades. Different approaches have been developed in order to find the solution in many different segmentation situations. In this paper we propose a novel edge detection approach aimed to generate useful information to achieve segmentation. The proposed method is based on analysis of the information provided by the time matrix generated from a pulse coupled neural network, PCNN. This information represents gray level differences among the pixel images. Two different schemes for edge detection are presented. The first scheme is developed to generate edges from coarse images and the second one to deal with more detailed edges. Similarity of this method with a previous developed method based on fuzzy edge level detection is also covered in the paper. Final results show that the proposed method may be used as a new alternative to define image edges of different levels for further analysis. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Valdes:2008:ijcnn, author = "Julio J. Valdes and Antonio Pou and Robert Orchard", title = "Characterization of Climatic Variations in Spain at the Regional Scale: A Computational Intelligence Approach", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0185.pdf}, url = {}, size = {}, abstract = {Computational intelligence and other data mining techniques are used for characterising regional and time varying climatic variations in Spain in the period 1901-2005. Daily maximum temperature data from 10 climatic stations are analysed (with and without missing values) using principal components (PC), similarity-preservation feature generation, clustering, Kolmogorov-Smirnov dissimilarity analysis and genetic programming (GP). The new features were computed using hybrid optimisation (differential evolution and Fletcher- Reeves) and GP. From them, a scalar regional climatic index was obtained which identifies time landmarks and changes in the climate rhythm. The equations obtained with GP are simpler than those obtained with PC and they highlight the most important sites characterising the regional climate. Whereas the general consensus is that there has been a clear and smooth trend towards warming during the last decades, the results suggest that the picture may probably be much more complicated than what is usually assumed. }, keywords = {genetic algorithms, genetic programming}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Yu:2008:ijcnn, author = "Ganggang Yu and Fengqi Yu and Lei Feng", title = "A Three Dimensional Localisation Algorithm Using a Mobile Anchor Node under Wireless Channel", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0186.pdf}, url = {}, size = {}, abstract = {Localisation is one of the crucial issues in wireless sensor networks. In range-based mechanisms, the nodes obtain pairwise distances or angles with extra hardware for high localisation accuracy. On the other hand, the range-free schemes obtain lower localisation accuracy at low hardware cost. To improve location accuracy, we present a three dimensional range-free localisation scheme by using a mobile anchor node equipped with a GPS. The mobile anchor node carried in an aero plane flies over the sensor node area and broadcasts its current position periodically. A sensor node in the area computes its own location using the position of the mobile anchor node where the maximum RSSI is received by the sensor node. In our scheme, neither extra hardware on each sensor node nor communications between sensor nodes is needed. Our proposed scheme is simulated in Opnet and simulation results show that our scheme performs better than other range-free localisation algorithms using mobile beacon nodes. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Takahashi:2008:ijcnn, author = "Norikazu Takahashi and Yasuhiro Minetoma ", title = "On Asymptotic Behavior of State Trajectories of Piecewise-Linear Recurrent Neural Networks Generating Periodic Sequence of Binary Vectors", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0189.pdf}, url = {}, size = {}, abstract = {Recently a sufficient condition for the recurrent neural network with the piecewise-linear output characteristic to generate a prescribed periodic sequence of binary vectors such that every two consecutive vectors differ in exactly one component has been derived. If a recurrent neural network satisfies this condition, it is guaranteed that any state trajectory of the network passes through the periodic sequence of regions corresponding to the periodic sequence of binary vectors. However, the asymptotic behaviour of the state trajectories has not been clarified yet. In this paper, we study asymptotic behaviour of state trajectories of recurrent neural networks satisfying the above-mentioned sufficient condition, and derive a criterion for state trajectories to converge a unique limit cycle. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Feng:2008:ijcnn, author = "Lei Feng and Fengqi Yu", title = "A Contention-Based MAC for Wireless Sensor Networks Including a Mobile Node", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0191.pdf}, url = {}, size = {}, abstract = {Wireless sensor networks with many inexpensive sensor nodes allow users to accurately monitor a remote environment. Usually one or several mobile nodes are used to collect and combine sensor data from each individual stationary node. These networks require robust wireless communication protocols that are energy efficient and provide low latency. Motivated by these applications, we develop a novel medium access control (MAC) protocol for this kind of applications. The sensor nodes are expected to remain inactive for most of time, but become active when a mobile node is nearby. A few techniques for energy saving are developed in our protocol, e.g., true-sleep and pseudo-sleep modes are introduced to reduce energy consumption. They are implemented by radio-triggered circuit which wakes up the stationary nodes as a mobile node moves into the stationary nodes' reception range. The proposed protocol is simulated in OPNET Modeller 10.5. The simulation results show that the proposed protocol has better performance than S-MAC and EAR (Eavesdrop-And-Register) in terms of latency, energy efficiency, and throughput. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Kurosawa:2008:ijcnn, author = "Yoshiaki Kurosawa ", title = "Incremental Learning for Feature Extraction Filter Mask Used in Similar Pattern Classification", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0194.pdf}, url = {}, size = {}, abstract = {The incremental learning system for a feature extraction unit in the character recognition system is described and experimental results are shown. The relationship between this learning system and Neural Networks (NN) are explained and the specifications of this method are described as an NN application.}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Yu2:2008:ijcnn, author = "Zhiwen Yu and Xing Wang and Hau-San Wong", title = "Ensemble Based 3D Human Motion Classification", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0196.pdf}, url = {}, size = {}, abstract = {Due to the rapid development of motion capture technology, more and more human motion databases appear. In order to effectively and efficiently manage human motion database, human motion classification is necessary. In this paper, we propose an Ensemble based Human Motion Classification Approach (EHMCA). Specifically, EHMCA first extracts the descriptors from human motion sequences. Then, singular value decomposition (SVD) is adopted to reduce the dimensionality of all the feature vectors. In the following step, a cluster ensemble approach is designed to construct the consensus matrix from the feature vectors. Finally, the normalised cut algorithm is applied to partition the consensus matrix and assign the feature vectors into the corresponding clusters. Experiments on the CMU database illustrate that the proposed approach achieves good performance. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Yuan:2008:ijcnn, author = "Jin Yuan and Kesheng Wang and Tao Yu and Xuemei Liu", title = "Incorporating Fuzzy Prior Knowledge into Relevance Vector Machine Regression", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0198.pdf}, url = {}, size = {}, abstract = {Although supervised learning has been widely used to tackle problems of function approximation and regression estimation, prior knowledge fails to be incorporated into the data-driven approach because the form of input-output data pairs are not applied. To overcome this limitation, focusing on the fusion between rough fuzzy system and very rare samples of input-output pairs with noise, this paper presents a simple but effective re-sampling algorithm based on piecewise differential interpolation and it is integrated with the sparse Bayesian learning framework for fuzzy model fused Relevance Vector Machine (RVM) regression. By using re-sampling algorithm encoded derivative regularisation, the prior knowledge is translated into a pseudo training data-set, which finally is trained by the adaptive Gaussian kernel RVM to obtain more sparse solution. A preliminary empirical study shows that combining prior knowledge with training examples can dramatically improve the regression performance, particularly when the training data-set is limited. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Olier:2008:ijcnn, author = "Ivan Olier and Alfredo Vellido", title = "A Variational Formulation for GTM Through Time", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0201.pdf}, url = {}, size = {}, abstract = {Generative Topographic Mapping (GTM) is a latent variable model that, in its original version, was conceived to provide clustering and visualisation of multivariate, real valued, i.i.d. data. It was also extended to deal with noni. i.d. data such as multivariate time series in a variant called GTM Through Time (GTM-TT), defined as a constrained Hidden Markov Model (HMM). In this paper, we provide the theoretical foundations of the reformulation of GTM-TT within the Variational Bayesian framework and provide an illustrative example of its application. This approach handles the presence of noise in the time series, helping to avert the problem of data overfitting. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Vellido:2008:ijcnn, author = "Alfredo Vellido and Jorge Velazco", title = "The Effect of Noise and Sample Size on an Unsupervised Feature Selection Method for Manifold Learning", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0202.pdf}, url = {}, size = {}, abstract = {The research on unsupervised feature selection is scarce in comparison to that for supervised models, despite the fact that this is an important issue for many clustering problems. An unsupervised feature selection method for general Finite Mixture Models was recently proposed and subsequently extended to Generative Topographic Mapping (GTM), a manifold learning constrained mixture model that provides data visualisation. Some of the results of a previous partial assessment of this unsupervised feature selection method for GTM suggested that its performance may be affected by insufficient sample size and by noisy data. In this brief study, we test in some detail such limitations of the method. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Cao:2008:ijcnn, author = "Yi Cao and Yaochu Jin and Michal Kowalczykiewicz and Bernhard Sendhoff", title = "Prediction of Convergence Dynamics of Design Performance Using Differential Recurrent Neural Networks", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0203.pdf}, url = {}, size = {}, abstract = {Computational Fluid Dynamics (CFD) simulations have been extensively used in many aerodynamic design optimisation problems, such as wing and turbine blade shape design optimization. However, it normally takes very long time to solve such optimization problems due to the heavy computation load involved in CFD simulations, where a number of differential equations are to be solved. Some efforts have been seen using feedforward neural networks to approximate CFD models. However, feedforward neural network models cannot capture well the dynamics of the differential equations. Thus, training data from a large number of different designs are needed to train feedforward neural network models to achieve reliable generalisation. In this work, a technique using differential recurrent neural networks has been proposed to predict the performance of candidate designs before the CFD simulation is fully converged. Compared to existing methods based on feedforward neural networks, this approach does not need a large number of previous designs. Case studies show that the proposed method is very promising. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Li3:2008:ijcnn, author = "Bo Li and De-Shuang Huang and Kun-Hong Liu", title = "Constrained Maximum Variance Mapping", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0210.pdf}, url = {}, size = {}, abstract = {In this paper, an efficient feature extraction method named as Constrained Maximum Variance Mapping (CMVM) is developed for dimensionality reduction. The proposed algorithm can be viewed as a linear approximation of multi-manifolds based learning approach, which takes the local geometry and manifold labels into account. After the local scatters have been characterised, the proposed method focuses on developing a linear transformation that can maximise the distances matrix between all the manifolds under the constraint of locality preserving. Then, YALE face database, ORL face database are all taken to examine the effectiveness and efficiency of the proposed method. Experimental results validate that the proposed approach is superior to other widely used feature extraction methods. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Xu3:2008:ijcnn, author = "Gang Xu and Jie Gao", title = "A New Method of Weak Signal Detection Based on Improved Matching Pursuit Algorithm", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0212.pdf}, url = {}, size = {}, abstract = {In this paper, the theory of sparse decomposition is introduced to weak signal detection, and the improved matching pursuit (MP) algorithm is studied to accomplish anti-interference process of some typical signals, such as a weak sine wave signal submerged in strong noises. The improved matching pursuit algorithm uses dual-parameter Gabor dictionary, and the iterative times can be modified in accordance with the signal to noise ratio (SNR), the genetic algorithm is also used to improve the efficiency of searching time-frequency atoms, thereby achieving high searching efficiency of time-frequency atoms and rapid noise restraining. The results of experiments indicated that the improved algorithm can effectively increase the searching speed by approximately 100 times and reduce the noises above SNR-15. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Xu4:2008:ijcnn, author = "Gang Xu and Xu Meng", title = "Detection and Recognition on Parameters of Object's Internal Structure", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0213.pdf}, url = {}, size = {}, abstract = {The detection of objects, the key technology in the field of image recognition, is the base of accuracy improvement of image recognition process. In this paper, a model is provided for detection and recognition of a object's internal structure. This model, which is based on Moment and Hough, combines geometric features as its parameter identification, and its evaluation criteria is matching percent and algorithm efficiency. The recognition, position and detection of graphics' internal structure can be completed effectively and accurately. In addition, good experimental results were obtained by using this algorithm, even though the objects were covered by each other or nested, which proves the model is applicable on practical application. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Isaacs:2008:ijcnn, author = "Amitay Isaacs and Vishwas Puttige and Tapabrata Ray and Warren Smith and Sreenatha Anavatti", title = "Development of a Memetic Algorithm for Dynamic Multi-Objective Optimization and Its Applications for Online Neural Network Modeling of UAVs", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0215.pdf}, url = {}, size = {}, abstract = {Dynamic Multi-objective Optimisation (DMO) is one of the most challenging class of optimization problems where the objective functions change over time and the optimization algorithm is required to identify the corresponding Pareto optimal solutions with minimal time lag. DMO has received very little attention in the past and none of the existing multi-objective algorithms perform satisfactorily on test problems and a handful of such applications have been reported. In this paper, we introduce a Memetic Algorithm (MA) and illustrate its performance for online Neural Network (NN) identification of the Multi-Input Multi-Output Unmanned Aerial Vehicle (UAV) system. As a typical case, the longitudinal model of the UAV is considered and the performance of a NN trained with the memetic algorithm is compared to another trained with Levenberg-Marquardt training algorithm using mini-batches. The memetic algorithm employs an orthogonal epsilon-constrained formulation to deal with multiple objectives and a Sequential Quadratic Programming (SQP) solver is embedded as its local search mechanism to improve the rate of convergence. The performance of the memetic algorithm is presented for two benchmarks Fisher's Discriminant Analysis (FDA), FDA1 and modified FDA2 before highlighting its benefits for online NN model identification for UAVs. Observations from our recent work [1] indicated that Mean Square Error (MSE) alone may not always be a good measure for training the networks. Hence the MSE and maximum absolute value of the instantaneous error is considered as objectives to be minimised which requires a Dynamic MO algorithm. The proposed memetic algorithm is aimed to solve such identification problems and the same can be extended to control problems. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Teh:2008:ijcnn, author = "Chee Siong Teh and Md. Sarwar ZahanTapan", title = "A Hybrid Supervised ANN for Classification and Data Visualization", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0217.pdf}, url = {}, size = {}, abstract = {Supervised ANNs such as Learning Vector Quantisation (LVQs) and Multi-Layer Perceptrons (MLPs) usually do not support data visualisation beside classification. Unsupervised visualisation focused ANNs such as Self-organising Maps (SOM) and its variants such as Visualization induced SOM (ViSOM) on the other hand, usually do not optimise data classification as compared with supervised ANNs such as LVQ. Thus to provide supervised classification and data visualisation simultaneously, this work is motivated to propose a novel hybrid supervised ANN of LVQ with AC by hybridising LVQ and modified Adaptive Coordinate (AC) approach. Empirical studies on benchmark data sets proven that, LVQwithAC was able to provide superior classification accuracy than SOM and ViSOM. Beside LVQwithAC was able to provide data topology, data structure, and inter-neuron distance preserve visualisation. LVQwithAC was also proven able to perform promising classification among other supervised classifiers besides its additional data visualisation ability over them. Thus, for applications requiring data visualization and classification LVQwithAC demonstrated its potential if supervised learning is all possible. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wu:2008:ijcnn, author = "Ketong Wu and Fan Cen and Huizhi Cai", title = "SVR-Based Approach to Improve Active Sonar Detection in Reverberation", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0219.pdf}, url = {}, size = {}, abstract = {Whitening method is widely used for improving active sonar detection in reverberation environment, which is equivalent to AR model estimation. However, traditional whitening methods suffer from several problems due to the varying statistics and nonlinearity of reverberation noise. In this paper, we use Support Vector Regression (SVR) to obtain the parameters of a whitening filter. The algorithm of SMO without bias is used to train SVR and three speed-up approaches are proposed. The SVR parameters C and p are selected by evaluating the detection performance. The ability of SVR prewhitener is verified on real lake data. Experimental results show that SVR prewhitener outperforms traditional methods significantly and provides an excellent performance even under low signal-to-reverberation ratio (SRR) and low-Doppler conditions. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Yu3:2008:ijcnn, author = "Zhiwen Yu and Zhongkai Deng and Hau-San Wong and Xing Wang ", title = "Fuzzy Cluster Ensemble and its Application on 3D Head Model Classification", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0220.pdf}, url = {}, size = {}, abstract = {In this paper, we propose a new algorithm called fuzzy cluster ensemble algorithm (FCEA) which integrates the fuzzy logic theory and traditional cluster ensembles for 3D head model classification. Specifically, FCEA consists of two parts: (i) data processing on the distributed locations and (ii) data fusion on the centralised location. In the distributed locations, data processing includes (i) extracting feature vectors from 3D head models, (ii) performing basic fuzzy clustering algorithm to obtain fuzzy membership matrix, while data fusion on the centralized location contains (i) creating a fuzzy cluster ensemble constructor by integrating different fuzzy membership matrices from the distributed locations, and (ii) obtaining the final results of 3D head model classification based on the fuzzy logic theory and the fuzzy cluster ensemble constructor. The experiments show that FCEA works well on 3D head model database. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Sun:2008:ijcnn, author = "Zhanquan Sun and Yinglong Wang and Jingshan Pan", title = "Short-Term Traffic Flow Forecasting Based on Clustering and Feature Selection", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0221.pdf}, url = {}, size = {}, abstract = {Traffic flow forecasting is an important issue for the application of Intelligent Transportation Systems (ITS). How to improve the traffic flow forecasting precision is a crucial problem. Traffic models in different time sections have great differences. The forecasting precision could be improved if the traffic flow forecasting models were built on different time sections respectively. Traffic flow forecasting usually is real-time and too many forecasting variables will reduce the real-time performance. So the selection of the most informative forecasting variable combination is significant. It can save computation cost and improve forecasting precision. In this paper, information bottleneck theory based on extended entropy is used to partition traffic flow of a day into different time sections. Corresponding to each time section, feature selection based on mutual information is generalised to regression problems and is used to select the most informative variable combination. Selected variables are input to Support Vector Machines (SVM) for traffic flow forecasting. Bayesian inference is used to determine the kernel parameters of SVM. The efficiency of the method is illustrated through analysing the traffic data of Jinan urban transportation. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Yu4:2008:ijcnn, author = "Zhiwen Yu and Xing Wang and Hau-San Wong and Zhongkai Deng ", title = "Pattern Mining Based on Local Distribution", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0222.pdf}, url = {}, size = {}, abstract = {Pattern mining gains more and more attention due to its useful applications in many areas, such as machine learning, database, multimedia, biology, and so on. Though there exist a lot of approaches for pattern mining, few of them consider the local distribution of the data. In the paper, we not only design six challenge datasets related to the local patterns, but also propose a new pattern mining algorithm based on local distribution. Unlike traditional pattern mining algorithms, our new algorithm first creates a local distribution for each data point by a random approach. Then, the distribution curve of each data point is simulated by the sum of low frequency curves obtained by the wavelet approach. In the third step, the coefficients of these low frequency curves for each data point are clustered by the normalised cut approach. Finally, the patterns of the datasets are obtained by the new pattern mining algorithm. The experiments show that our new algorithm outperforms traditional unsupervised learning approaches, such as K-means, EM, spectral clustering algorithm (SCA), and so on, on these six new datasets. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Zhongkai:2008:ijcnn, author = "Zhiwen Yu Zhongkai and Deng Hau-SanWong", title = "Knowledge based Cluster Ensemble", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0223.pdf}, url = {}, size = {}, abstract = {Although there exist a lot of cluster ensemble approaches, few of them consider the prior knowledge of the datasets. In this paper, we propose a new cluster ensemble approach called knowledge based cluster ensemble (KCE) which incorporates the prior knowledge of the dataset into the cluster ensemble framework. Specifically, the prior knowledge of the dataset is first represented by the side information which is encoded as pairwise constraints. Then, KCE generates a set of cluster solutions by the basic clustering algorithm. Next, KCE transforms the pairwise constraints to the confidence factor of the cluster solutions. In the following, the new data matrix is constructed by considering all the cluster solutions and their corresponding confidence factor. Finally, the results are obtained by partitioning the consensus matrix. The experiments illustrate that (1) KCE works well on the real datasets; (2) KCE outperforms most of the state-of-art cluster ensemble approaches. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Che:2008:ijcnn, author = "Xi-Long Che and Liang Hu", title = "Parallel Multidimensional Step Search Algorithm for Epsilon-Insensitive Support Vector Regression in Time Series Prediction", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0225.pdf}, url = {}, size = {}, abstract = {Recently, Epsilon-Insensitive Support Vector Regression (ε-SVR) has been introduced to solve regression and prediction problems. However, the preprocessing of data set and the selection of parameters can become a real computational burden to developer and user. Improper parameters usually lead to prediction performance degradation. In this paper, by introducing Parallel Multidimensional Step Search (PMSS) method, standard ε-SVR method is extended to a systematic approach for user to finish model selection with high prediction accuracy. Experiments with both simulation data set and practical data set were performed on computing nodes in Grid environment. Experimental results were analysed with statistical method to validate the effectiveness and accuracy of the proposed method. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Dozono:2008:ijcnn, author = "Hiroshi Dozono and Masanori Nakakuni", title = "An Integration Method of Multi-Modal Biometrics Using Supervised Pareto Learning Self Organizing Maps", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0226.pdf}, url = {}, size = {}, abstract = {This paper proposes a method for the integration of multi-modal biometrics. As the conventional authentication method, password system is mostly used. But, password mechanism has many issues. In order to solve the problems, biometric authentication methods are often used. But, the authentication method using biological characteristics, such as fingerprint, also has some problems. In this paper, we propose a authentication method using multi-modal behaviour biometrics sampled from keystroke timings and handwritten patterns. And Supervised Pareto learning Self Organising maps which integrate the multimodal vectors is proposed. The performance of this method is confirmed by the authentication experiments. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Qinghua:2008:ijcnn, author = "Wang Qinghua and Zhang Youyun and Zhu Yongshen and Yang Junyan", title = "Fault Diagnosis of Time-Frequency Images Based on Non-Negative Factorization and Neural Network Ensemble", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0227.pdf}, url = {}, size = {}, abstract = {Considering unstable characteristics of vibration signals with mechanical failure, the Wigner-Ville distributions (WVD) of vibration acceleration signals, which were acquired from the cylinder head in eight different states of valve train, were calculated and displayed in grey images. Non-negative matrix factorisation (NMF) as a useful decomposition for multivariate data and neural network ensembles (NNE) with better generalisation capability for classification than a single NN were introduced to perform intelligent diagnosis without further fault feature (such as eigenvalues or symptom parameters) extraction from time-frequency distributions. The experimental results show that the time-frequency images can be classified accurately by the proposed methods. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Taguchi:2008:ijcnn, author = "Y-h. Taguchi and M. Michael Gromiha", title = "Gene Ontology Term Prediction Based Upon Acid Occurence", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0229.pdf}, url = {}, size = {}, abstract = {Usually prediction of molecular functions of proteins from their amino acid sequences is based upon sequence similarity with proteins of known functions. However, it is well known that function is mainly dependent upon protein structures than sequences. Since structures are often independent of sequence, it is important to predict function without sequence similarities. Here we propose a method based upon amino acid occurrence for predicting Gene Ontology (GO) term. We have tested the method in a set of 3212 proteins in Protein Data Bank with less than 40percent sequence identity. Our method achieved more than 50percent sensitivity and 20percent precision for c.a. 20 selected GO terms among the most frequent 557 GO terms. Mean sensitivity, Specificity, precision, and accuracy for relatively rare (but majority) 402 GO terms among 557 GO terms are 13percent, 99percent, 9percent and 99percent, respectively. They are significantly larger than expected values of less than 2percent under assuming random selection. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Li4:2008:ijcnn, author = "Boyang Li and Jinglu Hu and Kotaro Hirasawa", title = "Financial Time Series Prediction Using a Support Vector Regression Network", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0231.pdf}, url = {}, size = {}, abstract = {This paper presents a novel support vector regression (SVR) network for financial time series prediction. The SVR network consists of two layers of SVR: transformation layer and prediction layer. The SVRs in the transformation layer forms a modular network; but distinguished with conventional modular networks, the partition of the SVR modular network is based on the output domain that has much smaller dimension. Then the transformed outputs from the transformation layer are used as the inputs for the SVR in prediction layer. The whole SVR network gives an online prediction of financial time series. Simulation results on the prediction of currency exchange rate between US dollar and Japanese Yen show the feasibility and the effectiveness of the proposed method. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Thakur:2008:ijcnn, author = "R. S. Thakur and R. C. Jain and K. R. Pardasani", title = "Graph Theoretic Based Algorithm for Mining Frequent Patterns", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0234.pdf}, url = {}, size = {}, abstract = {The primary goals of any frequent pattern mining algorithm are to reduce the number of candidates generated and tested as well as number of scan of database required and scan the database as small as possible. In this paper, we focus on reducing database scans and avoiding candidate generation. To achieve this objective a graph theoretic algorithm has been developed. The whole database is compressed by converting into pattern base in the form of a directed graph which is stored in the form of an Adjacency Matrix. This Adjacency Matrix is very small as compared to the size of database. This frequent pattern mining is done by performing operation on adjacency matrix of directed graph. The prominent feature of this method is it requires only single scan of the database for finding frequent patterns. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Zhu:2008:ijcnn, author = "Ming Zhu and Weidong Jin and Laizhao Hu", title = "Radar Emitter Signal Recognition Based on Atomic Decomposition", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0236.pdf}, url = {}, size = {}, abstract = {In this paper, a novel approach based on Gaussian Chirplet Atoms is presented to automatically recognise radar emitter signals. Firstly, based on the over-completed dictionary of Gaussian Chirplet atoms, the improved matching pursuit (MP) algorithm is applied to extract the features of the time-frequency atoms from the typical radar emitter signals, and FFT is introduced to effectively reduce the time complexity of searching step of MP. Secondly, reduce dimension of the feature parameters to re-extract the classification feature vectors. Finally, adopt the hierarchy decision strategy to realise automatic classification. The simulation experiment result shows that the classification feature vector has good properties of clustering the same and separating the different kind of radar emitter signals. Over 90percent recognition accuracy can be achieved as the signal-to-noise ratio is greater than -4dB. Therefore, the approach of signal recognition is feasible in the practical engineering area. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Jiang3:2008:ijcnn, author = "Zhu Jiang and Yan Zhang and Yong-xuan Huang and Ji-sheng Li", title = "Calibration of Traffic Dynamics Models with Data Mining", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0237.pdf}, url = {}, size = {}, abstract = {Speed-density relationships are one of models used by a mesoscopic traffic simulator to represent traffic dynamics. While the classical speed-density relationships provide a useful insight into the traffic dynamics problem and have theoretical value to traffic flow, for such applications they are limited. This paper focuses on calibrating parameters for the speed-density relationships by using data mining methods such as locally weighted regression, k-means, k-nearest neighbourhood classification and agglomerative hierarchical clustering. Meanwhile, in order to improve the precision of the parametric calibration, we also use densities and flows as variables to calibrate parameters. The proposed approach is tested with sensor data from the 3rd ring road in Beijing. The test results show that the proposed algorithm has great performance on the parametric calibration of the speed-density relationships. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Siwek:2008:ijcnn, author = "K. Siwek and S. Osowski and K. Garanty and M. Sowiński", title = "Ensemble of Neural Predictors for Forecasting the Atmospheric Pollution", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0239.pdf}, url = {}, size = {}, abstract = {The paper presents the application of an ensemble of neural predictors for forecasting the daily meteorological PM10 pollution. The Support Vector Machine has been used as the basic predicting network. The bagging technique has been applied to adapt different predictors. The results of many predictors have been combined together to form final forecasting. The blind source separation has been applied as the integration tool. The results of forecasting of the real pollution measured in the northern region of Poland have been presented and discussed. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Lemaire:2008:ijcnn, author = "Vincent Lemaire and Raphael Feraud and Nicolas Voisine ", title = "Contact Personalization Using a Score Understanding Method", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0240.pdf}, url = {}, size = {}, abstract = {This paper presents a method to interpret the output of a classification (or regression) model. The interpretation is based on two concepts: the variable importance and the value importance of the variable. Unlike most of the state of art interpretation methods, our approach allows the interpretation of the model output for every instance. Understanding the score given by a model for one instance can for example lead to an immediate decision in a Customer Relational Management (CRM) system. Moreover the proposed method does not depend on a particular model and is therefore usable for any model or software used to produce the scores. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Tang:2008:ijcnn, author = "Yaohua Tang and Jinghuai Gao and Guangzhao Cui", title = "Ensemble Learning with Generalization Performance Measurement and Negative Correlation", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0243.pdf}, url = {}, size = {}, abstract = {Conventional ensemble learning algorithms based on ambiguity decomposition and negative correlation learning theory are carried out on the basis of empirical risk minimisation principle. When SVM is used as the component learner, the generalisation ability of ensemble learning system may not be improved. In this paper, based on the estimation of the generalization performance of SVM and negative correlation learning theory, a new selective SVM ensemble learning method is proposed. Experiments on real world data sets from UCI were carried out to demonstrate the effectiveness of this method. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Fu:2008:ijcnn, author = "Yu Fu and Antony Browne", title = "Investigating the Influence of Feature Correlations on Automatic Relevance Determination", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0244.pdf}, url = {}, size = {}, abstract = {Feature selection is the technique commonly used in machine learning to select a subset of relevant features for building robust learning models. Ensemble feature relevance determination can properly group the most relevant features together and separate the relevant features from the irrelevant and redundant features. However, it cannot provide reliable local feature relevance rank. In this paper, we demonstrate that the predicted local relevance rank for the relevant features could be influenced by their highly correlated redundant features, according to the strength of their correlations. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Luo:2008:ijcnn, author = "Zhihui Luo and David Bell and Barry McCollum and Qingxiang Wu", title = "Learning to Select Relevant Perspective in a Dynamic Environment", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0246.pdf}, url = {}, size = {}, abstract = {When an agent observes its environment, there are two important characteristics of the perceived information. One is the relevance of information and the other is redundancy. The irrelevant and redundant features which commonly exists within an environment, commonly leads to agent state explosion and associated high computational cost within the learning process. This paper presents an efficient method concerning both the relevance of information and the correlation in order to improve the learning of reinforcement learning agent. We introduce a new concurrent online learning method to calculate the match count C(s) and relevance degree I(s) to quantify the redundancy and correlation of features with respect to a desired learning task. Our analysis shows that the correlation relationship of the features can be extracted and projected to concurrent biased learning threads. By comparing the commonalities of these learning threads, we can evaluate the relevance degree of a feature that contributes to a particular learning task. We explain the method using random walk examples and then demonstrate the method on the chase object domain. Our validation results show that, using the concurrent learning method, we can efficiently detect redundancy and irrelevant features from the environment on sequential tasks, and significantly improve the efficiency of learning. After relevant features are extracted, the agent can remarkably accelerate its succeeding learning speed. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Bondu:2008:ijcnn, author = "Alexis Bondu and Vincent Lemaire", title = "Adaptive Curiosity for Emotions Detection in Speech", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0247.pdf}, url = {}, size = {}, abstract = {Exploratory activities seem to be crucial for our cognitive development. According to psychologists, exploration is an intrinsically rewarding behaviour. The developmental robotics aims to design computational systems that are endowed with such an intrinsic motivation mechanism. There are possible links between developmental robotics and machine learning. Affective computing takes into account emotions in human machine interactions for intelligent system design. The main difficulty to implement automatic detection of emotions in speech is the prohibitive labelling cost of data. Active learning tries to select the most informative examples to build a training set for a predictive model. In this article, the adaptive curiosity framework is used in terms of active learning terminology, and directly compared with existing algorithms on an emotion detection problem. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Meftah:2008:ijcnn, author = "B. Meftah and A. Benyettou and O.Lezoray and W. QingXiang", title = "Image Clustering with Spiking Neuron Network", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0248.pdf}, url = {}, size = {}, abstract = {The process of segmenting images is one of the most critical ones in automatic image analysis whose goal can be regarded as to find what objects are presented in images. Artificial neural networks have been well developed. First two generations of neural networks have a lot of successful applications. Spiking Neuron Networks (SNNs) are often referred to as the 3rd generation of neural networks which have potential to solve problems related to biological stimuli. They derive their strength and interest from an accurate modelling of synaptic interactions between neurons, taking into account the time of spike emission. SNNs overcome the computational power of neural networks made of threshold or sigmoidal units. Moreover, SNNs add a new dimension, the temporal axis, to the representation capacity and the processing abilities of neural networks. In this paper, we present how SNN can be applied with efficacy in image segmentation. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Meuth:2008:ijcnn, author = "Ryan J. Meuth and Paul Robinette and Donald C. Wunsch II", title = "Computational Intelligence Meets the NetFlix Prize", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0253.pdf}, url = {}, size = {}, abstract = {The NetFlix Prize is a research contest that will award 1 Million to the first group to improve NetFlix's movie recommendation system by 10percent. Contestants are given a dataset containing the movie rating histories of customers for movies. From this data, a processing scheme must be developed that can predict how a customer will rate a given movie on a scale of 1 to 5. An architecture is presented that uses the Fuzzy-Adaptive Resonance Theory clustering method to create an interesting set of data attributes that are input to a neural network for mapping to a classification. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Ni:2008:ijcnn, author = "Yizhao Ni and Carlton Chu and Craig J Saunders and John Ashburner", title = "Kernel Methods for fMRI Pattern Prediction", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0254.pdf}, url = {}, size = {}, abstract = {In this paper, we present an effective computational approach for learning patterns of brain activity from the fMRI data. The procedure involved correcting motion artifacts, spatial smoothing, removing low frequency drifts and applying multivariate linear and non-linear kernel methods. Two novel techniques are applied: one uses the Cosine Transform to remove low-frequency drifts over time and the other involves using prior knowledge about the spatial contribution of different brain regions for the various tasks. Our experiment results on the PBAIC2007 competition data set show a great improvement for brain activity prediction, especially on some sensory experience such as hearing and vision. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wang2:2008:ijcnn, author = "Dianhui Wang ", title = "Modeling Performance Enhancement with Constrained Linear Filters", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0255.pdf}, url = {}, size = {}, abstract = {Estimation of plant Jacobian is necessary for successful control of nonlinear systems using neural networks with the specialised learning scheme. Our previous study showed that neuro-emulators provide a better estimation of the plant Jacobian using a new cost function for learning during the course of dynamic modelling and control. This paper presents an approach for further enhancing the estimation of the plant Jacobian, where a constrained linear filter is proposed to improve the quality of Jacobian teacher signals for on-line modelling. Simulations, including both modeling and adaptive control of a unknown nonlinear system, were carried out to demonstrate the usefulness of the proposed strategy. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Tivive:2008:ijcnn, author = "Fok Hing Chi Tivive and Abdesselam Bouzerdoum", title = "A Biologically Inspired Visual Pedestrian Detection System", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0257.pdf}, url = {}, size = {}, abstract = {In this paper, we present a biologically inspired method for detecting pedestrians in images. The method is based on a convolutional neural network architecture, which combines feature extraction and classification. The proposed network architecture is much simpler and easier to train than earlier versions. It differs from its predecessors in that the first processing layer consists of a set of pre-defined nonlinear derivative filters for computing gradient information. The subsequent processing layer has trainable shunting inhibitory feature detectors, which are used as inputs to a pattern classifier. The proposed pedestrian detection system is evaluated on the DaimlerChrysler pedestrian classification benchmark database and its performance is compared to the performance of support vector machines and Adaboost classifiers. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Cheung:2008:ijcnn, author = "Chi-Chung Cheung and Sin-Chun Ng", title = "Backpropagation with Two-Phase Magnified Gradient Function", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0258.pdf}, url = {}, size = {}, abstract = {Backpropagation (BP) learning algorithm is the most widely supervised learning technique which is extensively applied in the training of multi-layer feed-forward neural networks. Many modifications have been proposed to improve the performance of BP, and BP with Magnified Gradient Function (MGFPROP) is one of the fast learning algorithms which improve both the convergence rate and the global convergence capability of BP [19]. MGFPROP outperforms many benchmarking fast learning algorithms in different adaptive problems [19]. However, the performance of MGFPROP is limited due to the error overshooting problem. This paper presents a new approach called BP with Two-Phase Magnified Gradient Function (2P-MGFPROP) to overcome the error overshooting problem and hence speed up the convergence rate of MGFPROP. 2P-MGFPROP is modified from MGFPROP. It divides the learning process into two phases and adjusts the parameter setting of MGFPROP based on the nature of the phase of the learning process. Through simulation results in two different adaptive problems, 2P-MGFPROP outperforms MGFPROP with optimal parameter setting in terms of the convergence rate, and the improvement can be up to 50percent. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Rui:2008:ijcnn, author = "Lin Rui and Du Zhijiang and He Fujun and Kong Minxiu and Sun Lining", title = "Tracking a Moving Object with Mobile Robot Based on Vision", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0260.pdf}, url = {}, size = {}, abstract = {The paper proposes a real-time tracking algorithm for a moving object with mobile robot based on vision using adaptive colour matching and Kalman filter. The adaptive colour matching can limit the region containing moving object on vision image plane. It can adjust colour matching threshold to reduce the influence of lighting variations in the scene. Kalman filter is used as our prediction module to calculate motion vectors of moving object in the robot coordinate system. A view window containing the position of moving object estimated by Kalman filter is determined on image plane to reduce the image processing area. Colour matching threshold can adjust itself adaptively in view window, which is used as an updating module. Experimental results show that the algorithm can adapt to lighting variations and has good tracking precision. It can also be implemented in real time. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Han:2008:ijcnn, author = "Xue Han and Ma Hong-xu", title = "Bio-Inspired Stochastic Chance-Constrained Multi-Robot Task Allocation Using WSN", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0261.pdf}, url = {}, size = {}, abstract = {The multi-robot task allocation (MRTA) especially in unknown complex environment is one of the fundamental problems, a mostly important object in research of multi-robot. The MRTA problem is initially formulated as a chance-constrained optimisation problem. Monte Carlo simulation is used to verify the accuracy of the solution provided by the algorithm. Ant colony optimization (ACO) algorithm based on bionic swarm intelligence was used. A hybrid intelligent algorithm combined Monte Carlo simulation and neural network is used for solving stochastic chance constrained models of MRTA. A practical implementation with real WSN and real mobile robots were carried out. In environment the successful implementation of tasks without collision validates the efficiency, stability and accuracy of the proposed algorithm. The convergence curve shows that as iterative generation grows, the utility increases and finally reaches a stable and optimal value. Results show that using sensor information fusion can greatly improve the efficiency. The algorithm is proved better than tradition algorithms without WSN for MRTA in real time. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Zhibin:2008:ijcnn, author = "Liu Zhibin and Jin Lianwen", title = "LATTICESVM A New Method for Multi-class Support Vector Machines", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0262.pdf}, url = {}, size = {}, abstract = {Multi-class approaches for SVM (Support Vector Machines) is a very important issue for solving many practical problems (such as OCR and face recognition), since SVM was originally designed for binary class classification. Lots of methods based on traditional binary SVM have been proposed, each with its advantages and disadvantages. Among them, one-versus-one, one-versus-all, directed acyclic graph and binary tree are four most widely used methods. In this paper a novel LATTICESVM method, which can significantly reduce the storage and computational complexity, is proposed for multi-class SVM. A comparison in terms of storage, classification speed and accuracy against the four traditional multi-class approaches is given through both theoretic analysis and experiments on large scale handwritten Chinese character recognition. The results obtained clearly show the effectiveness of the proposed method. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Xiang:2008:ijcnn, author = "Kui Xiang and Xixiu Wu and Jian Fu and Jing Chen ", title = "Input-Output Model of Time Series Based on ESN", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0263.pdf}, url = {}, size = {}, abstract = {Echo state networks (ESN) is a novel time series model stemming from RNN. The reservoir of ESN provides a rich set of dynamics whose weighted combination can approximate teacher signal effectively. Its excellent predicting capability in deterministic system has been proved by several benchmarks. Yet analysing an input-output system using ESN has not discussed. In the paper a new I/O model is presented to address both input and output series as the observation of systems which comprise a teacher vector. Learning the vector by ESN can establish the mapping from input to output and predict the system output on the basis of new input. Though learning only the output series can also predict the unknown quantity, repeating simulations demonstrate that our model can restrain the instability of network state and improve the predicting performance. Such model gives us new choice to analyse input-output system. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Hidalgo:2008:ijcnn, author = "Denisse Hidalgo and Oscar Castillo and Patricia Melin", title = "Optimization with Genetic Algorithms of Modular Neural Networks Using Interval Type-2 Fuzzy Logic for Response Integration: The Case of Multimodal Biometry", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0266.pdf}, url = {}, size = {}, abstract = {We describe in this paper a comparative study of Fuzzy Inference Systems as methods of integration in modular neural networks (MNN's) for multimodal biometry. These methods of integration are based on type-1 and type-2 fuzzy logic. Also, the fuzzy systems are optimised with simple genetic algorithms. First, we considered the use of type-1 fuzzy logic and later the approach with type-2 fuzzy logic. The fuzzy systems were developed using genetic algorithms to handle fuzzy inference systems with different membership functions, like the triangular, trapezoidal and Gaussian; since these algorithms can generate the fuzzy systems automatically. Then the response integration of the modular neural network was tested with the optimised fuzzy integration systems. The comparative study of type-1 and type-2 fuzzy inference systems was made to observe the behaviour of the two different integration methods of modular neural networks for multimodal biometry. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Bai:2008:ijcnn, author = "Xue Bai and Vladimir Cherkassky", title = "Gender Classification of Human Faces Using Inference Through Contradictions", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0269.pdf}, url = {}, size = {}, abstract = {We present an empirical study of gender classification of human faces, using new learning methodology called inference through contradictions, introduced in [9]. This approach allows to incorporate a priori knowledge in the form of additional (unlabelled) samples, called the Universum, into the supervised learning process. Application of this methodology to gender classification shows that using this approach enables better generalisation over standard SVM classification (using labeled data alone). }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Zhang3:2008:ijcnn, author = "Liyan Zhang and Shuhai Quan and Kui Xiang ", title = "Recurrent Neural Network Optimization for Model Predictive Control", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0270.pdf}, url = {}, size = {}, abstract = {High computational burden in solving quadratic programming problem is a major obstacle when we apply model predictive control to industrial process. Recurrent neural networks offer a new quadratic programming optimisation approach due to its parallel computational performance. In this paper, we present a new architecture of solving model predictive control (MPC) problem based on one layer recurrent neural network. We give algorithm of model predictive control based on recurrent neural network and prove convergence property of one layer recurrent neural network at each sample step. Two examples demonstrate the effectiveness and efficient of the proposed recurrent neural network based MPC. Simulation results show that this approach can use fast converge property and the parallel computation ability of recurrent neural network and apply to real-time industrial process control. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Han2:2008:ijcnn, author = "Min Han and Ru Wei and Decai Li ", title = "Multivariate Chaotic Time Series Analysis and Prediction Using Improved Nonlinear Canonical Correlation Analysis", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0271.pdf}, url = {}, size = {}, abstract = {This paper proposes an improved nonlinear canonical correlation analysis algorithm named radial basis function canonical correlation analysis (RBFCCA) for multivariate chaotic time series analysis and prediction. This algorithm follows the key idea of kernel canonical correlation analysis (KCCA) method to make a nonlinear mapping of the original data sets firstly with a RBF network and a linear neural network. Then linear CCA is performed using the transformed nonlinear data sets, which corresponds to make nonlinear CCA of the original data. A modified cost function of the neural network with Lagrange multipliers and a joint learning rule based on gradient ascent algorithm which maximises the correlation coefficient of the network outputs is used to extract the maximal correlation pattern between the input and output of a prediction model. Finally, a regression model is constructed to implement the prediction problem. The performance of RBFCCA prediction algorithm is demonstrated via the prediction problem of Lorenz time series and some practical observed time series. The results compared with the traditional neural network method and the KCCA method indicate that the RBFCCA algorithm proposed in this paper is able to capture the dynamics of complex systems and give reliable prediction accuracy. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Guo3:2008:ijcnn, author = "Chen Guo and Campbell Wilson", title = "Use of Self-Organizing Maps for Texture Feature Selection in Content-Based Image Retrieval", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0273.pdf}, url = {}, size = {}, abstract = {The ``Semantic Gap'' observed in content-based image retrieval (CBIR) has become a highly active research topic in last twenty years, and it is widely accepted that domain specification is one of the most effective methods of addressing this problem. However, along with the challenge of making a CBIR system specific to a particular domain comes the challenge of making those features object dependent. Independent Component Analysis (ICA) is a powerful tool for detecting underlying texture features in images. However, features detected in this way often contain groups of features which are essentially shifted or rotated versions of each other. Thus, a method of dimensionality reduction that takes this self-similarity into account is required. In this paper, we proposed a Self-Organising Map (SOM) based clustering method to reduce the dimensionality of feature space. This method comprises two phases: clustering as well as representative selection. The result of the implementation confirms this method offers effective CBIR dimensionality reduction when using the ICA method of texture feature extraction. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Han3:2008:ijcnn, author = "Min Han and Xinzhe Wang and Yijie Wang", title = "Applying ICA on Neural Network to Simplify BOF Endpiont Predicting Model", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0274.pdf}, url = {}, size = {}, abstract = {This paper proposes an improved method to modelling the dynamic process of basic oxygen furnace (BOF) and the main idea is simplification. Aiming at the problem that it is usually difficult to build a precise endpoint dynamic model because of the high dimensional input variables which affect the final results - carbon content and temperature, this paper builds endpoint carbon content prediction model and endpoint temperature prediction model separately. First, the more important variables are chosen for two models by analysing the mechanism. The independent component analysis (ICA) is applied to reduce the input dimension for temperature prediction model. Results show that the model simplification is essential and effective. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Xu5:2008:ijcnn, author = "Gang Xu and Yuqing Lei", title = "A New Image Recognition Algorithm Based on Skeleton", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0276.pdf}, url = {}, size = {}, abstract = {Traditional recognition methods which mainly match object images with their skeleton couldn't resolve well complex objects' recognition problems. So in the paper, with an introduction and improvement of moment invariants, a new image recognition method is proposed with the combination of skeleton and moment invariants. Firstly, the paper analyses the thoughts of method. Then, the concept of object main skeleton and its extraction method is described, and with view to the characteristics of the skeleton, an extended Hu moment invariants algorithm is brought forward to calculate moment invariants of the skeleton. At the recognition stage, a two-layer generalised regression radial Basis (RBF) neural network is adopted to do machine self-learning and target- identifying. Compared with the present recognition methods based on similarity matching with skeleton, the algorithm doesn't need to face many problems such as the difficulties in matching and realising based on skeleton graph, the complexity of the Shock Graphs, the object selectivity of the Reeb Graphs and the order of the nodes which can't be guaranteed in SA-tree and so on. Compared with traditional moment recognition methods, the method not only can make calculation results meet scale, translation and rotation invariance, but also can reduce the number of related efficient pixels during moment calculation. In the meanwhile, it overcomes the difficulties that traditional moment recognition methods encountered when they deal with the fuzzy object boundary, and thus is effective. Finally, some experiments prove that the algorithm has better results for general object recognition. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Han4:2008:ijcnn, author = "Min Han and Jia Yin and Yang Li", title = "The Learning Algorithm Based on Multiresolution Analysis for Neural Networks", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0278.pdf}, url = {}, size = {}, abstract = {The multiresolution analysis learning algorithm (MRAL) for neural networks is proposed to get a more precious model from the noisy data set, which based on Multi-resolution Analysis (MRA) of the wavelet transformation and nondominated sorting genetic algorithm-II (NSGA-II). Several different scaled signals of the error function are used as the objections, and NSGA-II algorithm is applied to optimise this multiobjective problem. The new algorithm can improve the study ability of the neural networks. Two examples are provided to illustrate the efficiency of the MRAL algorithm. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Sandberg:2008:ijcnn, author = "David Sandberg and Mattias Wahde", title = "Particle Swarm Optimization of Feedforward Neural Networks for the Detection of Drowsy Driving", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0288.pdf}, url = {}, size = {}, abstract = {The work presented in this paper concerns the detection of drowsy driving based on time series measurements of driving behaviour. Artificial neural networks, trained using particle swarm optimisation, have been used to combine several indicators of drowsy driving based on a data set originating from a large study carried out in the driving simulator at the Swedish National Road and Transportation Institute. The neural networks obtained outperform the best individual indicators by a few percentage points, the best network reaching a performance (average of sensitivity and specificity) of around 75percent on previously unseen test data. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Nguwi:2008:ijcnn, author = "Yok-Yen Nguwi and Siu-Yeung Cho", title = "Two-Tier Self-Organizing Visual Model for Road Sign Recognition", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0289.pdf}, url = {}, size = {}, abstract = {This paper attempts to model human brain's cognitive process at the primary visual cortex to comprehend road sign. The cortical maps in visual cortex have been widely focused in recent research. We propose a visual model that locates road sign in an image and identifies the localised road sign. Gabor wavelets are used to encode visual information and extract features. Self-organizing maps are used to cluster and classify the road sign images. We evaluate the system with various test sets. The experimental results show encouraging recognition hit rates. There are quite a number of literatures [1]-[13] introducing different approaches to classify road sign, but none has adopted unsupervised approach. This work makes use of two-tier topological maps to recognise road signs. First-tier map, called detecting map, filters out non-road sign images and regions. Second-tier map, called recognizing map, classifies a road sign into appropriate class. }, keywords = { Self-Organizing Map, Gabor feature, Visual Model, road sign recognition }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Zheng:2008:ijcnn, author = "Lei Zheng and Siu-Yeung Cho and Chai Quek ", title = "A Memory-Based Reinforcement Learning Algorithm for Partially Observable Markovian Decision Processes", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0291.pdf}, url = {}, size = {}, abstract = {This paper presents a modified version of U-Tree [1], a memory-based reinforcement learning (RL) algorithm that uses selective perception and short-term memory to handle partially observable Markovian decision processes (POMDP). Conventional RL algorithms rely on a set of pre-defined states to model the environment, even though it can learn the state transitions from experience. U-Tree is not only able to do that, it can also build the state model by itself based on raw sensor inputs. This paper enhances U-Tree's model generation process. The paper also shows that because of the simplified and yet effective state model generated by U-Tree, it is feasible and preferable to adopt the classical Dynamic Programming (DP) algorithm for average reward MDP to solve some difficult POMDP problems. The new U-Tree is tested using a car-driving task with 31,224 world states, with the agent having very limited sensory information and little knowledge about the dynamics of the environment. }, keywords = { Reinforcement Learning Algorithm, Partially Observable Markovian Decision Processes, Dynamic Programming, Average Reward.}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Uda:2008:ijcnn, author = "Yoichi Uda and Yuko Osana", title = "Knowledge Processing System Using Kohonen Feature Map Associative Memory with Refractoriness Based on Area Representation", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0292.pdf}, url = {}, size = {}, abstract = {In this paper, we propose a knowledge processing system using Kohonen feature map associative memory with refractoriness based on area representation. The proposed system is based on the Kohonen feature map associative memory with refractoriness based on area representation. In the conventional Kohonen feature map associative memory, only one-to-one associations can be realised. In contrast, one-tomany associations are realised by the refractoriness of neurons in the Map Layer in the Kohonen feature map associative memory with refractoriness based on area representation. In this research, the Kohonen feature map associative memory with refractoriness based on area representation is applied to knowledge processing in which the knowledge is represented in a form of semantic network. The proposed system has the following features: (1) it can deal with the knowledge which is represented in a form of semantic network; (2) it can deal with characteristics inheritance; (3) it is robust for noisy input. We carried out a series of computer experiment and confirmed the effectiveness of the proposed system. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Tanimizu:2008:ijcnn, author = "Hiroyuki Tanimizu and Yuko Osana", title = "Similarity-Based Image Retrieval from Plural Key Images by Self-Organizing Map with Refractoriness", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0295.pdf}, url = {}, size = {}, abstract = {In this research, we propose a similarity-based image retrieval from plural key images by self-organising map with refractoriness. In the self-organizing map with refractoriness, the plural neurons in the Map Layer corresponding to the input can fire sequentially because of the refractoriness. The proposed image retrieval system from plural key images using the self-organizing map with refractoriness makes use of this property in order to retrieve plural similar images. In this image retrieval system, as the image feature, not only colour information but also spectrum, impression words and keywords are employed. In the proposed system, the similarity-based image retrieval from plural key images can be realised. We carried out a series of computer experiments and confirmed that the effectiveness of the proposed system. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Shiratori:2008:ijcnn, author = "Tomonori Shiratori and Yuko Osana", title = "Kohonen Feature Map Associative Memory with Area Representation for Sequential Analog Patterns", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0297.pdf}, url = {}, size = {}, abstract = {In this paper, we propose a Kohonen feature map associative memory with area representation for sequential analog patterns. This model is based on the Kohonen feature map associative memory with area representation for sequential patterns. Although the conventional Kohonen feature map associative memory with area representation for sequential patterns can deal with only binary (bipolar) patterns, the proposed model can deal not only binary (bipolar) patterns but also analog patterns. The proposed model can learn sequential analog patterns successively, and has robustness for damaged neurons. We carried out a series of computer experiments and confirmed that the effectiveness of the proposed model. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Hirata:2008:ijcnn, author = "Takanori Hirata and Takuya Tokuda and Yuko Osana", title = "Melody Retrieval by Self-Organizing Map with Refractoriness", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0300.pdf}, url = {}, size = {}, abstract = {In this research, we propose a similarity-based melody retrieval by self-organising map with refractoriness. In the self-organizing map with refractoriness, the plural neurons in the Map Layer corresponding to the input can fire sequentially because of the refractoriness. The proposed melody retrieval system using the self-organizing map with refractoriness makes use of this property in order to retrieve plural similar melodies. In this melody retrieval system, as the melody features, tone, rhythm and keyword (genre of music) are employed. We carried out a series of computer experiments and confirmed that the effectiveness of the proposed system. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Chan:2008:ijcnn, author = "Chien-Lung Chan and Yu-Chen Liu and Shih-Hui Luo", title = "Investigation of Diabetic Microvascular Complications Using Data Mining Techniques", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0307.pdf}, url = {}, size = {}, abstract = {This study theoretically analyses and numerically explores the relationship between the physiological data and three diabetic microvascular complications: diabetic retinopathy, diabetic nephropathy, and diabetic neuropathy (foot problem). Method: The analysis results of 8,736 diabetic patients in northern Taiwan by using two data mining models: C5.0 and neural network were presented and compared. Results: It is found that Creatinine is the most important predictor for diabetic retinopathy. If Creatinine is out of control, diabetic patients will easily suffer from diabetic retinopathy in spite of many other laboratory evaluations are normal. The sensitivity and specificity for diabetic retinopathy prediction using C5.0 are 58.62 and 74.73, and those using neural network are 59.48 and 99.86, respectively. In addition, diabetic nephropathy will happen when several laboratory evaluation values are worse than target values. Female diabetics with diabetic family history are easier to undergo this complication. The sensitivity and specificity for diabetic nephropathy prediction using C5.0 are 69.44 and 81.36, and those using neural network are 74.44 and 98.55, respectively. For diabetic neuropathy, female diabetics feature unqualified BMI, HbA1c and AC sugar, while male diabetics mostly have uncontrolled blood pressure. Besides, smoking diabetics are more difficult to avoid this complication. The sensitivity and specificity for diabetic foot problem prediction using C5.0 are 64.71 and 83.48, and those using neural network are 67.63 and 99.70, respectively. }, keywords = { Diabetes Mellitus, Retinopathy, Nephropathy, Neuropathy, C5.0, Neural Network.}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Lao:2008:ijcnn, author = "Jian Lao and Quansheng Ren and Jianye Zhao", title = "A Novel Chaotic Stream DS-UWB System", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0309.pdf}, url = {}, size = {}, abstract = {The novel Chaotic Stream DS-UWB system proposed in this paper accomplishes synchronisation, modulation and encryption of data in only one channel transmission mechanism. The architecture of the system combines the chaotic pulse position modulation, the complex chaotic stream ciphers encryption and the chaotic direct spread codes with the PAM based DS-UWB communication system. The synchronisation of the system is robust with noise and distortion. To overcome problems caused by the digital finite precision, the cipher systems are designed carefully to guarantee Shannon's three principles of secure systems. The chaotic direct spread code is changing with time just as the chaotic stream ciphers, which provides a better performance on security, spectrum and multi-access. Results of Simulations show that the Chaotic Stream DS-UWB has much better BER performances (3 ~ 5dB) than the ordinary DS-UWB when the channel is multi-path channel. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Masaru:2008:ijcnn, author = "Fujita Masaru and Takase Haruhiko and Kita Hidehiko and Hayashi Terumine ", title = "Shape of Error Surfaces in SpikeProp", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0310.pdf}, url = {}, size = {}, abstract = {In this paper, we discuss the shape of error surfaces, which represent error depending on parameters, in Spiking Neural Networks for SpikeProp[1]. SpikeProp is a learning algorithm that adjusts timing of spikes. The discussion is held in the viewpoint of the difference between analogue computation and digital computation (especially in discrete time). Since the error is defined by timing of spikes, quantisation error brought by digital computation changes the shape. We show typical shapes of error surfaces through some experiments. Digital computation bring rough error surfaces, which have many false local minima. These local minima will disturb effective acceleration of learning process by sophisticated algorithms. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Aly:2008:ijcnn, author = "Saleh Aly and Naoyuki Tsuruta and Rin-Ichiro Taniguchi and Atsushi Shimada", title = "Visual Feature Extraction Using Variable Map-Dimension Hypercolumn Model", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0311.pdf}, url = {}, size = {}, abstract = {Hyper-column model (HCM) is a neural network model previously proposed to solve image recognition problem. In this paper, we propose an improved version of HCM network and demonstrate its ability to solve face recognition problem. HCM network is a hierarchical model based on self-organising map (SOM) that closely follows the organization of visual cortex and builds an increasingly complex and invariant feature representation. This invariance achieved by alternating between feature extraction and feature integration operation. To improve the recognition rate of HCM, we propose a variable dimension for each map in the feature extraction layer. The number of neurons in each map-side is decided automatically from training data. We demonstrate the performance of the approach using ORL face database. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Fu2:2008:ijcnn, author = "Wei Fu and Xiaodong Gu and Yuanyuan Wang ", title = "Image Quality Assessment Using Edge and Contrast Similarity", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0312.pdf}, url = {}, size = {}, abstract = {Measurement of visual quality is of fundamental importance to some image processing applications. And the perceived image distortion of any image strongly depends on the local features, such as edges, flats and textures. Since edges often convey much information of an image, we propose a novel algorithm for image quality assessment based on the edge and contrast similarity between the distorted image and the reference(perfect) image. We demonstrate its promise through a set of intuitive examples, as well as validate its performance with subjective ratings. We also compare our method with two other state-of-the-art objective ones, which uses 550 images with different distortion types and BP neural network. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Chan2:2008:ijcnn, author = "Chien-Lung Chan and Chien-Wei Chen", title = "Discovery of Association Rules in Metabolic Syndrome Related Diseases", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0313.pdf}, url = {}, size = {}, abstract = {Since 1980, the Hypertension and Diabetes Mellitus in Metabolic Syndrome have appeared in the top ten causes of death every year in Taiwan. This research aims to study Metabolic Syndrome related disease by using data mining technique, and to understand the strength of association between Diabetes Mellitus, Hypertension and Hyperlipidemia. The data of this research came from the National Health Insurance Research Database provided by the Bureau of National Health Insurance, Department of Health. It includes the Diabetes Mellitus patients' health insurance record during 2003-2005 in Taiwan. We used association rules to find diseases patterns of Metabolic Syndrome related disease. Using data mining technique can find and confirm the relation between diseases. We found Diabetes Mellitus is related to oral diseases and blear eyes. We also found that patients with Metabolic Syndrome have higher connection with liver diseases than patients with Diabetes Mellitus. }, keywords = { Association Rules, Metabolic Syndrome.}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Zhou:2008:ijcnn, author = "Jingchao Zhou and Baihua Xiao and Qiudan Li", title = "A No Reference Image Quality Assessment Method for JPEG2000", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0314.pdf}, url = {}, size = {}, abstract = {This paper presents a novel no reference method to assess image quality. Firstly, the image is divided into many blocks. Textured blocks are selected and their amplitude fall-off curves are employed for quality prediction based on natural scene statistics. Secondly, projections of wavelet coefficients between adjacent scales with the same orientation are used to measure the positional similarity. At last, general regression neural network is adopted to conduct quality prediction according to features from above two aspects. The performance of our method is evaluated on a public data set and experimental results confirm its effectiveness. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Huang2:2008:ijcnn, author = "Tingwen Huang and Hui Huang", title = "Exponential Stability of Periodic Solution of Impulsive Fuzzy BAM Neural Networks with Time-Varying Delays", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0316.pdf}, url = {}, size = {}, abstract = {In this paper, we study impulsive fuzzy BAM neural networks. Criteria are obtained for exponential stability of globally exponential stability of periodic solution of time varying delayed fuzzy neural networks with impulses.The criteria obtained in this paper is easily verifiable. It is believed that it is useful in design neural networks in practices. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Chen7:2008:ijcnn, author = "Jing Chen and Guangcheng Xi", title = "Entropy Partition Method and Its Application for Discrete Variables and Continuous Variables", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0317.pdf}, url = {}, size = {}, abstract = {Entropy partition method for complex system has been applied in many kinds of fields. In this paper, we improve the calculation of correlative measure for both discrete variables and continuous variables, and apply this method in vascular endothelial dysfunction (ED) discrete data and neuro-endocrine-immune (NEI) continuous data respectively. The partition results show this entropy partition method's broad availability and obvious advantage in dealing with complex, multiple, nonlinear data. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Serrano:2008:ijcnn, author = "J. Ignacio Serrano and M. Dolores del Castillo and Á ngel Iglesias and Jesús Oliva", title = "Characterizing Prior Knowledge-Attention Relationship by a Computational Model of Cognitive Reading", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0318.pdf}, url = {}, size = {}, abstract = {Interest and prior knowledge are supposed to influence reading comprehension and learning from natural language texts. The effects of interest have been well studied in the literature, but little effort has been made on empirically establishing the influences of prior knowledge in reading attention and engagement, and therefore in comprehension and learning. A quantitative characterisation of this relationship is proposed in this paper by means of a connectionist and computational method, a model of cognitive reading which allows to configure and isolate inferential depth and memory issues, which are well-known to be strongly related to attention and engagement. Results have pointed out a clear and straight relationship between prior knowledge and the latter issues and they have shown the computational model to be suitable as experimental framework for the validation of further hypothesis related to human language processing. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Zeng:2008:ijcnn, author = "Zhigang Zeng and Huangqiong Chen and Shiping Wen ", title = "Global Exponential Stability of Recurrent Neural Networks with Pure Time-Varying Delays", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0320.pdf}, url = {}, size = {}, abstract = {This paper presents some theoretical results on the global exponential stability of recurrent neural networks with pure time-varying delays. It is shown that the recurrent neural network is globally exponentially stable, if the pure time varying delays satisfy some limitations. In addition to providing new criteria for recurrent neural networks with pure time varying delays, these stability conditions also improve upon the existing ones with constant time delays and without time delays. Furthermore, it is convenient to estimate the exponential convergence rates of the neural networks by using the results. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Zhang4:2008:ijcnn, author = "Xuejie Zhang and Alex Leng Phuan Tay", title = "Neural Classification of Objects Based on Gabor Signature", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0324.pdf}, url = {}, size = {}, abstract = {This paper uses a combination of K-Iterations Fast Learning Artificial Neural Network (KFLANN) and Gabor filters to create a Gabor signature classifier. Gabor filters are known to be useful in modelling responses of the receptive fields and the properties of simple cells in the visual cortex. The responses produced by Gabor filters produce good quantifiers of the visual content in any given image. A robust edge and edge orientation detection method using a combination of antisymmetric and symmetric Gabor filters is described in detail. The edge and edge orientation information are subsequently used to construct a Gabor signature that is size and orientation invariant. Some experimental results are provided to present the effectiveness and robustness of this signature construction for object classification. In addition to the KFLANN implementation, results were also obtained from a nearest neighbor classifier, back-propagation neural network and k-means clustering for the purposes of comparison. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Yu5:2008:ijcnn, author = "Zhijun Yu and Jianming Wei and Haitao Liu ", title = "A New Adaptive Maneuvering Target Tracking Algorithm Using Artificial Neural Networks", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0325.pdf}, url = {}, size = {}, abstract = {A new neural network (NN) aided adaptive unscented Kalman filter (UKF) is presented for tracking high maneuvering target. In practice, the dynamic systems of many target tracking problems are usually nonlinear and incompletely observed, moreover, there may be large modeling errors when the target is maneuverable or some parameters of the system models are inaccurate or incorrect. The adaptive capability of filters is known to be increased by incorporating a neural network into the filtering procedure. On the other hand, some nonlinear filtering methods such as extended Kalman filter (EKF) have been used to train a NN with fast convergence speed by augmenting the state with unknown connecting weights. Tackling the natural coalescent between the filtering algorithm and the NN described above, first a more efficient learning algorithm based on unscented Kalman filter (UKF) is derived, which can give a more accurate estimate of the weights and possess faster convergence rate. We then extend the algorithm to form a new NN aided adaptive UKF algorithm and use it in maneuvering target tracking applications. The NN in this algorithm is used to approximate the uncertainty of system models and is trained online, together with the target state estimation. Some simulations are also given to validate that the proposed method can give well state estimation of a highly maneuvering target. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Kretinin:2008:ijcnn, author = "A. V. Kretinin and Yu. A. Bulygin and S. G. Valyuhov", title = "Intelligent Algorithm for Forecasting of Optimum Neurons Quantity in Perceptron with One Hidden Layer", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0327.pdf}, url = {}, size = {}, abstract = {The research performed focus on the development of methods of building-up of the intelligent neural network modeling solutions database as well as methods of approximation aiming at empirical knowledge conservation and representation to find the best structure of the artificial neural network (ANN). The learning sample is made up of solutions of approximation of one-dimensional functions defined in the uniform grid nodes with the help of perceptrontype ANN with one hidden layer (single-layer perceptron-SLP). Computational experiment plan is made up of the points with uniform grid nodes abscissas, and the ordinates are defined by means of using of Sobol-Statnikov generator of the semi-uniform sequence of numbers. The training uses the stochastic approximation algorithm that is a modification of the back propagation algorithm. As a result of SLP given points training the minimum number of neurons in the hidden layer is defined at which the target accuracy is achieved. Numerous solutions of neural network approximations of one-dimensional functions of different topology are used to build-up neural network database to determine the best neuron number in the hidden layer of single-layer perceptron in order to attain the required approximation quality. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Abdel-Gawad:2008:ijcnn, author = "Ahmed H. Abdel-Gawad and Amir F. Atiya", title = "A New Accurate Approximation for the Gaussian Process Classification Problem", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0328.pdf}, url = {}, size = {}, abstract = {Gaussian processes is a very promising novel technology that has been applied for both the regression problem and the classification problem. While for the regression problem it yields simple exact solutions, this is not the case for the classification case. The reason is that we encounter intractable integrals. In this paper we propose a new approximate solution for the Gaussian process classification problem. The approximating formula is based on certain transformations of the variables and manipulations that lead to orthant multivariate Gaussian integrals. An approximation is then applied that leads to a very simple formula. In spite of its simplicity, the formula gives better results in terms of classification accuracy and speed compared to some of the well-known competing methods. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Cheng2:2008:ijcnn, author = "Wen-Chang Cheng ", title = "3D Human Face Reconstruction with Three Images Based on Constrained ICA", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0330.pdf}, url = {}, size = {}, abstract = {In this paper, we propose an improved photometric stereo scheme based on the Lambertian reflectance model and the constrained independent component analysis (CICA) method. When we obtain the object's surface normal vector on each point of an image by ICA model to reconstruct 3-D shape, we will find the normal vector's coordinates whose x-axis, y-axis and z-axis value are not arranged in turn. So we use CICA method to solve the problem. Then we obtain correct normal vector's sequence form surface, and using the enforcing integrability method to reconstruct 3-D object. Finally, we test our algorithm on a number of real images captured from the Yale Face Database B. The experimental results demonstrate that the proposed CICA method is work to find the order of normal vector. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Mu2:2008:ijcnn, author = "Chaoxu Mu and Hua Liang and Changyin Sun", title = "Inverse System Identification of Nonlinear Systems Using Least Square Support Vector Machine Based on FCM Clustering", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0331.pdf}, url = {}, size = {}, abstract = {The algorithm of least square support vector machine (LSSVM) based on fuzzy c-means (FCM) clustering is presented in this paper, which can select the number of clusters automatically depending on different parameters and samples. We adopt the method to identify the inverse system with crucial spanless process variables and the inenarrable nonlinear character. In the course of identification, we construct the allied inverse system by the left inverse soft-sensing function and the right inverse system, then use the proposed method to approach the nonlinear allied inverse system via offline training. Simulation experiments are performed and indicate that the proposed method is effective and provides satisfactory performance with excellent accuracy and low computational cost comparing with the conventional method using LSSVM. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Yang3:2008:ijcnn, author = "Wenlu Yang and Liqing Zhang", title = "Spatiotemporal Feature Extraction Based on Invariance Representation", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0333.pdf}, url = {}, size = {}, abstract = {This paper investigates spatiotemporal feature extraction from temporal image sequences based on invariance representation. Invariance representation is one of important functions of the visual cortex. We propose a novel hierarchical model based on invariance and independent component analysis for spatiotemporal feature extraction. Training the model from patches sampled from natural scenes, we can obtain image basis with properties of translational, scaling, and rotational features. Further experiments on TV videos and facial image sequences show different characteristics of spatiotemporal features are achieved by training the proposed model. All these computer simulations verify that our proposed model is successful for spatiotemporal feature extraction. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Hassan:2008:ijcnn, author = "Mostafa M. Hassan and Amir F. Atiya", title = "A New Multidimensional Penalized Likelihood Regression Method", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0334.pdf}, url = {}, size = {}, abstract = {Penalized likelihood regression is a concept whereby the log-likelihood of the observations is combined with a term measuring the smoothness of the fit, and the resulting expression is then optimized. This concept vies for achieving a compromise between goodness of fit (as typified by the likelihood function) and smoothness of the data. Penalized likelihood regression, which has been developed in the statistics literature since the seventies, has focused mostly on the onedimensional case. Attempts to consider the general multidimensional case have been limited. In this paper we propose a new multidimensional penalized likelihood regression method. The approach is based on proposing a roughness term based on the discrepancy between the function values among the Knearest- neighbors. The proposed formulation yields a simple solution in terms of a system of linear equations. We also derive an iterative solution to the problem that sheds light on its basic functionality. The iteration consists of repeatedly taking the weighted average of the target output value and the estimated function values of the K-nearest-neighbors. We show that the proposed model is fairly versatile in that it exhibits nice features in handling user-defined function constraints and data imperfections. Experimental results confirm that it is competitive with the Gaussian process regression method (one of the best methods out there), and exhibits significant speed advantage. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Shimada:2008:ijcnn, author = "Atsushi Shimada and Madoka Kanouchi and Daisaku Arita and Rin-ichiro Taniguchi", title = "Robust Estimation of Human Posture Using Incremental Learnable Self-Organizing Map", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0335.pdf}, url = {}, size = {}, abstract = {We propose an approach to improve the accuracy of estimating feature points of human body on a vision-based motion capture system (MICS) by using the Variable-density Self Organizing Map (VDSOM). The VDSOM is a kind of Self Organizing Map (SOM) and has an ability to learn training samples incrementally. We let VDSOM learn 3-D feature points of human body when the MCS succeeded in estimating them correctly. On the other hand, one or more 3-D feature point could not be estimated correctly, we use the VDSOM for the other purpose. The SOM including VDSOM has an ability to recall a part of weight vector which have learned in the learning process. We use this ability dto recall correct patterns and complement such incorrect feature points by replacing such incorrect feature points with them. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Tang2:2008:ijcnn, author = "Yaohua Tang and Jinghuai Gao and Guangzhao Cui", title = "Feature Selection Based on Kernel Pattern Similarity", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0336.pdf}, url = {}, size = {}, abstract = {Reduction of feature dimensionality is of considerable importance in machine learning. The generalization performance of classification system improves when correlated and redundant features are removed. In order to reduce the dimensionality of pattern similarity measurement in kernel space, class separability is deduced and we explore the use of the class separability in feature selection, The key idea of our method is that the feature whose removal downgrades the class separability in kernel space most is relevance to the classification. Experiments on linear and nonlinear synthetic problems and real world data sets have been carried out to demonstrate the effectiveness of this method. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Mammone:2008:ijcnn, author = "Nadia Mammone and Fabio La Foresta and Mario Versaci and Umberto Aguglia", title = "Mutual Information for Measuring Independence of STLmax Time Series in the Epileptic Brain", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0338.pdf}, url = {}, size = {}, abstract = {Results in literature show that the convergence of the Short-Term Maximum Lyapunov Exponent (STLmax) time series, extracted from intracranial EEG recorded from patients affected by intractable temporal lobe epilepsy, is linked to the seizure onset. When the STLmax profiles of different electrode sites converge (high entrainment) a seizure is likely to occur. In this paper Renyi's Mutual information (MI) is introduced in order to investigate the independence between pairs of electrodes involved in the epileptogenesis. A scalp EEG recording and an intracranial EEG recording, including two seizures each, were analysed. STLmax was estimated for each critical electrode and then MI between couples of STLmax profiles was measured. MI showed sudden spikes that occurred 8 to 15 min before the seizure onset. Thus seizure onset appears related to a burst in MI: this suggests that seizure development might restore the independence between STLmax of critical electrode sites. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Velde:2008:ijcnn, author = "Frank van der Velde and Marc de Kamps", title = "A Neural Architecture for Grounded Cognition: Representation, Structure, Dynamics and Learning", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0339.pdf}, url = {}, size = {}, abstract = {Human cognition is characterised by three important features: productivity, dynamics and grounding. These features can be integrated in a neural architecture. The representations in this architecture are not symbol tokens, that can be copied and transported. Instead, the representations always remain "in situ", because they are grounded in perception, action, emotion, associations and (semantic) relations. The neural architecture shows how these representations can be combined in a productive manner, and how dynamics influences this process. The constraints that each of these features impose on each other could result in an architecture in which the local and the global aspects of cognition interact in processing and learning. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Fieres:2008:ijcnn, author = "Johannes Fieres and Johannes Schemmel", title = "Realizing Biological Spiking Network Models in a Configurable Wafer-Scale Hardware System", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0342.pdf}, url = {}, size = {}, abstract = {An analog VLSI hardware architecture for the distributed simulation of large-scale spiking neural networks has been developed. Several hundred integrated computing nodes, each hosting up to 512 neurons, will be interconnected and operated on un-cut silicon wafers. The electro-technical aspects and the details of the hardware implementation are covered in a separate contribution to this conference. This paper focuses on the usability of the system by demonstrating that biologically relevant network models can in fact be mapped to this system. Different network configurations are established on the hardware by programmable switch matrices, repeaters, and address decoders. Systematic routing algorithms are presented to map a given network model to the hardware system. Routing is simulated for several network examples, proving the system's practical applicability. Furthermore, the routing simulations are used to fix values for yet open hardware parameters. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Beigi:2008:ijcnn, author = "Majid M. Beigi and Andreas Zell", title = "FIR-Based Classifiers for Animal Behavior Classification", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0343.pdf}, url = {}, size = {}, abstract = {In this paper, we implement a new method for classification of biological signals in general, and use it in the animal behavior classification as an example. The forced swimming test of rats or mice is a frequently used behavioral test to evaluate the efficacy of drugs in rats or mice. Frequently used features for that evaluation are obtained through observing three states: immobility, struggling/climbing and swimming in activity profiles.We consider that those activity profiles (signals) inherently contain undesired and interference noise that should be removed before feature extraction and classification. We use a Finite Impulse Response (FIR) filter to filter out that additive noise from the activity profile. The parameters of the FIR filter are obtained via maximizing the accuracy of a classifier that tries to make a discrimination between two classes of the activity profiles (e.g. drug vs. control). We use the kernel Fisher discriminant criterion as a criterion for the discrimination, the DIviding RECTangles (DIRECT) search method for solving the optimization problem and Support Vector Machines (SVMs) for the classification task. We show that Autoregressive (AR) coefficients are suitable features for the extraction of the dynamic behavior of rats and also the classification of activity profiles. Our proposed behavior classification method provides a reliable discrimination of different classes of antidepressant drugs (imipramine and desipramine) administered to rats versus a vehicle-treated group. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Alba:2008:ijcnn, author = "Enrique Alba and Davide Anguita and Alessandro Ghio and Sandro Ridella", title = "Using Variable Neighborhood Search to Improve the Support Vector Machine Performance in Embedded Automotive Applications", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0344.pdf}, url = {}, size = {}, abstract = {In this work we show that a metaheuristic, the Variable Neighborhood Search (VNS), can be effectively used in order to improve the performance of the hardware-friendly version of the Support Vector Machine (SVM). Our target is the implementation of the feed-forward phase of SVM on resource- limited hardware devices, such as Field Programmable Gate Arrays (FPGAs) and Digital Signal Processors (DSPs). The proposal has been tested on a machine-vision benchmark dataset for embedded automotive applications, showing considerable performance improvements respect to previously used techniques. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Tamminen:2008:ijcnn, author = "Satu Tamminen and Ilmari Juutilainen and Juha Roning ", title = "Product Design Model for Impact Toughness Estimation in Steel Plate Manufacturing", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0346.pdf}, url = {}, size = {}, abstract = {The purpose of this study was to develop a product design model for impact toughness estimation of low-alloy steel plates. Based on these estimates, the rejection probability of steel plates can be approximated. The target variable was formulated from three Charpy-V measurements with a LIB transformation, because the mean of the measurements would have lost valuable information. The method is suitable for all steel grades in production and it is not restricted to a few test temperatures. There were differences between the performances of different product groups, but overall performance was promising. Next the developed model will be implemented into a graphical simulation tool that is in daily use in the product planning department and already contains some other mechanical property models. The model will guide designers in predicting the related risk of rejection and in producing desired properties in the product at lower cost. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Jain:2008:ijcnn, author = "Brijnesh Jain and Klaus Obermayer ", title = "On the Sample Mean of Graphs", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0347.pdf}, url = {}, size = {}, abstract = {We present an analytic and geometric view of the sample mean of graphs. The theoretical framework yields efficient subgradient methods for approximating a structural mean and a simple plug-in mechanism to extend existing central clustering algorithms to graphs. Experiments in clustering protein structures show the benefits of the proposed theory. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Sanchez:2008:ijcnn, author = "E. N. Sanchez and E. A. Hernandez and C. Cadet", title = "Discrete-Time Recurrent High Order Neural Observer for Activated Sludge Wastewater Treatment", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0350.pdf}, url = {}, size = {}, abstract = {This paper presents a recurrent neural observer to estimate substrate and biomass concentrations in an activated sludge waste water treatment. The observer is based on a discrete time high order neural network (RHONN) trained on-line with an extended Kalman filter (EKF)-based algorithm. This observer is then associated with a hybrid intelligent system to control the substrate/biomass concentration ratio. The neural observer performance is illustrated via simulations. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Greer:2008:ijcnn, author = "Douglas S. Greer ", title = "Stable Reciprocal Image Associations in Cognitive Systems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0352.pdf}, url = {}, size = {}, abstract = {Sensory inputs such as visual images or audio spectrograms can act as symbols in a new cognitive model. The stability of direct image association operators allows the discrete bit patterns in a general-purpose symbol processing system to be replaced with continuous real-world signals. Analogous to an SR flip-flop, two reciprocal images recursively connected by association processors, can ``lock'' each other in place. A computational model of the Brodmann areas, whose boundaries are defined by the thickness of the exterior and interior lamina in cerebral cortex, closely resembles this structure. The recurrence between the cells in the cortical columns allows local connections in small regions to form overall global image associations. An implementation, based on neurotransmitter field theory, demonstrates the stability of the reciprocal-image attractors. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Alanis:2008:ijcnn, author = "Alma Y. Alanis and Edgar N. Sanchez", title = "Real-Time Discrete Recurrent High Order Neural Observer for Induction Motors", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0355.pdf}, url = {}, size = {}, abstract = {A nonlinear discrete-time neural observer for the state estimation of a discrete-time induction motor model, in presence of external and internal uncertainties is presented. The observer is based on a discrete time recurrent high order neural network (RHONN) trained with an extended Kalman filter (EKF)-based algorithm. This observer estimates the state of the unknown discrete-time nonlinear system, using a parallel configuration. The paper also includes the stability proof on the basis of the Lyapunov approach. To illustrate the applicability real-time results are included. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Huemer:2008:ijcnn, author = "Andreas Huemer and Mario Gongora and David Elizondo", title = "Evolving a Neural Network Using Dyadic Connections", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0356.pdf}, url = {}, size = {}, abstract = {Since machine learning has become a tool to make more efficient design of sophisticated systems, we present in this paper a novel methodology to create powerful neural network controllers for complex systems while minimising the design effort. Using a robot task as a case study, we have shown that using the feedback from the robot itself, the system can learn from experience, or example provided by an expert.We present a system where the processing of the feedback is integrated entirely in the growing of a spiking neural network system. The feedback is extracted from a measurement of a reward interpretation system provided by the designer, which takes into consideration the robot actions without the need for external explicit inputs.Starting with a small basic neural network, new connections are created. The connections are separated into artificial dendrites, which are mainly used for classification issues, and artificial axons, which are responsible for selecting appropriate actions. New neurons are then created using a special connection structure and the current reward interpretation of the robot.We show that dyadic connections can also make an artificial neural network acting and learning faster because they reduce the total number of neurons and connections needed in the resulting neural system.The main contribution of this research is the creation of a novel unsupervised learning system where the designer needs to define only the interface between the robot and the neural network in addition to the feedback system which includes a calculation of a reward value depending on the performance of the robot (or task aim of the system being developed). }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Lindgren2:2008:ijcnn, author = "Jussi T. Lindgren and Aapo Hyvärinen", title = "On the Learning of Nonlinear Visual Features from Natural Images by Optimizing Response Energies", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0357.pdf}, url = {}, size = {}, abstract = {The operation of V1 simple cells in primates has been traditionally modelled with linear models resembling Gabor filters, whereas the functionality of subsequent visual cortical areas is less well understood. Here we explore the learning of mechanisms for further nonlinear processing by assuming a functional form of a product of two linear filter responses, and estimating a basis for the given visual data by optimising for robust alternative of variance of the nonlinear model outputs. By a simple transformation of the learnt model, we demonstrate that on natural images, both minimisation and maximisation in our setting lead to oriented, band-pass and localised linear filters whose responses are then nonlinearly combined. In minimisation, the method learns to multiply the responses of two Gabor-like filters, whereas in maximization it learns to subtract the response magnitudes of two Gabor-like filters. Empirically, these learnt nonlinear filters appear to function as conjunction detectors and as opponent orientation filters, respectively. We provide a preliminary explanation for our results in terms of filter energy correlations and fourth power optimisation. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Achananuparp:2008:ijcnn, author = "Palakorn Achananuparp and Xiaohua Zhou and Xiaohua Hu and Xiaodan Zhang", title = "Semantic Representation in Text Classification Using Topic Signature Mapping", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0360.pdf}, url = {}, size = {}, abstract = {Document representation is one of the crucial components that determine the effectiveness of text classification tasks. Traditional document representation approaches typically adopt a popular bag-of-word method as the underlying document representation. Although it's a simple and efficient method, the major shortcoming of bag-of-word representation is in the independent of word feature assumption. Many researchers have attempted to address this issue by incorporating semantic information into document representation. In this paper, we study the effect of semantic representation on the effectiveness of text classification systems. We employed a novel semantic smoothing technique to derive semantic information in a form of mapping probability between topic signatures and single-word features. Two classifiers, NaÏve Bayes and Support Vector Machine, were selected to carry out the classification experiments. Overall, our topic-signature semantic representation approaches significantly outperformed traditional bag-of-word representation in most datasets. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wang3:2008:ijcnn, author = "Zhisong Wang and Alexander Maier", title = "Single-Trial Bistable Perception Classification Based on Sparse Nonnegative Tensor Decomposition", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0361.pdf}, url = {}, size = {}, abstract = {The study of the neuronal correlates of the spontaneous alternation in perception elicited by bistable visual stimuli is promising for understanding the mechanism of neural information processing and the neural basis of visual perception and perceptual decision-making. In this paper we apply a sparse nonnegative tensor factorisation (NTF) based method to extract features from the local field potential (LFP) in monkey visual cortex for decoding its bistable structure-frommotion (SFM) perception. We apply the feature extraction approach to the multichannel time-frequency representation of intracortical LFP data collected from the middle temporal area (MT) in a macaque monkey performing a SFM task. The advantages of the sparse NTF based feature extraction approach lies in its capability to yield components common across the space, time and frequency domains and at the same time discriminative across different conditions without prior knowledge of the discriminative frequency bands and temporal windows for a specific subject. We employ the support vector machines (SVM) classifier based on the features of the NTF components to decode the reported perception on a single-trial basis. Our results suggest that although other bands also have certain discriminability, the gamma band feature carries the most discriminative information for bistable perception, and that imposing the sparseness constraints on the nonnegative tensor factorization improves extraction of this feature. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Iftekharuddin:2008:ijcnn, author = "Khan M. Iftekharuddin and Yaqin Li ", title = "A Biologically-Inspired Computational Model for Transformation Invariant Target Recognition", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0362.pdf}, url = {}, size = {}, abstract = {Transformation invariant image recognition has been an active research area due to its widespread applications in a variety of fields such as military operations, robotics, medicalpractices, geographic scene analysis, and many others. One of theprimary challenges is detection and recognition of objects in thepresence of transformations such as resolution, rotation,translation, scale and occlusion. In this work, we investigate abiologically-inspired computational modeling approach thatexploits reinforcement learning (RL) for transformationinvariantimage recognition. The RL is implemented in anadaptive critic design (ACD) framework to approximate theneuro-dynamic programming. Two ACD algorithms such asHeuristic Dynamic Programming (HDP) and Dual Heuristicdynamic Programming (DHP) are investigated and compared fortransformation invariant recognition. The two learningalgorithms are evaluated statistically using simulatedtransformations in 2-D images as well as with a large-scaleUMIST 2-D face database with pose variations. Our simulationsshow promising results for both HDP and DHP fortransformation-invariant image recognition as well as faceauthentication. Comparing the two algorithms, DHPoutperforms HDP in learning capability, as DHP takes fewersteps to perform a successful recognition task in general. On theother hand, HDP is more robust than DHP as far as success rateacross the database is concerned when applied in a stochastic and uncertain environment, and the computational complexity involved in HDP is much less. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Rosselló:2008:ijcnn, author = "Jose L. Rosselló and Vincent Canals and Ivan de Paul and Jaume Segura ", title = "Using Stochastic Logic for Efficient Pattern Recognition Analysis", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0363.pdf}, url = {}, size = {}, abstract = {We present a pattern recognition methodology based on stochastic logic. The technique implements a parallel comparison of input data from a set of sensors to various prestored categories. Smart pulse-based stochastic-logic blocks are constructed to provide an efficient architecture that is able to implement Bayesian techniques, thus providing a low-cost solution in terms of gate count and power dissipation. The proposed architecture is applied to a specific navigation problem demonstrating that the system provides an almost optimal solution. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Liu5:2008:ijcnn, author = "Weifeng Liu and Jose C. Príncipe", title = "The Wellposedness Analysis of the Kernel Adaline", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0364.pdf}, url = {}, size = {}, abstract = { In this paper, we investigate the wellposedness of the kernel adaline. The kernel adaline finds the linear coefficients in a radial basis function network using deterministic gradient descent. We will show that the gradient descent provides an inherent regularisation as long as the training is properly early-stopped. Along with other popular regularisation techniques, this result is investigated in a unifying regularization-function concept. This understanding provides an alternative and possibly simpler way to obtain regularised solutions comparing with the cross-validation approach in regularization networks. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Lassez:2008:ijcnn, author = "Jean-Louis Lassez and Ryan Rossi and Stephen Sheel and Srinivas Mukkamala", title = "Signature Based Intrusion Detection Using Latent Semantic Analysis", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0365.pdf}, url = {}, size = {}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Liang:2008:ijcnn, author = "Fengmei Liang and Keming Xie ", title = "Classified Image Interpolation Using Neural Networks", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0367.pdf}, url = {}, size = {}, abstract = {An improved classified image interpolation algorithm is presented. The algorithm obtains high-resolution pixels by filtering with parameters that are optimal for the selected class. By applying the highly flexible neural network model in the proposed algorithms, classified image data is extended into a nonlinear model in each class while enhancing the sharpness and edge characteristic. Meantime the interpolation performance is improved and computer complexity is reduced. Besides emulation, the technology has been applied to the visual presenter with low-resolution image sensor. Results demonstrate that the new algorithm improves substantially the subjective and objective quality of the interpolated images over original interpolation algorithms, and meets the requirements of real time image processing. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Gordon:2008:ijcnn, author = "V. Scott Gordon ", title = "Neighbor Annealing for Neural Network Training", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0368.pdf}, url = {}, size = {}, abstract = {An extremely simple technique for training the weights of a feedforward multilayer neural network is described and tested. The method, dubbed ``neighbor annealing'' is a simple random walk through weight space with a gradually decreasing step size. The approach is compared against backpropagation and particle swarm optimization on a variety of training tasks. Neighbor annealing is shown to perform as well or better on the test suite, and is also shown to have pragmatic advantages. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Gordon2:2008:ijcnn, author = "V. Scott Gordon and Jeb Crouson", title = "Self-Splitting Modular Neural Network — Domain Partitioning at Boundaries of Trained Regions", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0370.pdf}, url = {}, size = {}, abstract = {A modular neural network works by dividing the input domain into segments, assigning a separate neural network to each sub-domain. This paper introduces the self-splitting modular neural network, in which the partitioning of the input domain occurs during training. It works by first attempting to solve a problem with a single network. If that fails, it finds the largest chunk of the input domain that was successfully solved, and sets that aside. The remaining unsolved portion(s) of the input domain are then recursively solved according to the same strategy. Using standard back-propagation, several large problems are shown to be solved quickly and with excellent generalisation, with very little tuning, using this divide-and-conquer approach. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Xu6:2008:ijcnn, author = "Chenfeng Xu and Jian Yang and Hongsheng Xi and Qi Jiang and Baoqun Yin ", title = "Event-Related Optimization for a Class of Resource Location with Admission Control", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0372.pdf}, url = {}, size = {}, abstract = {A class of resource location service for distributed VoD system, which combines one-hop k-random walk and global centralised indexing service, is studied. First, in order to minimising the cost of communication and guaranteeing the response time performance, a Markov model is proposed to describe the queue phenomenon, admission control and the process of location. In this model, control is related with not only states but also events, which introduce more information as the control basis. Then, an optimisation algorithm that combines policy gradient estimation and stochastic approximation is proposed. This algorithm can deal with constraints and depend on no system parameter. Finally, an illustrative simulation is performed to demonstrate the effectiveness of model and algorithm. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wang4:2008:ijcnn, author = "Na Wang and Xia Li and Xuehui Luo", title = "Semi-Supervised Kernel-Based Fuzzy C-Means with Pairwise Constraints", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0378.pdf}, url = {}, size = {}, abstract = {Clustering with constraints is an active area in machine learning and data mining. In this paper, a semi-supervised kernel-based fuzzy C-means algorithm called PCKFCM is proposed which incorporates both semi-supervised learning technique and the kernel method into traditional fuzzy clustering algorithm. The clustering is achieved by minimizing a carefully designed objective function. A kernel-based fuzzy term defined by the violation of constraints is included. The proposed PCKFCM is compared with other clustering techniques on benchmark and the experimental results convince that effective use of constraints improves the performance of kernel-based clustering. As for the effect of key parameter selection and the non-linear capability, it outperforms a similar semi-supervised fuzzy clustering approach Pairwise Constrained Competitive Agglomeration (PCCA). }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Sun2:2008:ijcnn, author = "Koun-Tem Sun and Chun-Huang Wang and Yi-Chun Lin and Yueh-Min Huang", title = "Develop a Novel Technique for a Virtual Reality Environment", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0379.pdf}, url = {}, size = {}, abstract = {In recent years, 3D virtual reality technology has been growing fast. The user interface of the Internet is facing a new challenge. Many organizations have begun to propose various kinds of specifications and standards for Web3D. The specification draft of VRML1.0 was proposed in 1994. In 2000, Java3D, Extensible 3D (X3D) and MPEG-4 Binary Format for Scene (BIFS) were formally included as important components of Web3D development by the Web3D Consortium. Quickly developing information science and technology have been shortening the half-life of knowledge. The time and the cost have become important factors in developing programs and software. Among all kinds of platforms and compiling devices, the portable bytecode of Java can reduce the waste caused by different platforms. Java technology is growing up, and its compression and security technology have improved in recent years. All of these aspects serve in making Java extremely competitive in the future. This research uses Java and Java3D API to develop a Web3D virtual reality learning environment, and proposes the development of techniques and praxis from four aspects; the virtual scene construction, learning history record, file compression technology, and security. Designers will be able to use this particular VR as a reference for developing Web3D virtual reality in the future. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Hadi:2008:ijcnn, author = "Ahmed S. Hadi ", title = "Linear Block Code Decoder Using Neural Network", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0382.pdf}, url = {}, size = {}, abstract = {In this paper the linear block code decoder is constructed by neural network. The neural network will be adapted for a single-bit error. Each layer of a neural network will simulate a linear block code decoder stage. The syndrome generator, the error detection, and the error correction stages of the linear block code decoder will be simulated by the proposed neural network. }, keywords = {Linear Block Code, Neural Network, Syndrome, Error detection, and Error Correction. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Nakano:2008:ijcnn, author = "Hidehiro Nakano and Akihide Utani", title = "Synchronization-Based Data Gathering Scheme Using Chaotic Pulse-Coupled Neural Networks in Wireless Sensor Networks", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0385.pdf}, url = {}, size = {}, abstract = {Wireless sensor networks (WSNs) have attracted significant interests of many researchers because they have great potential as a means of obtaining information of various environments remotely. WSNs have their wide range of applications, such as natural environmental monitoring in forest regions and environmental control in office buildings. In WSNs, hundreds or thousands of micro-sensor nodes with such resource limitation as battery capacity, memory, CPU, and communication capacity are deployed without control in a region and used to monitor and gather sensor information of environments. Therefore, scalable and efficient network control and/or data gathering scheme for saving energy consumption of each sensor node is needed to prolong WSN lifetime. In this paper, assuming that sensor nodes synchronize to intermittently communicate with each other only when they are active for realizing the longterm employment of WSNs, we propose a new synchronization scheme for gathering sensor information using chaotic pulsecoupled neural networks (CPCNN). We evaluate the proposed scheme using computer simulation and discuss its development potential. In simulation experiment, the proposed scheme is compared with previous synchronization scheme based on a pulse-coupled oscillator model to verify its effectiveness. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Watanabe:2008:ijcnn, author = "Kenji Watanabe and Akinori Hidaka and Takio Kurita", title = "Automatic Factorization of Biological Signals by Using Boltzmann Non-Negative Matrix Factorization", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0386.pdf}, url = {}, size = {}, abstract = {We propose an automatic factorization method for time series signals that follow Boltzmann distribution. Generally time series signals are fitted by using a model function for each sample. To analyze many samples automatically, we have to apply a factorization method. When the energy dynamics are measured in thermal equilibrium, the energy distribution can be modeled by Boltzmann distribution law. The measured signals are factorized as the non-negative sum of the probability density function of Boltzmann distribution. If these signals are composed from several components, then they can be decomposed by using the idea of non-negative matrix factorization (NMF). In this paper, we modify the original NMF to introduce the probability density function modeled by Boltzmann distribution. Also the number of components in samples is estimated by using model selection method. We applied our proposed method to actual data that was measured by fluorescence correlation spectroscopy (FCS). The experimental results show that our method can automatically factorize the signals into the correct components. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wang5:2008:ijcnn, author = "Xue Wang and Daowei Bi and Liang Ding and Sheng Wang", title = "Bootstrap Gaussian Process Classifiers for Rotating Machinery Anomaly Detection", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0387.pdf}, url = {}, size = {}, abstract = {Rotating machinery anomaly detection is of paramount significance for industries to prevent catastrophic breakdown and improve productivity and personnel safety. The kernel classifier support vector machine (SVM) has shown excellent performance towards this purpose, but it is difficult to optimize relevant hyper-parameters. In this paper, we propose a new anomaly detection approach by merging Gaussian process classifiers (GPCs) and bootstrap methods. GPCs are Bayesian probabilistic kernel classifiers and provide a well established Bayesian framework to determine the optimal or near optimal kernel hyper-parameters. They are largely unexplored for anomaly detection applications; consequently we take the initiatives to investigate GPCs' performance in these scenarios. Bootstrap methods are incorporated to improve GPCs' performance for small machinery anomaly samples by resampling at random. The proposed approach is evaluated on a motor testbed and wavelet packet is used to perform vibration analysis. Experiment results show bootstrap GPCs are highly effective and outperform GPCs and SVM with cross validation for anomaly detection. Moreover GPCs also prove to outperform SVM. Thus the proposed approach is promising for rotating machinery anomaly detection. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Li5:2008:ijcnn, author = "Jianwei Li and Weiyi Liu ", title = "A Novel Heuristic Q-Learning Algorithm for Solving Stochastic Games", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0388.pdf}, url = {}, size = {}, abstract = {We solve Nash equlibrium of stochastic games using heuristic Q-learning method on "heuristic learning" + "Q-learning" under the framework of noncooperative general sum games. Determining whether a strategy Nash equilibrium exists in a stochastic game is NP-hard even if the game is finite. Therefore normal Q-learning method based on iterative learning can't solve stochastic games with larger scale. We attempt to make heuristic evaluations for the rewards of each stage game encountered during learning and improve continually the relevant heuristic Q-values in order to approach the optimal learning. Based on such thought, we proposed Multi-agent Heuristic Q-Learning (MHQL) method and proved that its correctness, convergence and acceptable solving time complexity. The experimentation shows that our method can drastically decrease inefficient and repetitive learning thus speed up convergence than iterative Q-learning. Our method can be regarded as a basic framework for general heuristic Q-learning to design better heuristic learning rules. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Tao:2008:ijcnn, author = "Chi-Chung Tao ", title = "An Integrated Approach to Segment Mobile Commerce Market on the Train", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0389.pdf}, url = {}, size = {}, abstract = {An integrated approach based on innovation diffusion theory and lifestyle theory for customer segmentation of mobile commerce on the train using multivariate statistical analysis is proposed for Taiwan Railway Administration. Firstly, the contents of mobile commerce on the train are identified as segmentation variables and key factor facets for mobile commerce are redefined by using factor analysis. Then, the cluster analysis is used to classify customer groups which are named by analysis of variance (ANOVA) and market segmentations are described with demographic, lifestyle and train patronage variables by using cross analysis and Chi-squared independence tests. Finally, this paper discusses empirical results to provide valuable implications for better mobile commerce marketing strategies in the future. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wang6:2008:ijcnn, author = "Sheng Wang and Xue Wang and Daowei Bi and Liang Ding and Zheng You", title = "Collaborative Statistical Learning with Rough Feature Reduction for Visual Target Classification", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0390.pdf}, url = {}, size = {}, abstract = {To implement visual target classification, this paper proposes a collaborative statistical learning algorithm for online support vector machine (SVM) classifier learning in wireless multimedia sensor network (WMSN). For achieving robust target classification, classifier learning should be carried out iteratively for updating classifiers according to various situations. Because only unlabeled samples can be acquired, semi-supervised learning is desired to make full use of unlabeled samples. According to the restrict limitation in energy and bandwidth, the proposed algorithm incrementally implement classifier learning with the selected features from multiple sensor nodes, where rough set based feature reduction is used for retaining most of the intrinsic information. Furthermore, some metrics are introduced to evaluate the effectiveness of the samples in specific sensor nodes, and a sensor node selection strategy is also proposed to reduce the impact of inevitable missing detection and false detection. Experimental results demonstrate that the collaborative statistical learning algorithm can effectively implement target classification in WMSN. With the rough set based feature reduction, the proposed algorithm has outstanding performance in energy efficiency and time cost. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wu2:2008:ijcnn, author = "Jianhua Wu and Qinbao Song and Junyi Shen", title = "Missing Nominal Data Imputation Using Association Rule Based on Weighted Voting Method", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0391.pdf}, url = {}, size = {}, abstract = {With the rapid increase in the use of databases, missing data make up an important and unavoidable problem in data management and analysis. Because the mining of association rules can effectively establish the relationship among items in databases, therefore, discovered rules can be applied to predict the missing data. In this paper, we present a new method that uses association rules based on weighted voting to impute missing data. Three databases were used to demonstrate the performance of the proposed method. Experimental results prove that our method is feasible in some databases. Moreover, the proposed method was evaluated using five classification problems with two incomplete databases. Experimental results indicate that the accuracy of classification is increased when the proposed method is applied for missing attribute values imputation. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Hidaka:2008:ijcnn, author = "Akinori Hidaka and Takio Kurita", title = "Fast Training Algorithm by Particle Swarm Optimization and Random Candidate Selection for Rectangular Feature Based Boosted Detector", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0393.pdf}, url = {}, size = {}, abstract = {Adaboost is an ensemble learning algorithm that combines many base-classifiers to improve their performance. Starting with Viola and Jones' researches, Adaboost has often been used to local feature selection for object detection. Adaboost by Viola-Jones consists of following two optimization schemes: (1) training of the local features to make baseclassifiers, and (2) selection of the best local feature. Because the number of local features becomes usually more than tens of thousands, the learning algorithm is time consuming if the two optimizations are completely performed. To omit the unnecessary redundancy of the learning, we propose fast boosting algorithms by using Particle Swarm Optimization (PSO) and random candidate selection (RCS). Proposed learning algorithm is 50 times faster than the usual Adaboost while keeping comparable classification accuracy. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Pontin:2008:ijcnn, author = "David R. Pontin and Michael J. Watts and S. P. Worner", title = "Using Multi-Layer Perceptrons to Predict the Presence of Jellyfish of the Genus Physalia at New Zealand Beaches", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0395.pdf}, url = {}, size = {}, abstract = {The apparent increase in number and magnitude of jellyfish blooms in the worlds oceans has lead to concerns over potential disruption and harm to global fishery stocks. Because of the potential harm that jellyfish populations can cause and to avoid impact it would be helpful to model jellyfish populations so that species presence or absence can be predicted. Data on the presence or absence of jellyfish of the genus Physalia was modelled using Multi-Layer Perceptrons (MLP) based on oceanographic data. Results indicated that MLP are capable of predicting the presence or absence of Physalia in two regions in New Zealand and of identifying significant biological variables. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Cabanes:2008:ijcnn, author = "Guenaël Cabanes and Younès Bennani", title = "A Local Density-Based Simultaneous Two-Level Algorithm for Topographic Clustering", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0400.pdf}, url = {}, size = {}, abstract = {Determining the optimum number of clusters is an ill posed problem for which there is no simple way of knowing that number without a priori knowledge. The purpose of this paper is to provide a simultaneous two-level clustering algorithm based on self organizing map, called DS2L-SOM, which learn at the same time the structure of the data and its segmentation. The algorithm is based both on distance and density measures in order to accomplish a topographic clustering. An important feature of the algorithm is that the cluster number is discovered automatically. A great advantage of the proposed algorithm, compared to the common partitional clustering methods, is that it is not restricted to convex clusters but can recognize arbitrarily shaped clusters and touching clusters. The validity and the stability of this algorithm are superior to standard two-level clustering methods such as SOM+Kmeans and SOM+Hierarchical agglomerative clustering. This is demonstrated on a set of critical clustering problems. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Guan:2008:ijcnn, author = "Donghai Guan and Weiwei Yuan and Young-Koo Lee and Sungyoung Lee", title = "Semi-supervised Nearest Neighbor Editing", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0405.pdf}, url = {}, size = {}, abstract = {This paper proposes a novel method for data editing. The goal of data editing in instance-based learning is to remove instances from a training set in order to increase the accuracy of a classifier. To the best of our knowledge, although many diverse data editing methods have been proposed, this is the first work which uses semi-supervised learning for data editing. Wilson editing is a popular data editing technique and we implement our approach based on it. Our approach is termed semi-supervised nearest neighbor editing (SSNNE). Our empirical evaluation using 12 UCI datasets shows that SSNNE outperforms KNN and Wilson editing in terms of generalization ability. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Dahu:2008:ijcnn, author = "Wang Dahu and Yang Haizhu and Yu Fashan and Wang Xudong", title = "Research on A Novel One-way Trap-door Map Based on Improved Hyperbolic Function", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0412.pdf}, url = {}, size = {}, abstract = {In this paper, the properties of hyperbolic function are analysized at first; then a key exchange algorithm is proposed, which is based on improved hyperbolic function in combination with module computation.Moreover in comparison with the correspondent methods such as RSA and EIGamal etc., our algorithm is proven more secure and practical. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Zhao2:2008:ijcnn, author = "Guopeng Zhao and Zhiqi Shen and Chunyan Miao and Robert Gay", title = "Enhanced Extreme Learning Machine with Stacked Generalization", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0414.pdf}, url = {}, size = {}, abstract = {This paper first reviews Extreme Learning Machine (ELM) in light of Cover's theorem and interpolation for a comparative study with Radial-Basis Function (RBF) networks. To improve generalization performance, a novel method of combining a set of single ELM networks using stacked generalization is proposed. Comparisons and experiment results show that the proposed stacking ELM outperforms a single ELM network for both regression and classification problems. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Chen8:2008:ijcnn, author = "Omix Yu-Chian Chen and Guan-Wen Chen and WinstonYu-Chen Chen ", title = "A Novel Strategy for the Structure-Based Drug Design of Heat Shock Protein 90 Inhibitors", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0415.pdf}, url = {}, size = {}, abstract = {Heat shock protein 90 (HSP90) regulates the correct folding of nascent protein in tumor cells. Through the ATPase domain of HSP90, inhibition of its activity is a manipulation for anticancer treatment. Two series of anticancer compounds, flavonoids and YC-1 derivatives, were employed in this study. The reference ligand in the docking simulation showed the significant RMSD of 0.87 with respect to the template (PDB code: 1uy7). Six scoring functions (DockScore, PLP1, PLP2, LigScore1, LigScore2, and PMF) were employed to evaluate the binding affinity. The correlation coefficients (r2) between each scoring function and the biological activity were used to determine the accurate scoring function for virtual screening. The r2 values were 0.878, 0.696, 0.395, 0.276, 0.050, and 0.187 for DockScore, LigScore1, LigScore2, PLP1, PLP2, and PMF, respectively. According to the accurate DockScore, most of flavonoids and YC-1 derivatives had the higher binding affinities to HSP90 than controls and built the important hydrogen bond with the key residue ASP93. The structure-based de novo design by using Ludi program was performed to increase the binding affinity. Final thirteen potential compounds had higher binding affinity than the original ones. These candidates might guide drug design for novel HSP90 inhibitors in the future. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Fan:2008:ijcnn, author = "Yu-Neng Fan and Chun-Che Huang and Ching-Chin Chern", title = "Rule Induction Based on an Incremental Rough Set", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0417.pdf}, url = {}, size = {}, abstract = {The incremental technique is a way to solve the issue of added-in data without re-implementing the original algorithm in a dynamic database. There are numerous studies of incremental rough set based approaches. However, these approaches are applied to traditional rough set based rule induction, which may generate redundant rules without focus, and they do not verify the classification of a decision table. In addition, these previous incremental approaches are not efficient in a large database. In this paper, an incremental rule-extraction algorithm based on the previous Rule Extraction Algorithm is proposed to resolve the aforementioned issues. Applying this algorithm, while a new object is added to an information system, it is unnecessary to re-compute rule sets from the very beginning. The proposed approach updates rule sets by partially modifying the original rule sets, which increases the efficiency. This is especially useful while extracting rules in a large database. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Hu:2008:ijcnn, author = "Jinchun Hu and Badong Chen and Fuchun Sun and Zengqi Sun", title = "Adaptive Filtering for Desired Error Distribution Under Minimum Information Divergence Criterion", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0420.pdf}, url = {}, size = {}, abstract = {Conventional cost functions of adaptive filtering are usually related to the error's dispersion, such as error's moments or error's entropy, but neglect the shape aspects (peaks, kurtosis, tails, etc.) of the error distribution. In this work, we propose a new notion of filtering (or estimation) in which the error's probability density function (PDF) is shaped into a desired one. As PDFs contain all the probabilistic information, the proposed method can be used to achieve the desired error variance or error entropy, and is expected to be useful in the complex signal processing and learning systems. In our approach, the information divergence between the actual errors and the desired errors is used as the cost function. By kernel density estimation, we derive the associated stochastic gradient algorithm for the finite impulse response (FIR) filter. Simulation results emphasize the effectiveness of this new algorithm in adaptive system training. }, keywords = {Adaptive filtering, Information divergence, stochastic gradient algorithm, Kernel density estimation. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Santana:2008:ijcnn, author = "Laura E. A. Santana and Alberto Signoretti", title = "An Analysis of Data Distribution Methods in Classifier Combination Systems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0423.pdf}, url = {}, size = {}, abstract = {In systems that combine the outputs of classification methods (combination systems), such as ensembles and multi-agent systems, one of the main constraints is that the base components (classifiers or agents) should be diverse among themselves. In other words, there is clearly no accuracy gain in a system that is composed of a set of identical base components. One way of increasing diversity is through the use of feature selection or data distribution methods in combination systems. In this paper, an investigation of the impact of using data distribution methods among the components of combination systems will be performed. In this investigation, five different methods of data distribution will be used and an analysis of the combination systems, using several different configurations, will be performed. As a result of this analysis, it is aimed to detect which combination systems are more suitable to use feature distribution among the components. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Yin:2008:ijcnn, author = "Hong Li Yin and Yong Ming Wang and Nan Feng Xiao and Yan Rong Jiang", title = "Real-Time Remote Manipulation and Monitoring Architecture of an Industry Robot", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0424.pdf}, url = {}, size = {}, abstract = {Remote control and monitoring are very necessary in decentralized manufacturing environments. This is evidenced by today's distributed shop floors where agility and responsiveness are required to maintain high productivity and flexibility. However, there exists a lack of an effective system architecture that integrates remote condition monitoring and control of automated equipment; that give much consideration about the Internet data transfer time delay. This paper presented an Internet-based and sensor-driven architecture, which can guarantee the non-distortion-transfer of control information and reduce the action time difference between local simulated virtual robot and remote real robot, couple the remote monitoring and control together. For demonstrate and validate the architectural design, a 3 DOF industry robot remote operation and monitoring system has been developed. Experimental results are encouraging and demonstrate a promising application in advanced intelligent manufacturing environment. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Reznik:2008:ijcnn, author = "Leon Reznik and Gregory Von Pless", title = "Neural Networks for Cognitive Sensor Networks", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0425.pdf}, url = {}, size = {}, abstract = {The paper puts forward a concept of cognitive sensor networks and investigates a feasibility of artificial neural networks application for its realization. It describes a design of novel hierarchical configurations imitating the structural topology of brain-like architectures. They are composed from artificial neural networks distributed over network platforms with limited resources. The paper examines a cognition idea based on its implementation through the signal change detection. The novel multilevel neural networks architectures are designed and tested in sensor networks built from Crossbow Inc. sensor kits. The results are compared against conventional multilayer perceptron structures in terms of both functional efficiency and resource consumption. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Chen9:2008:ijcnn, author = "Omix Yu-Chian Chen ", title = "Pharmacoinformatics Approach to the Discovery of Novel Selective COX-2 Inhibitors by in silico Virtual Screening", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0426.pdf}, url = {}, size = {}, abstract = {Various potent anti-cancer compounds, defined as group A, B, D, and YC-1 derivatives, were recruited for the simulation trails of selective inhibition to human cyclooxygenase-2 (COX-2). From our modeling, Leu530 and Ile522 would lead to the COX-1 binding site with a tunnel-like configuration. Compounds of group B would be suitable in the lobby of COX-1. In contract, the larger compounds, group A, D, and YC-1 derivatives were more potential against COX-2. As more the compounds bound in the similar pose indicates more the possible docking poses could happen, and thus generated the consensus pose. The anthraquinonyl group could be more suitable near the Tyr371 and Trp373 of COX-2, and the added hydroxyl group that interacted with Arg106/Tyr341 gate led the ligand more stable. In aspect of group D, the fused hydroxyl and aldehydyl group on one candidate attempted to interact with the gate which induced the whole construction more stable. Besides, the hydrophobic groups, fusing on some candidates and bounding between Ser516 and Tyr371 could stabilize the whole conformations in active site. We found the H-bond interactions between the gate of active site and the hydrophobic region among Ser516 and Tyr371 were important for the bound ligands in COX-2 active site. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Nishida:2008:ijcnn, author = "Kenji Nishida and Takio Kurita", title = "Boosting with Cross-Validation Based Feature Selection for Pedestrian Detection", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0427.pdf}, url = {}, size = {}, abstract = {An example-based classification algorithm to improve generalization performance for detecting objects in images is presented. The classifier integrates component-based classifiers according to the AdaBoost algorithm. A probability estimate by a kernel-SVM is used for the outputs of base learners, which are independently trained for local features. The base learners are determined by selecting the optimal local feature according to sample weights determined by the boosting algorithm with cross-validation. Our method was applied to the MIT CBCL pedestrian image database, and 54 sub-regions were extracted from each image as local features. The experimental results showed a good classification ratio for unlearned samples. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Huang3:2008:ijcnn, author = "Kou-Yuan Huang and Ying-Liang Chou", title = "Simulated Annealing for Hierarchical Pattern Detection and Seismic Applications", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0428.pdf}, url = {}, size = {}, abstract = {A Hierarchical system is proposed by using simulated annealing for the detection of lines, circles, ellipses, and hyperbolas in image. The hierarchical detection procedures are type by type and pattern by pattern. The equation of ellipse and hyperbola is defined under translation and rotation. The distance from all points to all patterns is defined as the error. Also we use the minimum error to determine the number of patterns. The proposed simulated annealing parameter detection system can search a set of parameter vectors for the global minimal error. In the experiments, using the hierarchical system, the result of the detection of a large number of simulated image patterns is better than that of using the synchronous system. In the seismic experiments, both of two systems can well detect line of direct wave and hyperbola of reflection wave in the simulated one-shot seismogram and the real seismic data, but the hierarchical system can converge faster. The results of seismic pattern detection can improve seismic interpretation and further seismic data processing. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Senoussi:2008:ijcnn, author = "H. Senoussi and Chebel-Morello", title = "A New Contextual Based Feature Selection", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0429.pdf}, url = {}, size = {}, abstract = {The pre processing phase is essential in Knowledge Data Discovery process. We study too particularly the data filtering in supervised context, and more precisely the feature selection. Our objective is to permit a better use of the data set. Most of filtering algorithm use myopic measures, and give bad results in the case of the features correlated part by part. Consequently in the first time, we build two new contextual criteria. In the second part we introduce those criteria in an algorithm similar to the greedy algorithm. The algorithm is tested on a set of benchmarks and the results were compared with five reference algorithms: Relief, CFS, Wrapper (C4.5), consistancySubsetEval and GainRatio. Our experiments have shown its ability to detect the semi-correlated features. We conduct extensive experiments by using our algorithm like pre processing data for decision tree, nearest neighbours and Naïve Bays classifiers. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Nieto:2008:ijcnn, author = "Isidro B. Nieto and Jose Refugio Vallejo", title = "A Decision Boundary Hyperplane for the Vector Space of Conics Using a Polinomial Kernel in m-Euclidean Space", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0430.pdf}, url = {}, size = {}, abstract = {The concept of linear perceptron or spherical perceptron in confomal geometry is extended to the more general conic perceptron, namely the elliptical perceptron. By means of the d-uple embedding a polynomial kernel of degree d is used, which is widely known in SVM's for neural networks. By associating the Clifford algebra to the Vector space of conics the conic separator is introduced, generalizing the notion of separator to a decision boundary hyperconic; which is independent of the dimension of the input space. Experimental results are shown in 2-dimensional Euclidean space where we separate data that are naturally separated by some typical plane conic separators by this polynomial kernel. This is more general in the sense that it is independent of the dimension of the input data and hence we can speak of the hyperconic elliptic perceptron by using a higher degree polynomial kernel. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Cheng3:2008:ijcnn, author = "Jianlin Cheng and Zheng Wang and Gianluca Pollastri", title = "A Neural Network Approach to Ordinal Regression", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0431.pdf}, url = {}, size = {}, abstract = {Ordinal regression is an important type of learning, which has properties of both classification and regression. Here we describe an effective approach to adapt a traditional neural network to learn ordinal categories. Our approach is a generalization of the perceptron method for ordinal regression. On several benchmark datasets, our method (NNRank) outperforms a neural network classification method. Compared with the ordinal regression methods using Gaussian processes and support vector machines, NNRank achieves comparable performance. Moreover, NNRank has the advantages of traditional neural networks: learning in both online and batch modes, handling very large training datasets, and making rapid predictions. These features make NNRank a useful and complementary tool for large-scale data mining tasks such as information retrieval, web page ranking, collaborative filtering, and protein ranking in Bioinformatics. The neural network software is available at: http://www.cs.missouri.edu/ ~ chengji/cheng_software.html. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Peres:2008:ijcnn, author = "Sarajane M. Peres and Marcio L. de A. Netto", title = "The Meaningful Fractal Fuzzy Dimension Applied to the Design of Self Organizing Maps", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0432.pdf}, url = {}, size = {}, abstract = {This paper presents the principal results of a detailed study about the use of the Meaningful Fractal Fuzzy Dimension measure in the problem in determining adequately the topological dimension of output space of a Self-Organizing Map. This fractal measure is conceived by combining the Fractals Theory and Fuzzy Approximate Reasoning. In this work this measure was applied on the dataset in order to obtain a priori knowledge, which is used to support the decision making about the SOM output space design. Several maps were designed with this approach and their evaluations are discussed here. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Qiu2:2008:ijcnn, author = "Qinru Qiu and Daniel Burns and Michael Moore", title = "Accelerating Cogent Confabulation: An Exploration in the Architecture Design Space", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0433.pdf}, url = {}, size = {}, abstract = {Cogent confabulation is a computation model that mimics the Hebbian learning, information storage, inter-relation of symbolic concepts, and the recall operations of the brain. The model has been applied to cognitive processing of language, audio and visual signals. In this project, we focus on how to accelerate the computation which underlie confabulation based sentence completion through software and hardware optimization. On the software implementation side, appropriate data structures can improve the performance of the software by more than 5,000X. On the hardware implementation side, the cogent confabulation algorithm is an ideal candidate for parallel processing and its performance can be significantly improved with the help of application specific, massively parallel computing platforms. However, as the complexity and parallelism of the hardware increases, cost also increases. Architectures with different performance-cost tradeoffs are analyzed and compared. Our analysis shows that although increasing the number of processors or the size of memories per processor can increase performance, the hardware cost and performance improvements do not always exhibit a linear relation. Hardware configuration options must be carefully evaluated in order to achieve good cost performance tradeoffs. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Tepvorachai:2008:ijcnn, author = "Gorn Tepvorachai and Chris Papachristou", title = "Multi-Label Imbalanced Data Enrichment Process in Neural Net Classifier Training", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0435.pdf}, url = {}, size = {}, abstract = {Semantic scene classification, robotic state recognition, and many other real-world applications involve multilabel classification with imbalanced data. In this paper, we address these problems by using an enrichment process in neural net training. The enrichment process can manage the imbalanced data and train the neural net with high classification accuracy. Experimental results on a robotic arm controller show that our method has better generalization performance than traditional neural net training in solving the multi-label and imbalanced data problems. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Bergamini:2008:ijcnn, author = "Cheila Bergamini and Luiz S. Oliveira and Alessandro L. Koerich and Robert Sabourin", title = "Fusion of Biometric Systems using One-Class Classification", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0436.pdf}, url = {}, size = {}, abstract = {One of the main requirements of biometric systems is the ability of producing very low false acceptation rate, which very often can be achieved only by combining different biometric traits. The literature has shown that the pattern classification approach usually surpasses the classifier combination approach for this task. In this work we take into account the pattern classification approach, but considering the one-class classification approach. We show that one-class classification could be considered as an alternative for biometric fusion specially when the data is highly unbalanced or data from a single class is available. The results for one-class classification reported in this paper compares to the standard two-class SVM and surpasses all the conventional classifier combination rules tested. }, keywords = { One-class classification, multimodal biometric systems}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Gharavol:2008:ijcnn, author = "Ebrahim A. Gharavol and Ooi Ban Leong", title = "Blind Source Separation and Bearing Estimation Using Fourier- and Wavelet-Based Spectrally Condensed Data and Artificial Neural Networks for Indoor Environments", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0442.pdf}, url = {}, size = {}, abstract = {A new method for blind separation and bearing estimation of wavefronts in a smart antenna scheme, which is based on the usage of artificial neural networks (ANN) is presented here. Because of ``the curse of dimensionality,'' especially in the cases having many antenna elements, in uniform linear, circular or planar arrays, it is important to find a method which makes it feasible to use the ANNs. The proposed method, do not walk along the road of well-known method of correlation-coefficient training. In contrast this method uses the truncated version of their spectral representations. The Fast Fourier Transform (FFT) and Discrete Wavelet Transform (DWT) are employed to provide the spectral representations. The simulation scenario is set up to demonstrate that the results is applicable to realistic cases such as urban, non-line of sight, and indoor environments. For the sake of this purpose, coherent signals are employed in simulations. In this case, most conventional methods are not applicable, because they are built on some statistical assumptions which implies that the received signals by array must be independent. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(He:2008:ijcnn, author = "Haibo He and Yang Bai and Edwardo A. Garcia and Shutao Li", title = "ADASYN: Adaptive Synthetic Sampling Approach for Imbalanced Learning", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0444.pdf}, url = {}, size = {}, abstract = {This paper presents a novel adaptive synthetic (ADASYN) sampling approach for learning from imbalanced data sets. The essential idea of ADASYN is to use a weighted distribution for different minority class examples according to their level of difficulty in learning, where more synthetic data is generated for minority class examples that are harder to learn compared to those minority examples that are easier to learn. As a result, the ADASYN approach improves learning with respect to the data distributions in two ways: (1) reducing the bias introduced by the class imbalance, and (2) adaptively shifting the classification decision boundary toward the difficult examples. Simulation analyses on several machine learning data sets show the effectiveness of this method across five evaluation metrics. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Mendoza:2008:ijcnn, author = "Olivia Mendoza and Patricia Melín and Guillermo Licea", title = "Estimating Module Relevance with Sugeno Integration of Modular Neural Networks using Interval Type-2 Fuzzy Logic", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0445.pdf}, url = {}, size = {}, abstract = {In this paper a Fuzzy Logic approach to determine the relevance of each module in Modular Neural Networks for images recognition is presented. The tests were made with Type-1 and Interval Type-2 Fuzzy Inference Systems, to compare the performance of the proposed approach. In both cases the fusion operator for the modules is the Sugeno Integral, and the estimated parameters are the fuzzy densities. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Lu3:2008:ijcnn, author = "Bing Lu and Alireza Dibazar and Theodore W. Berger ", title = "Nonlinear Hebbian Learning for Noise-Independent
Vehicle Sound Recognition
", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0446.pdf}, url = {}, size = {}, abstract = {In this paper we propose using a new approach, a nonlinear Hebbian learning, to implement acoustic signature recognition of running vehicles. The proposed learning rule processes both time and frequency components of input data. The spectral analysis is realized using auditory gammatone filterbanks. The gammatone-filtered feature vectors are then assembled over multiple temporal frames to establish a highdimensional spectro-temporal representation (STR). With the exact acoustic signature of running vehicles being unknown, a nonlinear Hebbian learning (NHL) rule is employed to extract representative independent features from the spectro-temporal ones and to reduce the dimensionality of the feature space. During learning, synaptic weights between input and output neurons are adaptively learned. Motivated by neurobiological synaptic transmission in the brain, one specific nonlinear activation function, which can represent multiple independent neural signaling pathways, is proposed to process nonlinear Hebbian learning. It is shown that this function satisfies the requirements of the activation function in nonlinear neural learning, and that its derivative matches the implicit distribution of vehicle sounds, thus leading to a statistically optimal learning. Simulation results show that both STR and NHL can accurately extract critical features from original input data. The proposed model achieves better performance under noisy environments than its counterparts. For additive white Gaussian noise and common coloured noise, the proposed model demonstrates excellent robustness. It can decrease the error rate to 3percent with improvement 21 ~ 34percent at signal-to-noise ratio (SNR) = 0 dB, and can function efficiently with error rate 7 ~ 8percent at low SNR6dB when its counterparts cannot work properly at this situation. To summarize, this study not only provides an efficient way to capture important features from high-dimensional input signals but also offers robustness against severe background noise. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Kumar:2008:ijcnn, author = "Swagat Kumar and Laxmidhar Behera", title = "Implementation of a Neural Network Based Visual Motor Control Algorithm for a 7 DOF Redundant Manipulator", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0449.pdf}, url = {}, size = {}, abstract = {This paper deals with visual-motor coordination of a 7 dof robot manipulator for pick and place applications. Three issues are dealt with in this paper — finding a feasible inverse kinematic solution without using any orientation information, resolving redundancy at position level and finally maintaining the fidelity of information during clustering process thereby increasing accuracy of inverse kinematic solution. A 3-dimensional KSOM lattice is used to locally linearize the inverse kinematic relationship. The joint angle vector is divided into two groups and their effect on end-effector position is decoupled using a concept called function decomposition. It is shown that function decomposition leads to significant improvement in accuracy of inverse kinematic solution. However, this method yields a unique inverse kinematic solution for a given target point. A concept called sub-clustering in configuration space is suggested to preserve redundancy during learning process and redundancy is resolved at position level using several criteria. Even though the training is carried out off-line, the trained network is used online to compute the required joint angle vector in only one step. The accuracy attained is better than the current state of art. The experiment is implemented in real-time and the results are found to corroborate theoretical findings. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Stubberud:2008:ijcnn, author = "Stephen C. Stubberud and Kathleen A. Kramer", title = "System Identification Using the Neural-Extended Kalman
Filter for State-Estimation and Controller Modification", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0455.pdf}, url = {}, size = {}, abstract = {The neural extended Kalman filter (NEKF) is an adaptive state estimation technique that can be used in target tracking and directly in a feedback loop. It improves state estimates by learning the difference between the a priori model and the actual system dynamics. The neural network training occurs while the system is in operation. Often, however, due to stability concerns, such an adaptive component in the feedback loop is not considered desirable by the designer of a control system. Instead, the tuning of parameters is considered to be more acceptable. The ability of the NEKF to learn dynamics in an open-loop implementation, such as with target tracking and intercept prediction, can be used to identify mismodeled dynamics external to the closed-loop system. The improved nonlinear system model can then be used at given intervals to adapt the state estimator and the state feedback gains in the control law, providing better performance based on the actual system dynamics. This variation to neural extended Kalman filter control operations is introduced in this paper using applications to the nonlinear version of the standard cartpendulum system. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Kimura:2008:ijcnn, author = "Masahiro Kimura and Kazumasa Yamakawa and Kazumi Saito and Hiroshi Motoda", title = "Community Analysis of Influential Nodes for Information
Diffusion on a Social Network", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0456.pdf}, url = {}, size = {}, abstract = {We consider the problem of finding influential nodes for information diffusion on a social network under the independent cascade model. It is known that the greedy algorithm can give a good approximate solution for the problem. Aiming to obtain efficient methods for finding better approximate solutions, we explore what structual feature of the underlying network is relevant to the greedy solution that is the approximate solution by the greedy algorithm. We focus on the SR-community structure, and analyze the greedy solution in terms of the SR-community structure. Using real large social networks, we experimentally demonstrate that the SRcommunity structure can be more strongly correlated with the greedy solution than the community structure introduced by Newman and Leicht. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Gu:2008:ijcnn, author = "Bin Gu and Jiandong Wang and Haiyan Chen ", title = "On-line Off-line Ranking Support Vector Machine and Analysis", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0459.pdf}, url = {}, size = {}, abstract = {Ranking Support Vector Machine (RSVM) learning is equivalent to solving a convex quadratic programming problem. Currently there exists some difficulties for exact online ranking learning. This paper presents an exact and effective method that can solve the on-line ranking learning problem and shows the feasibility and finite convergence of the algorithm from the perspective of theoretical analysis. Additionally, this paper extends this method for on-line learning to off-line ranking learning and offers another algorithm for solving largescale RSVM}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Makki:2008:ijcnn, author = "B. Makki and M. Noori Hosseini and S. A. Seyyedsalehi", title = "Unsupervised Extraction of Meaningful Nonlinear Principal Components Applied for Voice Conversion", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0461.pdf}, url = {}, size = {}, abstract = {Nonlinear Principal Component Analysis (NLPCA) is one of the most progressive computational tools developed during the last two decades. However, in spite of its proper performance in feature extraction and dimension reduction, it is considered as a blind processor which can not extract physical or meaningful factors from dataset. This paper presents a new distributed model of autoassociative neural network which increases meaningfulness degree of the extracted parameters. The model is implemented to perform Voice Conversion (VC) and, as it will be seen through comparisons, results in proper conversion quality. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Minku:2008:ijcnn, author = "Fernanda L. Minku and Xin Yao", title = "On-line Bagging Negative Correlation Learning", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0463.pdf}, url = {}, size = {}, abstract = {Negative Correlation Learning (NCL) has been showing to outperform other ensemble learning approaches in off-line mode. A key point to the success of NCL is that the learning of an ensemble member is influenced by the learning of the others, directly encouraging diversity. However, when applied to on-line learning, NCL presents the problem that part of the diversity has to be built a priori, as the same sequence of training data is sent to all the ensemble members. In this way, the choice of the base models to be used is limited and the use of more adequate neural network models for the problem to be solved may be not possible. This paper proposes a new method to perform on-line learning based on NCL and On-line Bagging. The method directly encourages diversity, as NCL, but sends a different sequence of training data to each one of the base models in an on-line bagging way. So, it allows the use of deterministic base models such as Evolving Fuzzy Neural Networks (EFuNNs), which are specifically designed to perform on-line learning. Experiments show that on-line bagging NCL using EFuNNs have better accuracy than NCL applied to online learning using on-line Multi-Layer Perceptrons (MLPs) in 4 out of 5 classification databases. Besides, on-line bagging NCL using EFuNNs manage to attain similar accuracy to NCL using off-line MLPs. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Korsrilabutr:2008:ijcnn, author = "Teesid Korsrilabutr and Boonserm Kijsirikul", title = "Pseudometrics for Time Series Classification by Nearest Neighbor", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0464.pdf}, url = {}, size = {}, abstract = {Despite the success of its applications in many areas, the Dynamic Time Warping (DTW) distance does not satisfy the triangle inequality (subadditivity). Once we have a subadditive distance measure for time series, the measure will have at least one significant advantage over DTW; one can directly plug such distance measure into systems which exploit the subadditivity to perform faster similarity search techniques. We propose two frameworks for designing subadditive distance measures and a few examples of distance measures resulting from the frameworks. One framework is more general than the other and can be used to tailor distances from the other framework to gain better classification performance. Experimental results of nearest neighbor classification showed that the designed distance measures are practical for time series classification. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Calster:2008:ijcnn, author = "Ben Van Calster and Vanya Van Belle and George Condous and Tom Bourne", title = "Multi-class AUC metrics and weighted alternatives", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0466.pdf}, url = {}, size = {}, abstract = {The area under the receiver operating characteristic curve (AUC) is a useful and widely used measure to evaluate the performance of binary and multi-class classification models. However, it does not take into account the exact numerical output of the models, but rather looks at how the output ranks the cases. AUC metrics that incorporate the exact numerical output have been developed for binary classification. In this paper, we try to extend such weighted metrics to the multiclass case. Several metrics are suggested. Using real world data, we investigate intercorrelations between these metrics and demonstrate their use. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Duan:2008:ijcnn, author = "Haibin Duan and Senqi Liu and Xiujuan Lei", title = "Air Robot Path Planning Based on Intelligent Water Drops Optimization", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0469.pdf}, url = {}, size = {}, abstract = {Path planning of air robot is a complicated global optimum problem. Intelligent Water Drops (IWD) algorithm is newly presented under the inspiration of the dynamic of river systems and the actions that water drops do in the rivers, and it is easy to combine with other methods in optimization. In this paper, we propose an improved IWD optimization algorithm for solving the air robot path planning problems in various environments. The water drops can act as an agent in searching the optimal path. The detailed realization procedure for this novel approach is also presented. Series experimental comparison results show the proposed IWD optimization algorithm is more effective and feasible in the air robot path planning than the basic IWD model. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Tao2:2008:ijcnn, author = "Dacheng Tao and Jimeng Sun and Jialie Shen", title = "Bayesian Tensor Analysis", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0470.pdf}, url = {}, size = {}, abstract = {Vector data are normally used for probabilistic graphical models with Bayesian inference. However, tensor data, i.e., multidimensional arrays, are actually natural representations of a large amount of real data, in data mining, computer vision, and many other applications. Aiming at breaking the huge gap between vectors and tensors in conventional statistical tasks, e.g., automatic model selection, this paper proposes a decoupled probabilistic algorithm, named Bayesian tensor analysis (BTA). BTA automatically selects a suitable model for tensor data, as demonstrated by empirical studies. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Shrestha:2008:ijcnn, author = "Durga Lal Shrestha and Dimitri P. Solomatine", title = "Comparing Machine Learning Methods in Estimation of Model Uncertainty", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0471.pdf}, url = {}, size = {}, abstract = {The paper presents a generalization of the framework for assessment of predictive models uncertainty using machine learning techniques. Historical model errors which are mismatch between observed and predicted values are assumed to be indicators of total model uncertainty; it is measured in the form of prediction intervals, and comprises all sources of uncertainty including model structure, model parameters, input and output data. Several machine learning methods are compared. The approach is tested on a conceptual hydrological model set up to predict stream flows of the Brue catchment in the United Kingdom. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Liu6:2008:ijcnn, author = "Derong Liu and Ning Jin", title = "ε-Adaptive Dynamic Programming for Discrete-Time Systems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0474.pdf}, url = {}, size = {}, abstract = {Dynamic programming for discrete-time systems is difficult due to the "curse of dimensionality": one has to find a series of control actions that must be taken in sequence. This sequence will lead to the optimal performance cost, but the total cost of those actions will be unknown until the end of that sequence. In this paper, we present our work on dynamic programming for discrete-time system, which is referred as ε-Adaptive Dynamic Programming. A single controller, ε-optimal controller u(ε)*, which is determined from an ε-optimal cost J(ε)*, is given to approximate the optimal controller. The ε-optimal controller u(ε)* can always control the state to approach to the equilibrium state, while the performance cost is close to the biggest lower bound of all performance costs within an error according to ε. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Saha:2008:ijcnn, author = "Sriparna Saha and Sanghamitra Bandyopadhyay and Chingtham Tejbanta Singh", title = "A New Line Symmetry Distance Based Pattern Classifier", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0475.pdf}, url = {}, size = {}, abstract = {In this paper, a new line symmetry based classifier (LSC) is proposed to deal with pattern classification problems. In order to measure total amount of line symmetry of a particular point in a class, a new definition of line symmetry based distance is also proposed in this paper. The proposed line symmetry based classifier (LSC) uses this new definition of line symmetry distance for classifying an unknown test sample. LSC assigns an unknown test sample pattern to that class with respect to whose major axis it is most symmetric. The mean of all the training patterns belonging to that particular class is taken as the prototype of that class. Thus training constitutes of computing only the class prototypes and the major axes of those classes. Kd-tree based nearest neighbor search is used for reducing complexity of line symmetry distance computation. The performance of LSC is demonstrated in classifying twelve artificial and real-life data sets of varying complexities. Experimental results show that LSC achieves, in general, higher classification accuracy compared to k-NN classifier. Results indicate that the proposed novel line symmetry based classifier is well-suited for classifying data sets having symmetrical classes, irrespective of any convexity, overlap and size. Statistical analysis, ANOVA is also performed to compare the performance of these classifications techniques. }, keywords = { Pattern Classification, Kd-tree, Symmetry based distance, Nearest Neighbor Rule, Line Symmetry }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Miao:2008:ijcnn, author = "Qingliang Miao and Qiudan Li", title = "An opinion search system for consumer products", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0476.pdf}, url = {}, size = {}, abstract = {With the rapid progress of e-commerce, many people like purchasing product on the e-commerce website, and giving their personal reviews to the product they purchased, so the number of customer reviews grows rapidly. Generally, a potential customer will browse product reviews before they purchase the product. However, retrieving opinions relevant to customer's desire still remains challenging. To provide efficient opinion information for customers, we propose an opinion search system for consumer products, which uses data mining and information retrieval technology. A ranking mechanism taking temporal dimension into account and a method for results visualization are developed in the system. Experimental results on a real-world data set show the system is feasible and effective. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Zhang5:2008:ijcnn, author = "Jun Zhang and Ning Li and Y. F. Li ", title = "The Detection of Multiple Moving Objects Using Fast Level Set Method", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0477.pdf}, url = {}, size = {}, abstract = {A novel method for the detection of multiple moving objects is proposed in this paper. In order to get the detailed information of the objects, fast level set method which is only based on the evolution of single link list is mainly used to detect the boundaries of moving objects. The whole process consists of two main procedures: the coarse detection and the fine localization. During the coarse detection procedure, the velocity function is defined according to the modified Otsu method which is more effective to eliminate the split phenomena of the whole motion area and get the consecutive boundaries. As to the fine localization, improved region competition is applied to obtain the smooth and exact contours. The proposed method has been tested on several different video sequences, and the efficiency of the method has been verified. }, keywords = {: Object detection, object tracking, level set method, region competition, moving objects, video sequences.}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Liu7:2008:ijcnn, author = "Wenju Liu and Yun Tang and Shouye Peng", title = "Fast and Robust Stochastic Segment Model for Mandarin Digital
String Recognition", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0480.pdf}, url = {}, size = {}, abstract = {Based on the analysis and comparisons of complexity between stochastic segment model (SSM) and hidden Markov model (HMM) in this paper, we presented a fast and robust SSM, which yields a 94.75percent speaker-independent performance on Mandarin digit string recognition. This result is better than HMM based system at the same level of computational complexity and just only a little slower than HMM in the running time. We also studied a region based discriminative method, which achieves 18.0percent error rate reduction for substitution error and 95.08percent accuracy for Mandarin digit string recognition. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Jiang4:2008:ijcnn, author = "Aiwen Jiang and Chunheng Wang and Yuanping Zhu", title = "Calibrated Rank-SVM for Multi-Label Image Categorization", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0482.pdf}, url = {}, size = {}, abstract = {In the area of multi-label image categorization, there are two important issues: label classification and label ranking. The former refers to whether a label is relevant or not, and the latter refers to what extent a label is relevant to an image. However, few existing papers have considered them in a holistic way. In this paper we will suggest a concrete improved method, named calibrated RankSVM, to bridge the gap between multi-label classification and label ranking. Through incorporating a virtual label as a calibrated scale [1], the threshold selection stage is embedded into ranking learning stage. This holistic way is essentially different from conventional rank methods, making our proposed method more suitable for multi-label classification task. The experiments on image have demonstrated that our algorithm has better multi-label classification performances than conventional ranksvm while preserving its good ranking characteristics. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Angelov:2008:ijcnn, author = "Plamen Angelov and Ramin Ramezani and Xiaowei Zhou ", title = "Autonomous Novelty Detection and Object Tracking in Video Streams Using Evolving Clustering and Takagi-Sugeno Type Neuro-Fuzzy System", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0483.pdf}, url = {}, size = {}, abstract = {Autonomous systems for surveillance, security, patrol, search and rescue are the focal point of extensive research and interest from defense and the security related industry, traffic control and other institutions. A range of sensors can be used to detect and track objects, but optical cameras or camcorders are often considered due to their convenience and passive nature. Tracking based on colour intensity information is often preferred than the motion cues due to being more robust. The technique presented in this paper can also be used in conjunction with infra-red cameras, 3D lasers which result in a grey scale image. Novelty detection and tracking are two of the key elements of such systems. Most of the currently reported techniques are characterized by high computational, memory storage costs and are not autonomous because they usually require a human operator in the loop. This paper presents new approaches to both the problem of novelty detection and object tracking in video streams. These approaches are rooted in the recursive techniques that are computationally efficient and therefore potentially applicable in real-time. A novel approach for recursive density estimation (RDE) using a Cauchy type of kernel (as opposed to the usually used Gaussian one) is proposed for visual novelty detection and the use of the recently introduced evolving Takagi-Sugeno (eTS) neuro-fuzzy system for tracking the object detected by the RDE approach is proposed as opposed to the usually used Kalman filter (KF). In fact, eTS can be seen as a fuzzily weighted mixture of KF. The proposed technique is significantly faster than the well known kernel density estimation (KDE) approach for background subtraction for novelty detection and is more precise than the usually used KF. Additionally the overall approach removes the need of manually selecting the object to be tracked which makes possible a fully autonomous system for novelty detection and tracking to be developed. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Bamford:2008:ijcnn, author = "Simeon A. Bamford and Alan F. Murray and David J. Willshaw", title = "Large Developing Axonal Arbors Using a Distributed and
Locally-Reprogrammable Address-Event Receiver", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0484.pdf}, url = {}, size = {}, abstract = {We have designed a distributed and locally reprogrammable address event receiver. Incoming address-events are monitored simultaneously by all synapses, allowing for arbitrarily large axonal fan-out without reducing channel capacity. Synapses can change input address, allowing neurons to implement a biologically realistic learning rule locally, with both synapse formation and elimination. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Julia:2008:ijcnn, author = "Fatema N. Julia and Khan M. Iftekharuddin ", title = "Dialog Act Classification using Acoustic and Discourse Information of MapTask Data", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0485.pdf}, url = {}, size = {}, abstract = {In this work, we analyze both acoustic and discourse information for Dialog Act (DA) classification of HCRC MapTask dataset. We extract several different acoustic features and exploit these features in a Hidden Markov Model (HMM) to classify acoustic information. For discourse feature extraction, we propose a novel parts-of-speech (POS) tagging technique that effectively reduces the dimensionality of discourse features manyfold. To classify discourse information, we exploit two classifiers such as a HMM and a Support Vector Machine (SVM) respectively. We further obtain classifier fusion between HMM and SVM to improve discourse classification. Finally, we perform an efficient decision-level classifier fusion for both acoustic and discourse information to classify twelve different DAs in HCRC MapTask data. We obtain accuracy of rate 65.2percent (58.06percent with cross validation) and 55.4percent (51.08percent with cross validation) DA classification using acoustic and discourse information respectively. Furthermore, we obtain combined accuracy of 68.6percent (61.02percent with cross validation) for DA classification. These accuracy rates of DA classification are comparable to previously reported results for the same HCRC MapTask dataset. In terms of average Precision and Recall, we obtain accuracy of 74.89percent and 69.83percent (without cross validation) respectively. Therefore, we obtain much better precision and recall rate for most of the classified DAs when compared to existing works on the same dataset }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Souto:2008:ijcnn, author = "Marcilio C. P. de Souto and Rodrigo G. F. Soares and Alixandre Santana and Anne M. P. Canuto", title = "Empirical Comparison of Dynamic Classifier Selection Methods Based on Diversity and Accuracy for Building Ensembles", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0487.pdf}, url = {}, size = {}, abstract = {In the context of Ensembles or Multi-Classifier Systems, the choice of the ensemble members is a very complex task, in which, in some cases, it can lead to ensembles with no performance improvement. In order to avoid this situation, there is a great deal of research to find effective classifier member selection methods. In this paper, we propose a selection criterion based on both the accuracy and diversity of the classifiers in the initial pool. Also, instead of using a static selection method, we use a Dynamic Classifier Selection (DSC) procedure. In this case, the member classifiers to form the ensemble are chosen at the test (use) phase. That is, different testing patterns can be classified by different ensemble configurations. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Tsaih:2008:ijcnn, author = "Rua-Huan Tsaih and Yat-wah Wan and Shin-Ying Huang", title = "The Rule-Extraction Through the Preimage Analysis", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0489.pdf}, url = {}, size = {}, abstract = {This study reveals the properties of the input/output relationship for a real-valued single-hidden layer feed-forward neural network (SLFN) with the tanh activation function on all hidden-layer nodes and the linear activation function on output node. Specifically, the rule-extraction of the SLFN is done through mathematically analyzing its preimage, which is the set of input values for a given output value. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Al-Mamory:2008:ijcnn, author = "Safaa O. Al-Mamory and Zhang Hongli and Ayad R. Abbas", title = "Modeling Network Attacks for Scenario Construction", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0491.pdf}, url = {}, size = {}, abstract = {The Intrusion detection system (IDS) is a security technology that attempts to identify network intrusions. Defending against multistep intrusions which prepare for each other is a challenging task. In this paper, the Context-Free Grammar (CFG) was used to describe the multistep attacks using alerts classes. Based on the CFGs, the modified LR parser was employed to generate the parse trees of the scenarios presented in the alerts. Instead of searching all the received alerts for those that prepare for a new alert, we only search for the latest alert's type of each scenario. Consequently, the proposed system has an attractive time complexity. The experiments were performed on two different sets of network traffic traces, using different open-source and commercial IDSs. The detected scenarios are represented by Correlation Graphs (CGs). The experimental results show that the CFG can describe multistep attacks explicitly and the modified LR parser, based on the CFG, can construct scenarios successfully. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Lopez:2008:ijcnn, author = "Miguel Lopez and Patricia Melin", title = "Response Integration in Ensemble Neural Networks Using Interval
Type-2 Fuzzy Logic", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0493.pdf}, url = {}, size = {}, abstract = {This paper describes a new approach for response integration in Ensemble Neural Networks using interval type-2 Fuzzy logic. When using ensemble neural networks it is important to choose a good method of Response Integration to obtain a better identification in pattern recognition. In this paper a comparative analysis between interval type-2 fuzzy logic, type-1 fuzzy logic and the Sugeno Integral, as response integration methods, in ensemble neural networks is presented. Based on Simulation results Interval type-2 fuzzy logic is shown to be a superior method for response integration. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Zhang6:2008:ijcnn, author = "Wenfeng Zhang and Zhongke Shi and Zhiyong Luo", title = "Prediction of Urban Passenger Transport Based-On Wavelet SVM with Quantum-Inspired Evolutionary Algorithm", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0496.pdf}, url = {}, size = {}, abstract = {Based on least squares wavelet support vector machines (LS-WSVM) with quantum-inspired evolutionary algorithm (QEA), the prediction model of urban passenger transport is proposed, that can provide the theoretical foundation of forecasting passenger volume of urban transport accurately. The prediction model of urban passenger transport is established by using LS-WSVM, whose regularization parameter and kernel parameter are adjusted using quantum-inspired evolutionary algorithm. QEA with quantum chromosome and quantum mutation has better global search capacity. The parameters of LS-WSVM can be adjusted using quantuminspired evolutionary optimization. Combining with the data of the urban volume of passenger transport of Xi'an over years, the prediction model of urban passenger transport is validated, the simulation results indicate that the prediction model is effective, and based on LS-WSVM has more improvement than LS-SVM with Gaussian kernel in predicting precision, and then the improved LS-WSVM with QEA is efficient than with crossvalidation method for tuning parameters. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Goh:2008:ijcnn, author = "Hanlin Goh and Joo-Hwee Lim and Chai Quek", title = "Learning Associations of Conjuncted Fuzzy Sets for Data Prediction", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0497.pdf}, url = {}, size = {}, abstract = {Fuzzy Associative Conjuncted Maps (FASCOM) is a fuzzy neural network that represents information by conjuncting fuzzy sets and associates them through a combination of unsupervised and supervised learning. The network first quantizes input and output feature maps using fuzzy sets. They are subsequently conjuncted to form antecedents and consequences, and associated to form fuzzy if-then rules. These associations are learnt through a learning process consisting of three consecutive phases. First, an unsupervised phase initializes based on information density the fuzzy membership functions that partition each feature map. Next, a supervised Hebbian learning phase encodes synaptic weights of the input-output associations. Finally, a supervised error reduction phase fine-tunes the fine-tunes the network and discovers the varying influence of an input dimension across output feature space. FASCOM was benchmarked against other prominent architectures using data taken from three nonlinear data estimation tasks and a realworld road traffic density prediction problem. The promising results compiled show significant improvements over the stateof-the-art for all four data prediction tasks. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Krasnopolsky:2008:ijcnn, author = "Vladimir Krasnopolsky and Michael S. Fox-Rabinovitz and Alexei Belochitski", title = "Using Neural Network Emulations of Model Physics in Numerical
Model Ensembles", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0498.pdf}, url = {}, size = {}, abstract = {In this paper the use of the neural network emulation technique, developed earlier by the authors, is investigated in application to ensembles of general circulation models used for the weather prediction and climate simulation. It is shown that the neural network emulation technique allows us: (1) to introduce fast versions of model physics (or components of model physics) that can speed up calculations of any type of ensemble up to 2-3 times; (2) to conveniently an naturally introduce perturbations in the model physics (or a component of model physics) and to develop a fast versions of perturbed model physics (or fast perturbed components of model physics), and (3) to make the computation time for the entire ensemble (in the case of short term perturbed physics ensemble introduced in this paper) comparable with the computation time that is needed for a single model run. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Kim2:2008:ijcnn, author = "Hyun-Soo Kim and Bryan G. Morris and Seung-Soo Han and Gary S. May", title = "A Comparison of Genetic and Particle Swarm Optimization for Contact Formation in High-Performance Silicon Solar Cells", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0499.pdf}, url = {}, size = {}, abstract = {In this paper, statistical experimental design is used to characterize the contact formation process for high-performance silicon solar cells. Central composite design is employed, and neural networks trained by the error back-propagation algorithm are used to model the relationships between several input factors and solar cell efficiency. Subsequently, both genetic algorithms and particle swarm optimization are used to identify the optimal process conditions to maximize cell efficiency. The results of the two approaches are compared, and the optimized efficiency found via the particle swarm method was slightly larger than the value determined via genetic algorithms. More importantly, repeated applications of particle swarm optimization yielded process conditions with smaller standard deviations, implying greater consistency in recipe generation. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Valdez:2008:ijcnn, author = "Fevrier Valdez and Patricia Melin and Olivia Mendoza", title = "A New Evolutionary Method with Fuzzy Logic for Combining Particle Swarm Optimization and Genetic Algorithms: The Case of Neural Networks Optimization", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0500.pdf}, url = {}, size = {}, abstract = {We describe in this paper a new hybrid approach for optimization combining Particle Swarm Optimization (PSO) and Genetic Algorithms (GAs) using Fuzzy Logic to integrate the results. The new evolutionary method combines the advantages of PSO and GA to give us an improved PSO+GA hybrid method. Fuzzy Logic is used to combine the results of the PSO and GA in the best way possible. The new hybrid PSO+GA approach is compared with the PSO and GA methods with a set of benchmark mathematical functions. The proposed hybrid method is also tested with the problem of neural network optimization. The new hybrid PSO+GA method is shown to be superior with respect to both the individual evolutionary methods. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(He2:2008:ijcnn, author = "Fei He and Martin Brown and Hong Yue", title = "Robust Experimental Design and Feature Selection in Signal
Transduction Pathway Modeling", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0503.pdf}, url = {}, size = {}, abstract = {Due to the general lack of experimental data for biochemical pathway model identification, cell-level time series experimental design is particularly important in current systems biology research. This paper investigates the problem of experimental design for signal transduction pathway modeling, and in particular, focuses on methods for parametric feature selection. An important problem is the estimation of parametric uncertainty which is a function of the true (but unknown) parameters. In this paper, two "robust" feature selection strategies are investigated. The first is a mini-max robust experimental design approach, the second is a sampled experimental design method inspired by the Morris global sensitivity analysis. The two approaches are analyzed and interpreted in terms of a generalized optimal experimental design criterion, and their performance has been compared via simulation on the IκB-NF-κB pathway feature selection problem. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wang7:2008:ijcnn, author = "Hui Wang and Chuandong Li and Yongguang Yu", title = "An Estimate of Impulse Bounds in Delayed BAM Neural Networks", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0504.pdf}, url = {}, size = {}, abstract = {This paper further studies the exponential stability of delayed bidirectional associative memory neural networks and focuses on the impulse effect on the exponential stability property. It is shown that if the corresponding impulse-free DBAM is globally exponentially stable the impulsive analog will remain its stability property even if the measurements of the states are magnified to some extent at the impulse instants. Furthermore, the admissible upper bound of impulse is estimated in terms of exponential convergence degree of the corresponding impulse-free DBAM and the length of impulse interval. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Guo4:2008:ijcnn, author = "Baosheng Guo and Ruoen Ren", title = "Nonparametric Approach for Estimating Dynamics of Stock Index", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0505.pdf}, url = {}, size = {}, abstract = {Parametric and nonparametric methods are used in estimating stochastic diffusion process. Nonparametric method has its own advantages; this paper uses nonparametric method to estimate drift and diffusion term. Two nonparametric methods have been studied, which are kernel estimation and local linear estimation. Local linear estimation has been used in estimating dynamics of Shanghai Stock Exchange Composite Index. }, keywords = {: Stochastic diffusion process, kernel regression, local linear estimation, stock indices.}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Ferreira:2008:ijcnn, author = "Rita Ferreira and Bernardete Ribeiro and Catarina Silva and Qingzhong Liu and Andrew H Sung", title = "Building Resilient Classifiers for LSB Matching Steganography", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0506.pdf}, url = {}, size = {}, abstract = {One of the Internet's hallmark is the rapid spread of the use of information and communication technology. This has boosted methods for hiding stego information inside digital cover content images which is a concerning issue in information security. On the other hand, attack of steganographic schemes has leveraged methods for steganalysis which is a challenging problem. In this paper, first we look at the design of classifiers, such as, Support Vector Machines (SVM) and neural networks (RBF and MLP) which are able to detect the presence of Least Significant Bit (LSB) matching steganography of gray scale images. Second, by combining with feature ranking methods (SVM-Recursive Feature Elimination, Kruskal Wallis) and reduction techniques (PCA) pattern classification of stego is successfully achieved. It is of utmost importance to look at the large set of features extracted from images and find ranking methods able, namely, to exclude correlated and redundant features, avoid the curse of dimensionality or circumvent the need of the steganalyzer to be re-designed. Results show that desirable properties of robustness and resilience are attained by designing classifiers able to deal with redundancy and noise. Moreover, comparison of classifiers performance emphasizes the chosen model for the steganalyser. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Olier2:2008:ijcnn, author = "Ivan Olier and Alfredo Vellido", title = "On the Benefits for Model Regularization of a Variational
Formulation of GTM", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0508.pdf}, url = {}, size = {}, abstract = {Generative Topographic Mapping (GTM) is a manifold learning model for the simultaneous visualization and clustering of multivariate data. It was originally formulated as a constrained mixture of distributions, for which the adaptive parameters were determined by Maximum Likelihood (ML), using the Expectation-Maximization (EM) algorithm. In this formulation, GTM is prone to data overfitting unless a regularization mechanism is included. The theoretical principles of Variational GTM, an approximate method that provides a full Bayesian treatment to a Gaussian Process (GP)-based variation of the GTM, were recently introduced as alternative way to control data overfitting. In this paper we assess in some detail the generalization capabilities of Variational GTM and compare them with those of alternative regularization approaches in terms of test log-likelihood, using several artificial and real datasets. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Jin:2008:ijcnn, author = "Fengxiang Jin and Shifei Ding", title = "An Improved PCA Algorithm Based on WIF", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0509.pdf}, url = {}, size = {}, abstract = {In this paper, we analyze the information feature of principal component analysis (PCA) deeply based on information entropy. According to idea of entropy function, a new weighted information functions (WIF) is proposed, and the information content of data matrix X is measured by it. Based on WIF, the information compression rate (ICR, RIC) and accumulated information compression rate (AICR, RAIC) are set up, by which the degree of information compression is measured. At last, an improved PCA algorithm (IPCA) based on WIF is constructed. Through simulated application in practice, the results show that the IPCA proposed here is efficient and satisfactory. It provides a new research approach of feature compression for pattern recognition, machine learning, data mining and so on. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Policastro:2008:ijcnn, author = "Claudio A. Policastro and Giovana Zuliani and Renato R. da Silva", title = "Hybrid Knowledge Representation Applied to the Learning of the
Shared Attention", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0510.pdf}, url = {}, size = {}, abstract = {Sociable robots are embodied agents that are part of a heterogeneous society of robots and humans. They are able to recognize human beings and each other, and engage in social interactions. The use of a robotic architecture may strongly reduce the time and effort required to construct a sociable robot. However, a robotic architecture for sociable robots must have structures and mechanisms to allow social interaction, behavior control and learning from environment. In this article, a new hybrid knowledge representation is proposed and integrated to our robotic architecture inspired on Behavior Analysis. This new hybrid knowledge representation enables incremental learning and knowledge generalization by incorporating an ART2 Neural Network combined with a relational presentation of first order. The new representation has been evaluated in the context of the learning of the shared attention and the results obtained show that it is a very promising approach. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Yan:2008:ijcnn, author = "Weizhong Yan and Feng Xue", title = "Jet Engine Gas Path Fault Diagnosis Using Dynamic Fusion of Multiple Classifiers", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0512.pdf}, url = {}, size = {}, abstract = {Jet engine gas path fault diagnosis is not only important in modern condition-based maintenance of aircraft engines, but also a challenging classification problem. Exploring more advanced classification techniques for achieving improved classification performance for gas path fault diagnosis, therefore, has been increasingly active in recent years in PHM community. To that end, in this paper, we apply a recently developed dynamic fusion scheme to gas path fault diagnosis. Through designing a real-world gas path fault diagnostic system, we demonstrate that dynamic fusion of multiple classifiers can be effective in improving classification performance of gas path diagnosis. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Lee:2008:ijcnn, author = "Hyekyoung Lee and Seungjin Choi", title = "CUR+NMF for Learning Spectral Features from Large Data Matrix", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0515.pdf}, url = {}, size = {}, abstract = {Nonnegative matrix factorization (NMF) is a popular method for multivariate analysis of nonnegative data. It was successfully applied to learn spectral features from EEG data. However, the size of a data matrix grows, NMF suffers from 'out of memory' problem. In this paper we present a memory-reduced method where we downsize the data matrix using CUR decomposition before NMF is applied. Experimental results with two EEG data sets in BCI competition, confirm the useful behavior of the proposed method. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Syrris:2008:ijcnn, author = "Vassilis Syrris and Vassilios Petridis", title = "Classification Through Hierarchical Clustering and Dimensionality Reduction", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0516.pdf}, url = {}, size = {}, abstract = {This work describes a two-mode clustering hierarchical model capable of dealing with high dimensional data spaces. The algorithm seeks a transformed subspace which can represent the initial data, simplify the problem and possibly lead to a better categorization level. We test the algorithm on two hard classification problems, the phoneme and the pedestrian recognition; both are typical classification problems from real-life applications. Finally, the model is compared with many other algorithms. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Huang4:2008:ijcnn, author = "Rongbing Huang and Minghui Du and Dexin Xie", title = "A Human Face Recognition Approach Based on Spatially Weighted Pseudo-Zernike Moments", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0517.pdf}, url = {}, size = {}, abstract = {A new modified pseudo-Zernike moments feature, namely, "spatial weighted pseudo- Zernike moments" (SWPZM) is proposed for face recognition in this paper. Since different facial region plays a different important role for face recognition, the new modified pseudo-Zernike feature is weighted with a weight function derived from the spatial information of the human face; hence the most important regions such as the eyes, nose, and mouth regions are intensified for face discrimination. Experimental results based on the AT&T/ORL, Yale, and their combined face database show that SWPZM can obtain 95.7percent, 92.3percent, and 92.5percent recognition rates with the nearest neighbor rule and have better identification power than other methods. }, keywords = {:Face recognition, pseudo-Zernike moments, feature extraction, principal component analysis, nearest neighbor.}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Hirose:2008:ijcnn, author = "Akira Hirose ", title = "An Adaptive Ground Penetrating Radar Imaging System Based on Complex-Valued Self-Organizing Map — Recent Progress and
Experiments in Cambodia", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0519.pdf}, url = {}, size = {}, abstract = {This paper reports recent progress in an adaptive ground penetrating radar imaging system based on a complexvalued neural network (CVNN), i.e., a complex-valued selforganizing map (CSOM). In the CSOM processing, we deal with feature vectors that represent complex-amplitude texture in space and frequency domains. We developed a switched walled linearly tapered slot antenna (walled-LTSA) array for the frontend. A higher resolution results in a better classification quality. To realize a high resolution in range and azimuth directions, we use a wide frequency bandwidth in frequency stepping operation, and a special switching scheme for the walled LTSA. We conducted experiments in Cambodia. In this paper, we report successful plastic landmine visualization, not only for targets buried in normal sand but also for those in wet laterite soil at the Siem Reap test site. Adaptive coherent radar imaging is one of the most potential application fields of the CVNNs. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Chen10:2008:ijcnn, author = "Yajie Chen and Liam McDaid and Steve Hall and Peter Kelly", title = "A Programmable Facilitating Synapse Device", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0520.pdf}, url = {}, size = {}, abstract = {We present a programmable dynamic Charge Transfer Synapse (CTS) in a single semiconductor device. The CTS comprises a Metal Oxide Semiconductor (MOS) transistor operating in subthreshold and two MOS capacitors in proximity to the transistor. One of the capacitors is permanently biased in strong inversion where the associated density of charge in the well implements the weighting. When a presynaptic spike is applied to the gate of the second MOS capacitor the charge density in the well falls producing a current spike at the output. The amplitude of the spike is correlated with the equilibrium charge density in the well, which is controlled by the associated gate voltage. Aggregation of spikes from an array of CTSs is achieved by using a current mirror configuration whose output postsynaptic potential can be used to stimulate a point neuron circuit. The function of the MOS transistor is to restore the charge in the well where the duration of this process is dictated by the associated gate voltage. Therefore, the synapse is capability of operating in the facilitating state over a large frequency range. The CTS is compact and since it operates in transient mode, its power consumption is negligible. Simulation results are presented which clearly demonstrate its operation. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Bologna:2008:ijcnn, author = "G. Bologna and B. Deville and M. Vinckenbosch and T. Pun", title = "A Perceptual Interface for Vision Substitution in a Colour Matching Experiment", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0523.pdf}, url = {}, size = {}, abstract = {In the context of vision substitution by the auditory channel several systems have been introduced. One such system that is presented here, See Colour, is a dedicated interface part of a mobility aid for visually impaired people. It transforms a small portion of a coloured video image into spatialized instrument sounds. In this work the purpose is to verify the hypothesis that sounds from musical instruments provide an alternative way to vision for obtaining colour information from the environment. We introduce an experiment in which several participants try to match pairs of coloured socks by pointing a head mounted camera and by listening to the generated sounds. Our experiments demonstrated that blindfolded individuals were able to accurately match pairs of coloured socks. The advantage of the See Colour interface is that it allows the user to receive a feedback auditory signal from the environment and its colours, promptly. Our perceptual auditory coding of pixel values opens the opportunity to achieve more complicated experiments related to vision tasks, such as perceiving the environment by interpreting its colours. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Ho:2008:ijcnn, author = "Charlotte Yuk Fan Ho and Bingo Wing Kuen Ling and Muhammad H U Nasir and Hak Keung Lam", title = "Properties of an Invariant Set of Weights of Perceptrons", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0524.pdf}, url = {}, size = {}, abstract = {In this paper, the dynamics of weights of perceptrons are investigated based on the perceptron training algorithm. In particular, the condition that the system map is not injective is derived. Based on the derived condition, an invariant set that results to a bijective invariant map is characterized. Also, it is shown that some weights outside the invariant set will be moved to the invariant set. Hence, the invariant set is attracting. Computer numerical simulation results on various perceptrons with exhibiting various behaviors, such as fixed point behaviors, limit cycle behaviors and chaotic behaviors, are illustrated. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) abstract = {Conventional speaker identification and speech recognition algorithms cannot deal with noisy and multiple speaker environments. For example, IBM via Voice has low recognition rates if dictation is done in a noisy environment. In order to achieve high performance in speaker identification and speech recognition, we propose an integrated approach that takes every facet of the process into account. Here we summarize some preliminary results from the application of this integrated approach to robust speaker identification and speech recognition. A real-time stand-alone software prototype has been developed to evaluate the effectiveness of the approach. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Kwan:2008:ijcnn, author = "C. Kwan and S. Chu and J. Yin and X. Liu and M. Kruger and I. Sityar", title = "Enhanced Speech in Noisy Multiple Speaker Environment", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0526.pdf}, url = {}, size = {}, abstract = {Noisy environments seriously degrade the performance of speech recognition systems. Here we implement a high performance speech enhancement algorithm. Data from Speech Separation Challenge [1] were used to evaluate the method. It was observed that the enhanced speech significantly improved the recognition performance. In 2 out of 4 SNR cases, over 100percent relative percentage improvements were achieved. Standalone software prototype has been developed and evaluated. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Kwan2:2008:ijcnn, author = "C. Kwan and J. Yin and B. Ayhan and S. Chu and X. Liu", title = "Speech Separation Algorithms for Multiple Speaker Environments", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0527.pdf}, url = {}, size = {}, abstract = {Conventional speaker identification and speech recognition algorithms do not perform well if there are multiple speakers in the background. For high performance speaker identification and speech recognition applications in multiple speaker environments, a speech separation stage is essential. Here we summarize the implementation of three speech separation techniques. Advantages and disadvantages of each method are highlighted, as no single method can work under all situations. Stand-alone software prototypes for these methods have been developed and evaluated. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Ferreira2:2008:ijcnn, author = "Aida A. Ferreira and Teresa B. Ludermir and Ronaldo R. B. de Aquino", title = "Investigating the Use of Reservoir Computing for Forecasting the Hourly Wind Speed in Short-Term", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0529.pdf}, url = {}, size = {}, abstract = {This paper presents the results of the models created for forecasting the hourly wind speed in 24-step-forward using Reservoir Computing (RC). RC is a new paradigm that offers an intuitive methodology for using the temporal processing power of recurrent neural networks (RNN) without the inconvenience of training them. Originally, introduced independently as Liquid State Machine [5] or Echo State Network [6], whose basic concept is randomly construct a RNN and leave the weights unchanged. In this work we used Echo State Network (ESN) to create the models and Multi-Layer Networks (MLP) to compare the results. The results showed that the ESN performed significantly better than MLP networks, even though it presents a significantly simpler, and faster, training algorithm. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Baruch:2008:ijcnn, author = "Ieroham S. Baruch and Rosalba Galvan-Guerra and Carlos-Roman Mariaca-Gaspar and Patricia Melin", title = "Decentralized Indirect Adaptive Fuzzy-Neural Multi-Model Control of a Distributed Parameter Bioprocess Plant", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0530.pdf}, url = {}, size = {}, abstract = {The paper proposed to use recurrent Fuzzy-Neural Multi-Model (FNMM) identifier for decentralized identification of a distributed parameter anaerobic wastewater treatment digestion bioprocess, carried out in a fixed bed and a recirculation tank. The distributed parameter analytical model of the digestion bioprocess is reduced to a lumped system using the orthogonal collocation method, applied in three collocation points (plus the recirculation tank), which are used as centers of the membership functions of the fuzzyfied plant output variables with respect to the space variable. The local and global weight parameters and states of the proposed FNMM identifier are implemented by a Hierarchical Fuzzy-Neural Multi-Model Sliding Mode Controller (HFNMM-SMC). The comparative graphical simulation results of the digestion wastewater treatment system identification and control, obtained via learning, exhibited a good convergence, and precise reference tracking outperforming the optimal control. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Hu2:2008:ijcnn, author = "Yunlong Hu and Yongchen Li", title = "LS-SVM for Bad Debt Risk Assessment in Enterprises", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0531.pdf}, url = {}, size = {}, abstract = {With the development of market economy in China, the problem of bad debt becomes increasingly serious in enterprises. In this paper, a bad-debt-risk evaluation model is established based on LS-SVM classifier, using a new set of index system which combines financial factors with non-financial factors on the basis of the 5C system evaluation method. The bad debt rating is separated into four classes- normality, attention, doubt and loss through analyzing accounts payable. Then the LS-SVM classifier is trained with 220 samples which are stochastically extracted from listed companies of China in industry, and the four classes are identified by the trained classifier using 80 samples. Then, BP neural network is also used to assess the same data. The experiment results show that LS-SVM has an excellent performance on training accuracy and reliability in credit risk assessment and achieves better performance than BP neural network. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Zhang7:2008:ijcnn, author = "Tianhao Zhang and Dacheng Tao and Xuelong Li", title = "A Unifying Framework for Spectral Analysis Based Dimensionality Reduction", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0532.pdf}, url = {}, size = {}, abstract = {Past decades, numerous spectral analysis based algorithms have been proposed for dimensionality reduction, which plays an important role in machine learning and artificial intelligence. However, most of these existing algorithms are developed intuitively and pragmatically, i.e., on the base of the experience and knowledge of experts for their own purposes. Therefore, it will be more informative to provide some a systematic framework for understanding the common properties and intrinsic differences in the algorithms. In this paper, we propose such a framework, i.e., "patch alignment", which consists of two stages: part optimization and whole alignment. With the proposed framework, various algorithms including the conventional linear algorithms and the manifold learning algorithms are reformulated into a unified form, which gives us some new understandings on these algorithms. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Islam:2008:ijcnn, author = "Atiq Islam and Khan M. Iftekharuddin and E. Olusegun. George", title = "Class Specific Gene Expression Estimation and Classification in
Microarray Data", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0536.pdf}, url = {}, size = {}, abstract = {In this work, we characterize genes using an Oligonucleotide Affymetrix gene expression dataset and propose a novel gene selection method based on samples from the posterior distributions of class-specific gene expression measures. We construct a hierarchical Bayesian framework for a random effect ANOVA model that allows us to obtain the posterior distributions of the class-specific gene expressions. We also formalize a novel class prediction scheme based on the samples from new posterior distributions of group specific gene expressions. Our experimental results show the classdiscriminating power of the selected genes. Furthermore, we demonstrate that our prediction scheme classifies tissue samples into appropriate treatment groups with high accuracy. The computations are implemented by using Gibbs sampling. We compare the efficacy of our proposed gene selection and prediction methods with that of Pomeroy et al. (Nature, 2002) on the same CNS tumor sample dataset. }, keywords = {: Affymetrix, hierarchical Bayesian, ANOVA model, clustering, CNS tumor, Gibbs sampling, marker genes,
parallel coordinate, Semantic Gene Organizer.}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Amiri:2008:ijcnn, author = "Mahmood Amiri and Mohammad Bagher Menhaj and Mohammad Javad Yazdanpanh", title = "A Neural-Network-Based Controller for a Single-Link Flexible Manipulator: Comparison of FFNN and DRNN Controllers", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0538.pdf}, url = {}, size = {}, abstract = {This paper employs two types of neural networks to control a single-link flexible arm. To train each network, we use a gradient-based approach with adaptive learning rate. We first apply the Diagonal Recurrent Neural Network (DRNN) to a single-link flexible arm, which is a challenging control problem, in order to investigate the ability of this type of recurrent neural network. We then apply a feed-forward neural network (FFNN) to this problem and perform some case studies for the purpose of performance comparisons of the two structures. Several simulations presented in this paper verify that the DRNN-based controller significantly improves the precision of the tip motion tracking, suppresses the tip deflections of the manipulator more effectively and simultaneously produces more appropriate control voltages. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Khanmohammadi:2008:ijcnn, author = "S. Khanmohammadi and H. Ghadiri", title = "Modeling of Helper Robots in Manufacturing Systems Using Petri Nets", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0539.pdf}, url = {}, size = {}, abstract = {In this paper the Timed Petri Net (TPN) is used for modeling the operations of co-operative robots in Flexible Manufacturing Systems (FMS). The effect of fault parameters and the number of co-operative robots -we call them the helpers- in the performance of the system is investigated. These special purpose robots are used to decrease the operation times, as well as to increase the total performance of the system. These robots perform the preliminary tasks on the waiting jobs (parts) in the wait lines or input buffers of machines. Also some Automatic Guided Vehicles (AGVs) are used as material handlers between machines while performing some simple preliminary tasks during the material handling. Some sub- TPNs are used for modeling of FMS and helper robots, as special purpose robots. Simulation results show that by using the helpers, there will be a decrease of 23.7 percent in mean operation time of system, and a decrease of 25.4 percent in the mean waiting time of jobs at input buffers. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Davande:2008:ijcnn, author = "Hamed Davande and Mahmood Amiri and Alireza Sadeghian and Sylvain Chartier", title = "Auto-Associative Memory Based on a New Hybrid Model of SFNN and GRNN: Performance Comparison with NDRAM, ART2 and MLP", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0540.pdf}, url = {}, size = {}, abstract = {Currently, associative neural networks (AsNNs) are among the most extensively studied and understood neural paradigms. In this paper, we use a hybrid model of neural network for associative recall of analog and digital patterns. This hybrid model which consists of self-feedback neural network structures (SFNN) parallel with generalized regression neural network (GRNN) were first proposed by authors of this paper. Firstly, patterns are stored as the asymptotically stable fixed points of the SFNN. In the retrieving process, each new pattern is applied to the GRNN to make the corresponding initial conditions of that pattern which initiate the dynamical equations of the SFNN. In this way, the corresponding stored patterns and noisy version of them are retrieved. Several simulations are provided to show that the performance of the hybrid model is better than those of recurrent associative memory, feed-forward multilayer perceptron and is equally comparable with the performance of hard-competitive models. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Khanmohammadi2:2008:ijcnn, author = "S. Khanmohammadi and A. Mahdizadeh", title = "A New Technique for Optimizing and Smoothing Randomized Paths", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0541.pdf}, url = {}, size = {}, abstract = {In this paper we propose a new technique which uses a combination of two powerful path planning methods, in order to gain a fast path planning technique, which benefit's the advantages of both methods. First a feasible path is generated by a randomized path planner, and then it is leaded to a sub-optimal path by using Distance Transform in a bounded area. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Rong:2008:ijcnn, author = "Hai-Jun Rong and Guang-Bin Huang and Yew-Soon Ong", title = "Extreme Learning Machine for Multi-Categories
Classification Applications", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0543.pdf}, url = {}, size = {}, abstract = {In the paper, the multi-class pattern classification using extreme learning machine (ELM) is studied. The study is based on either a series of ELM binary classifiers or a single ELM classifier. When using binary ELM classifiers, the multi-class problem is decomposed into two-class problem using the one-against-all (OAA) and one-against-one (OAO) schemes, which are named as ELM-OAA and ELM-OAO respectively for brevity. In a single ELM classifier, the multi-class problem is implemented with an architecture of multi-output nodes which is equal to the number of pattern classes. Their performance is evaluated using some multi-class benchmark problems and simulation results show that ELM-OAA and ELM-OAO requires fewer hidden nodes than the single ELM classifier. In addition ELM-OAO usually has similar or less computation burden than the single ELM classifier when the pattern class labels is not larger than 10. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Ye:2008:ijcnn, author = "Mao Ye and Yongguo Liu and Hong Wu and Qihe Liu", title = "A Few Online Algorithms for Extracting Minor Generalized Eigenvectors", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0544.pdf}, url = {}, size = {}, abstract = {Recently, a few adaptive algorithms for generalized eigen-decomposition have been proposed, which are very useful in many applications such as digital mobile communications, Blind signal separation, etc. These algorithms are all focusing on extracting principal generalized eigenvectors. However, in many practical applications such as dimension reduction and signal processing, extracting the minor generalized eigenvectors adaptively are needed. Because of little literatures in the community, we discuss several approaches that lead to a few novel algorithms for extracting minor generalized eigenvectors. First, we derive an adaptive algorithms by using a singlelayer linear forward neural network from the viewpoint of linear discriminant analysis(LDA). And the algorithm to extract multiple minor generalized eigenvectors are also proposed by using orthogonality property. Second, by using gradient ascent approach of some objective functions, we can derive more algorithms and explain the first algorithm. Then, we extend these algorithms to minor generalized eigenvector problem. Theoretical analysis shows that these algorithms are stable and convergent to the minor generalized eigenvectors. Simulations have been conducted for illustration of the efficiency and effectiveness of our algorithms. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Tan:2008:ijcnn, author = "Tuan Zea Tan and Gary Kee Khoon Lee and Shie-Yui Liong and Tian Kuay Lim and and Terence Hung ", title = "Rainfall Intensity Prediction by a Spatial-Temporal Ensemble", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0546.pdf}, url = {}, size = {}, abstract = {Accurate rainfall intensity nowcasting has many applications such as flash flood defense and sewer management. Conventional computational intelligence tools do not take into account temporal information, and the series of rainfall is treated as continuous time series. Unfortunately, rainfall intensity is not a continuous time series as it has different dry periods in between raining seasons. Hence, conventional computational intelligence tools sometimes are not able to offer acceptable accuracy. An ensemble constitutes of classification, regression and reward models is proposed. The classification model identifies rain or no rain episodes, whereas the regression model predicts the rainfall intensity. The error of the regression model is then predicted by the reward regression model. Through that, the spatial information is captured by the classification model, and the temporal information is captured by the regression and reward models. Preliminary experimental results are encouraging. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Tadeusiewicz:2008:ijcnn, author = "Ryszard Tadeusiewicz and Marek R. Ogiela", title = "Medical Pattern Understanding Based on Cognitive Linguistic Formalisms and Computational Intelligence Methods", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0547.pdf}, url = {}, size = {}, abstract = {This paper will present the achievements of its authors related to the development of new cognitive information systems classes used in the tasks of automatic understanding of image data semantics. Such systems are a practical implementation of the paradigm for machine semantics understanding of selected image data types, with special regards to various classes of medical images. The development of such systems is possible owing to defining computer procedures of cognitive resonance, as used by the developed new classes of intelligent systems for pattern recognition and semantic reasoning. In particular, we shall present UBIAS (Understanding Based Image Analysis Systems) system classes, proposed by the authors for the interpretation of medical planar diagnostic images and for the modelling of spatial anatomy structures. }, keywords = {: computational intelligence, medical pattern understanding, image recognition, mathematical linguistic,
cognitive reasoning}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Tan2:2008:ijcnn, author = "Wi-Meng Tan and Hiok-Chai Quek", title = "Adaptive Training Schema in Mamdani-Type Neuro-Fuzzy Models for Data-Analysis in Dynamic System Forecasting", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0549.pdf}, url = {}, size = {}, abstract = {This paper investigates the possibility of a pseudoonline adaptive training schema for Mamdani-type neuro-fuzzy models that have robust linguistic interpretability. As such verbatim models are incapable of complex constructs available to Takagi-Sugeno-type neuro-fuzzy models, a heuristic approach is developed to allow the rule bases to adapt accordingly to fundamental shifts in the characteristics of time-varying dynamic systems for the purpose of forecasting. Experimental results showed that the proposed model is capable of adapting its rule base over time, and uses a relatively fewer number of rules for generalization in dynamic systems. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Gromiha:2008:ijcnn, author = "M. Michael Gromiha and Shandar Ahmad", title = "Neural Network based Prediction of Protein Structure and Function: Comparison with Other Machine Learning Methods", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0550.pdf}, url = {}, size = {}, abstract = {We have used neural networks in different applications of bioinformatics such as discrimination of β-barrel membrane proteins, mesophilic and thermophilic proteins, different folding types of globular proteins, different classes of transporter proteins and predicting the secondary structures of β-barrel membrane proteins. In these methods, we have used the information about amino acid composition, neighboring residue information, inter-residue contacts and amino acid properties as features. We observed that the performance with neural networks is comparable to or better than other widely used machine learning techniques. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Andrabi:2008:ijcnn, author = "Munazah Andrabi and Shandar Ahmad and Kenji Mizuguchi and Akinori Sarai", title = "Benchmarking and Analysis of DNA-Binding Site Prediction
Using Machine Learning", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0554.pdf}, url = {}, size = {}, abstract = {We benchmarked the performance of machine learning based publicly available web servers, predicting DNA-binding residues. A blind test on data sets derived from protein-DNA complexes submitted to PDB after the publication of these web servers was performed. It was discerned that models trained on unusually large number of parameters show exaggerated performance during training, which could not be sustained on new proteins submitted after these publications. Also discussed are the optimum definition of binding site and a correspondence between residue propensity and its bias for predictability in positive and negative class. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Pampara:2008:ijcnn, author = "G. Pampara and A. P. Engelbrecht and T. Cloete", title = "CIlib: A Collaborative Framework for Computational Intelligence Algorithms — Part I", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0555.pdf}, url = {}, size = {}, abstract = {Research in Computational Intelligence (CI) has produced a huge collection of algorithms, grouped into the main CI paradigms. Development of a new CI algorithm requires such algorithm to be thoroughly benchmarked against existing algorithms, which requires researchers to implement already published algorithms. This re-implementation of existing algorithms unnecessarily wastes valuable time, and may be the cause of incorrect results due to unexpected bugs in the code. It is also the case that more, new CI algorithms are hybrids of algorithms from different paradigms. This illustrates a demand for a comprehensive library of CI algorithms, to minimize development time and the occurrence of programming errors, and to facilitate combination of components to form hybrid models. This paper presents such a library, called CIlib. }, keywords = {genetic algorithms, genetic programming}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Zheng2:2008:ijcnn, author = "Chaoxin Zheng and Dermot Kelleher and Khurshid Ahmad", title = "A Semi-Automatic Indexing System for Cell Images", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0556.pdf}, url = {}, size = {}, abstract = {A method is described that can be used for annotating and indexing an arbitrary set of images with texts collateral to the images. The collateral texts comprise digitised texts, e.g. journal papers and newspapers in which the images appear, and digitised speech, e.g. a commentary on the contents of the images. The annotation 'vector' comprises image features and keywords in the collateral texts; our method can be used to generate both the image features and keywords automatically. Terminology extraction techniques are incorporated into the system to form a domain specific lexicon, which can then be used or help to annotate the images. Our method can be used as the basis of the autonomous learning of associations between images and their collateral descriptions, for example using Kohonen feature maps. We focus on images that show the migration and the division of cells within live systems. We show how the annotations can be collected by using the state-of-the-art speech recognition techniques that convert audio input into descriptive text on cell migration. A system based on the method has been developed and has reduced the annotation time to around two minutes per image, on a set of 429 cell images — which is significantly smaller than 5 minutes for manual annotation. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Cloete:2008:ijcnn, author = "T. Cloete and A. P. Engelbrecht and G. Pampara", title = "CIlib: A Collaborative Framework for Computational Intelligence Algorithms — Part II", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0560.pdf}, url = {}, size = {}, abstract = {CIlib is a recently developed open source library of Computational Intelligence (CI) algorithms. Developed in Java, and designed to be a generic framework of pluggable components, CIlib provides the CI researcher with a powerful tool to facilitate research in new CI techniques, and to easily benchmark against existing CI algorithms on a variety of problems. Consisting of a number of frameworks, including a framework for most CI paradigms, CIlib also allows components from different frameworks to be weaved together to form hybrid CI models. This paper provides a detailed illustration of how CIlib can be used to easily setup simulations using different algorithms to solve various problems. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Fontanari:2008:ijcnn, author = "Jose F. Fontanari and and Leonid I. Perlovsky", title = "Object Perception in the Neural Modeling Fields Framework", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0561.pdf}, url = {}, size = {}, abstract = {Movement seems to be the key ingredient to understanding how children perceive (and hence name) objects in their environment. This insight, which is based on Spelke's experiments on language acquisition by children, is akin to Aristotle's conception of object as a "continuous thing" that has one and only one movement. Here we test this idea with a computer experiment in which the perceptual system of the individual is modeled by the Neural Modeling Fields categorization mechanism, and the environment is composed of two complex objects that can move with respect to each other. Rather remarkably, we find that movement indeed makes possible the differentiation of objects which the individual would deem indistinguishable if motionless. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Ferrari:2008:ijcnn, author = "Silvia Ferrari and Bhavesh Mehta and Gianluca Di Muro and Antonius M. J. VanDongen and Craig Henriquez", title = "Biologically Realizable Reward-Modulated Hebbian Training for Spiking Neural Networks", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0562.pdf}, url = {}, size = {}, abstract = {Spiking neural networks have been shown capable of simulating sigmoidal artificial neural networks providing promising evidence that they too are universal function approximators. Spiking neural networks offer several advantages over sigmoidal networks, because they can approximate the dynamics of biological neuronal networks, and can potentially reproduce the computational speed observed in biological brains by enabling temporal coding. On the other hand, the effectiveness of spiking neural network training algorithms is still far removed from that exhibited by backpropagating sigmoidal neural networks. This paper presents a novel algorithm based on reward-modulated spike-timing-dependent plasticity that is biologically plausible and capable of training a spiking neural network to learn the exclusive-or (XOR) computation, through rate-based coding. The results show that a spiking neural network model with twenty-three nodes is able to learn the XOR gate accurately, and performs the computation on time scales of milliseconds. Moreover, the algorithm can potentially be verified in light-sensitive neuronal networks grown in vitro by determining the spikes patterns that lead to the desired synaptic weights computed in silico when induced by blue light in vitro. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Fiori:2008:ijcnn, author = "Simone Fiori ", title = "Generation of Pseudorandom Numbers with Arbitrary Distribution by Learnable Look-Up-Table-Type Neural Networks", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0564.pdf}, url = {}, size = {}, abstract = {The aim of the present manuscript is to propose a pseudo-random number generation algorithm based on a learnable non-linear neural network whose implementation is based on look-up tables. The proposed neural network is able to generate pseudo-random numbers with arbitrary distribution on the basis of standard variate generators available within programming environments. The proposed method is not computationally demanding and easy to implement on a computer. Numerical tests confirm the agreement between the desired and obtained distributions of the generated pseudo-random number batches. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wang8:2008:ijcnn, author = "Yu-Chiang Frank Wang and David Casasent", title = "Soft-Decision Hierarchical Classification Using
SVM-Type Classifiers", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0565.pdf}, url = {}, size = {}, abstract = {In this paper, we address both recognition of true object classes and rejection of false (non-object) classes as occurs in many realistic pattern recognition problems. We modified our hierarchical binary-decision classifier to produce analog outputs at each node, with values proportional to the class conditional probabilities at that node. This yields a new soft-decision hierarchical system. The hierarchical classification structure is designed by our weighted support vector k-means clustering method, which selects the classes to be separated at each node in the hierarchy. Use of our SVRDM (support vector representation and discrimination machine) classifiers at each node provides generalization and rejection ability. Compared to the standard SVM, use of the Gaussian kernel function and a looser constraint in the classifier design give our SVRDM an improved rejection ability. The soft-decision SVRDM output allows us to use the confidence level of each class to improve the classification (for true class inputs) and rejection (for false class inputs) performance of the hierarchical classifier. False class rejection is a major new aspect of this work. It is not present in most prior work. Excellent test results on a real infra-red (IR) database are presented. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Fiori2:2008:ijcnn, author = "Simone Fiori ", title = "Learning Stepsize Selection for the Geodesic-Based Neural Blind Deconvolution Algorithm", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0566.pdf}, url = {}, size = {}, abstract = {The present paper illustrates a geodesic-based learning algorithm over a curved parameter space for blind deconvolution application. The chosen deconvolving structure appears as a single neuron model whose learning rule arises from criterion-function minimization over a smooth manifold. In particular, we propose here a learning stepsize selection theory for the algorithm at hand. We consider the blind deconvolution performances of the algorithm as well as its computational burden. Also, a numerical comparison with seven blind-deconvolution algorithms known from the scientific literature is illustrated and discussed. Results of numerical tests conducted on a noiseless as well as a noisy system will confirm that the algorithm discussed in the present paper performs in a satisfactory way. Also, the performances of the presented algorithm will be compared with those exhibited by other blind deconvolution algorithms known from the literature. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Yu6:2008:ijcnn, author = "Wen Yu and Xiaoou Li", title = "Robust Adaptive Control Via Neural Linearization and Four Types of Compensation", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0567.pdf}, url = {}, size = {}, abstract = {In this paper, we propose a new type of neural adaptive control via dynamic neural networks. For a class of unknown nonlinear systems, a neural identifierbased feedback linearization controller is first used. Deadzone and projection techniques are applied to assure the stability of neural identification. Then four types of compensator are addressed. The stability of closed-loop system is also proven. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Woodside:2008:ijcnn, author = "Joseph M. Woodside ", title = "Neuro-Fuzzy CBR Hybridization: Healthcare Application", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0568.pdf}, url = {}, size = {}, abstract = {As the total cost of healthcare continues to rise, computerized methods are sought to improve the overall efficiency and effectiveness of healthcare systems. In this application, the focus is on healthcare claim payment processing, which is a major component of administrative healthcare costs. Due to the complexity of healthcare data, current methods require a large amount of healthcare claim payment processing to occur through manual intervention by human operators. This limitation necessitates the inclusion of machine learning techniques to create a hybrid system for automation of healthcare claim payments. Further automation of claims payment processing will lead to improved quality cost components of healthcare delivery. Machine learning techniques are used to demonstrate the feasibility of a hybrid system for healthcare claim payment automation, leading to reduced administrative costs and increased efficiencies. When the administrative cost savings are applied to the industry, this contributes to lowering the overall cost of healthcare. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Castillo:2008:ijcnn, author = "Oscar Castillo and Patricia Melin", title = "Computational Intelligence Software: Type-2 Fuzzy Logic and Modular Neural Networks", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0569.pdf}, url = {}, size = {}, abstract = {This paper presents the development and design of two software tools for computational intelligence. The software tools include a graphical user interface for construction, edition and observation of the intelligent systems. The software tool are for Interval Type-2 Fuzzy Logic and Modular Neural Networks. The Interval Type-2 Fuzzy Logic System Toolbox (IT2FLS), is an environment for interval type-2 fuzzy logic inference system development. Tools that cover the different phases of the fuzzy system design process, from the initial description phase, to the final implementation phase, build the Toolbox. The Toolbox's best qualities are the capacity to develop complex systems and the flexibility that permits the user to extend the availability of functions for working with the use of type-2 fuzzy operators, linguistic variables, interval type-2 membership functions, defuzzification methods and the evaluation of Interval Type-2 Fuzzy Inference Systems. The toolbox for modular neural networks has similar advantages. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Choi:2008:ijcnn, author = "Seungjin Choi ", title = "Algorithms for Orthogonal Nonnegative Matrix Factorization", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0570.pdf}, url = {}, size = {}, abstract = {Nonnegative matrix factorization (NMF) is a widely-used method for multivariate analysis of nonnegative data, the goal of which is decompose a data matrix into a basis matrix and an encoding variable matrix with all of these matrices allowed to have only nonnegative elements. In this paper we present simple algorithms for orthogonal NMF, where orthogonality constraints are imposed on basis matrix or encoding matrix. We develop multiplicative updates directly from the true gradient (natural gradient) in Stiefel manifold, whereas existing algorithms consider additive orthogonality constraints. Numerical experiments on face image data for a image representation task show that our orthogonal NMF algorithm preserves the orthogonality, while the goodness-off-it (GOF) is minimized. We also apply our orthogonal NMF to a clustering task, showing that it works better than the original NMF, which is confirmed by experiments on several UCI repository data sets. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Azcarraga:2008:ijcnn, author = "Arnulfo P. Azcarraga and Aldrich C. Caw", title = "Enhancing SOM Digital Music Archives Using
Scatter-Gather", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0571.pdf}, url = {}, size = {}, abstract = {The MarB system is a digital archive of music files that are clustered and laid out as a self-organized map, following the SOM methodology for large digital archives. The system has the usual music archive features as follows: (1) automatic clustering and organization of music files into "islands of related music"; (2) classification of music clusters into various music genres; (3) playback of music files selected by the user; and (4) automatic generation of related music files for every music file that is chosen. In addition to these rather common features found in most Self-Organizing Maps (SOM) based digital music archives, MarB also allows for an interactive selection and clustering of sets and subsets of music files until a specific music file is found. This is done using a Scatter/Gather interface that allows the user to select interesting clusters of music files (gather mode), which are then re-organized and re-clustered (scatter mode) for the user to visually inspect and possibly listen to. The user is then asked to select new interesting clusters (gather mode again). This alternating selection and re-clustering process continues until the user chooses a specific music file, and is provided with a set of most related music files. A novel album dispersal measure is used to objectively assess the quality of the clusters produced both by the SOM and the special k-means algorithm employed in the Scatter-Gather module. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Li6:2008:ijcnn, author = "Tao Li and Dongbin Zhao and Jianqiang Yi", title = "Adaptive Dynamic Neuro-Fuzzy System for Traffic Signal Control", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0574.pdf}, url = {}, size = {}, abstract = {This paper aims at developing near optimal traffic signal control for multi-intersection in city. Fuzzy control is widely used in traffic signal control. For improving fuzzy control's adaptability in fluctuant states, a controller combined with neuro-fuzzy system and adaptive dynamic programming (ADP) is designed. This controller can be used for cooperative control of multi-intersection. The adaptive dynamic programming gives reinforcement for good neuro-fuzzy system behavior and punishment for poor behavior. The neuro-fuzzy system adjusts its parameters according to the reinforcement and punishment. Then, those actions leading to better results tend to be chosen preferentially in the future. Comparing with traditional ADP, this controller uses neuro-fuzzy system as the action network. The neuro-fuzzy system offers some existing knowledge and reduces the randomness of traditional ADP. In this paper, the objective of the controller is to minimize the average vehicular delay. The controller can be trained to adapt fluctuant traffic states by real-time traffic data, and achieves a near optimal control result in a long run. Simulation results show that the trained controller achieves shorter average vehicular delay than the controller with initial membership function. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Bai2:2008:ijcnn, author = "Xuerui Bai and Dongbin Zhao and Jianqiang Yi", title = "Ramp Metering Based on On-Line ADHDP (λ) Controller", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0575.pdf}, url = {}, size = {}, abstract = {Increasing dependence on car-based travel has led to the daily occurrence of freeway congestions around the world. In order to improve the worse and worse traffic congestion situation and solve the problems brought with it, a new kind of effective, fast, and robust method should be presented. Ramp metering has been developed as a traffic management strategy to alleviate congestion on freeways. But, it doesn't work well in uncertainty situations. In this paper, in order to solve the problems in uncertainty conditions, an on-line learning control method based on the fundamental principle of reinforcement learning is proposed. The method is ADP (adaptive dynamic programming) and in order to expedite the learning rate, the concept about eligibility traces is introduced here. Then eligibility trace and ADP is combined to present a new kind of traffic responsive control method. The new method is called action-dependent heuristic dynamic programming based on eligibility traces (ADHDP (λ)). ADHDP (λ) is an approximate optimal ramp metering method. Simulation studies on a hypothetical freeway indicate good control performance of the proposed real-time traffic controller. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Li7:2008:ijcnn, author = "Wei Li and Yannan Zhao and Yixu Song and Zehong Yang", title = "COX-2 Activity Prediction in Chinese Medicine Using Neural Network Based Ensemble Learning Methods", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0576.pdf}, url = {}, size = {}, abstract = {In this paper, neural network based ensemble learning methods are introduced in predicting activities of COX-2 inhibitors in Chinese medicine Quantitative Structure-Activity Relationship (QSAR) research. Three different ensemble learning methods: bagging, boosting and random subspace are tested using neural networks as basic regression rules. Experiments show that all three methods, especially boosting, are fast and effective ways in the activity prediction of Chinese medicine QSAR research, which is generally based on a small amount of training samples. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Lan:2008:ijcnn, author = "Yuan Lan and Yeng Chai Soh and Guang-Bin Huang", title = "Extreme Learning Machine Based Bacterial Protein Subcellular Localization Prediction", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0577.pdf}, url = {}, size = {}, abstract = {In this paper, Extreme Learning Machine (ELM) is introduced to predict the subcellular localization of proteins based on the frequent subsequences. It is proved that ELM is extremely fast and can provide good generalization performance. We evaluated the performance of ELM on four localization sites with frequent subsequences as the feature space. A new parameter called Comparesup was introduced to help the feature selection. The performance of ELM was tested on data with different number of frequent subsequences, which were determined by different range of Comparesup. The results demonstrated that ELM performed better than previously reported results, for all of the four localization sites. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Papadopoulos:2008:ijcnn, author = "George Papadopoulos and Martin Brown", title = "Minimum Entropy Parameter Estimation: Application to the RKIP Regulated ERK Signaling Pathway", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0579.pdf}, url = {}, size = {}, abstract = {Parameter estimation plays an important role in Systems Biology in helping to understand the complex behavior of signal transduction networks. The problem becomes more intense as the inherent stochasticity of the signaling mechanism involves noise components of non-Gaussian nature. A novel stochastic parameter estimation method has been developed where the aim is to obtain the optimal parameters corresponding to a lower entropy measure on the residual joint probability density function. The residual joint PDF is approximated using Kernel Density Estimation methods and the method is designed to handle general multivariable dynamic ODE systems where the measurement noise is not necessarily Gaussian. The analysis on the proposed minimum entropy parameter estimation involves an application to the RKIP regulated ERK pathway where the demonstrated simulation results clearly indicate its effectiveness. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Beran:2008:ijcnn, author = "Peter Paul Beran and Elisabeth Vinek and Erich Schikuta and Thomas Weishäupl", title = "ViNNSL — The Vienna Neural Network Specification Language", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0580.pdf}, url = {}, size = {}, abstract = {We present ViNNSL (Vienna Neural Network Specification Language), an XML based language for describing, training and running neural networks on a Grid infrastructure. ViNNSL allows for dynamic client-server communication regarding the semantics of neural network resources. As a proof-of-concept the language is used in N2Grid, a system for the use of neural network resources on a worldwide basis. Based on the Grid infrastructure N2Grid allows to build up a virtual community enabling arbitrary users to exchange knowledge (neural network resources, such as neural network objects and neural network paradigms) and to exploit the available computing resources for neural network specific tasks, leading to a Grid based, world-wide distributed, neural network knowledge and simulation system. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Liu8:2008:ijcnn, author = "Yong Liu ", title = "Reduction of Difference among Trained Neural Networks by Re-Learning", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0581.pdf}, url = {}, size = {}, abstract = {It is often that the learned neural networks end with different decision boundaries under the variations of training data, learning algorithms, architectures, and initial random weights. Such variations are helpful in designing neural network ensembles, but are harmful for making unstable performances, i.e., large variances among different learnings. This paper discusses how to reduce such variances for learned neural networks by letting them re-learn on those data points on which they disagrees with each other. Experimental results have been conducted on four real world applications to explain how and when such re-learning works. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Chandrakala:2008:ijcnn, author = "S. Chandrakala and C. Chandra Sekhar", title = "A Density based Method for Multivariate Time Series Clustering in Kernel Feature Space", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0583.pdf}, url = {}, size = {}, abstract = {Time series clustering finds applications in diverse fields of science and technology. Kernel based clustering methods like kernel k-means method need number of clusters as input and cannot handle outliers or noise. In this paper, we propose a density based clustering method in kernel feature space for clustering multivariate time series data of varying length. This method can also be used for clustering any type of structured data, provided a kernel which can handle that kind of data is used. We present heuristic methods to find the initial values of the parameters used in our proposed algorithm. To show the effectiveness of this method, this method is applied to two different online handwritten character data sets which are mutivariate time series data of varying length, as a real world application. The performance of the proposed method is compared with the spectral clustering and kernel k-means clustering methods. Besides handling outliers, the proposed method performs as well as the spectral clustering method and outperforms the kernel k-means clustering method. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Yang4:2008:ijcnn, author = "Bingbing Yang and Qian Yin and Shengyong Xu and Ping Guo", title = "Software Quality Prediction Using Affinity Propagation Algorithm", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0585.pdf}, url = {}, size = {}, abstract = {Software metrics are collected at various phases of the software development process. These metrics contain the information of the software and can be used to predict software quality in the early stage of software life cycle. Intelligent computing techniques such as data mining can be applied in the study of software quality by analyzing software metrics. Clustering analysis, which can be considered as one of the data mining techniques, is adopted to build the software quality prediction models in the early period of software testing. In this paper, a new clustering method called Affinity Propagation is investigated for the analysis of two software metric datasets extracted from real-world software projects. Meanwhile, K-Means clustering method is also applied for comparison. The numerical experiment results show that the Affinity Propagation algorithm can be applied well in software quality prediction in the very early stage, and it is more effective on reducing Type II error. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Jin2:2008:ijcnn, author = "Feng Jin and Shiliang Sun", title = "Neural Network Multitask Learning for Traffic Flow Forecasting", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0586.pdf}, url = {}, size = {}, abstract = {Traditional neural network approaches for traffic flow forecasting are usually single task learning (STL) models, which do not take advantage of the information provided by related tasks. In contrast to STL, multitask learning (MTL) has the potential to improve generalization by transferring information in training signals of extra tasks. In this paper, MTL based neural networks are used for traffic flow forecasting. For neural network MTL, a backpropagation (BP) network is constructed by incorporating traffic flows at several contiguous time instants into an output layer. Nodes in the output layer can be seen as outputs of different but closely related STL tasks. Comprehensive experiments on urban vehicular traffic flow data and comparisons with STL show that MTL in BP neural networks is a promising and effective approach for traffic flow forecasting. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Oentaryo:2008:ijcnn, author = "Richard J. Oentaryo and Michel Pasquier ", title = "Towards A Novel Integrated Neuro-Cognitive Architecture (INCA)", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0587.pdf}, url = {}, size = {}, abstract = {Artificial intelligence research is now flourishing which aims at achieving general, human-level intelligence. Accordingly, cognitive architectures are increasingly employed as blueprints for building intelligent agents to be endowed with various perceptive and cognitive abilities. This paper presents a novel Integrated Neuro-Cognitive Architecture (INCA) which emulate the putative functional aspects of various salient brain sub-systems via a learning memory modeling approach. The strength of INCA lies in self-organizing connectionist learning to induce high-level symbolic knowledge autonomously, and support for meta-cognitive functions. Its overall operations are governed by its consolidation and inference cycles, which posit a human-plausible way for forming and exploiting knowledge. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Huang5:2008:ijcnn, author = "Shian-Chang Huang and Tung-Kuang Wu", title = "Forecasting Stock Indices with Wavelet-based Kernel Partial
Least Square Regressions", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0589.pdf}, url = {}, size = {}, abstract = {This study combines wavelet-based feature extractions with kernel partial least square (PLS) regression for international stock index forecasting. Wavelet analysis is used as a preprocessing step to decompose and extract most important time scale features from high dimensional input data. Owing to the high dimensionality and heavy multi-collinearity of the input data, a kernel PLS regression model is employed to create the most efficient subspace that keeping maximum covariance between inputs and outputs, and perform the final forecasting. Compared with neural networks, pure SVMs or traditional GARCH models, the proposed model performs best. The root-mean-squared forecasting errors are significantly reduced. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Yang5:2008:ijcnn, author = "Yingjie Yang and Chris Hinde and David Gillingwater", title = "Airport Noise Simulation Using Neural Networks", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0591.pdf}, url = {}, size = {}, abstract = {Aircraft noise is influenced by many complex factors and it is difficult to devise an accurate mathematical model to simulate it with respect to operations at an airport. This paper presents an investigation in simulating airport noise using artificial neural networks. The results show that it is possible to establish a simple neural network model with monitored data for a specific airport and specific aircraft under local conditions. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Vassilas:2008:ijcnn, author = "Nikolaos Vassilas ", title = "Batch Self-Organizing Map Algorithm: A Theoretical Study of
Self-Organization of a 1-D Network Under Quantization Effects", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0592.pdf}, url = {}, size = {}, abstract = {In this paper, we examine necessary and sufficient conditions that ensure self-organization of the batch variant of the self-organizing map algorithm for 1-D networks and for quantized weights and inputs. Using Markov chain formalism, it is shown that the existing analysis for the original algorithm can be extended to also include the more general batch variant. Finally, simulations verify the theoretical results, relate the speed of weight ordering to the distribution of the inputs and show the existence of metastable states of the Markov chain. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Castañeda:2008:ijcnn, author = "Carlos E. Castañeda and Edgar N. Sanchez and Alexander G. Loukianov", title = "Discrete-Time Recurrent Neural DC Motor Control using Kalman Learning", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0593.pdf}, url = {}, size = {}, abstract = {An adaptive tracking controller for a discrete-time direct current (DC) motor model in presence of bounded disturbances is presented. A high order neural network is used to identify the plant model; this network is trained with an extended Kalman filter. Then, the discrete-time block control and sliding modes techniques are used to develop the reference tracking control. This paper includes also the respective stability analysis and a strategy to avoid specific adaptive weights zero-crossing. The scheme is illustrated via simulations for a discrete-time nonlinear model of an electric DC motor. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Silva:2008:ijcnn, author = "Kelly P. Silva and Rodrigo G. F. Soares and Francisco A. T. de Carvalho and Teresa B. Ludermir", title = "Evolving Both Size And Accuracy of RBF Networks Using Memetic Algorithm", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0595.pdf}, url = {}, size = {}, abstract = {One of the main obstacles to obtain an artificial neural network with reasonable performance is the parameter setting. This work proposes a methodology to the automatic definition of RBF (Radial Basis Function) networks with an appropriate configuration for the selected classification problems. We propose the use of a Memetic Algorithm in order to perform the search for networks with minimum architecture and error rate. A set of experiments was made with four datasets and we were able to show the effectiveness of The method. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Soares:2008:ijcnn, author = "Rodrigo G. F. Soares and Kelly P. Silva and Teresa B. Ludermir and Francisco A. T. de Carvalho", title = "An Evolutionary Approach for the Clustering Data Problem", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0597.pdf}, url = {}, size = {}, abstract = {The clustering problem consists in the discovery of interesting groups in a dataset. Such task is very important and widely tackled in the literature. In this paper, we propose an evolutionary method in order to obtain well formed and spatially separated clusters. The proposed algorithm uses a complete solution representation, each partition is represented by a length-variable chromosome. The variation operators were chosen to facilitate the exchange of clustering information between individuals.We have put two complementary clustering criteria together in the fitness function, so that the method can find clusters with arbitrary shapes. The k-means algorithm was the basis of the local search operator, such operator might refine the clustering solutions. The population diversity was an important issue for the algorithm, so a diversity maintenance scheme was employed. Differently from other existing clustering algorithms, our algorithm does not need the setting of the number of clusters in advance. We evaluated the method in different contexts, using both real and simulated data. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Zanchettin:2008:ijcnn, author = "Cleber Zanchettin and Teresa B. Ludermir", title = "Feature Subset Selection in a Methodology for Training and Improving Artificial Neural Network Weights and Connections", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0600.pdf}, url = {}, size = {}, abstract = {This paper investigates the problem of feature subset selection as part of a methodology that integrates heuristic tabu search, simulated annealing, genetic algorithms and backpropagation. This technique combines both global and local search strategies for the simultaneous optimization of the number of connections and connection values of Multi-Layer Perceptron neural networks. We compare the performance of the proposed method for feature subset selection to five classical feature selection methods in three different classification problems. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Pan:2008:ijcnn, author = "Hong Pan and W. C. Siu and N. F. Law ", title = "Efficient and Low-Complexity Image Coding with the Lifting
Scheme and Modified SPIHT", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0602.pdf}, url = {}, size = {}, abstract = {In this paper, we propose an efficient and low complexity image coding algorithm based on the lifting wavelet transform and listless modified SPIHT (LWT-LMSPIHT). LWT-LMSPIHT jointly considers the advantages of progressive transmission and spatial scalability that were not fully provided by the SPIHT algorithm, thus it outperforms the SPIHT at low bit rates coding. The coding efficiency of LWT-LMSPIHT comes from three aspects. The lifting scheme lowers the number of arithmetic operations of the wavelet transform. Moreover, a significance reordering of the modified SPIHT ensures that it codes more significant information earlier in the bit stream belonging to the lower frequency bands than SPIHT to better exploit the energy compaction of the wavelet coefficients. Finally, a listless structure further reduces the amount of memory and improves the speed of compression by more than 47percent for a 512 × 512 image, as compared with the SPIHT algorithm. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Fyfe:2008:ijcnn, author = "Colin Fyfe and Tseng Wen-Ching and Wu Chia-Ti and Chien Shih-Yu and Pei Ling Lai", title = "A Topology Preserving Mapping for Face Recognition", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0603.pdf}, url = {}, size = {}, abstract = {We review a recent form of topology preserving mapping which uses the same underlying structure as the Generative Topographic Mapping (GTM) but organises the projections of the latent points into data space based on the method of Harmonic K-means. We show that projections of the Olivetti Face Database onto this latent space show good performance in terms of identifying all images of a particular individual as lying in the same section of the latent space. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Lam:2008:ijcnn, author = "Benson S. Y. Lam and Hong Yan", title = "Robust Clustering Algorithm for High Dimensional Data Classification based on Multiple Supports", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0604.pdf}, url = {}, size = {}, abstract = {High dimensionality, noisy features and outliers can cause problems in cluster analysis. Many existing methods can handle one of the problems well but not the others. In this paper, we propose a new clustering algorithm to solve these problems. The basic idea is to control the support of the optimization procedure so that the effect produced by those contaminated samples and dimensions is greatly reduced. This is achieved by using multiple supports. Initially, a large support is used and then its size is reduced and eventually only a subgroup of data samples is considered for clustering. This procedure can filter out lots of contaminated information. Experiment results show that the proposed method effectively resolves all these problems. It outperforms existing ones for real world high dimensional datasets. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Tzortzis:2008:ijcnn, author = "Grigorios Tzortzis and Aristidis Likas", title = "The Global Kernel k-Means Clustering Algorithm", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0606.pdf}, url = {}, size = {}, abstract = {Kernel k-means is an extension of the standard kmeans clustering algorithm that identifies nonlinearly separable clusters. In order to overcome the cluster initialization problem associated with this method, in this work we propose the global kernel k-means algorithm, a deterministic and incremental approach to kernel-based clustering. Our method adds one cluster at each stage through a global search procedure consisting of several executions of kernel k-means from suitable initializations. This algorithm does not depend on cluster initialization, identifies nonlinearly separable clusters and, due to its incremental nature and search procedure, locates near optimal solutions avoiding poor local minima. Furthermore a modification is proposed to reduce the computational cost that does not significantly affect the solution quality. We test the proposed methods on artificial data and also for the first time we employ kernel k-means for MRI segmentation along with a novel kernel. The proposed methods compare favorably to kernel k-means with random restarts. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Chen11:2008:ijcnn, author = "Dan Chen and YueChao Wang and and XuSheng Tang", title = "GPC Scheme for the Internet-Based Teleoperation", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0607.pdf}, url = {}, size = {}, abstract = {The variable time delay and the packet loss degrade the performance of Internet based teleoperation system seriously, even make the system unstable. To overcome the trouble, an idea that using CARIMA model of the linearized slave robot to design a controller based on the Generalized Predictive Control (GPC) method is proposed in this paper. We place the GPC controller at the remote site. First of all, The CARIMA model of the linearized slave robot is derived. Secondly, a GPC controller is designed at slave site to generate the redundant control information to diminish the influence of the packet loss and the large time delay in the internet to the system. Moreover, in order to solve the problem caused by the variable time delay, the reference information signed with time stamp is used and fedback to the operator, so the operator can predict the next round trip time delay(RTT) according to the preceding RTT we got. Finally, stability condition is achieved. Simulation results show that these strategies can dynamically compensate for the variable time delay and reduce the performance degradation induced by packet loss. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Zhou2:2008:ijcnn, author = "Bo Zhou and Jianda Han", title = "Dynamic Feedback Tracking Control of Tracked Mobile Robots with Estimated Slipping Parameters", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0608.pdf}, url = {}, size = {}, abstract = {The trajectory tracking control problem of a tracked vehicle with slipping is considered in this paper. The slipping effects are analyzed and modeled as three time-varying parameters, which can be estimated simultaneously with robot's pose using nonlinear estimators such as unscented Kalman filter. Dynamic feedback linearization integrated with a globally exponential stabilizing state feedback is applied to achieve the tracking control objective. Simulation results are provided to demonstrate the effectiveness of proposed method. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Tan3:2008:ijcnn, author = "T. Z. Tan and G. S. Ng and C. Quek ", title = "Improving Tractability of Clinical Decision Support System", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0609.pdf}, url = {}, size = {}, abstract = {Clinical Decision Support System (CDSS) is a promising tool that can alleviate the high medical error rate. However, most of the CDSS are not adopted in clinical settings due to the lack of trust amongst the physicians. Thus, the development of CDSS should cater to the psychological need of physicians. One major issue preventing the wide acceptance of CDSS is the tractability of the system. Hence, in this paper, an attempt is made to improve the system tractability. One possible approach is proposed to improve the tractability of present CDSS. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Islam2:2008:ijcnn, author = "Md. Monirul Islam and Md. Faijul Amin and Suman Ahmmed and Kazuyuki Murase", title = "An Adaptive Merging and Growing Algorithm for Designing Artificial Neural Networks", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0610.pdf}, url = {}, size = {}, abstract = {This paper presents a new algorithm, called adaptive merging and growing algorithm (AMGA), for designing artificial neural networks (ANNs). The new algorithm merges and adds hidden neuron during training. The merging operation introduced here is a kind mixed mode operation that is equivalent to pruning two neurons and adding one neuron. Unlike most previous studies on designing ANNs, AMGA puts emphasis on adaptive functioning in designing ANNs. This is the main reason why AMGA merges and adds hidden neurons repeatedly (or alternatively) based on the learning ability of hidden neurons and training progress of ANNs, respectively. AMGA has been tested on five benchmark problems including the Australian credit card, cancer, diabetes, glass and thyroid problems. The experimental results show that AMGA can produce ANNs with good generalization ability compared to other algorithms. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Bozakov:2008:ijcnn, author = "Zdravko Bozakov and Lars Graening and Stephan Hasler and Heiko Wersing and Stefan Menzel", title = "Unsupervised Extraction of Design Components for a 3D parts-based Representation", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0612.pdf}, url = {}, size = {}, abstract = {During CAD development and any kind of design optimisation over years a huge amount of geometries accumulate in a design department. To organize and structure these designs with respect to reusability, a hierarchical set of components on different scalings is extracted by the designers. This hierarchy allows to compose designs from several parts and to adapt the composition to the current task. Nevertheless, this hierarchy is imposed by humans and relies on their experiences. In the present paper a computational method is proposed for an unsupervised extraction of design components from a large repository of geometries. Methods known from the field of object and pattern recognition in images are transferred to the 3D design space to detect relevant features of geometries. The non-negative matrix factorization algorithm (NMF) is extended and tuned to the given task for an autonomous detection of design components. The results of the NMF additionally provide an overview on the distribution of these components in the design repository. The extracted components sum up in a parts-based representation which serves as a base for manual or computational design development or optimisation respectively. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Botteldooren:2008:ijcnn, author = "Dick Botteldooren and Bert De Coensel", title = "A Model for Long-Term Environmental Sound Detection", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0613.pdf}, url = {}, size = {}, abstract = {In recent years, knowledge on primary processing of sound by the human auditory system has tremendously increased. This paper exploits the opportunities this creates for assessing the impact of (unwanted) environmental noise on quality of life of people. In particular the effect of auditory attention in a multisource context is focused on. The typical application envisaged here is characterized by very long term exposure (days) and multiple listeners (thousands) that need to be assessed. Therefore, the proposed model introduces many simplifications. The results obtained show that the approach is nevertheless capable of generating insight in the emergence of annoyance and the appraisal of open area soundscapes. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Shahjahan:2008:ijcnn, author = "Md. Shahjahan and Md. Asaduzzaman and K. Murase", title = "How to Maintain Information Content in Artificial Neural Networks with Coherence Adaptation", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0616.pdf}, url = {}, size = {}, abstract = {This paper presents a learning approach called adaptive coherence scheme (CAS) that adaptively reduces information on input patterns in hidden layer(s) of a neural network. The hidden units in a neural network store information continuously during training session. As a result the network becomes extremely familiar with every details of input patterns. This is not desirable in training. Therefore, we attempt to limit this information automatically with a regularization function consisting of activations of hidden units. We proposed standard coherence learning (SCL) where a constant coherence strength was used in order to solve the problem. Here, we attempt to develop a coherence adaptation scheme in order to maintain small amount of information in the network automatically. We have applied the algorithm to the breast cancer classification and Mackey-Glass chaotic time series prediction problems with single and double hidden layered networks. The results show that the network maintains small amount of information with good classification and prediction accuracies. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Okun2:2008:ijcnn, author = "Oleg Okun and Helen Priisalu", title = "Ensembles of K-Nearest Neighbors and Dimensionality Reduction", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0617.pdf}, url = {}, size = {}, abstract = {In this paper, ensembles of k-nearest neighbors classifiers are explored for gene expression cancer classification, where each classifier is linked to a randomly selected subset of genes. It is experimentally demonstrated using five datasets that such ensembles can yield both good accuracy and dimensionality reduction. If a characteristic called dataset complexity guides which random subset to include into an ensemble, then the ensemble achieves even better performance. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wong:2008:ijcnn, author = "Max H. Y. Wong and Raymond S. T. Lee", title = "Wind Shear Forecasting by Chaotic Oscillatory-Based Neural Networks (CONN) with Lee Oscillator (Retrograde Signalling) Model", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0618.pdf}, url = {}, size = {}, abstract = {Wind shear is a conventionally unpredictable meteorological phenomenon which presents a common danger to aircraft, particularly on takeoff and landing at airports. This paper describes a method for forecasting wind shear using an advanced paradigm from computational intelligence, Chaotic Oscillatory-based Neural Networks (CONN). The method uses weather data to predict wind velocities and directions over a short time period. This approach may have a wide variety of applications but from the aviation forecast perspective, it can be used in aviation to generate wind shear alerts. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Liang2:2008:ijcnn, author = "Lichen Liang and Vladimir Cherkassky", title = "Connection Between SVM+ and Multi-Task Learning", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0619.pdf}, url = {}, size = {}, abstract = {Exploiting additional information to improve traditional inductive learning is an active research in machine learning. When data are naturally separated into groups, SVM+[7] can effectively use this structure information to improve generalization. Alternatively, we can view learning based on data from each group as an individual task, but all these tasks are somehow related; so the same problem can also be formulated as a multi-task learning problem. Following the SVM+ approach, we propose a new multi-task learning algorithm called svm+MTL, which can be thought as an adaptation of SVM+ for solving MTL problem. The connections between SVM+ and svm+MTL are discussed and their performance is compared using synthetic data sets. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Silva2:2008:ijcnn, author = "Renato R. da Silva and Claudio A. Policastro", title = "An Enhancement of Relational Reinforcement Learning", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0621.pdf}, url = {}, size = {}, abstract = {Relational reinforcement learning is a technique that combines reinforcement learning with relational learning or inductive logic programming. This technique offers greater expressive power than that one offered by traditional reinforcement learning. However, there are some problems when one wish to use it in a real time system. Most of recent research interests on incremental relational learning structure, that is a great challenge in this area. In this work, we are proposing an enhancement of TG algorithm and we illustrate the approach with a preliminary experiment. The algorithm was evaluated on a Blocks World simulator and the obtained results shown it is able to produce appropriate learn capability. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(DalleMole:2008:ijcnn, author = "Vilson L. DalleMole and Aluizio F. R. Araújo", title = "The Growing Self-Organizing Surface Map", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0622.pdf}, url = {}, size = {}, abstract = {This paper presents a new Self-organizing Map suitable for recovering a 2D surface starting from points sampled on the object surface. Growing Self-organizing Surface Map (GSOSM), is a new algorithm of the growing SOM family that reproduce the surface as an incremental mesh composed of triangles which are approximately equilateral. GSOSM introduces a new connection learning rule, called Competitive Connection Hebbian Learning (CCHL), that produces a complete triangulation where CHL fails. Differently from other models such as Neural Meshes (NM), GSOSM recovers a surface topology from homogeneous samples distribution according to any presentation sequence. GSOSM map is a mesh that represents the object surface with a detail level established by a parameter, allowing different versions of a same object surface. Moreover, GSOSM reconstructions are very often meshes free of false or overlapping faces, and then GSOSM is a potential tool for virtual reconstruction of real objects. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(DeLooze:2008:ijcnn, author = "Lori L. DeLooze ", title = "Eclectic Method for Feature Reduction Using Self-Organizing Maps", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0625.pdf}, url = {}, size = {}, abstract = {This paper presents an eclectic method for extracting simple classification rules using a combination of a genetic algorithm, a Self-Organizing Map and the ID3 decision tree algorithm. After outlining the method for extracting rules, we assess them for effectiveness, complexity and precision and compare them with similar methods which use Support Vector Machines. While it is no surprise that the method proposed reduced the complexity of classification, it was surprising that the simple rules extracted from the SOMs were both more effective and more precise than the SOM from which they were extracted. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Kamimura:2008:ijcnn, author = "Ryotaro Kamimura ", title = "Conditional Information and Information Loss for Flexible Feature Extraction", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0627.pdf}, url = {}, size = {}, abstract = {In this paper, we propose a new informationtheoretic approach to competitive learning and self-organizing maps. We use several information-theoretic measures such as conditional information and information losses to extract main features in input patterns. First, conditional information content is used to show how much information is contained in a competitive unit or an input pattern. Then, information content in each variable is detected by information losses. The information loss is defined by difference between information with all input units and information without an input unit. We applied the method to an artificial data, the Iris problem, a student survey, a CPU classification problem and a company survey. In all cases, experimental results showed that main features in input patterns were clearly detected. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Liu9:2008:ijcnn, author = "Xiaoxiang Liu and Henry Leung and George A. Lampropoulous", title = "An Intelligent Through-the-Wall Recognition System for Homeland Security", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0628.pdf}, url = {}, size = {}, abstract = {The increasing demands for homeland security boost the development of an intelligent recognition system for through-the-wall sensing. A novel intelligent through-the-wall life recognition engine based on support vector machine (SVM) is provided herein. In this system, micro-Doppler signatures detected from through-the-wall radar are extracted and fed into a SVM classifier. Micro-Doppler effect has great potential for life recognition of human activities, nonhuman but vital subjects, and lifeless targets. Due to time-varying nonstationary characteristic of micro-Doppler feature and its high dimensionality, the SVM classifier is found effective in achieving both computation efficiency and accuracy for this application. Simulation results show that high classification performance is achieved using the proposed recognition system. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Soltic:2008:ijcnn, author = "S. Soltic and S. G. Wysoski and N. K. Kasabov", title = "Evolving Spiking Neural Networks for Taste Recognition", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0629.pdf}, url = {}, size = {}, abstract = {The paper investigates the use of the spiking neural networks for taste recognition in a simple artificial gustatory model. We present an approach based on simple integrate-and-fire neurons with rank order coded inputs where the network is built by an evolving learning algorithm. Further, we investigate how the information encoding in a population of neurons influences the performance of the networks. The approach is tested on two real-world datasets where the effectiveness of the population coding and network's adaptive properties are explored. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Watanabe2:2008:ijcnn, author = "Sumio Watanabe ", title = "A Formula of Equations of States in Singular Learning Machines", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0630.pdf}, url = {}, size = {}, abstract = {Almost all learning machines used in computational intelligence are not regular but singular statistical models, because they are nonidentifiable and their Fisher information matrices are singular. In singular learning machines, neither the Bayes a posteriori distribution converges to the normal distribution nor the maximum likelihood estimator satisfies the asymptotic normality, resulting that it has been difficult to estimate generalization performances. In this paper, we establish a formula of equations of states which holds among Bayes and Gibbs generalization and training errors, and show that two generalization errors can be estimated from two training errors. The equations of states proved in this paper hold for any true distribution, any learning machine, and a priori distribution, and any singularities, hence they define widely applicable information criteria. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Yuan2:2008:ijcnn, author = "Changsong Yuan and Xiangyang Zhu and Guangquan Liu and Min Lei ", title = "Classification of the Surface EMG Signal Using RQA Based Representations", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0631.pdf}, url = {}, size = {}, abstract = {Feature extraction is a key element of pattern recognition for myoelectric control. In this paper, recurrence plots and recurrence quantification analysis (RQA) are used as the feature extractor for surface EMG signals. For eight different hand motions, two-channel EMG signals are recorded. Ten individual RQA parameters are calculated for each channel of EMG signals. With different combinations of individual RQA parameters, a set of feature vectors with dimensions varying from 2 to 20 are obtained. The feature vectors are used as the input to a BP neural network for motion classification. Experimental results show that with appropriate selections of feature vectors, the motion classification algorithm achieves desirable accurate rate. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Siek:2008:ijcnn, author = "Michael Siek and Dimitri Solomatine", title = "Multivariate Chaotic Models vs Neural Networks in Predicting Storm Surge Dynamics", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0632.pdf}, url = {}, size = {}, abstract = {The recently developed methods in nonlinear dynamics and chaos time series analysis are used in this study to analyze, delineate and quantify the underlying coastal water level and surge dynamics in the North Sea along several locations at the Dutch coast. This study analyzes seven water level and surge data sets, five of which characterize coastal locations and two relate to the open sea locations. Both the water level data and the surge data (with the astronomical tide removed) are analyzed. The main objective of this analysis is to delineate and quantify the underlying dynamics of the coastal water levels and to quantify the variability and predictability of the coastal dynamics along the Dutch coast based on time series of observables. Based on the reconstructed multivariate phase space of the water level and surge dynamics, adaptive multivariate local models were built which typically yield more reliable and accurate short-term predictions compared to neural networks. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Akhand:2008:ijcnn, author = "M. A. H. Akhand and Md. Monirul Islam and Kazuyuki Murase", title = "Training of Neural Network Ensemble Through Progressive Interaction", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0633.pdf}, url = {}, size = {}, abstract = {This paper presents an interactive training method for neural network ensembles (NNEs). For an NNE, proposed method trains component neural networks (NNs) one after another sequentially and interactions among the NNs are maintained indirectly via an intermediate space, called information center (IC). IC manages outputs of all previously trained NNs. Update rule, to train an NN in conjunction with IC, is developed from negative correlation learning (NCL) and defined the proposed method as progressive NCL (pNCL). The introduction of such an information center in ensemble methods reduces the training time interaction among component NNs. The effectiveness of the proposed method is evaluated on several benchmark classification problems. The experimental results show that the proposed approach can improve the performance of NNEs. pNCL is incorporated with two popular NNE methods, bagging and boosting. It is also found that the performance of bagging and boosting algorithms can be further improved by incorporating pNCL with their training processes. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Liu10:2008:ijcnn, author = "Song Liu and Jagath C. Rajapakse", title = "Protein Localization on Cellular Images with Markov Random Fields", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0634.pdf}, url = {}, size = {}, abstract = {There has been an increasing interest recently in identifying subcellular proteins from cellular images in order to understand subcellular activities of cells. However, accuracies of the prediction tend to decrease with the number of protein subcellular localization classes. Therefore in this paper, we introduce a multiple-cell model with a higher-order Markov random fields (MRF) to combine predictions on multiple cells to make inferences on protein localizations of individual cells. The proposed method showed a significant improvement in discrimination of protein subcellular localization patterns over the predictions by single cells. We also introduce structure learning of MRF, which indeed enhanced the predictions especially when the number of cells in the model becomes large. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Sri:2008:ijcnn, author = "Kavuri Swathi Sri and Jagath C. Rajapakse", title = "Extracting EEG Rhythms Using ICA-R", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0635.pdf}, url = {}, size = {}, abstract = {Extracting brain rhythms from EEG signals has many applications including Brain Computer Interfacing. Here, we demonstrate how ICA with Reference (ICA-R) is used to extract brain rhythms, using appropriate reference signals. In particular, we evaluate four criteria for generating reference signals to use with ICA-R. We demonstrate the performance of these techniques in extracting μ and β rhythms from two real EEG datasets. The results indicate that ICA-R can be effectively used for extracting brain rhythms. The blind source separation technique decomposing autocorrelation for extracting reference signals outperformed other methods. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Heerden:2008:ijcnn, author = "Willem S. van Heerden and Andries P. Engelbrecht", title = "A Comparison of Map Neuron Labeling Approaches for Unsupervised
Self-Organizing Feature Maps", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0636.pdf}, url = {}, size = {}, abstract = {The self-organizing map (SOM) is an unsupervised neural network approach that reduces a high-dimensional data set to a representative and compact two-dimensional grid. In so doing, a SOM reveals emergent clusters within the data. Research has shown that SOMs lend themselves to visual and computational analysis for exploratory and data mining purposes. However, an important requirement for many SOM interpretations is the characterization of the map's emergent clusters. This process is often addressed by either a manual or automated map neuron labeling approach. This paper discusses techniques for the labeling of the unsupervised, supervised and semi-supervised variants of the SOM, and proposes some new methods. It also presents empirical results characterizing the performance of two automated labeling approaches for fully unsupervised SOMs when applied for example classification of experimental data sets. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Yang6:2008:ijcnn, author = "Cheng-San Yang and Li-Yeh Chuang and Jung-Chike Li and Cheng-Hong Yang", title = "A Novel BPSO Approach for Gene Selection and Classification of Microarray Data", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0637.pdf}, url = {}, size = {}, abstract = {Selecting relevant genes from microarray data poses a huge challenge due to the high-dimensionality of the features, multi-class categories and a relatively small sample size. The main task of the classification process is to decrease the microarray data dimensionality. In order to analyze microarray data, an optimal subset of features (genes) which adequately represents the original set of features has to be found. In this study, we used a novel binary particle swarm optimization (NBPSO) algorithm to perform microarray data selection and classification. The K-nearest neighbor (K-NN) method with leave-one-out cross-validation (LOOCV) served as a classifier. The experimental results showed that the proposed method not only effectively reduced the number of gene expression levels, but also achieved lower classification error rates. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Qiao:2008:ijcnn, author = "Yuanhua Qiao and Jun Miao and Lijuan Duan and Yunfeng Lu", title = "Image Segmentation Using Dynamic Mechanism Based PCNN Model", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0638.pdf}, url = {}, size = {}, abstract = {Pulse-coupled neuron networks (PCNN) can be efficiently applied to image segmentation. However, the performance of segmentation depends on the suitable PCNN parameters, which are obtained by manual experiment, and the effect of the segmentation needs to be improved for images with noise. In this paper, dynamic mechanism based PCNN(DMPCNN) is brought forward to simulate the integrate-and-fire mechanism, and it is applied to segment images with noise effectively. Parameter selection is based on dynamic mechanism. Experimental results for image segmentation show its validity and robustness. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Pattaraintakorn:2008:ijcnn, author = "Puntip Pattaraintakorn ", title = "Analysis of Distributed Databases with a Hybrid Rough Sets Approach", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0639.pdf}, url = {}, size = {}, abstract = {The aim of this paper is to offer mathematical proofs of Pawlak's rough set theory about distributed knowledge based on rough sets and relational databases. A case study on actual self-reported geriatric data for survival analysis is presented to provide a computational evidence of the distributed knowledge. Risk factors, prolongation time prediction rules and validation are also computed and discussed. We illustrate that dividing a decision table (or database) into smaller units will in general result in the loss of some information by rough set theory. }, keywords = {: Rough set theory, relational database, distributed knowledge, survival analysis, artificial intelligence.}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Argha:2008:ijcnn, author = "Ahmadreza Argha and Paknoosh Karimaghaee and Mehdi Roopaei", title = "Iterative Learning Control for 2-D Systems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0642.pdf}, url = {}, size = {}, abstract = {In this paper, the application of iterative learning control (ILC) in two-dimensional systems is considered and a method of ILC for 2-D systems is introduced so that the output of the process follows a desired trajectory. In this method the input of process in each iteration is determined by an innovative method called two-dimensional method by means of the obtained error between the output of the process and the desired trajectory which was given in previous iteration and the ability of this new method is illustrated by computerized simulation and the obtained results are compared with the results of one-dimensional method which was adapted to a 2-D one. Also the convergence of these methods is considered. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Saeed:2008:ijcnn, author = "Mehreen Saeed and Haroon Babri", title = "Classifiers Based on Bernoulli Mixture Models for Text Mining and Handwriting Recognition Tasks", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0643.pdf}, url = {}, size = {}, abstract = {In this paper we describe a model for classifying binary data using classifiers based on Bernoulli mixture models. We show how Bernoulli mixtures can be used for feature extraction and dimensionality reduction of raw input data. The extracted features are then used for training a classifier for supervised labeling of individual sample points. We have applied this method to two different types of datasets, i.e., one from the text mining domain and one from the handwriting recognition area. Empirical experiments demonstrate that we can obtain up to 99.9percent reduction in the dimensionality of the original feature set for sparse binary features. Classification accuracy also increases considerably when the combined model is used. This paper compares the performance of different classification algorithms when used in conjunction with the new feature set generated by Bernoulli mixtures. Using this hybrid model of learning we have achieved one of the best accuracy rates on the NOVA and GINA datasets of the 'agnostic vs. prior knowledge' competition held by the International Joint Conference on Neural Networks in 2007. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Esaki:2008:ijcnn, author = "Tomohito Esaki and Tomonori Hashiyama", title = "Extracting Human Players' Shogi Game Strategies from Game Records Using Growing SOM", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0644.pdf}, url = {}, size = {}, abstract = {Shogi game is similar to Chess and very popular in Japan. Computer Shogi programs are still in developing to defeat the professional human players. One of the main problems exists in estimating the circumstances of the game phases. It is said that there are three phases in the Shogi game, so called, opening, middle and endgame phase. The appropriate strategy to be selected differs depending on the proceeding phase. The professional human players classify states of the game phases properly. In this paper, we have carried out some experiments to extract human players' strategies on shogi game from game records using growing SOM. The results show the promising feature of the proposed method. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Xu7:2008:ijcnn, author = "Xin Xu and Hongyu Zhang and Bin Dai and Han-gen He ", title = "Self-Learning Path-Tracking Control of Autonomous Vehicles Using Kernel-Based Approximate Dynamic Programming", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0645.pdf}, url = {}, size = {}, abstract = {With the fast development of robotics and intelligent vehicles, there has been much research work on modeling and motion control of autonomous vehicles. However, due to model complexity, and unknown disturbances from dynamic environment, the motion control of autonomous vehicles is still a difficult problem. In this paper, a novel self-learning path-tracking control method is proposed for a car-like robotic vehicle, where kernel-based approximate dynamic programming (ADP) is used to optimize the controller performance with little prior knowledge on vehicle dynamics. The kernel-based ADP method is a recently developed reinforcement learning algorithm called kernel least-squares policy iteration (KLSPI), which uses kernel methods with automatic feature selection in policy evaluation to get better generalization performance and learning efficiency. By using KLSPI, the lateral control performance of the robotic vehicle can be optimized in a self-learning and data-driven style. Compared with previous learning control methods, the proposed method has advantages in learning efficiency and automatic feature selection. Simulation results show that the proposed method can obtain an optimized path-tracking control policy only in a few iterations, which will be very practical for real applications. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Attard:2008:ijcnn, author = "Conrad Attard and Andreas A. Albrecht", title = "On Axon Delay Functions and Spiking Activity", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0646.pdf}, url = {}, size = {}, abstract = {Over the past few years, the importance of axonal conduction delays has been emphasized by a number of authors. Different models are proposed for the approximation of signal delays, where some of them have been evaluated in the context of the optimal neuronal layout problem. Our paper presents computational experiments on the impact of two wiring cost functions, proposed by Chklovskii and Shefi et al., when applied to interneuronal connections in small ML neuronal networks. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Haranadh:2008:ijcnn, author = "G. Haranadh and C. Chandra Sekhar", title = "Hyperparameters of Gaussian Process as Features for Trajectory Classification", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0648.pdf}, url = {}, size = {}, abstract = {In this paper, we address the trajectory classification problem in Gaussian process framework without using Gaussian process based classification directly. Properties of the function corresponding to a trajectory are captured into the hyperparameters of a Gaussian process. As different trajectories have different properties, hyperparameters are different for these trajectories. In the hyperparametric space, different clusters are formed for noisy, shifted versions of the trajectories. The hyperparameters are used as features representing a trajectory and the classification task is performed in the hyperparametric space. Classification performance of the proposed method is evaluated on simulated data and also on realworld time series data. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Alnajjar:2008:ijcnn, author = "Fady Alnajjar and Kazuyuki Murase", title = "Sensor-Fusion in Spiking Neural Network that Generates Autonomous Behavior in Real Mobile Robot", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0649.pdf}, url = {}, size = {}, abstract = {We here introduce a novel adaptive controller for autonomous mobile robot that binds N types of sensory information. For each sensory modality, sensory-motor connection is made by a three-layered spiking neural network (SNN). The synaptic weights in the model have the property of spike timing-dependent plasticity (STDP) and regulated by presynaptic modulation signal from the sensory neurons. Each synaptic weight is incrementally adapted depending upon the firing rate of the presynaptic modulation signal and that of the hidden-layer neuron(s). Information from different types of sensors are bound at the motor neurons. A real mobile robot Khepera with the SNN controller quickly adapted into an open environment and performed the desired task successfully. This approach could be applicable to a robot with inputs of various sensory modalities and various types of motor outputs. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Alnajjar2:2008:ijcnn, author = "Fady Alnajjar and Indra Bin Mohd Zin", title = "A Spiking Neural Network with Dynamic Memory for a Real Autonomous Mobile Robot in Dynamic Environment", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0651.pdf}, url = {}, size = {}, abstract = {This work concerns practical issues surrounding the application of learning and memory in a real mobile robot towards optimal navigation in dynamic environments. A novel control system that contains two-level units (low-level and high-level) is developed and trained in a physical mobile robot "e-Puck". In the low-level unit, the robot's task is to navigate in a various local environments, by training N numbers of Spiking Neural Networks (SNN) that have the property of spike time-dependent plasticity. All the trained SNNs are stored in a tree-type memory structure, which is located in the high-level unit. These stored networks are used as experiences for the robot to enhance its navigation ability in new and previously trained environments. The memory is designed to hold memories of various lengths and has a simple searching mechanism. For controlling the memory size, forgetting and on-line dynamic clustering techniques are used. Experimental results have proved that the proposed model can provide a robot with learning and memorizing capabilities enable it to survive in complex and dynamic environments. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Li8:2008:ijcnn, author = "Yuanqing Li and Chuanchu Wang and Haihong Zhang and Cuntai Guan ", title = "An EEG-Based BCI System for 2D Cursor Control", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0654.pdf}, url = {}, size = {}, abstract = {In this paper, an electroencephalogram (EEG)- based brain computer interface (BCI) is proposed for two dimensional cursor control. The horizontal and vertical movements of the cursor are controlled by mu/beta rhythm and P300 potential respectively. The main advantages of this system are: (i) two almost independent control signals are produced simultaneously; (ii) the cursor can be moved from a random position to another random position in a screen. These advantages have been demonstrated in our experiment and data analysis. }, keywords = {: Brain-computer interface (BCI), electroencephalogram (EEG), cursor control, mu/beta rhythm, P300 potential.}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Lian:2008:ijcnn, author = "Feng Lian and Chongzhao Han and Yong Shi", title = "Adaptive On-Line Registration Algorithm Based on GLR", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0655.pdf}, url = {}, size = {}, abstract = {In practical system, the sensor biases may jump abruptly. An adaptive on-line algorithm is presented in this paper for this situation. The algorithm can detect the jump onset time and estimate the jump level base on General Likelihood Ratio (GLR) test. The Monte Carlo results show, our algorithm can adaptively estimate the bias jump level well and the estimation error will not increase remarkably as other previous registration algorithms. The bias estimation error also converges to the Cramer-Rao lower bound (CRLB) after the jumping. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Yang7:2008:ijcnn, author = "Jian Yang and Xi Huang and Ying Tan and Xingui He ", title = "Forecast of Driving Load of Hybrid Electric Vehicles by Using Discrete Cosine Transform and Support Vector Machine", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0656.pdf}, url = {}, size = {}, abstract = {As advances in green automotives, hybrid electric vehicle (HEV) has being given more and more attention in recent years. The power management control strategy of HEV is the key problem that determines the efficiency and pollution emission level of the HEV, which requires the forecast of driving load situation of HEV in advance. This paper proposes an efficient approach for forecasting the driving load of the HEV by using Discrete Cosine Transform (DCT) and Support Vector Machine (SVM). The DCT is used to extract features from raw data, and reduce the dimensionality of feature which will result in an efficient SVM classification. The SVM is used to classify the current driving load into one of five presetting levels of the driving load of the HEV. In such way, we can predict the driving load efficiently and accurately, which leads to a reasonable control to the HEV and gives as a high efficiency and low emission level as possible. Finally, a number of experiments are conducted to verify the validity of our proposed approach. Compared to current methods, our proposed approach gives a considerably promising performance through extensive experiments and comparison tests. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Almeida:2008:ijcnn, author = "Leandro M. Almeida and Teresa Ludermir", title = "An Improved Method for Automatically Searching Near-Optimal Artificial Neural Networks", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0657.pdf}, url = {}, size = {}, abstract = {This paper describes an improved version of a method that automatically searches near-optimal Multilayer feedforward Artificial Neural Networks using Genetic Algorithms. This method employs an evolutionary search for simultaneous choices of initial weights, transfer functions, architectures and learning rules. Experimental results have shown that the developed method can produce compact, efficient networks with a satisfactory generalization power and with shorter training times when compared to other methods found in the literature. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wang9:2008:ijcnn, author = "Wenjia Wang ", title = "Some Fundamental Issues in Ensemble Methods", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0658.pdf}, url = {}, size = {}, abstract = {The ensemble paradigm for machine learning has been studied for more than two decades and many methods, techniques and algorithms have been developed, and increasingly used in various applications. Nevertheless, there are still some fundamental issues remaining to be addressed, and an important one is what factors affect the accuracy of an ensemble, and to what extent they do, which is thus taken as the main topic of this paper. The factors studied include the accuracy of individual models, the diversity among the individual models in an ensemble, decision-making strategy, and the number of the members used for constructing an ensemble. This paper firstly describes the conceptual and theoretical analyses on these factors, and then presents the possible relationships between them. The experiments have been conducted by using some benchmark data sets and some typical results are presented in the paper. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Savitha:2008:ijcnn, author = "R. Savitha and S. Suresh and N. Sundararajan and P. Saratchandran", title = "Complex-Valued Function Approximation Using an Improved BP Learning Algorithm for Feed-Forward Networks", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0659.pdf}, url = {}, size = {}, abstract = {In a fully complex-valued feed-forward network, the convergence of the complex-valued backpropagation learning algorithm depends on the choice of the activation function, minimization criterion, initial weights and the learning rate. The minimization criteria used in the existing learning algorithms do not approximate the phase well in complex-valued function approximation problems. This aspect is very important in telecommunication and medical imaging applications. In this paper, we propose an improved complex-valued back propagation algorithm using an exponential activation function and a logarithmic minimization criterion, which approximates both the magnitude and phase well. Performance of the proposed scheme is evaluated using the complex XOR problem and a synthetic complex-valued function approximation problem. Also, a comparative analysis on the convergence of the existing fully complex and split complex networks is presented. }, keywords = {:- Split complex network, fully complex-valued networks, multi-layer perceptron, complex-valued elementary transcendental functions and its derivatives.}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Matsushita:2008:ijcnn, author = "Haruna Matsushita and Yoshifumi Nishio", title = "Batch-Learning Self-Organizing Map with False-Neighbor Degree Between Neurons", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0660.pdf}, url = {}, size = {}, abstract = {This study proposes a Batch-Learning Self- Organising Map with False-Neighbor degree between neurons (called BL-FNSOM). False-Neighbor degrees are allocated between adjacent rows and adjacent columns of BL-FNSOM. The initial values of all of the false-neighbor degrees are set to zero, however, they are increased with learning, and the false neighbour degrees act as a burden of the distance between map nodes when the weight vectors of neurons are updated. BLFNSOM changes the neighbourhood relationship more flexibly according to the situation and the shape of data although using batch learning. We apply BL-FNSOM to some input data and confirm that FN-SOM can obtain a more effective map reflecting the distribution state of input data than the conventional Batch-Learning SOM. }, keywords = {genetic algorithms, genetic programming}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Guile:2008:ijcnn, author = "Geoffrey R. Guile and Wenjia Wang", title = "Relationship Between Depth of Decision Trees and Boosting Performance", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0661.pdf}, url = {}, size = {}, abstract = {We have investigated strategies for enhancing ensemble learning algorithms for the analysis of high dimensional biological data. Specifically we investigated strategies to force classifiers to consider the possible interactions between features. As a result an algorithm that induces decision trees with a feature non-replacement mechanism has been devised and tested on DNA microarray and proteomic datasets. The results show that feature nonreplacement enables decision trees deeper than simple stumps to be used, thereby allowing feature interaction to be taken into account. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Haraguchi:2008:ijcnn, author = "Taku Haraguchi and Haruna Matsushita and Yoshifumi Nishio", title = "Lazy Self-Organizing Map and its Behaviors", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0662.pdf}, url = {}, size = {}, abstract = {The Self-Organising Map (SOM) is a famous algorithm for the unsupervised learning and visualisation introduced by Teuvo Kohonen. This study proposes the Lazy Self-Organising Map (LSOM) algorithm which reflects the world of worker ants. In LSOM, three kinds of neurons exist: worker neurons, lazy neurons and indecisive neurons. We apply LSOM to various input data set and confirm that LSOM can obtain a more effective map reflecting the distribution state of the input data than the conventional SOM. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Lin:2008:ijcnn, author = "Han Lin and Liu Xuegong and Zhang Yanning", title = "Interpretation of River Main-Flow from Remote Sensing Images: Studying on Dynamic Transmission Cross-Correlation Method", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0664.pdf}, url = {}, size = {}, abstract = {The main-flow is wandering for sedimentation in downstream channel of the Yellow River, which threatened security greatly for flood control in the lower. It is a very difficult issue to interpret the main-flow information using remote sensing image. In this paper, with the flow characteristics of direction and continuously similarity in river channel, a Dynamic Transmission Cross-Correlation (DTCC) algorithm was proposed and employed to try to extract the main-flow information based on Landsat TM images in wandering reaches of the lower Yellow River. The results show that the interpretation main-flow is coincident with the field observed. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Torres-Sospedra:2008:ijcnn, author = "Joaquín Torres-Sospedra and Carlos Hernandez-Espinosa and Mercedes Fernandez-Redondo", title = "Researching on Combining Boosting Ensembles", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0667.pdf}, url = {}, size = {}, abstract = {As shown in the bibliography, training an ensemble of networks is an interesting way to improve the performance with respect to a single network. The two key factors to design an ensemble are how to train the individual networks and how to combine them to give a single output. Boosting is a well known methodology to build an ensemble. Some boosting methods use an specific combiner (Boosting Combiner) based on the accuracy of the network. Although the Boosting combiner provides good results on boosting ensembles, the simple combiner Output Average worked better in three new boosting methods we successfully proposed in previouses papers. In this paper, we study the performance of sixteen different combination methods for ensembles previously trained with Adaptive Boosting and Average Boosting in order to see which combiner fits better on these ensembles. Finally, the results show that the accuracy of the ensembles trained with these original boosting methods can be improved by using the appropriate alternative combiner. In fact, the Output average and the Weighted average on low/medium sized ensembles provide the best results in most of the cases. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Pan2:2008:ijcnn, author = "Yunpeng Pan and Jun Wang", title = "Nonlinear Model Predictive Control Using a Recurrent Neural Network", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0671.pdf}, url = {}, size = {}, abstract = {As linear model predictive control (MPC) becomes a standard technology, nonlinear MPC (NMPC) approach is debuting both in academia and industry. In this paper, the NMPC problem is formulated as a convex quadratic programming problem based on nonlinear model prediction and linearization. A recurrent neural network for NMPC is then applied for solving the quadratic programming problem. The proposed network is globally convergent to the optimal solution of the NMPC problem. Simulation results are presented to show the effectiveness and performance of the neural network approach. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Silva3:2008:ijcnn, author = "João M. M. Silva and Eugenius Kaszkurewicz", title = "An LMI-Neural Network Based Solution to the Load Balancing Problem for Heterogeneous Local Clusters", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0672.pdf}, url = {}, size = {}, abstract = {A solution for the load balancing problem in local clusters of heterogeneous processors is proposed within the setting of delayed artificial neural networks, optimal control and Linear Matrix Inequalities (LMI) theory. Based on a mathematical model that includes delays and processors with different processing velocities, this model is transformed into a special case of Delayed Cellular Neural Networks model. A systematic method of controller synthesis is derived, based on two coupled Linear Matrix Inequalities — one guaranteeing global convergence and the other guaranteeing performance in the linear region of operation. Simulations and computational experiments show the efficiency of this approach, reducing load balancing time. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Cai:2008:ijcnn, author = "Song Cai and William W. Hsieh and Alex J. Cannon", title = "A Comparison of Bayesian and Conditional Density Models in Probabilistic Ozone Forecasting", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0674.pdf}, url = {}, size = {}, abstract = {Probabilistic models were developed to provide predictive distributions of daily maximum surface level ozone concentrations. Five forecast models were compared at two stations (Chilliwack and Surrey) in the Lower Fraser Valley of British Columbia, Canada, with local meteorological variables used as predictors. The models were of two types, conditional density models and Bayesian models. The Bayesian models (especially the Gaussian Processes) gave better forecasts for extreme events, namely poor air quality events defined as having ozone concentration ≥82 ppb. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Daneshyari:2008:ijcnn, author = "Moayed Daneshyari ", title = "A Neurochaotic PSO-Guided Network Based Upon Perturbed Duffing Oscillator", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0675.pdf}, url = {}, size = {}, abstract = {This paper introduces a neurochaotic information processor based upon perturbed Duffing equation. The proposed chaotic neural network has parameters to tune by which decision is made to behave either chaotically or periodically. The neurochaotic nonlinear network adopts the chaotic dynamics of so-called Duffing oscillator for the chaotic movement in the search space. It then uses the benefits of fast convergence of particle swarm optimization to settle down into the attractors of periodic solutions. }, keywords = {: Chaos, neural network, particle swarm optimization, pattern recognition, Duffing oscillator.}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Kurogi:2008:ijcnn, author = "Shuichi Kurogi and Yohei Koshiyama", title = "Model Switching Predictive Control Using Bagging CAN2 and
First-Difference Signals for Temperature Control of RCA Cleaning Solutions", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0676.pdf}, url = {}, size = {}, abstract = {The RCA cleaning method is the industry standard way to clean silicon wafers, where temperature control is important for a stable cleaning performance. However, it is difficult mainly because the RCA solutions cause nonlinear and time-varying exothermic chemical reactions. So far, the MSPC (model switching predictive controller) using the CAN2 (competitive associative net 2) has been developed and the effectiveness has been validated. However, we have observed that the control performance, such as the settling time and the overshoot, does not always improve with the increase of the number of learning iterations of the CAN2. To solve this problem, we introduce the bagging method for the CAN2 and first-difference signals for the MSPC. The effectiveness of the present method is shown by means of computer simulation. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Kurogi2:2008:ijcnn, author = "Shuichi Kurogi and Daisuke Wakeyama and Hideaki Koya and Shota Okada", title = "Application of CAN2 to Plane Extraction from 3D Range Images", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0677.pdf}, url = {}, size = {}, abstract = {An application of CAN2 (competitive associative net 2) to plane extraction from 3D range images obtained by a LRF (laser range finder) is presented. The CAN2 basically is a neural net which learns efficient piecewise linear approximation of nonlinear functions, and in this application it is used for learning piecewise planner surfaces from the range image. As a result of the learning, the obtained piecewise planner surfaces are much smaller and much more than the actual planner surfaces, so that we introduce a method to gather piecewise planner surfaces for reconstructing the actual planner surfaces. We apply this method to real range images, and examine the performance and the comparative advantage to other methods. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Chiang:2008:ijcnn, author = "Ching-Tsan Chiang and Yu-Bin Lin", title = "The Learning Convergence of High Dimension CMAC_GBF", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0678.pdf}, url = {}, size = {}, abstract = {High-Dimension Cerebellar Model Articulation Controller with General Basis Function (CMAC_GBF [2]) is developed and its learning convergence is also proved in this study. Up till now, the applications of CMAC are mainly used as controller or system identification (function mapping). Due to the guaranteed convergence and learning speed of CMAC, all the applications have shown good performance. But for high-dimensional mapping or control, it requires a lot of memories; the consequence is not able to use CMAC_GBF or to use enormous resources to complete its mission. When CMAC_GBF is employed, the necessary memory is growing exponentially with increasing input dimensions, and this slows down the learning speed or turns out to be impossible. In this project, S_CMAC_GBF [4] (A simple structure for CMAC_GBF) is employed to realize high-dimension application ability. Two 6-input nonlinear systems are employed to demonstrate the learning performance and the required practical memories of S_CMAC_GBF in high-dimensional applications. Briefly, the learning convergence is also proved. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Li9:2008:ijcnn, author = "XuQin Li and Carlos Ramirez and Evor L. Hines and Mark S. Leeson and Phil Purnell and Mark Pharaoh", title = "Pattern Recognition of Fiber-Reinforced Plastic Failure Mechanism Using Computational Intelligence Techniques", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0679.pdf}, url = {}, size = {}, abstract = {Acoustic Emission (AE) can be used to discriminate the different types of damage occurring in composite materials, because any AE signal contains useful information about the damage mechanisms. A major issue in the use of the AE technique is how to discriminate the AE signatures which are due to the different damage mechanisms. Conventional studies have focused on the analysis of different parameters of such signals, say the frequency. But in previous publications where the frequency is employed to differentiate between events, only one frequency is considered and this frequency was not enough to thoroughly describe the behavior of the composite material. So we introduced the second frequency. A Fast Fourier Transform (FFT) is then applied to the signals resulting from the two frequencies to discriminate different failure mechanisms. This was achieved by using self-organizing map and Fuzzy C-means to cluster the AE data. The result shows that the two approaches have been very successful. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Cai2:2008:ijcnn, author = "Chenghui Cai and Silvia Ferrari", title = "A Q-Learning Approach to Developing an Automated Neural Computer Player for the Board Game of CLUE®", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0682.pdf}, url = {}, size = {}, abstract = {The detective board game of CLUE® can be viewed as a benchmark example of the treasure hunt problem, in which a sensor path is planned based on the expected value of information gathered from targets along the path. The sensor is viewed as an information gathering agent that makes imperfect measurements or observations from the targets, and uses them to infer one or more hidden variables (such as, target features or classification). The treasure hunt problem arises in many modern surveillance systems, such as demining and reconnaissance robotic sensors. Also, it arises in the board game of CLUE®, where pawns must visit the rooms of a mansion to gather information from which the hidden cards can be inferred. In this paper, Q-Learning is used to develop an automated neural computer player that plans the path of its pawn, makes suggestions about the hidden cards, and infers the answer, often winning the game. A neural network is trained to approximate the decision-value function representing the value of information, for which there exists no general closed-form representation. Bayesian inference, test (suggestions), and action (motion) decision making are unified using an MDP framework. The resulting computer player is shown to outperform other computer players implementing Bayesian networks, or constraint satisfaction. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Muro:2008:ijcnn, author = "Gianluca Di Muro and Silvia Ferrari", title = "A Constrained-Optimization Approach to Training Neural Networks for Smooth Function Approximation and System Identification", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0683.pdf}, url = {}, size = {}, abstract = {A constrained-backpropagation training technique is presented to suppress interference and preserve prior knowledge in sigmoidal neural networks, while new information is learned incrementally. The technique is based on constrained optimization, and minimizes an error function subject to a set of equality constraints derived via an algebraic training approach. As a result, sigmoidal neural networks with long term procedural memory (also known as implicit knowledge) can be obtained and trained repeatedly on line, without experiencing interference. The generality and effectiveness of this approach is demonstrated through three applications, namely, function approximation, solution of differential equations, and system identification. The results show that the long term memory is maintained virtually intact, and may lead to computational savings because the implicit knowledge provides a lasting performance baseline for the neural network. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Yang8:2008:ijcnn, author = "Jufeng Yang and Guangshun Shi and Qingren Wang and Yong Zhang", title = "Recognition of On-line Handwritten Chemical Expressions", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0686.pdf}, url = {}, size = {}, abstract = {In this paper, we study the major modules of on-line handwritten chemical expressions recognition. We propose a novel algorithm that combines two separate methods to segment expressions, one of which is based on structural information and the other on partial recognition. The algorithm improves the traditional algorithm at the stage of recognition, which consists of a substance recognizer and a character recognizer. To meet the demand of actual applications, the paper also designs a standard feature set to deal with the related issues and presents a flexible process of human-computer interaction to help users modify the recognition result. The experimental results show that the presented algorithm is reliable. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Li10:2008:ijcnn, author = "Jinbo Li and Shiliang Sun", title = "Energy Feature Extraction of EEG Signals and a Case Study", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0687.pdf}, url = {}, size = {}, abstract = {Energy is very important in electroencephalogram (EEG) signal classification. In this paper, a criterion called extreme energy difference (EED) is devised, which is a discriminative objective function to guide the process of spatially filtering EEG signals. The energy of the filtered EEG signals has the optimal discriminative capability under the EED criterion, and therefore EED can be considered as a feature extractor. The solution which optimizes the EED criterion is presented in this paper and according to experimental results, EED is a promising method for extracting energy features in EEG signal classification. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Liu11:2008:ijcnn, author = "Panzhi Liu and Chongzhao Han and Jing Jie", title = "A Threshold Factor Approach Method for CFAR Detector Based on Stochastic Particle Swarm Optimization", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0691.pdf}, url = {}, size = {}, abstract = {Based on the perfect properties of stochastic particle swarm optimization (SPSO), such as the property of robust and quick convergence, a new scheme is applied to estimate scaling factor for radar constant false alarm rate (CFAR) detectors. Owing to few constraints, it can estimate scaling factor for single radar as well as radar netting system. The numerical results indicate that the particle swarm optimizer has been found to be accuracy and fast in searching the threshold factor T of CFAR detector under any designed probability of false alarm. }, keywords = { CFAR detector, Scaling factor, and stochastic particle swarm optimization (SPSO)}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Lee2:2008:ijcnn, author = "S. L. A. Lee and A. Z. Kouzani and E. J. Hu", title = "From Lung Images to Lung Models: A Review", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0693.pdf}, url = {}, size = {}, abstract = {Automated 3D lung modeling involves analyzing 2D lung images and reconstructing a realistic 3D model of the lung. This paper presents a review of the existing works on automatic formation of 3D lung models from 2D lung images. A common framework for 3D lung modeling is proposed. It consists of eight components: image acquisition, image preprocessing, image segmentation, boundary creation, image recognition, image registration, 3D surface reconstruction, and 3D rendering and visualization. The algorithms used by the existing systems to implement these components are also reviewed. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Notsu:2008:ijcnn, author = "Akira Notsu and Hidetomo Ichihashi and Katsuhiro Honda ", title = "State and Action Space Segmentation Algorithm in Q-Learning ", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0694.pdf}, url = {}, size = {}, abstract = {In this Paper, we propose a novel Q-learning algorithm that segmentalizes the agent environment and action. This algorithm is learned through interation with an environment and action. This algorithm is learned through interaction with an environment and provides deterministic space segmentation. The purposes of this study can be divided into two main groups: search domain reduction and heuristic space segmentation. In our method, the most activated space segment is divided into new two segments with the learning by a heuristic and recognizable method. Appropriate search domain reduction can minimize the learning time and enables us to recognize the evolutionary process. This segmentation method is also designed for social simulation models. Social space segmentation, such as language systems and culture, is revealed by multi-agent social simulation with our method. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Ang:2008:ijcnn, author = "Kai Keng Ang and Zheng Yang Chin and Haihong Zhang and Cuntai Guan", title = "Filter Bank Common Spatial Pattern (FBCSP) in Brain-Computer Interface", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0695.pdf}, url = {}, size = {}, abstract = {In motor imagery-based Brain Computer Interfaces (BCI), discriminative patterns can be extracted from the electroencephalogram (EEG) using the Common Spatial Pattern (CSP) algorithm. However, the performance of this spatial filter depends on the operational frequency band of the EEG. Thus, setting a broad frequency range, or manually selecting a subject-specific frequency range, are commonly used with the CSP algorithm. To address this problem, this paper proposes a novel Filter Bank Common Spatial Pattern (FBCSP) to perform autonomous selection of key temporalspatial discriminative EEG characteristics. After the EEG measurements have been bandpass-filtered into multiple frequency bands, CSP features are extracted from each of these bands. A feature selection algorithm is then used to automatically select discriminative pairs of frequency bands and corresponding CSP features. A classification algorithm is subsequently used to classify the CSP features. A study is conducted to assess the performance of a selection of feature selection and classification algorithms for use with the FBCSP. Extensive experimental results are presented on a publicly available dataset as well as data collected from healthy subjects and unilaterally paralyzed stroke patients. The results show that FBCSP, using a particular combination feature selection and classification algorithm, yields relatively higher crossvalidation accuracies compared to prevailing approaches. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Iwamura:2008:ijcnn, author = "Kazuki Iwamura and Shigeo Abe", title = "Sparse Support Vector Machines Trained in the Reduced Empirical Feature Space", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0699.pdf}, url = {}, size = {}, abstract = {We discuss sparse support vector machines (sparse SVMs) trained in the reduced empirical feature space. Namely, we select the linearly independent training data by the Cholesky factorization of the kernel matrix, and train the SVM in the dual form in the reduced empirical feature space. Since the mapped linearly independent training data span the empirical feature space, the linearly independent training data become support vectors. Thus if the number of linearly independent data is smaller than the number of support vectors trained in the feature space, sparsity is increased. By computer experiments we show that in most cases we can reduce the number of support vectors without deteriorating the generalization ability. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Tan4:2008:ijcnn, author = "Chue Poh Tan and Chen Change Loy and Weng Kin Lai and Chee Peng Lim", title = "Robust Modular Artmap For Multi-Class Shape Recognition", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0700.pdf}, url = {}, size = {}, abstract = {This paper presents a Fuzzy ARTMAP (FAM) based modular architecture for multi-class pattern recognition known as Modular Adaptive Resonance Theory Map (MARTMAP). The prediction of class membership is made collectively by combining outputs from multiple novelty detectors. Distance-based familiarity discrimination is introduced to improve the robustness of MARTMAP in the presence of noise. The effectiveness of the proposed architecture is analyzed and compared with ARTMAP-FD network, FAM network, and One-Against-One Support Vector Machine (OAOSVM). Experimental results show that MARTMAP is able to retain effective familiarity discrimination in noisy environment, and yet less sensitive to class imbalance problem as compared to its counterparts. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Junyao:2008:ijcnn, author = "Gao Junyao and Gao Xueshan and Zhu Wei and Zhu Jianguo and Wei Boyu", title = "Fault-Tolerant and High Reliability Space Robot Design and Research", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0701.pdf}, url = {}, size = {}, abstract = {Space robot reliability is a fatal important problem. Any fault and error may damage spacecraft. This paper suggests a space robot system with two alternate drive system, two alternate control system, redundant freedom, two alternate communicate system. The space robot is a fault-tolerant system. Fault tree is used to analysis space robot. Any one element on the space robot fault doesn't influence robot work. Space robot reliability is raised to a high level through this design. This design scheme is a practice scheme for space robot. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Alnajjar3:2008:ijcnn, author = "Fady Alnajjar and Abdul Rahman Hafiz", title = "Vision-Sensorimotor Abstraction and Imagination Towards Exploring Robot's Inner World", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0702.pdf}, url = {}, size = {}, abstract = {Based on indications from the neuroscience and psychology, both perception and action can be internally simulated by activating sensor and motor areas in the brain without external sensory input or without any resulting overt behavior. This hypothesis, however, can be highly useful in the real robot applications. The robot, for instance, can cover some of the corrupted sensory inputs by replacing them with its internal simulation. The accuracy of this hypothesis is strongly based on the agent's experiences. As much as the agent knows about the environment, as much as it can build a strong internal representation about it. Although many works have been presented regarding to this hypothesis with various levels of success. At the sensorimotor abstraction level, where extracting data from the environment occur, however, none of them have so far used the robot's vision as a sensory input. In this study, vision-sensorimotor abstraction is presented through memory-based learning in a real mobile robot "Hemisson" to investigate the possibilities of explaining its inner world based on internal simulation of perception and action at the abstract level. The analysis of the experiments illustrate that our robot with vision sensory input has developed some kind of simple associations or anticipation mechanism through interacting with the environment, which enables, based on its history and the present situation, to guide its behavior in the absence of any external interaction. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Zhang8:2008:ijcnn, author = "W. Zhang and B. Li and W. Zhou", title = "A LLE-Based Approach to Sensor Fault Detection", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0703.pdf}, url = {}, size = {}, abstract = {Feature extraction has been widely used in sensor fault detection. Commonly used feature extraction methods such as PCA and MDS involve signal process of liner time-invariant systems, which are less effective in dealing with the nonlinear systems. In this paper, we will present that Local Linear Embedding (LLE) concept is adopted to solve the fault detection problems and that certain enhancement have been made to make LLE approach more efficient and robust in the extraction of signal features. Test results are given to demonstrate the effectiveness of the enhanced LLE method. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Ahmad:2008:ijcnn, author = "Shandar Ahmad and Zulfiqar Ahmad", title = "ATP-Binding Site as a Further Application of Neural Networks to Residue Level Prediction", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0704.pdf}, url = {}, size = {}, abstract = {Similar neural network models based on single sequence and evolutionary profiles of residues have been successfully used in the past for predicting secondary structure, solvent accessibility, protein-, DNA- and carbohydrate- binding sites. ATP is a ubiquitous ligand in all living-systems, involved in most biological functions requiring energy and charge transfer. Prediction of ATP-binding site from single sequences and their evolutionary profiles at a high throughput rate can be used at genomic level as well as quick clues for site-directed mutagenesis experiments. We have developed a method for such predictions to demonstrate yet another application of sequence-base prediction algorithms using neural networks. This method can achieve 81percent sensitivity and 69percent specificity which are mutually adjustable in a wide range on a three-fold cross-validation data set. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Hu3:2008:ijcnn, author = "Yanjie Hu and Juanjuan Pang", title = "Financial Crisis Early-Warning Based on Support Vector Machine", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0705.pdf}, url = {}, size = {}, abstract = {Analyzing the principle of typical financial crisis early-warning model, this study summarizes the limitations of them and their requirement of variance. An empirical research is carried out on how to sample the Chinese listed companies of A-stock market in Shanghai and Shenzhen, and how to determine the core parameters of support vector machine (SVM) as well. This research also studies the predicting accuracy in 1-3 years and the performance on condition that some data are missing. At last the contrastive analysis is made between SVM model and the Logistic model. Our experimentation results demonstrate that SVM outperforms the Logistic model and SVM also has a sound accuracy under the data missing. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Gao:2008:ijcnn, author = "Changjian Gao and Mazad S. Zaveri and Dan Hammerstrom", title = "CMOS/CMOL Architectures for Spiking Cortical Column", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0707.pdf}, url = {}, size = {}, abstract = {We present a spiking cortical column model based on neural associative memory, and demonstrate architectures for emulating the cortical column model with nanogrid molecular circuitry. We investigate a number of options for cost-effective hardware with digital CMOS and mixed-signal CMOL, a hybrid CMOS/nanogrid technology. We also give an example of a dynamic learning algorithm that is a suitable match to CMOL implementation. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Junyao2:2008:ijcnn, author = "Gao Junyao and Gao Xueshan and Zhu Wei and Zhu Jianguo and Wei Boyu", title = "Light Mobile Robot's Weight Design and Research", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0708.pdf}, url = {}, size = {}, abstract = {Light mobile robot's weight design is a important problem which decides function and ability of mobile robot. In this paper, a total design method is advanced. There are many factors to consider, include mechanical part, electrical part, and task part. Each parts of robot must be carefully calculated and designed. There are many experiences in it. A light mobile robot is carefully analyzed as an example. The method can help mobile robot designers on how to design a robot's weight quickly and not waste time and money. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Ishi:2008:ijcnn, author = "Tsuneyoshi Ishi and Shigeo Abe", title = "Feature Selection Based on Kernel Discriminant Analysis for Multi-Class Problems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0711.pdf}, url = {}, size = {}, abstract = {We propose a feature selection criterion based on kernel discriminant analysis (KDA) for an n-class problem, which finds n-1 eigenvectors on which the projected class data are locally maximally separated. The proposed criterion is the sum of the objective function values of KDA associated with the n-1 eigenvectors. The criterion results in calculating the sum of n-1 eigenvalues associated with the eigenvectors and is shown to be monotonic for the deletion or addition of features. Using the backward feature selection strategy, for several multi-class data sets, we evaluated the proposed criterion and the criterion based on the recognition rate of the support vector machine (SVM) evaluated by cross-validation. From the standpoint of generalization ability the proposed criterion is comparable with the SVM-based recognition rate, although the proposed method does not use cross-validation. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Lu4:2008:ijcnn, author = "Shijian Lu and Cuntai Guan and Haihong Zhang", title = "Learning Adaptive Subject-Independent P300 Models for EEG-Based Brain-Computer Interfaces", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0712.pdf}, url = {}, size = {}, abstract = {This paper proposes an approach to learn subject-independent P300 models for EEG-based brain-computer interfaces. The P300 models are first learned using a pool of existing subjects and Fisher linear discriminant, and then autonomously adapted to the unlabeled data of a new subject using an unsupervised machine learning technique. In data analysis, we apply this technique to a set of EEG data of 10 subjects performing word spelling in an oddball paradigm. The results are very positive: the adapted models with unlabeled data yield virtually the same classification accuracy as the conventional methods with labeled data. Therefore, it proves the feasibility of P300-based BCIs which can be applied directly to a new subject without training sessions. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Liu12:2008:ijcnn, author = "Xiao-Hua Liu and Cheng-Lin Liu and Xinwen Hou", title = "A Pooled Subspace Mixture Density Model for Pattern Classification in High-Dimensional Spaces", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0714.pdf}, url = {}, size = {}, abstract = {Density estimation in high-dimensional data spaces is a challenge due to the sparseness of data which is known as ``the curse of dimensionality''. Researchers often resort to low-dimensional subspaces for such tasks, while discard the distribution in the complementary subspace. In this paper, we propose a new mixture density model based on pooled subspace. In our method, the Gaussian components of each class share a subspace and the complementary subspace is incorporated in the density function. The subspace and Gaussian mixture density are estimated simultaneously in EM iteration steps. We apply the density model to pattern classification in experiments on UCI datasets and compare the proposed method with previous ones. The experimental results demonstrate the superiority of the proposed method. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Torikai:2008:ijcnn, author = "Hiroyuki Torikai and Sho Hashimoto ", title = "A Hardware-Oriented Learning Algorithm for a Digital Spiking Neuron", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0715.pdf}, url = {}, size = {}, abstract = {The digital spiking neuron is a wired system of shift registers and behaves like a simplified neuron model. By adjusting the wirings among the registers, the neuron can generate various spike-trains. In this paper some basic relations between the wiring pattern and spike-train characteristics are analyzed. Based on the analysis results, a hardware-oriented learning algorithm is proposed. The learning algorithm and the digital neuron are implemented by a hardware description language (HDL). It is shown that the learning algorithm enables the digital neuron to approximate various spike-trains generated by an analog spiking neuron model. In addition, some basic experimental measurements are provided by using a field programmable gate array (FPGA). }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Oh:2008:ijcnn, author = "Jiyong Oh and Chong-Ho Choi and Chunghoon Kim", title = "Kernel Discriminant Analysis Using Composite Vectors", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0716.pdf}, url = {}, size = {}, abstract = {In this paper, we propose a new kernel discriminant analysis using composite vectors (C-KDA). We show that employing composite vectors is similar to using more samples by analysis, which is a great advantage in classification problems when the size of training samples is small. Motivated by this, we apply composite vectors to kernel-based methods, which may have overfitting problems when training samples are not sufficient. Experimental results using several data sets from UCI machine learning repository show that C-KDA gives a better performance compared to other methods based on primitive input variables and linear discriminant analysis using composite vectors (C-LDA) when the training sample size is relatively small. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Pang:2008:ijcnn, author = "Shaoning Pang and Nikola Kasabov", title = "r-SVMT: Discovering the Knowledge of Association Rule Over SVM Classification Trees", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0719.pdf}, url = {}, size = {}, abstract = {This paper presents a novel method of rule extraction by encoding the knowledge of the data into an SVM classification tree (SVMT), and decoding the trained SVMT into a set of linguistic association rules. The method of rule extraction over the SVMT (r-SVMT), in the spirit of decision-tree rule extraction, achieves rule extraction not only from SVM, but also over the obtained decision-tree structure. The benefits of r-SVMT are that the decision-tree rule provides better comprehensibility, and the support-vector rule retains the good classification accuracy of SVM. Furthermore, the r-SVMT is capable of performing a very robust classification on such datasets that have seriously, even overwhelmingly, class-imbalanced data distribution, which profits from the super generalization ability of SVMT owing to the aggregation of a group of SVMs. Experiments with a gaussian synthetic data, seven benchmark cancers diagnosis have highlighted the utility of SVMT and r-SVMT on encoding and decoding rule knowledge, as well as the superior properties of r-SVMT as compared to a completely support-vector based rule extraction. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Abe:2008:ijcnn, author = "Tohru Abe and Toshimichi Saito", title = "An Approach to Prediction of Spatio-Temporal Patterns Based on Binary Neural Networks and Cellular Automata", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0720.pdf}, url = {}, size = {}, abstract = {This Paper studies application of binary neural networks (BNN) to prediction for spatio-temporal patterns. In the approach, we assume that the objective spatio-temporal patterns can be approximated by a cellular automaton (CA). Teacher signals are extracted from a part of objective pattern and are used for learning of the BNN. The BNN is used to govern dynamics of CA that outputs prediction patterns. Performing basic numerical experiments, we have investigated relation among the number of teacher signals, the number of hidden neurons and prediction performance. The results provide basic information for development of robust prediction method for digital spatio-temporal patterns. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Amin:2008:ijcnn, author = "Md. Faijul Amin and Md. Monirul Islam", title = "Single-Layered Complex-Valued Neural Networks and Their Ensembles for Real-Valued Classification Problems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0722.pdf}, url = {}, size = {}, abstract = {This paper presents a complex-valued neuron (CVN) model for real-valued classification problems incorporating a new activation function. The activation function maps complex-valued net-inputs (sum of weighted inputs) of a neuron into bounded real-values, and its role is to divide the net-input space into different regions for different classes. A gradient-descent learning rule has been derived to train the CVN. Such a CVN is able to solve all possible twoinput Boolean functions. For further investigation, single layered complex-valued neural networks (i.e. without hidden units) are applied on the real-world multi-class classification problems. The results are comparable to the conventional multilayer real-valued neural networks. It is also shown that the performance can be improved further by using their ensembles. Negative correlation learning (NCL) algorithm has been used to create the ensembles. Since NCL is a gradientdescent based algorithm, the proposed activation function is well suited for it. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Ono:2008:ijcnn, author = "Aiko Ono and Shigeo Sato and Mitsunaga Kinjo and Koji Nakajima", title = "Study on the Performance of Neuromorphic Adiabatic Quantum Computation Algorithms", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0725.pdf}, url = {}, size = {}, abstract = {Quantum computation algorithms indicate possibility that non-deterministic polynomial time (NP-time) problems can be solved much faster than by classical methods. Farhi et al. [2], [3] have proposed an adiabatic quantum computation (AQC) for solving the three-satisfiability problem (3-SAT). We have proposed a neuromorphic quantum computation algorithm based on AQC, in which an analogy to an artificial neural network (ANN) is considered in order to design a Hamiltonian. However, in the neuromorphic AQC, the relation between its computation time and the probability of correct answers is not clear yet. In this paper, we study both of residual energy and the probability of finding solution as a function of computation time. The results show that the performance of the neuromorphic AQC depends on the characteristic of Hamiltonians. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Ferreira3:2008:ijcnn, author = "Leonardo V. Ferreira and Eugenius Kaszkurewicz and Amit Bhaya", title = "Image Restoration Using L1-Norm Regularization and a Gradient-Based Neural Network with Discontinuous Activation Functions", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0726.pdf}, url = {}, size = {}, abstract = {The problem of restoring images degraded by linear position invariant distortions and noise is solved by means of a L1-norm regularization, which is equivalent to determining a L1- norm solution of an overdetermined system of linear equations, which results from a data-fitting term plus a regularization term that are both in L1 norm. This system is solved by means of a gradient-based neural network with a discontinuous activation function, which is ensured to converge to a L1-norm solution of the corresponding system of linear equations. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Park:2008:ijcnn, author = "Dong-Chul Park and Dong-Min Woo", title = "Image Classification Using Gradient-Based Fuzzy c-Means with Divergence Measure", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0727.pdf}, url = {}, size = {}, abstract = {This paper proposes a novel classification method for image retrieval using Gradient-Based Fuzzy c-Means with Divergence Measure (GBFCM(DM)). GBFCM(DM) is a neural network-based algorithm that uses the Divergence Measure to exploit the statistical nature of the image data and thereby improve the classification accuracy. Experiments and results on various data sets demonstrate that the proposed classification algorithm outperforms conventional algorithms such as the traditional Self-Organizing Map (SOM) and Fuzzy c-Means (FCM) by 27percent-28.5percent in terms of accuracy. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Lin2:2008:ijcnn, author = "Minlong Lin and Ke Tang and Xin Yao", title = "Selective Negative Correlation Learning Algorithm for Incremental Learning", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0728.pdf}, url = {}, size = {}, abstract = {Negative correlation learning (NCL) is a successful scheme for constructing neural network ensembles. In batch learning mode, NCL outperforms many other ensemble learning approaches. Recently, NCL is also shown to be a potentially powerful approach to incremental learning, while the advantage of NCL has not yet been fully exploited. In this paper, we propose a selective NCL approach for incremental learning. In the proposed approach, the previously trained ensemble is cloned when a new data set presents and the cloned ensemble is trained on the new data set. Then, the new ensemble is combined with the previous ensemble and a selection process is applied to prune the whole ensemble to a fixedsize. Simulation results on several benchmark datasets show that the proposed algorithm outperforms two recent incremental learning algorithms based on NCL. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Matsuda:2008:ijcnn, author = "Yoshitatsu Matsuda and Kazunori Yamaguchi", title = "A Connection-limited Neural Network by Infomax and Infomin", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0733.pdf}, url = {}, size = {}, abstract = {It is well known that edge filters in the visual system can be generated by the InfoMax principle. But, such models are nonlinear and employ fully-connected network structures. In this paper, a new artificial network model is proposed, which is based on the ``InfoMin'' principle and linear multilayer ICA (LMICA). This network uses cumulantbased objective functions which are derived from the InfoMax and InfoMin principles with large noise. Because the objective functions do not rely on any nonlinear models, a linear model can be employed. It simplifies the model considerably. Besides, this network can deal with quite large number of neurons by employing a connection-limited structure as in LMICA. In addition, it is more efficient than even LMICA because it does not need any prewhitening. Numerical experiments show that this network generates hierarchical edge filters from large-size natural scenes and verify the validity of the InfoMin principle. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Petreska:2008:ijcnn, author = "Biljana Petreska and Yossi Yovel", title = "A Neural Model of Demyelination of the Mouse Spinal Cord", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0734.pdf}, url = {}, size = {}, abstract = {This paper presents a neural network model of demyelination of the mouse motor pathways, coupled to a central pattern generation (CPG) model for quadruped walking. Demyelination is the degradation of the myelin layer covering the axons which can be caused by several neurodegenerative autoimmune diseases such as multiple sclerosis. We use this model - to our knowledge first of its kind - to investigate the locomotion deficits that appear following demyelination of axons in the spinal cord. Our model meets several physiological and behavioral results and predicts that whereas locomotion can still occur at high percentages of demyelination damage, the distribution and location of the lesion are the most critical factors for the locomotor performance. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Huang6:2008:ijcnn, author = "Kou-Yuan Huang and Liang-Chi Shen and Chun-Yu Chen", title = "Higher Order Neural Networks for Well Log Data Inversion", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0735.pdf}, url = {}, size = {}, abstract = {Multilayer perceptron is adopted for well log data inversion. The input of the neural network is the apparent resistivity (Ra) of the well log and the desired output is the true formation resistivity (Rt). The higher order of the input features and the original features are the network input for training. Gradient descent method is used in the back propagation learning rule. From our experimental results, we find the expanding input features can get fast convergence in training and decrease the mean absolute error between the desired output and the actual output. The multilayer perceptron network with 10 input features, the expanding input features to the third order, 8 hidden nodes, and 10 output nodes can get the smallest average mean absolute error on simulated well log data. And then the system is applied on the real well log data. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Sollacher:2008:ijcnn, author = "Rudolf Sollacher and Huaien Gao", title = "Efficient Online Learning with Spiral Recurrent Neural Networks", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0736.pdf}, url = {}, size = {}, abstract = {Distributed intelligent systems like self-organizing wireless sensor and actuator networks are supposed to work mostly autonomous even under changing environmental conditions. This requires robust and efficient self-learning capabilities implementable on embedded systems with limited memory and computational power. We present a new solution called Spiral Recurrent Neural Networks with an online learning based on an extended Kalman filter and gradients as in Realtime Recurrent Learning. We illustrate its performance using artificial and reallife time series and compare it to other approaches. Finally we describe a few potential applications. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Guile2:2008:ijcnn, author = "Geoffrey R. Guile and Wenjia Wang", title = "Boosting for Feature Selection for Microarray Data Analysis", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0739.pdf}, url = {}, size = {}, abstract = {We have investigated the use of boosting techniques for feature selection for microarray data analysis. We propose a novel algorithm for feature selection and have tested it on three datasets. The results clearly show that our boosting technique for feature selection outperformed the Wilcoxon-Mann-Whitney U-test commonly used in microarray data analysis, and produced more accurate boosting ensembles when they were constructed with the features selected by our technique. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Dhahri:2008:ijcnn, author = "H. Dhahri and Adel. M. Alimi and F. Karray", title = "Designing Beta Basis Function Neural Network for Optimization Using Particle Swarm Optimization", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0740.pdf}, url = {}, size = {}, abstract = {Many methods for solving optimization problems, whether direct or indirect, rely upon gradient information and therefore may converge to a local optimum. Global optimization methods like Evolutionary algorithms, overcome this problem. In this work it is investigated how to construct a quality BBF network for a specific application can be a time-consuming process as the system must select both a suitable set of inputs and a suitable BBF network structure. Evolutionary methodologies offer the potential to automate all or part of these steps. This study illustrates how a hybrid BBFN-PSO system can be constructed, and applies the system to a number of datasets. The utility of the resulting BBFNs on these optimization problems is assessed and the results from the BBFN-PSO hybrids are shown to be competitive against the best performance on these datasets using alternative optimization methodologies. The results show that within these classes of evolutionary methods, particle swarm optimization algorithms are very robust, effective and highly efficient in solving the studied class of optimization problems. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Mahdavi:2008:ijcnn, author = "Nariman Mahdavi and Ali A.Gorji and Mohammad B. Menhaj and Saeedeh Barghinia", title = "A Variable Structure Neural Network Model For Mid-Term Load Forecasting of Iran National Power System", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0741.pdf}, url = {}, size = {}, abstract = {Mid-term load forecasting is taken into account as one of the most important policies in the electricity market and brings about many financial, commercial and, even, political benefits. In this paper, artificial neural networks are represented for mid-term load forecasting of Iran national power system. To do so, the multi layer perceptron (MLP) neural network as well as radial basis function (RBF) networks are considered as parametric structures. Moreover, because of some problems such as a limitation on the number of data for training networks, the number of neurons and basis functions is also adjusted during the training process. The obtained optimal networks are used to forecast the electricity pick load of the next 52 weeks. Simulation results show the superiority of both proposed structures in the mid-term load forecasting of Iran national power system. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Karatzas:2008:ijcnn, author = "Kostas D. Karatzas and George Papadourakis and Ioannis Kyriakidis", title = "Understanding and Forecasting Atmospheric Quality Parameters with the Aid of ANNs", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0743.pdf}, url = {}, size = {}, abstract = {A problem solving domain for the application of artificial intelligence (AI) methods towards knowledge discovery for the purposes of modelling and forecasting is urban air quality. This domain has the specific characteristic that the key parameters of interest (pollutant concentration criteria) have multiple temporal (and spatial) scales. The present paper applies ANNs for the operational forecasting of the 8-hour running average for Ozone, 24 hours in advance, for two locations in Athens, Greece. Results verify the ability of the methods to analyze and model this knowledge domain and to forecast the levels of key parameters that provide direct input to the environmental decision making process. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Schneegass:2008:ijcnn, author = "Daniel Schneegass and Steffen Udluft and Thomas Martinetz", title = "Uncertainty Propagation for Quality Assurance in Reinforcement Learning", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0744.pdf}, url = {}, size = {}, abstract = {In this paper we address the reliability of policies derived by Reinforcement Learning on a limited amount of observations. This can be done in a principled manner by taking into account the derived Q-function's uncertainty, which stems from the uncertainty of the estimators used for the MDP's transition probabilities and the reward function. We apply uncertainty propagation parallelly to the Bellman iteration and achieve confidence intervals for the Q-function. In a second step we change the Bellman operator as to achieve a policy guaranteeing the highest minimum performance with a given probability. We demonstrate the functionality of our method on artificial examples and show that, for an important problem class even an enhancement of the expected performance can be obtained. Finally we verify this observation on an application to gas turbine control. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Fu3:2008:ijcnn, author = "Siyao Fu and Qi Zuo and Zeng-Guang Hou and Zize Liang and Min Tan and Fengshui Jing and Xiaoling Fu", title = "Unsupervised Learning of Categories from Sets of Partially Matching Image Features for Power Line Inspection Robot", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0745.pdf}, url = {}, size = {}, abstract = {Object recognition and categorization are considered as fundamental steps in the vision based navigation for inspection robot as it must plan its behaviors based on various kinds of obstacles detected from the complex background. However, current approaches typically require some amount of supervision, which is viewed as a expensive burden and restricted to relatively small number of applications in practice. For this purpose, we present an computationally efficient approach that does not need supervision and is capable of learning object categories automatically from unlabeled images which are represented by an set of local features, and all sets are clustered according to their partial-match feature correspondences, which is done by a enhanced Spatial Pyramid Match algorithm (E-SPK). Then a graph-theoretic clustering method is applied to seek the primary grouping among the images. The consistent subsets within the groups are identified by inferring category templates. Given the input, the output of the approach is a partition of the images into a set of learned categories. We demonstrate this approach on a field experiment for a powerline inspection robot. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Kadlec:2008:ijcnn, author = "Petr Kadlec and Bogdan Gabrys", title = "Learnt Topology Gating Artificial Neural Networks", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0746.pdf}, url = {}, size = {}, abstract = {This work combines several established regression and meta-learning techniques to give a holistic regression model and presents the proposed Learnt Topology Gating Artificial Neural Networks (LTGANN) model in the context of a general architecture previously published by the authors. The applied regression techniques are Artificial Neural Networks, which are on one hand used as local experts for the regression modelling and on the other hand as gating networks. The role of the gating networks is to estimate the prediction error of the local experts dependent on the input data samples. This is achieved by relating the input data space to the performance of the local experts, and thus building a performance map, for each of the local experts. The estimation of the prediction error is then used for the weighting of the local experts predictions. Another advantage of our approach is that the particular neural networks are unconstrained in terms of the number of hidden units. It is only necessary to define the range within which the number of hidden units has to be generated. The model links the topology to the performance, which has been achieved by the network with the given complexity, using a probabilistic approach. As the model was developed in the context of process industry data, it is evaluated using two industrial data sets. The evaluation has shown a clear advantage when using a model combination and meta-learning approach as well as demonstrating the higher performance of LTGANN when compared to a standard combination method. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Shahjahan2:2008:ijcnn, author = "Md. Shahjahan and Kafi M. Nahin and Md. Shamim Ahsan and K. Murase", title = "An Implementation of On-Line Traffic Information System via Short Message Service (SMS) for Bangladesh", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0747.pdf}, url = {}, size = {}, abstract = {In this paper, a vision-based on-line traffic information system is discussed. The system objectives are to detect levels of traffic congestion on certain roads in Dhaka City and to make this information available to the travelers. To achieve this task, multiple Web Cams will be installed on designated roads. The system will capture digital images of the passing by traffic, analyze these images, and reach a clear decision about number of car. Users will then be able to reach this data by using the short messaging service in their mobile phones. Basically, the system is divided into three independent, yet interacting modules: the image capturing module which will automate the capture of images, the digital image processing module which will process the images, and the short message service (known as SMS) server module which will receive SMSs from a user and reply back to him by an SMS. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Xiaojing:2008:ijcnn, author = "Guo Xiaojing and Wu Xiaopei and Zhang Dexiang", title = "Motor Imagery EEG Detection by Empirical Mode Decomposition", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0748.pdf}, url = {}, size = {}, abstract = {The paper investigates the possibility of using empirical mode decomposition (EMD) method to detect the mu rhythm of motor imagery EEG signal. Recently the mu rhythm by motor imagination has been used as a reliable EEG pattern for brain-computer interface (BCI) system. Considering the non-stationary characteristics of the motor imagery EEG, the EMD method is proposed to detect the mu rhythm during left and right hand movement imagination. By analyzing the instantaneous amplitude and instantaneous frequency of the intrinsic mode functions (IMFs), the mu rhythm can be detected. And by Hilbert marginal spectrum, the ERD/ERS phenomenon of mu rhythm can be found. The results in this paper demonstrate that the EMD method is a effective time-frequency analysis tool for non-stationary EEG signal. }, keywords = { Empirical mode decomposition (EMD), Intrinsic mode functions (IMFs), motor imagery EEG, Brain-Computer Interface (BCI), instantaneous frequency, instantaneous amplitude, Hilbert marginal spectrum.}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Surówka:2008:ijcnn, author = "Grzegorz Surówka ", title = "Inductive Learning of Skin Lesion Images for Early Diagnosis of Melanoma", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0749.pdf}, url = {}, size = {}, abstract = {We take advantage of natural induction methods to build classifiers of the pigmented skin lesion images. This methodology can be treated as a non-invasive approach to early diagnosis of melanoma. We use the AQ21 application, which is based on the attributional calculus, to discover patterns in the skin images. Our classifier has good efficiency and may potentially be an important diagnostic aid. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Hsu2:2008:ijcnn, author = "Yu-Su Hsu and Tang-Jung Chiu and Hsin Chen", title = "Real-Time Recognition of Continuous-Time Biomedical Signals Using the Diffusion Network", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0753.pdf}, url = {}, size = {}, abstract = {Real-time recognition of multichannel, continuoustime physiological signals has been crucial for the development of implantable biomedical devices. This work investigates the feasibility of using the Diffusion Network, a stochastic recurrent neural network, to recognise continuous-time biomedical signals. In addition, a hardware-friendly approach for achieving real-time recognition is proposed and tested with both artificial and real biomedical data. Based on this approach, the Diffusion Network is demonstrated to exhibit great tolerance against noise and drifts in continuous-time signals being classified. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Guo5:2008:ijcnn, author = "Yimo Guo and Zhengguang Xu ", title = "Local Binary Pattern with New Decomposition Method for Face Recognition", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0755.pdf}, url = {}, size = {}, abstract = {As face is a topological object, spatial contents contained in facial images (i.e. eyes, nose...) play an important role in feature extraction. To preserve spatial information, region decomposition is an essential step in face recognition for local feature based methods. In this paper, a new region decomposition method is proposed based on Cellular Neural Network (CNN). This method, called Face Penta-Chotomy (FPC), can be factorized into two parts. First, a stable facial region is extracted by a CNN template. Then other four regions are depicted according to the stable facial region and facial proportion. The local binary pattern (LBP) is adopted as the region descriptor. This method is evaluated by conducting experiments on the Yale face database B and ORL database. Besides, it compared with six state-of-the-art methods. From experimental results, it outperforms all the compared methods and the feature dimension can be significantly reduced compared with the conventional uniform region decomposition method. Moreover, the proposed method is demonstrated to be robust under single training condition. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wall:2008:ijcnn, author = "Julie A. Wall and Liam J. McDaid and Liam P. Maguire and Thomas M. McGinnity ", title = "Spiking Neuron Models of the Medial and Lateral Superior Olive for Sound Localisation", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0756.pdf}, url = {}, size = {}, abstract = {Sound localisation is defined as the ability to identify the position of a sound source. The brain employs two cues to achieve this functionality for the horizontal plane, interaural time difference (ITD) by means of neurons in the medial superior olive (MSO) and interaural intensity difference (IID) by neurons of the lateral superior olive (LSO), both located in the superior olivary complex of the auditory pathway. This paper presents spiking neuron architectures of the MSO and LSO. An implementation of the Jeffress model using spiking neurons is presented as a representation of the MSO, while a spiking neuron architecture showing how neurons of the medial nucleus of the trapezoid body interact with LSO neurons to determine the azimuthal angle is discussed. Experimental results to support this work are presented. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wade:2008:ijcnn, author = "John J. Wade and Liam J. McDaid and Jose A. Santos and Heather M. Sayers", title = "SWAT: An Unsupervised SNN Training Algorithm for Classification Problems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0757.pdf}, url = {}, size = {}, abstract = {The work presented in this paper merges the Bienenstock-Cooper-Munro (BCM) learning rule with Spike Timing Dependent Plasticity (STDP) to develop a training algorithm for a Spiking Neural Network (SNN), stimulated using spike trains. The BCM rule is used to modulate the height of the plasticity window, associated with STDP. The SNN topology uses a single training neuron in the training phase where all classes are passed to this neuron, and the associated weights are subsequently mapped to the classifying output neurons: the weights are proportionally distributed across the output neurons to reflect similarities in the input data. The training algorithm also includes both exhibitory and inhibitory facilitating dynamic synapses that create a frequency routing capability allowing the information presented to the network to be routed to different hidden layer neurons. A variable neuron threshold level simulates the refractory period. The network is benchmarked against the non-linearly separable IRIS data set problem and results presented in the paper show that the proposed training algorithm exhibits a convergence accuracy comparable to other SNN training algorithms. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Zhao3:2008:ijcnn, author = "Qibin Zhao and Liqing Zhang and Andrzej Cichocki and Jie Li", title = "Incremental Common Spatial Pattern Algorithm for BCI", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0758.pdf}, url = {}, size = {}, abstract = {A major challenge in applying machine learning methods to Brain-Computer Interfaces (BCIs) is to overcome the on-line non-stationarity of the data blocks. An effective BCI system should be adaptive to and robust against the dynamic variations in brain signals. One solution to it is to adapt the model parameters of BCI system online. However, CSP is poor at adaptability since it is a batch type algorithm. To overcome this, in this paper, we propose the Incremental Common Spatial Pattern (ICSP) algorithm which performs the adaptive feature extraction on-line. This method allows us to perform the online adjustment of spatial filter. This procedure helps the BCI system robust to possible non-stationarity of the EEG data. We test our method to data from BCI motor imagery experiments, and the results demonstrate the good performance of adaptation of the proposed algorithm. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Oentaryo2:2008:ijcnn, author = "Richard J. Oentaryo and Michel Pasquier", title = "A Reduced Rule-Based Localist Network for Data Comprehension", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0759.pdf}, url = {}, size = {}, abstract = {Localist networks and especially neuro-fuzzy systems constitute promising techniques for data comprehension, but generally exhibit poor system interpretability and generalization ability. This paper aims at addressing the issues through a novel localist Reduced Fuzzy Cerebellar Model Articulation Controller (RFCMAC), that models the two-stage development of cortical memories in the human brain to compress and refine the formulated (fuzzy) rule base respectively. The proposed mechanisms allow the RFCMAC associative memory to induce a concise, interpretable rule base, and at the same time to improve generalization, fostering in turn system scalability and robustness. Experimental results on several benchmark tasks have demonstrated the potential of the proposed system as an effective tool for understanding data. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Almeida2:2008:ijcnn, author = "Gustavo M. de Almeida and Marcelo Cardoso and Danilo C. Rena and Song W. Park", title = "Graphical Representation of Cause-Effect Relationships among Chemical Process Variables using a Neural Network Approach", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0761.pdf}, url = {}, size = {}, abstract = {The visualization of relevant information from numerical data is not a natural task for human beings, mainly in case of multivariate systems. In compensation, graphical representations make the understanding easier since it explores the human capacity of processing visual information. Based on that, this study constructs a cause-effect map relating effects of operating process variables over the steam generated by a boiler. This is done after the identification of a neural predictive model for this response. The use of such data-driven technique is due to its capacity of performing a non linear input-output mapping given a reliable data base. The case study is based on the operations of a chemical recovery boiler belonging to a Kraft pulp mill located in Brazil. The utility of the obtained map is clear, once the visualization of the contributions of each process variable over the output steam, from this graphical representation, is more intuitive. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Sorjamaa:2008:ijcnn, author = "Antti Sorjamaa and Yoan Miche and Robert Weiss and Amaury Lendasse", title = "Long-Term Prediction of Time Series Using NNE-Based Projection and OP-ELM", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0762.pdf}, url = {}, size = {}, abstract = {This paper proposes a combination of methodologies based on a recent development -called Extreme Learning Machine (ELM)- decreasing drastically the training time of nonlinear models. Variable selection is beforehand performed on the original dataset, using the Partial Least Squares (PLS) and a projection based on Nonparametric Noise Estimation (NNE), to ensure proper results by the ELM method. Then, after the network is first created using the original ELM, the selection of the most relevant nodes is performed by using a Least Angle Regression (LARS) ranking of the nodes and a Leave-One-Out estimation of the performances, leading to an Optimally-Pruned ELM (OP-ELM). Finally, the prediction accuracy of the global methodology is demonstrated using the ESTSP 2008 Competition and Poland Electricity Load datasets. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Joshi:2008:ijcnn, author = "Sachin Joshi and Kishore Prahallad and B. Yegnanarayana", title = "AANN-HMM Models for Speaker Verification and Speech Recognition", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0764.pdf}, url = {}, size = {}, abstract = {Pattern classification is an important task in speech recognition and speaker verification. Given the feature vectors of an input the goal is to capture the characteristics of these features unique to each class. This paper deals with exploring Auto Associative Neural Network (AANN) models for the task of speaker verification and speech recognition.We show that AANN models produce comparable performance with that of GMM based speaker verification and speech recognition. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Valdes:2008:ijcnn, author = "A. Valdes and K. Khorasani", title = "Dynamic Neural Network-Based Pulsed Plasma Thruster (PPT) Fault Detection and Isolation for the Attitude Control System of a Satellite", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0765.pdf}, url = {}, size = {}, abstract = {The main objective of this paper is to develop a dynamic neural network-based fault detection and isolation (FDI) scheme for the Pulsed Plasma Thrusters (PPTs) of a satellite. The goal is to determine the occurrence of a fault in any one of the multiple thrusters that are employed in the attitude control subsystem of a satellite, and further to localize which PPT is faulty. In order to accomplish these objectives, a multilayer perceptron network embedded with dynamic neurons is proposed. Based on a given set of input-output data collected from the electrical circuit of the PPTs, the dynamic network parameters are adjusted to minimize the output estimation error. A Confusion Matrix approach is used to measure the effectiveness of our proposed dynamic neural network-based fault detection and isolation (FDI) scheme under various fault scenarios. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Satheeshkumar:2008:ijcnn, author = "J. Satheeshkumar and S. Arumugaperumal and R. Rajesh and C. Kesavadas", title = "Does Brain React On Indian Music? - A Functional Magnetic Resonance Imaging Study", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0766.pdf}, url = {}, size = {}, abstract = {Listening to music, as per clinical neuro science, involves many cognitive components with distinct brain substrates and its study has advanced greatly in the last three decades. But the studies of Indian music and its influence in the brain have not yet been studied. This article presents sequence of image processing steps using statistical parametric mapping for the analysis of fMRI brain structures for studying the influence of two Indian ragas namely Sankarabnam and Madhyamavathi on a non-musician brain. The results shows that ragas have a very good influence on non-musician and also shows that raga named Madhyamavathi has influenced the subject more than Sankarabranam. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Bhagat:2008:ijcnn, author = "K. K. Kiran Bhagat and Stefan Wermter and Kevin Burn", title = "Hybrid Learning Architecture for Unobtrusive Infrared Tracking Support", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0767.pdf}, url = {}, size = {}, abstract = {The system architecture presented in this paper is designed for helping an aged person to live longer independently in their own home by detecting unusual and potentially hazardous behaviours. The system consists of two major components. The first component is the tracking part which is responsible for monitoring the movements of the person within the home, while the second part is a learning agent which is responsible for learning the behavioural patterns of the person. For the tracking part of the system a simulation portraying a virtual room with passive infrared sensors has been designed, while for the learning agent a hybrid architecture has been implemented. The hybrid architecture consists of a Markov Chain Model, Template Matching, Fuzzy Logic and Memory-Based reasoning techniques. The hybrid structure was selected because it combined the strengths of the constituent algorithms and because it supports the learning with limited training data. The resultant system was able to not only classify between the normal and the abnormal paths but was also able to distinguish between different normal routes. We claim that passive infrared tracking combined with a hybrid learning architecture has potential for adaptive unobtrusive tracking support. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Gill:2008:ijcnn, author = "Arjun Singh Gill ", title = "A Novel Low Complexity Speech Recognition Approach", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0768.pdf}, url = {}, size = {}, abstract = {In the field of Digital Speech Recognition powerful ASR (Automatic Speech Recognizer) systems have been developed which employ highly intricate algorithms like the HMM, DTW and Neural Network based algorithms capable of recognizing up to 1000 different words. Their high complexity and computation requirements prove to be superfluous for less demanding tasks. In this paper is proposed a simple, less aggressive and computationally efficient algorithm that can parallel any of the above algorithms' ability to distinguish a few words (typically up to 5 different words) but demands considerably less computational power making it suitable for embedded systems. Also discussed is the technique of ``Menu Driven'' control where the number of words that can be recognized, pose no frontier to the number of tasks that can be performed by using very few (5 or less) words. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Yau:2008:ijcnn, author = "Chi-Yung Yau and Kevin Burn and Stefan Wermter ", title = "A Neural Wake-Sleep Learning Architecture for Associating Robotic Facial Emotions", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0769.pdf}, url = {}, size = {}, abstract = {A novel wake-sleep learning architecture for processing a robot's facial expressions is introduced. According to neuroscience evidence, associative learning of emotional responses and facial expressions occurs in the brain in the amygdala. Here we propose an architecture inspired by how the amygdala receives information from other areas of the brain to discriminate it and generate innate responses. The architecture is composed of many individual Helmholtz machines using the wake-sleep learning algorithm for performing information transformation and recognition. The Helmholtz machine is used since its re-entrant connections support both supervised and unsupervised learning. Potentially it can explain some aspects of human learning of emotional concepts and experience. In this research, a robotic head's facial expression dataset is used. The objective of this learning architecture is to demonstrate the neural basis for the association of recognized facial expressions and linguistic emotion labels. It implies the understanding of emotions from observation and is further used to generate facial expressions. In contrast with other facial expression recognition research, this work concentrates more on emotional information processing and neural concept development, rather than a technical recognition task. This approach has a lot of potential to contribute towards neurally inspired emotional experience in robotic systems. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Carvalho:2008:ijcnn, author = "Cesar A. M. Carvalho and George D. C. Cavalcanti", title = "An Artificial Neural Network Approach for User Class-Dependent Off-Line Sentence Segmentation", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0772.pdf}, url = {}, size = {}, abstract = {In this paper, we present an Artificial Neural Network (ANN) architecture for segmenting unconstrained handwritten sentences in the English language into single words. Feature extraction is performed on a line of text to feed an ANN that classifies each column image as belonging to a word or gap between words. Thus, a sequence of columns of the same class represents words and inter-word gaps. Through experimentation, which was performed using the IAM database, it was determined that the proposed approach achieved better results than the traditional Gap Metric approach for handwriting sentence segmentation. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Rast:2008:ijcnn, author = "Alexander D. Rast and Shufan Yang and Mukaram Khan and Steve B. Furber ", title = "Virtual Synaptic Interconnect Using an Asynchronous Network-on-Chip", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0773.pdf}, url = {}, size = {}, abstract = {Given the limited current understanding of the neural model of computation, hardware neural network architectures that impose a specific relationship between physical connectivity and model topology are likely to be overly restrictive. Here we introduce, in the SpiNNaker chip, an alternative approach: a mappable virtual topology using an asynchronous network-on-chip (NoC) that decouples the ``logical'' connectivity map from the physical wiring. Borrowing the established digital RAM model for synapses, we develop a concurrent memory access channel optimised for neural processing that allows each processing node to perform its own synaptic updates as if the synapses were local to the node. The highly concurrent nature of interconnect access, however, requires careful design of intermediate buffering and arbitration. We show here how a locally buffered, one-transaction-per-node model with multiple synapse updates per transaction enables the local node to offload continuous burst traffic from the NoC, allowing for a hardware-efficient design that supports biologically realistic speeds. The design not only presents a flexible model for neural connectivity but also suggests an ideal form for general-purpose high-performance on-chip interconnect. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Lau:2008:ijcnn, author = "Javy H. Y. Lau and Bertram E. Shi", title = "Improved Illumination Invariance Using a Colour Edge Representation Based on Double Opponent Neurons", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0774.pdf}, url = {}, size = {}, abstract = {We describe an evaluation framework that provides a quantitative measure on the performance of a neural network colour constancy model. In this framework, the responses of three models of colour constancy to a set of colour edges under varying illuminating conditions are computed. We study a model based on Double Opponent cells, as well as two variants of the Retinex model. Evaluation metrics on the models' capabilities to discriminate among different colour edges and resist illuminant induced changes are measured. Using this framework, we confirm the advantage of incorporating spectral opponency into the colour constancy model. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Chen12:2008:ijcnn, author = "Weiliang Chen and Rod Adams and Lee Calcraft", title = "Connectivity Graphs and the Performance of Sparse Associative Memory Models", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0775.pdf}, url = {}, size = {}, abstract = {This paper investigates the relationship between network connectivity and associative memory performance using high capacity associative memory models with different types of sparse networks. We found that the clustering of the network, measured by Clustering Coefficient and Local Efficiency, have a strong linear correlation to its performance as an associative memory. This result is important since a purely static measure of network connectivity appears to determine an important dynamic property of the network. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Casey:2008:ijcnn, author = "Matthew C. Casey and Athanasios Pavlou", title = "A Behavioral Model of Sensory Alignment in the Superficial and Deep Layers of the Superior Colliculus", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0776.pdf}, url = {}, size = {}, abstract = {The ability to combine sensory information is an important attribute of the brain. Multisensory integration in natural systems suggests that a similar approach in artificial systems may be important. Multisensory integration is exemplified in mammals by the superior colliculus (SC), which combines visual, auditory and somatosensory stimuli to shift gaze. However, although we have a good understanding of the overall architecture of the SC, as yet we do not fully understand the process of integration. While a number of computational models of the SC have been developed, there has not been a larger scale implementation that can help determine how the senses are aligned and integrated across the superficial and deep layers of the SC. In this paper we describe a prototype implementation of the mammalian SC consisting of self-organizing maps linked by Hebbian connections, modeling visual and auditory processing in the superficial and deep layers. The model is trained on artificial auditory and visual stimuli, with testing demonstrating the formation of appropriate spatial representations, which compare well with biological data. Subsequently, we train the model on multisensory stimuli, testing to see if the unisensory maps can be combined. The results show the successful alignment of sensory maps to form a multisensory representation. We conclude that, while simple, the model lends itself to further exploration of integration, which may give insight into whether such modeling is of benefit computationally. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Almeida3:2008:ijcnn, author = "Carlos W. D. de Almeida and Renata M. C. R. de Souza and Nicomedes L. Cavalcanti Júnior", title = "Image Retrieval Using the Curvature Scale Space (CSS) Descriptor and the Self-Organizing Map (SOM) Model Under Scale Invariance", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0777.pdf}, url = {}, size = {}, abstract = {In a previous work [4], we presented an approach for shape-based image retrieval using the curvature scale space (CSS) and self-organizing map (SOM) methods. Here, we examine the robustness of the representation with images under different scales. The shape features of images are represented by CSS images extracted from, for example, a large database and represented by median vectors that constitutes the training data set for a SOM neural network which, in turn, will be used for performing efficient image retrieval. Experimental results using a benchmark database are presented to demonstrate the usefulness of the proposed methodology. The evaluation of performance is based on accuracy and retrieval time assessed in the framework of a Monte Carlo experience. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wahid:2008:ijcnn, author = "Khan Wahid and Seok-Bum Ko and Daniel Teng ", title = "Efficient Hardware Implementation of an Image Compressor for Wireless Capsule Endoscopy Applications", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0778.pdf}, url = {}, size = {}, abstract = {The paper presents an area- and power-efficient implementation of an image compressor for wireless capsule endoscopy application. The architecture uses a direct mapping to compute the two-dimensional Discrete Cosine Transform which eliminates the need of transpose operation and results in reduced area and low processing time. The algorithm has been modified to comply with the JPEG standard and the corresponding quantization tables have been developed and the architecture is implemented using the CMOS 0.18um technology. The processor costs less than 3.5k cells, runs at a maximum frequency of 150 MHz, and consumes 10 mW of power. The test results of several endoscopic colour images show that higher compression ratio (over 85percent) can be achieved with high quality image reconstruction (over 30 dB). }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Hu4:2008:ijcnn, author = "Xiao Hu and Raj Subbu and Piero Bonissone and Hai Qiu and Naresh Iyer ", title = "Multivariate Anomaly Detection in Real-World Industrial Systems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0779.pdf}, url = {}, size = {}, abstract = {Anomaly detection is a critical capability enabling condition-based maintenance (CBM) in complex real-world industrial systems. It involves monitoring changes to system state to detect änomalous" behavior. Timely and reliable detection of anomalies that indicate faulty conditions can help in early fault diagnostics. This will allow for timely maintenance actions to be taken before the fault progresses and causes secondary damage to the system leading to downtime. When an anomaly is identified, it is important to isolate the source of the fault so that appropriate maintenance actions can be taken. In this paper, we introduce effective multivariate anomaly detection techniques and methods that allow fault isolation. We present experimental results from the application of these techniques to a high-bypass commercial aircraft engine. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Gudmundsson:2008:ijcnn, author = "Steinn Gudmundsson and Thomas Philip Runarsson and Sven Sigurdsson ", title = "Support Vector Machines and Dynamic Time Warping for Time Series", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0780.pdf}, url = {}, size = {}, abstract = {Effective use of support vector machines (SVMs) in classification necessitates the appropriate choice of a kernel. Designing problem specific kernels involves the definition of a similarity measure, with the condition that kernels are positive semi-definite (PSD). An alternative approach which places no such restrictions on the similarity measure is to construct a set of inputs and let each example be represented by its similarity to all the examples in this set and then apply a conventional SVM to this transformed data. Dynamic time warping (DTW) is a well established distance measure for time series but has been of limited use in SVMs since it is not obvious how it can be used to derive a PSD kernel. The feasibility of the similarity based approach for DTW is investigated by applying the method to a large set of time-series classification problems. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Bastos-Filho:2008:ijcnn, author = "Carmelo J. A. Bastos-Filho and Wesnaida H. Schuler and Adriano L. I. Oliveira", title = "A Fast and Reliable Routing Algorithm Based on Hopfield Neural Networks Optimized by Particle Swarm Optimization", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0783.pdf}, url = {}, size = {}, abstract = {Routing is very important for computer networks because it is one of the main factors that influences network performance. In this paper, we propose an improved intelligent method for routing based on Hopfield Neural Networks (HNN), which uses a discrete equation and the Particle Swarm Optimization (PSO) technique to optimize the HNN parameters. The fitness function for the PSO algorithm used here is a combination of the number of iterations for convergence and the percentage error when the HNN method tries to find the best path in a communication network. The simulation results show that PSO is a reliable approach to optimize the Hopfield network for routing in computer networks, since this method results in fast convergence and produces accurate results. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Silva4:2008:ijcnn, author = "Leandro Augusto da Silva and Humberto Sandmann and Emilio Del-Moral-Hernandez", title = "A Self-Organizing Architecture of Recursive Elements for Continuous Learning", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0786.pdf}, url = {}, size = {}, abstract = {This paper describes how recursive nodes with rich dynamics can be explored in a self-organizing artificial network for continuous learning tasks. The purpose of inserting the recursive elements is introducing chaos behavior in a modified Self-Organizing Map (SOM). This new structure is called CSOM. It incorporates some of the main features of SOM, but it also improves the capability of cluster input patterns through increasing the winning opportunities of the units. The proposal is to use the Lyapunov Exponent value to define the winner unit. In addition, the CSOM is introduced in continuous learning task, which is the capacity of learning a new pattern, without losing the patterns learned. The proposal addressed here is described, analyzed quantitatively and its performance is compared with that of conventional SOM. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Souto2:2008:ijcnn, author = "Marcilio C. P. de Souto and Daniel S. A. de Araujo and Ivan G. Costa and Teresa B. Ludermir and Alexander Schliep", title = "Comparative Study on Normalization Procedures for Cluster Analysis of Gene Expression Datasets", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0787.pdf}, url = {}, size = {}, abstract = {Normalization before clustering is often needed for proximity indices, such as Euclidian distance, which are sensitive to differences in the magnitude or scales of the attributes. The goal is to equalize the size or magnitude and the variability of these features. This can also be seen as a way to adjust the relative weighting of the attributes. In this context, we present a first large scale data driven comparative study of three normalization procedures applied to cancer gene expression data. The results are presented in terms of the recovering of the true cluster structure as found by five different clustering algorithms. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Polettini:2008:ijcnn, author = "Nicola Polettini and Diego Sona and Paolo Avesani", title = "A Relational Cascade Correlation for Structured Outputs", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0788.pdf}, url = {}, size = {}, abstract = {We propose a Relational Neural Network defined as a special instance of the Recurrent Cascade Correlation. The proposed model is designed to deal with classification tasks where classes are organized into generic graphs (e.g., taxonomies, ontologies, etc.). The open challenge is to exploit the knowledge encoded in the relationships among the classes. This is particularly useful when there are many classes poorly represented by labeled examples. Exploiting the relationships we increase the bias, making the generalization more robust. The novelty of the proposed model can be seen from two different perspectives. On one hand, the temporal encoding of the standard recurrent networks is revised with a notion of non-stationary structural unfolding. On the other hand, it can be seen as a novel constructive algorithm that generates the neural network architecture exploiting the class structure. We present the results of an empirical evaluation on a hierarchical document classification task. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Petlenkov:2008:ijcnn, author = "Eduard Petlenkov and Sven Nomm and Jüri Vain and Fujio Miyawaki", title = "Application of Self Organizing Kohonen Map to Detection of Surgeon Motions During Endoscopic Surgery", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0789.pdf}, url = {}, size = {}, abstract = {Segmentation of the surgeon's hand movements during the surgery into more primitive parts and recognition of those parts using Kohonen map is discussed in present paper. Main advantages of the proposed approach are that it allows to take into account dynamical characteristics of the hand movements and exclude probability of human error in building etalon segmentation. Ability to recognize current action of the surgeon has a crucial importance in developing a robot able to assist surgeon during the endoscopic surgical operation. One of the possible ways is to predefine a set of possible surgeon's actions and provide a recognition algorithm explored in the framework of present contribution. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Jin3:2008:ijcnn, author = "Xin Jin and Steve B. Furber and John V. Woods", title = "Efficient Modelling of Spiking Neural Networks on a Scalable Chip Multiprocessor", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0790.pdf}, url = {}, size = {}, abstract = {we propose a system based on the Izhikevich model running on a scalable chip multiprocessor - SpiNNaker - for large-scale spiking neural network simulation. The design takes into account the requirements for processing, storage, and communication which are essential to the efficient modelling of spiking neural networks. To gain a speedup of the processing as well as saving storage space, the Izhikevich model is implemented in 16-bit fixed-point arithmetic. An approach based on using two scaling factors is developed, making the precision comparable to the original. With the two scaling factors scheme, all of the firing patterns by the original model can be reproduced with a much faster execution speed. To reduce the communication overhead, rather than sending synaptic weights on communicating, we only send out event packets to indicate the neuron firings while holding the synaptic weights in the memory of the post-synaptic neurons, which is so-called event-driven algorithm. The communication based on event packets can be handled efficiently by the multicast system supported by the SpiNNaker machine. We also describe a system level model for spiking neural network simulation based on the schemes above. The model has been functionally verified and experimental results are included. An analysis of the performance of the whole system is presented at the end of the paper. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Levine:2008:ijcnn, author = "Daniel S. Levine and Leonid I. Perlovsky", title = "A Network Model of Rational Versus Irrational Choices on a Probability Maximization Task", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0791.pdf}, url = {}, size = {}, abstract = {Humans have a drive to maximize knowledge of the world, yet decision making data also suggest a contrary drive to minimize cognitive effort using simplifying heuristics. The trade-off between maximizing knowledge and minimizing effort is modeled by simulation of a challenging decision task. The task is to choose which of two gambles has the highest probability of success when the alternative with higher success probability also has lower success frequency. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Harb:2008:ijcnn, author = "Moufid Harb and Rami Abielmona and Emil Petriu and Kamal Naji", title = "Neural Control System of a Mobile Robot", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0792.pdf}, url = {}, size = {}, abstract = {Mobile robots could play a significant role in places where it is impossible for the human to work. In such environments, neural networks, instead of traditional methods, are suitable solutions to locally navigate and recognize the environment's subspaces. In order to learn and perform two important functions ``environmental recognition'' and ``local navigation'', multi-layered neural networks are trained to process distance measurements received from a laser rangefinder. This paper will focus on a computer based design and test of this neural system, that includes three neural controllers for local navigation, and two neural networks for environmental recognition, fed off-line by a simulated model of a laser range-finder. These neural networks are the major components of a control system that performs a global neural navigation of a mobile robot, which could be used to perform industrial missions within industrial environments. This control system can guide a mobile robot to track its predefined path to arrive to its final goal through a set of sub-goals, or autonomously plan its path to arrive to the desired final goal, and to avoid obstacles that are found along the way. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wong2:2008:ijcnn, author = "Aaron S. W. Wong and Stephan K. Chalup ", title = "Towards Visualisation of Sound-Scapes Through Dimensionality Reduction", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0793.pdf}, url = {}, size = {}, abstract = {Sound-scapes are useful for understanding our surrounding environments in applications such as security, source tracking or understanding human computer interaction. Accurate position or localisation information from sound-scape samples consists of many channels of high dimensional acoustic data. In this paper we demonstrate how to obtain a visual representation of sound-scapes by applying dimensionality reduction techniques to a range of artificially generated sound-scape datasets. Linear and non-linear dimensionality techniques were compared including principle component analysis (PCA), multidimensional scaling (MDS), locally linear embedding (LLE) and isometric feature mapping (ISOMAP). Results obtained by applying the dimensionality reduction techniques led to visual representations of affine positions of the sound source on its sound-scape manifold. These displayed clearly the order relationships of angles and intensities of the generated sound-scape samples. In a simple classification task with the artificial sound data, the successful combination of dimensionality reduction and classifier methods are demonstrated. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Stahlbock:2008:ijcnn, author = "Robert Stahlbock ", title = "Neural Classification Approach for Short Term Forecast of Exchange Rate Movement with Point and Figure Charts", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0795.pdf}, url = {}, size = {}, abstract = {In the domain of classification and forecasting tasks, artificial neural networks (ANNs) are prominent data mining methods. Neural network paradigms like learning vector quantization (LVQ) are suitable for solving classification problems. In this paper, we combine LVQ with the popular Point & Figure (P&F) chart analysis applied to a one day forecast of the exchange rate between Euro (EUR) and US Dollar (USD). We present two different P&F encoding schemes and analyze the classification accuracy and results of a trading system fed with our results from the LVQ. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Khan:2008:ijcnn, author = "M. M. Khan and D. R. Lester and L. A. Plana and A. Rast and X. Jin and E. Painkras and S. B. Furber ", title = "SpiNNaker: Mapping Neural Networks onto a Massively-Parallel Chip Multiprocessor", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0796.pdf}, url = {}, size = {}, abstract = {SpiNNaker is a novel chip - based on the ARM processor - which is designed to support large scale spiking neural networks simulations. In this paper we describe some of the features that permit SpiNNaker chips to be connected together to form scalable massively-parallel systems. Our eventual goal is to be able to simulate neural networks consisting of 109 neurons running in `real time', by which we mean that a similarly sized collection of biological neurons would run at the same speed.In this paper we describe the methods by which neural networks are mapped onto the system, and how features designed into the chip are to be exploited in practice. We will also describe the modelling and verification activities by which we hope to ensure that, when the chip is delivered, it will work as anticipated. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Kasturi:2008:ijcnn, author = "Jyotsna Kasturi and Raj Acharya", title = "A New Information-Theoretic Dissimilarity for Clustering Time-Dependent Gene Expression Profiles Modeled with Radial Basis Functions", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0797.pdf}, url = {}, size = {}, abstract = {The study and inference of biological pathways and gene regulation mechanisms has become a vital component of modern medicine and drug discovery. Gene expression studies make it possible to understand these mechanisms by simultaneously measuring the expression level of thousands of genes. These data though rich in information are also prone to many quality control issues that ultimately result in noisy data. A new method to smooth the data and measure expression dissimilarity between genes is proposed in this paper. A new dissimilarity measure is defined as an approximation of the Kullback-Leibler divergence between mixture models. Further, a noise reduction method is also proposed for use with data from time-course experiments. Results from real data and simulated data demonstrate that the method is well suited for clustering gene expression profiles. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Silva5:2008:ijcnn, author = "Kelly P. Silva and Francisco A. T. de Carvalho and M. Csernel", title = "Clustering of Symbolic Data Through a Dissimilarity Volume Based Measure", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0799.pdf}, url = {}, size = {}, abstract = {The recording of symbolic data has become a common practice with the recent advances in database technologies. This paper shows hard and fuzzy relational clustering in order to partition symbolic data. These methods optimize objective functions based on a dissimilarity function. The distance used is a volume based measure and may be applied to data described by set-valued, list-valued or interval-valued symbolic variables. Experiments with real and synthetic symbolic data sets show the usefulness of the proposed approach. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Cao2:2008:ijcnn, author = "Yuan Cao and Haibo He", title = "Learning from Testing Data: A New View of Incremental Semi-Supervised Learning", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0800.pdf}, url = {}, size = {}, abstract = {In this paper, we propose a novel method for incremental semi-supervised learning. Unlike the traditional way of incremental learning or semi-supervised learning, we try to answer a more challenging question: given inadequate labeled training data, can one use the unlabeled testing data to improve the learning and prediction accuracy? The objective here is to reinforce the learning system trained offline through online incremental semi-supervised learning based on the testing data distribution. To do this, we propose an iterative algorithm that can adaptively recover the labels for testing data based on their confidence levels, and then extend the training population by such recovered data to facilitate learning and prediction. Multiple hypotheses are developed based on different learning capabilities of different recovered data sets, and a voting method is used to integrate the decisions from different hypotheses for the final predicted labels. We compare the proposed algorithm with bootstrap aggregating (bagging) method for performance evaluation. Simulation results on various real-world data sets illustrate the effectiveness of the proposed method. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Park2:2008:ijcnn, author = "Sunho Park and Seungjin Choi", title = "Gaussian Process Regression for Voice Activity Detection and Speech Enhancement", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0802.pdf}, url = {}, size = {}, abstract = {Gaussian process (GP) model is a flexible nonparametric Bayesian method that is widely used in regression and classification. In this paper we present a probabilistic method where we solve voice activity detection (VAD) and speech enhancement in a single framework of GP regression, modeling clean speech by a GP smoother. Optimized hyperparameters in GP models lead us to a novel VAD method since learned lengthscale parameters in covariance functions are much different between voiced and unvoiced frames. Clean speech is estimated by posterior means in GP models. Numerical experiments confirm the validity of our method. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Brodu:2008:ijcnn, author = "Nicolas Brodu ", title = "Multifractal Feature Vectors for Brain-Computer Interfaces", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0803.pdf}, url = {}, size = {}, abstract = {This article introduces a new feature vector extraction for EEG signals using multifractal analysis. The validity of the approach is asserted on real data sets from the BCI competitions II and III. The feature extraction can be performed in real time with low-cost discrete wavelet transforms. Classification results obtained with the new feature vectors are close to the state of art techniques, while using a different information. Combining the new multifractal feature vector with existing ones may result in better performances, up to 5percent in the present case. This work thus offers an alternative to the usual feature-extraction techniques, and opens new possibilities in the field of Brain-Computer interfaces. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wang10:2008:ijcnn, author = "Qingguo Wang and Yi Shang and Dong Xu", title = "A New Clustering-Based Method for Protein Structure Selection", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0804.pdf}, url = {}, size = {}, abstract = {In protein tertiary structure prediction, it is a crucial step to select near-native structures from a large number of candidate structural models. Despite much effort to tackle the problem of protein structure selection, the discerning power of current scoring functions is still unsatisfactory.In this paper, we developed a new clustering-based method for selecting near-native protein structures. Our method consists of three phases: filtering, clustering and cluster reduction, and centroid construction. Given a set of Cα protein structures, we apply one or multiple existing scoring functions to filter out bad structures. Then, we group the remaining structures into clusters based on pair-wise similarity measured by RMSD. Each cluster is reduced iteratively to remove outliers and bad structures. Finally, we construct a centroid for each cluster by applying multi-dimensional scale techniques. The centroids are the final models. In experiments, we applied our method to a test set of representative proteins and obtained significant improvement over existing methods. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Mencía:2008:ijcnn, author = "Eneldo Loza Mencía and Johannes Fürnkranz", title = "Pairwise Learning of Multilabel Classifications with Perceptrons", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0806.pdf}, url = {}, size = {}, abstract = {Multiclass multilabel perceptrons (MMP) have been proposed as an efficient incremental training algorithm for addressing a multilabel prediction task with a team of perceptrons. The key idea is to train one binary classifier per label, as is typically done for addressing multilabel problems, but to make the training signal dependent on the performance of the whole ensemble. In this paper, we propose an alternative technique that is based on a pairwise approach, i.e., we incrementally train a perceptron for each pair of classes. Our evaluation on four multilabel datasets shows that the multilabel pairwise perceptron (MLPP) algorithm yields substantial improvements over MMP in terms of ranking quality and overfitting resistance, while maintaining its efficiency. Despite the quadratic increase in the number of perceptrons that have to be trained, the increase in computational complexity is bounded by the average number of labels per training example. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Santana2:2008:ijcnn, author = "Laura E. A. Santana and Anne M. P. Canuto", title = "An Analysis of Data Distribution Methods in Classifier Combination Systems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0807.pdf}, url = {}, size = {}, abstract = {In systems that combine the outputs of classification methods (combination systems), such as ensembles and multi-agent systems, one of the main constraints is that the base components (classifiers or agents) should be diverse among themselves. In other words, there is clearly no accuracy gain in a system that is composed of a set of identical base components. One way of increasing diversity is through the use of feature selection or data distribution methods in combination systems. In this paper, an investigation of the impact of using data distribution methods among the components of combination systems will be performed. In this investigation, five different methods of data distribution will be used and an analysis of the combination systems, using several different configurations, will be performed. As a result of this analysis, it is aimed to detect which combination systems are more suitable to use feature distribution among the components. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Shokri:2008:ijcnn, author = "Maryam Shokri and Hamid R. Tizhoosh and Mohamed S. Kamel", title = "Tradeoff Between Exploration and Exploitation of OQ(λ) with Non-Markovian Update in Dynamic Environments", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0809.pdf}, url = {}, size = {}, abstract = {This paper presents some investigations on tradeoff between exploration and exploitation of opposition-based Q(λ) with non-Markovian update (NOQ(λ) in a dynamic environment. In the previous work the authors applied NOQ(λ) to the deterministic GridWorld problem. In this paper, we have implemented the NOQ(λ) algorithm for a simple elevator control problem to test the behavior of the algorithm for nondeterministic and dynamic environment. We also extend the NOQ(λ) algorithm by introducing the opposition weight to find a better tradeoff between exploration and exploitation for the NOQ(λ) technique. The value of the opposition weight increases as the number of steps increases. Hence, it has more positive effects on the Q-value updates for opposite actions as the learning progresses. The performance of NOQ(λ) method is compared with Q(λ) technique. The experiments indicate that NOQ(λ) performs better than Q(λ). }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Lee3:2008:ijcnn, author = "Jong Chan Lee and Wu Jun and Won Don Lee", title = "Deterministic AdaBoost Algorithm Based on FLDF", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0811.pdf}, url = {}, size = {}, abstract = {AdaBoost is an algorithm with a procedure of selecting the data events from a dataset at each iteration sequence. The data events are selected stochastically using a random number generator. In this paper, a deterministic AdaBoost algorithm is proposed in contrast to the usual stochastic one. For doing this we derive the modified Fisher's formulas moderated to the deterministic method. These formulas contain a scheme to treat data set with weight vector.To verify the performance of proposed algorithm, we compare with the results of different measurements with the deterministic and the stochastic method, by gradually increasing the prune rate and the number of weak learner in the network structure. Through the result of these experiments, we show that our proposed method has higher performance than typical stochastic one. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Kim3:2008:ijcnn, author = "Jungmin Kim and Yountae Kim and Sungshin Kim", title = "An Accurate Localization for Mobile Robot Using Extended Kalman Filter and Sensor Fusion", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0812.pdf}, url = {}, size = {}, abstract = {This paper presents an accurate localization scheme for mobile robots based on the fusion of an ultrasonic satellite (U-SAT) with inertial navigation system (INS), i.e., sensor fusion. Our aim is to achieve an accuracy of less than 100 mm. The INS consists of a yaw gyro and two wheel-encoders, and the U-SAT consists of four transmitters and a receiver. Besides the proposed localization method, we will fuse these in an extended Kalman filter. The performance of the localization was verified by simulation and two actual data sets (straight and curve) gathered from about 0.5 m/s of actual driving data. The localization methods used were general sensor fusion and sensor fusion through a Kalman filter using data from the INS. Through simulation and actual data analysis, the experiment shows the effectiveness of the proposed method for autonomous mobile robots. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Neris:2008:ijcnn, author = "Marrony N. Neris and Alexandre J. Silva and Sarajane M. Peres and Franklin C. Flores", title = "Self Organizing Maps and Bit Signature: A Study Applied on Signal Language Recognition", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0814.pdf}, url = {}, size = {}, abstract = {Self Organizing Map (SOM) is a kind of artificial neural network with a competitive and unsupervised learning. This technique is commonly used to dataset clustering tasks and can be useful in patterns recognition problems. This paper presents an artificial neural network application to signals language recognition problem, where the image representation is given by bit signatures. The recognition results are promising and are presented in this paper. More, some analysis about the combination ``SOM + bit signature'' improved our understanding about the characteristics of the LIBRAS signals and the conclusions are also listed in this paper. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Peng:2008:ijcnn, author = "Ya-Fu Peng and Chih-Hui Chiu", title = "The Implementation of Wheeled Robot Using Adaptive Output Recurrent CMAC", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0815.pdf}, url = {}, size = {}, abstract = {In this study, an adaptive output recurrent cerebellar model articulation controller (AORCMAC) is investigated to control the two-wheeled robot. The main purpose is to develop a self-dynamic balancing and motion control strategy. The proposed AORCMAC has superior capability to the conventional cerebellar model articulation controller in efficient learning mechanism and dynamic response. The dynamic gradient descent method is adopted to online adjust the AORCMAC parameters. Therefore, AORCMAC has superior capability to the conventional cerebellar model articulation controller (CMAC) in efficient learning mechanism and dynamic response. Finally, the effectiveness of the proposed control system is verified by the experiments of the two-wheeled robot standing control. Experimental results show that the the two-wheeled robot can stand upright stably with uncertainty disturbance by using the proposed AORCMAC. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Mohammed:2008:ijcnn, author = "Dhafar S. Mohammed and Saeid Habibi and Danil Prokhorov", title = "Adaptive Parameter Robust Estimation", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0816.pdf}, url = {}, size = {}, abstract = {In this paper, we describe an adaptive technique for states and parameter estimation involving a combination of two methods, namely the Variable Structure Filter (VSF) and the Extend Kalman Filters (EKF).The VSF concept is a model-based robust state/parameter estimation. It has a secondary set of uncertainties that provide a measure of uncertainties in the filter model. It is not however an optimal method. When combined with the Kalman Filter, it provides near optimal solution (further to the assumption pertaining to the Kalman Filter). The combined strategy would then also benefit from the robustness and the additional indicators of performance of the VSF.These features of the combined strategy used for removing uncertainties in the estimation process by dynamic adaptation of the filter model.The modeling uncertainties in the combined VSF/EKF method are removed by using two forms of Neural Networks adaptation. These adaptation methods are based on the Simultaneous Perturbation Stochastic Approximation (SPSA) and the Algorithm Of Pattern Extraction (ALOPEX). The use of dynamic adaption can significantly improve the performance of the estimation process.Other attractive features include computational simplicity, fast rate of convergence, robustness and stability. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Goertzel:2008:ijcnn, author = "Ben Goertzel ", title = "A Pragmatic Path Toward Endowing Virtually-Embodied AIs with Human-Level Linguistic Capability", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0817.pdf}, url = {}, size = {}, abstract = {Current work is described wherein simplified versions of the Novamente Cognition Engine (NCE) are being used to control virtual agents in virtual worlds such as game engines and Second Life. In this context, an IRC (imitationreinforcement- correction) methodology is being used to teach the agents various behaviors, including simple tricks and communicative acts. Here we describe how this work may potentially be exploited and extended to yield a pathway toward giving the NCE robust, ultimately human-level natural language conversation capability. The pathway starts via using the current system to instruct NCE-controlled agents in semiosis and gestural communication; and then continues via integration of a particular sort of hybrid rule-based/statistical NLP system (which is currently partially complete) into the NCE-based virtual agent system, in such a way as to allow experiential adaptation of the rules underlying the NLP system, in a manner that builds on the agent's knowledge of semiosis and gesture. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Rao:2008:ijcnn, author = "A. Ravishankar Rao and Guillermo A. Cecchi and Charles C. Peck and James R. Kozloski ", title = "Efficient Segmentation in Multi-Layer Oscillatory Networks", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0818.pdf}, url = {}, size = {}, abstract = {In earlier work, we derived the dynamical behavior of a network of oscillatory units described by the amplitude and phase of oscillations. The dynamics were derived from an objective function that rewards both the faithfulness and the sparseness of representation. After unsupervised learning, the network is capable of separating mixtures of inputs, and also segmenting the inputs into components that most contribute to the classification of a given input object.In the current paper, we extend our analysis to multi-layer networks, and demonstrate that the dynamical equations derived earlier can be successfully applied to multi-layer networks. The topological connectivity between the different layers are derived from biological observations in primate visual cortex, and consist of receptive fields that are topographically mapped between layers. We explore the role of feedback connections, and show that increasing the diffusivity of feedback connections significantly improves segmentation performance, but does not affect separation performance. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Nitta:2008:ijcnn, author = "Tohru Nitta and Sven Buchholz", title = "On the Decision Boundaries of Hyperbolic Neurons", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0820.pdf}, url = {}, size = {}, abstract = {In this paper, the basic properties, especially decision boundary, of the hyperbolic neurons used in the hyperbolic neural networks are investigated. And also, a nonsplit hyperbolic sigmoid activation function is proposed. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Nguyen:2008:ijcnn, author = "G. H. Nguyen and A. Bouzerdoum and S. L. Phung", title = "Efficient Supervised Learning with Reduced Training Exemplars", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0823.pdf}, url = {}, size = {}, abstract = {In this article, we propose a new supervised learning approach for pattern classification applications involving large or imbalanced data sets. In this approach, a clustering technique is employed to reduce the original training set into a smaller set of representative training exemplars, represented by weighted cluster centers and their target outputs. Based on the proposed learning approach, two training algorithms are derived for feed-forward neural networks. These algorithms are implemented and tested on two pattern classification applications - skin detection and image classification. Experimental results show that with the proposed learning approach, it is possible to design networks in a fraction of time taken by the standard learning approach, without compromising the generalization ability and overall classification performance. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Sharma:2008:ijcnn, author = "Anand Sharma and Anthony Kuh", title = "Class Document Frequency as a Learned Feature for Text Categorization", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0824.pdf}, url = {}, size = {}, abstract = {Document classification uses different types of word weightings as features for representation of documents. In our findings we find the class document frequency, dfc, of a word is the most important feature in document classification. Machine learning algorithms trained with dfc of words show similar performance in terms of correct classification of test documents when compared to more complicated features. The importance of dfc is further verified when simple algorithms developed solely on the basis of dfc shows performance that compares closely with that of more complex machine learning algorithms. We also found improved performance when the dfc of links of documents in a class is used along with the dfc of the words of the document. We compared the algorithms for showing the importance of dfc on the Reuters-21578 text categorization test classification and the Cora data set. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Lee4:2008:ijcnn, author = "Hong Lee and Brijesh Verma", title = "A Novel Multiple Experts and Fusion Based Segmentation Algorithm for Cursive Handwriting Recognition", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0825.pdf}, url = {}, size = {}, abstract = {This paper presents a novel segmentation algorithm for offline cursive handwriting recognition. An over-segmentation algorithm is introduced to dissect the words from handwritten text based on the pixel density between upper and lower baselines. Each segment from the over-segmentation is passed to a multiple expert-based validation process. First expert compares the total foreground pixel of the segmentation point to a threshold value. The threshold is set and calculated before the segmentation by scanning the stroke components in the word. Second expert checks for closed areas such as holes. Third expert validates segmentation points using a neural voting approach which is trained on segmented characters before validation process starts. Final expert is based on oversized segment analysis to detect possible missed segmentation points. The proposed algorithm has been implemented and the experiments on cursive handwritten text have been conducted. The results of the experiments are very promising and the overall performance of the algorithm is more effective than the other existing segmentation algorithms. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Hsu3:2008:ijcnn, author = "Chi-I Hsu and Meng-Long Shih and Biing-Wen Huang and Bing-Yi Lin and Chun-Nan Lin", title = "Combining LISREL and Bayesian Network to Predict Tourism Loyalty", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0826.pdf}, url = {}, size = {}, abstract = {This study proposes an analytic approach that combines LISREL and Bayesian networks (BN) to examine factors influencing tourism loyalty and predict a tourist's loyalty level. LISREL is used to verify the hypothesized relationships proposed in the research model. Subsequently, the supported relationships are used as the BN network structure for prediction. 452 valid samples were collected from tourists with the tour experience of the Toyugi hot spring resort, Taiwan. Compared with other prediction methods, our approach yielded better results than those of back-propagation neural networks (BPN) or classification and regression trees (CART) for 10-fold cross-validation. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Zhang9:2008:ijcnn, author = "Jin Zhang and Guang Li and Meng Hu and Jiaojie Li and Zhiyuan Luo", title = "Recognition of Hypoxia EEG with a Preset Confidence Level Based on EEG Analysis", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0830.pdf}, url = {}, size = {}, abstract = {Though the olfactory model entitled KIII has been widely used to pattern recognition, it only can give bare prediction. Combining KIII model with the transductive confidence machine, a novel method to recognize hypoxia electroencephalogram (EEG) with a preset confidence level is proposed in this paper. This method can make prediction with confidence measure rather than bare prediction. The experimental results of classifying normal and hypoxia EEGs show that the method can set confidence level in advance for every prediction to control the risk of error effectively. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Ninomiya:2008:ijcnn, author = "Hiroshi Ninomiya and Qi-Jun Zhang", title = "Particle with Ability of Local Search Swarm Optimization: PALSO for Training of Feedforward Neural Networks", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0831.pdf}, url = {}, size = {}, abstract = {This paper describes a new technique for training feedforward neural networks. We employ the proposed algorithm for robust neural network training purpose. Conventional neural network training algorithms based on the gradient descent often encounter local minima problems. Recently, some evolutionary algorithms are getting a lot more attention about global search ability but are less-accurate for complicated training task of neural networks. The proposed technique hybridizes local training algorithm based on quasi-Newton method with a recent global optimization algorithm called Particle Swarm Optimization (PSO). The proposed technique provides higher global convergence property than the conventional global optimization technique. Neural network training for some benchmark problems is presented to demonstrate the proposed algorithm. The proposed algorithm achieves more accurate and robust training results than the quasi-Newton method and the conventional PSOs. }, keywords = { Feedforward neural networks, Particle swarm optimization, quasi-Newton method, Hybrid algorithm }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Brajard:2008:ijcnn, author = "Julien Brajard and Fouad Badran and Michel Crepon and Sylvie Thiria", title = "Validation of Model Simulations with Respect to in Situ Observations by the use of Probabilistic Estimations", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0836.pdf}, url = {}, size = {}, abstract = {The present work addresses the problem of validation of a synthetic dataset with respect to observations. It gives an index that determines locally how much a region of the synthetic dataset fits the observations. The method uses an estimation of the probability density function computed with the probabilistic self-organizing maps. Then, an index F was defined to quantify the areas of the synthetic datasets that correspond to the observations.The method was first applied to a ''toy'' example in 2 dimensions to see its potentiality and then applied to two real datasets of optics measurements of the surface ocean. The method allowed to characterize some simulations that have not been encountered during ship campaigns. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Achler:2008:ijcnn, author = "Tsvi Achler and Cyrus Omar and Eyal Amir", title = "Shedding Weights: More with Less", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0837.pdf}, url = {}, size = {}, abstract = {Traditional connectionist models place an emphasis on learned weights. Based on neurobiological evidence, a new approach is developed and experimentally shown to be more robust for disambiguating novel combinations of stimuli. It does not require variable weights and avoids many training related issues. This approach is compared with traditional weight-learning methods. The network is better able to function in different scenarios and can recognize multiple stimuli even if it is only trained on single stimuli. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Huynh:2008:ijcnn, author = "Hieu Trung Huynh and Yonggwan Won", title = "Evolutionary Algorithm for Training Compact Single Hidden Layer Feedforward Neural Networks", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0839.pdf}, url = {}, size = {}, abstract = {An effective training algorithm called extreme learning machine (ELM) has recently proposed for single hidden layer feedforward neural networks (SLFNs). It randomly chooses the input weights and hidden layer biases, and analytically determines the output weights by a simple matrix-inversion operation. This algorithm can achieve good performance at extremely high learning speed. However, it may require a large number of hidden units due to non-optimal input weights and hidden layer biases. In this paper, we propose a new approach, evolutionary least-squares extreme learning machine (ELS-ELM), to determine the input weights and biases of hidden units using the differential evolution algorithm in which the initial generation is generated not by random selection but by a least squares scheme. Experimental results for function approximation show that this approach can obtain good generalization performance with compact networks. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Turchetti:2008:ijcnn, author = "Claudio Turchetti and Francesco Gianfelici and Giorgio Biagetti and Paolo Crippa ", title = "A Computational Intelligence Technique for the Identification of Non-Linear Non-Stationary Systems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0840.pdf}, url = {}, size = {}, abstract = {This paper addresses nonlinear nonstationary system identification from stimulus-response data, a problem concerning a large variety of applications, in dynamic control as well as in signal processing, communications, physiological system modelling and so on. Among the different methods suggested in the vast literature for nonlinear system modelling, the ones based on the Volterra series and the Neural Networks are the most commonly used. However, a strong limitation for the applicability of these methods lies in the necessary property of stationarity, an assumption that cannot be considered as valid in general and strongly affecting the validity of results. Another weakness of the approaches currently used is that they refer to differential systems, thus being unsuitable to model systems described by integral equations. A computational intelligence technique that exploits the potentialities of both the Karhunen-Loève Transform (KLT) and Neural Networks (NNs) representation and without any of the limitations of the previous approaches is suggested in this paper. The technique is suitable for modelling the wide class of systems described by nonlinear nonstationary functionals, thus including both differential and integral systems. It takes advantage of the KLT separable kernel representation that is able to separate the dynamic and static behaviours of the system as two distinct components, and the approximation property of NNs for the identification of the nonlinear no-memory component. To validate the suggested technique comparisons with experimental results on both nonlinear nonstationary differential and integral systems are reported. }, keywords = { Non-Linear Non-Stationary System Identification (NLNSSI), Karhunen-Lo`eve Transform (KLT), Statistical Signal Processing, Polynomial Approximation, Volterra Series (VS), Lee-Schetzen Method.}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Theera-Umpon:2008:ijcnn, author = "Nipon Theera-Umpon and Sansanee Auephanwiriyakul and Sitawit Suteepohnwiroj and Kittichai Wantanajittikul", title = "River Basin Flood Prediction Using Support Vector Machines", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0841.pdf}, url = {}, size = {}, abstract = {This paper presents a river flood prediction technique using support vector machine (SVM). We investigated the 2-year data covering 2005 and 2006 and 7 crucial river floods that occurred in the downtown of Chiang Mai, Thailand. Past and current river levels of the 3 gauging stations are used as the input data of the SVM models to predict the river levels at the downtown station in 1 hour and 7 hours in advance. The performances of the SVM models are compared with that of the multilayer perceptrons (MLP) models. The experimental results show that the SVM models can perform better than the MLP models. Moreover, the results from the blind test sets demonstrate that the SVM models are appropriate for warning people before flood events. The proposed SVM prediction models are also implemented in a real-world flood warning system. The predicted river levels are accessible to public via a web site, electronic billboards, and warning stations all over the city. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Koskimäki:2008:ijcnn, author = "Heli Koskimäki and Ilmari Juutilainen and Perttu Laurinen and Juha Roning ", title = "Two-level Clustering Approach to Training Data Instance Selection: A Case Study For the Steel Industry", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0842.pdf}, url = {}, size = {}, abstract = {Nowadays, huge amounts of information from different industrial processes are stored into databases and companies can improve their production efficiency by mining some new knowledge from this information. However, when these databases becomes too large, it is not efficient to process all the available data with practical data mining applications. As a solution, different approaches for intelligent selection of training data for model fitting have to be developed. In this article, training instances are selected to fit predictive regression models developed for optimization of the steel manufacturing process settings beforehand, and the selection is approached from a clustering point of view. Because basic k-means clustering was found to consume too much time and memory for the purpose, a new algorithm was developed to divide the data coarsely, after which k-means clustering could be performed. The instances were selected using the cluster structure by weighting more the observations from scattered and separated clusters. The study shows that by using this kind of approach to data set selection, the prediction accuracy of the models will get even better. It was noticed that only a quarter of the data, selected with our approach, could be used to achieve results comparable with a reference case, while the procedure can be easily developed for an actual industrial environment. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Zhang10:2008:ijcnn, author = "Qing Zhang and Minho Lee", title = "Emotion Recognition in Natural Scene Images Based on Brain Activity and Gist", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0843.pdf}, url = {}, size = {}, abstract = {Artificial emotion study will be of utmost importance in future artificial intelligence research. In this paper, an emotion understanding system based on brain activity and ''GIST'' is newly proposed to categorize emotions reflected by natural scenes. According to the strong relationship of human emotion and the brain activity, functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) are used to analyze and classify emotional states stimulated by a natural scene. The ''GIST'' is used to represent the emotional gist of the natural scene. In other words, by taking the way human brain responding to the same stimulus into consideration, a machine will be able to visually extract the emotional features of natural scenes and achieve interaction with a human in terms of emotional sharing. The experimental results show that positive and negative emotions can be distinguished, and a monkey robot head that can share emotion with human subject during watching an image is implemented. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Zhang11:2008:ijcnn, author = "Jia-Rui Zhang and Shih-Yu Chiu and Leu-Shing Lan ", title = "Non-Uniqueness of Solutions of 1-Norm Support Vector Classification in Dual Form", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0844.pdf}, url = {}, size = {}, abstract = {Most of previous research efforts on support vector machines (SVMs) were directed toward efficient implementations and practical applications. In this work, we concentrate on a different aspect of SVMs. Specifically, we investigate the non-uniqueness of SVM solutions. The key features of this work include (1) we concentrate on the cases where the dual solutions are not unique, whereas the primal solutions are unique; (2) our test for non- uniqueness can be directly applied to data points without solving the SVC optimization problem, namely, the non-uniqueness information is obtained before any numerical optimization procedure is employed. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Thongkam:2008:ijcnn, author = "Jaree Thongkam and Guandong Xu and Yanchun Zhang", title = "AdaBoost Algorithm with Random Forests for Predicting Breast Cancer Survivability", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0846.pdf}, url = {}, size = {}, abstract = {In this paper we propose a combination of the AdaBoost and random forests algorithms for constructing a breast cancer survivability prediction model. We use random forests as a weak learner of AdaBoost for selecting the high weight instances during the boosting process to improve accuracy, stability and to reduce overfitting problems. The capability of this hybrid method is evaluated using basic performance measurements (e.g., accuracy, sensitivity and specificity), Receiver Operating Characteristic (ROC) curve and Area Under the receiver operating characteristic Curve (AUC). Experimental results indicate that the proposed method outperforms a single classifier and other combined classifiers for the breast cancer survivability prediction. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Molter:2008:ijcnn, author = "Colin Molter and David Colliaux and Yoko Yamaguchi", title = "Working Memory and Spontaneous Activity of Cell Assemblies. A Biologically Motivated Computational Model", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0849.pdf}, url = {}, size = {}, abstract = {Many cognitive tasks require the ability to maintain and manipulate simultaneously several chunks of information. Numerous neurobiological observations have reported that this ability, known as the working memory, is strongly associated with the activity of the prefrontal cortex. Furthermore, during resting state, the spontaneous activity of the cortex exhibits exquisite spatiotemporal patterns sharing similar features with the ones observed during specific memory tasks.}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Floares:2008:ijcnn, author = "Alexandru George Floares", title = "Automatic Inferring Drug Gene Regulatory Networks with Missing Information Using Neural Networks and Genetic Programming", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0852.pdf}, url = {}, size = {}, abstract = {Automatically inferring drug gene regulatory networks models from microarray time series data is a challenging task. The ordinary differential equations models are sensible, but difficult to build. We extended our reverse engineering algorithm for gene networks (RODES), based on genetic programming, by adding a neural networks feedback linearisation component. Thus, RODES automatically discovers the structure, estimate the parameter, and identify the molecular mechanisms, even when information is missing from the data. It produces systems of ordinary differential equations from experimental or simulated microarray time series data. On simulated data the accuracy and the CPU time were very good. This is due to reducing the reversing of an ordinary differential equations system to that of individual algebraic equations, and to the possibility of incorporating common a priori knowledge. To our knowledge, this is the first realistic reverse engineering algorithm, based on genetic programming and neural networks, applicable to large gene networks. }, keywords = {genetic algorithms, genetic programming}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Tanaka:2008:ijcnn, author = "Gouhei Tanaka and Kazuyuki Aihara", title = "Complex-Valued Multistate Associative Memory with Nonlinear Multilevel Functions for Gray-Level Image Reconstruction", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0854.pdf}, url = {}, size = {}, abstract = {The complex-signum function has been widely used as an activation function in complex-valued recurrent neural networks for multistate associative memory. This paper presents two alternative activation functions with circularity. One is the complex-sigmoid function based on a multilevel sigmoid function defined on a circle. The other is a characteristic of a bifurcating neuron represented by a circle map. The performance of the complex-valued neural networks with the two kinds of activation functions is investigated in multistate associative memory tests. In both networks, the connection weights to store the memory patterns are determined by the generalised projection rule. We also demonstrate gray-level image reconstruction as a possible application of the proposed methods. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Chang:2008:ijcnn, author = "Chuan-Yu Chang and Ming-Feng Tsai and Shao-Jer Chen", title = "Classification of the Thyroid Nodules Using Support Vector Machines", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0855.pdf}, url = {}, size = {}, abstract = {Most of the thyroid nodules are heterogeneous with various internal components, which confuse many radiologists and physicians with their various echo patterns in thyroid nodules. A lot of texture extraction methods were used to characterise the thyroid nodules. Accordingly, the thyroid nodules could be classified by the corresponding textural features. In this paper, five support vector machines (SVM) were adopted to select the significant textural features and to classify the nodular lesions of thyroid. Experimental results showed the proposed method classifies the thyroid nodules correctly and efficiently. The comparison results demonstrated that the capability of feature selection of the proposed method was similar to the sequential floating forward selection (SFFS) method. However, the proposed method is faster than the SFFS method. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Muslim:2008:ijcnn, author = "M. Aziz Muslim and Masumi Ishikawa", title = "Formation of Graph-based Maps for Mobile Robots using Hidden Markov Models", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0856.pdf}, url = {}, size = {}, abstract = {Ambiguity in sensory-motor signals from a mobile robot due mainly to noise and fluctuation makes a deterministic approach unsatisfactory. In this paper, a stochastic approach based-on Hidden Markov Models (HMMs) is proposed to recognize environment of a mobile robot. From this recognition a graph-based map is formed. Graph-based maps are important in decreasing memory and the computational cost. Two methods for constructing graph-based maps are proposed. The former is to estimate HMMs based on quantized sensory-motor signals. The latter is to estimate HMMs based on a sequence of labels obtained by modular network SOM (mnSOM). Although mnSOM learns non-linear dynamics of sensory-motor signals, it still generates labels from each subsequence separately. This might not be robust, because resulting sequence of labels may rapidly change, which rarely occurs in the real world. This motivates us to combine mnSOM and HMM to realize more robust segmentation of the environment. The resulting HMMs can be converted into a graph-based map in a straightforward way. The resulting graph-based map is also useful for goal seeking. Simulation results demonstrate that the proposed method can construct graph-based maps effectively, and can perform goal seeking in the changing environment. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Hsieh:2008:ijcnn, author = "Ji-Lung Hsieh and Chuen-Tsai Sun", title = "Building a Player Strategy Model by Analyzing Replays of Real-Time Strategy Games", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0857.pdf}, url = {}, size = {}, abstract = {Developing computer-controlled groups to engage in combat, control the use of limited resources, and create units and buildings in Real-Time Strategy(RTS) Games is a novel application in game AI. However, tightly controlled online commercial game pose challenges to researchers interested in observing player activities, constructing player strategy models, and developing practical AI technology in them. Instead of setting up new programming environments or building a large amount of agent's decision rules by player's experience for conducting real-time AI research, the authors use replays of the commercial RTS game StarCraft to evaluate human player behaviors and to construct an intelligent system to learn human-like decisions and behaviors. A case-based reasoning approach was applied for the purpose of training our system to learn and predict player strategies. Our analysis indicates that the proposed system is capable of learning and predicting individual player strategies, and that players provide evidence of their personal characteristics through their building construction order. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Tahersima:2008:ijcnn, author = "Fatemeh Tahersima and Babak Nadjar Araabi", title = "Approximation of a Map and its Derivatives with an RBF Network Using Input-Output Clustering", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0858.pdf}, url = {}, size = {}, abstract = {Radial Basis Function Networks (RBFNs) are widely used in curve-fitting problems and nonlinear dynamical systems modelling. Using the gradient of the function during the training phase leads to a smooth approximation of both the function itself, and its derivatives. The knowledge about gradient of the function in some identification and control tasks is desired, particularly when the stability and robustness of the system are studied. In this paper, a new clustering based algorithm for learning an Input-Output map along with its derivatives using RBFN is proposed. The input-output clustering (IOC) algorithm for the training of an RBFN is modified to improve the performance of the network in approximating a nonlinear single-input single-output map along with its derivatives using a set of input-output data and the first derivative of the function in each data point. The advantage of the proposed algorithm, in comparison with orthogonal least square (OLS), is demonstrated with an example in the field of data interpolation. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Szymański:2008:ijcnn, author = "Julian Szymański and Włodzisław Duch", title = "Knowledge Representation and Acquisition for Large-Scale Semantic Memory", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0859.pdf}, url = {}, size = {}, abstract = {Acquisition and representation of semantic concepts is a necessary requirement for the understanding of natural languages by cognitive systems. Word games provide an interesting opportunity for semantic knowledge acquisition that may be used to construct semantic memory. A task-dependent architecture of the knowledge base inspired by psycholinguistic theories of human cognition process is introduced. The core of the system is an algorithm for semantic search using a simplified vector representation of concepts. Based on this algorithm a 20 questions game has been implemented. This implementation provides an example of an application of the semantic memory, but also allows for testing the linguistic competence of the system. A web portal with Haptek-based talking head interface facilitates acquisition of a new knowledge while playing the game and engaging in dialogs with users. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Geng:2008:ijcnn, author = "Yang Geng and Jongdae Jung and Donggug Seol", title = "Sound-Source Localization System Based on Neural Network for Mobile Robots", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0860.pdf}, url = {}, size = {}, abstract = {In this paper we described a sound-source localization (SSL) system which can be applied to mobile robot and automatic control systems. A novel approach of using artificial neural network was proposed to obtain the horizontal direction angle (azimuth) of the sound source. According to humanoid characteristic only two microphones, which were attached symmetrically on both sides of the robot as its two ears, were used and tested. Sound wave signals were received from both microphones and analyzed directly by neural network. Two sets of training data were collected and used to train neural network, according to which, different performances of the SSL system were verified and compared. The strong recognizing and calculating ability of neural network made the system work effectively and accurately. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Jr.:2008:ijcnn, author = "Euclides Peres Farias Jr. and Júlio Cesar Nievola ", title = "Comparative of Data Base Evolution in Rule Association Algorithms in Incremental and Conventional Way", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0861.pdf}, url = {}, size = {}, abstract = {Many results in the literature indicate that the incremental approach to association mining leads to gain regarding the time needed to obtain the rules, but there is no evaluation about their quality, compared to non-incremental algorithms. This paper presents the comparison of usage of two typical algorithms representing each approach: A Priori and ZigZag. Execution time clearly shows the advantage of incremental approaches, but when someone needs accurate results concerning the association rules obtained, the matter should be taken with more caution, because the rules obtained are not necessarily in a relation one-to-one, according to the results obtained. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Guo6:2008:ijcnn, author = "Yimo Guo and Zhengguang Xu ", title = "Research on the Cellular Neural Network Template for Translation of Gray-scale Images", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0864.pdf}, url = {}, size = {}, abstract = {A number of templates for image translation using cellular neural network (CNN) have been proposed before. In this paper, all cases of the 3×3 uncoupled CNN template for translation of gray-scale images are investigated and their functions are discussed. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Seno:2008:ijcnn, author = "Bernardo Dal Seno and Matteo Matteucci", title = "A Genetic Algorithm for Automatic Feature Extraction in P300 Detection", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0865.pdf}, url = {}, size = {}, abstract = {A Brain-Computer Interface (BCI) is an interface that directly analyzes brain activity to transform user intentions into commands. Many known techniques use the P300 eventrelated potential by extracting relevant features from the EEG signal and feeding those features into a classifier. In these approaches, feature extraction becomes the key point, and doing it by hand can be at the same time cumbersome and suboptimal. In this paper we face the issue of feature extraction by using a genetic algorithm able to retrieve the relevant aspects of the signal to be classified in an automatic fashion. We have applied this algorithm to publicly available data sets (a BCI competition) and data collected in our lab, obtaining with a simple logistic classifier results comparable to the best algorithms in the literature. In addition, the features extracted by the algorithm can be interpreted in terms of signal characteristics that are contributing to the success of classification, giving new insights for brain activity investigation. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Zurada:2008:ijcnn, author = "Jacek M. Zurada and Janusz Wojtusiak and Fahmida Chowdhury and Cedric J. Jeannot and Maciej A. Mazurowski", title = "Computational Intelligence Virtual Community: Framework and Implementation Issues", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0866.pdf}, url = {}, size = {}, abstract = {This paper discusses the framework for virtual collaborative environment for researchers, practitioners, users and learners in the areas of computational intelligence and machine learning (CIML) that is currently developed by our group. It also outlines main features of the community portal under construction that will support communication and sharing of computational resources. In particular, selected aspects of structure of the portal such as common formats of data, models, software, publications and software documentation are discussed. The preliminary portal is available at URL: www.cimlcommunity.org. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Jiang5:2008:ijcnn, author = "Jun Jiang and Horace H S Ip", title = "Active Learning for the Prediction of Phosphorylation Sites", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0867.pdf}, url = {}, size = {}, abstract = {In this paper, we propose several active learning strategies to train classifiers for phosphorylation site prediction. When combined with support vector machine, we show that active learning SVM is able to produce classifiers that give comparable or better phosphorylation site prediction performance than conventional SVM techniques and, at the same time, require a significantly less number of annotated protein training samples. The result has both conceptual and practical implications in protein prediction: it exploits information inherent in the large scale database of non-annotated protein samples and reduces the amount of manual labor required for protein annotation. To the best of our knowledge, active learning has not been explored in phosphorylation sites prediction. Several active learning strategies: single running mode, batch running mode with sample and support vector diversity, were investigated for phosphorylation sites prediction in this work. Our experiments have shown that active learning with SVM is able to reduce the effort of protein annotation by 6.6percent to 25.7percent to yield similar prediction performance as compared with conventional SVM technique. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Ozkaya:2008:ijcnn, author = "N. Ozkaya and S. Sagiroglu", title = "Intelligent Face Mask Prediction System", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0869.pdf}, url = {}, size = {}, abstract = {Biometric based person identification systems are used to provide alternative solutions for security. Although many approaches and algorithms for biometric recognition techniques have been developed and proposed in the literature, relationships among biometric features have not been studied in the field so far. In this study, we have analysed the existence of any relationship between biometric features and we have tried to obtain a biometric feature of a person from another biometric feature of the same person. Consequently, we have designed and introduced a new and intelligent system using a novel approach based on artificial neural networks for generating face masks including eyes, nose and mouth from fingerprints with 0.75 - 3.60 absolute percent errors. Experimental results have demonstrated that it is possible to generate face masks from fingerprints without knowing any information about faces. In addition it is shown that fingerprints and faces are related to each other closely. In spite of the proposed system is initial study and it is still under development, the results are very encouraging and promising. Also proposed work is very important from view of the point that it is a new research area in biometrics. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Hamdani:2008:ijcnn, author = "Tarek M. Hamdani and Adel M. Alimi", title = "Enhancing the Structure and Parameters of the Centers for BBF Fuzzy Neural Network Classifier Construction Based on Data Structure", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0871.pdf}, url = {}, size = {}, abstract = {This paper aims at presenting different strategies for the construction of Beta Basis Function (BBF) Fuzzy Neural Network. These strategies lead to the determination of the network architecture by determining the structure of the hidden layer and parameters of its centers based on data structure. For that, we use Self Organizing Maps (SOM) clustering to construct a mapped structure of the real training data. By analyzing this structure, we proceed to neuron selection. Data sets were also analyzed with the Fuzzy C-Means (FCM) clustering technique to generate fuzzy membership values presenting fuzzy outputs for our Fuzzy Neural model. We propose to estimate the parameters of Beta Basis Function in order to obtain better data coverage. Experimental results show that the use of the proposed technique produces better results. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Docusse:2008:ijcnn, author = "Tiago A. Docusse and Jullyene R. Furlani and Rodolfo P. Romano and Shi-Huang Chen and Norian Marranghello and Aledir S. Pereira", title = "Microcalcification Enhancement and Classification on Mammograms using the Wavelet Transform", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0872.pdf}, url = {}, size = {}, abstract = {This paper presents a method to enhance microcalcifications and classify their borders by applying the wavelet transform. Decomposing an image and removing its low frequency sub-band the microcalcifications are enhanced. Analyzing the effects of perturbations on high frequency subband it's possible to classify its borders as smooth, rugged or undefined. Results show a false positive reduction of 69.27percent using a region growing algorithm. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Jung:2008:ijcnn, author = "Jae-Yoon Jung and Janice I. Glasgow and Stephen H. Scott", title = "A Hierarchical Ensemble Model for Automated Assessment of Stroke Impairment", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0876.pdf}, url = {}, size = {}, abstract = {Assessment of sensory, motor and cognitive function of stroke subjects provide important information to guide patient rehabilitation. As many of the currently used measures are inherently subjective and use course rating scales, here we propose a hierarchical ensemble network that can automatically identify stroke patients and assess their upper limb functionality objectively, based on experimental task data. We compare our neural network ensemble model with ten combinations of different classifiers and ensemble schemes, showing that it significantly outperforms competitors. We also demonstrate that our measure scale is congruent with clinical information, responsive with changes of patients motor function, and reliable in terms of test-retest configuration. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Lu5:2008:ijcnn, author = "Ruei-Shan Lu and Shang-Wu Yu and Yi-Hsien Lin", title = "The Prediction of Applying Smooth Support Vector Regression and Back Propagation Network in Mutual Fund Performance", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0878.pdf}, url = {}, size = {}, abstract = {This study used Smooth Support Vector Regression and Back Propagation Network as the basic theory in study of the mutual fund performance prediction. This paper used return on performance and return on market to make a comparison, and through the risk values, explored each model's advantages and disadvantages. This study used Taiwan's equity fund as the prediction target, the validation study period was January 2004 to December 2004. The empirical results showed that the SSVR application and BPN application can both increase return on investment, and will receive an even better return in the bull market. In addition, applying SSVR prediction model, in the bear market, will also result in excess return, and reduction of the investors' loss. This study thinks that with Smooth Support Vector Regression model and Back Propagation Networking model respectively, according to different risk preferences of investors, investors can, based on their personal risk preferences, choose a suitable prediction model in order to create the excess return in line with the expectations. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Castro:2008:ijcnn, author = "Ana Paula Abrantes de Castro and Jose Demisio Simões da Silva", title = "Restoring Images with a Multiscale Neural Network Based Technique", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0880.pdf}, url = {}, size = {}, abstract = {This paper describes a neural network based multiscale image restoration approach using multilayer perceptron neural networks trained with artificially degraded images of gray level co-centered circles. The main goal of the approach is to make the neural network learn inherent space relations of the degraded pixels in restoring the image. In the conducted experiment, the degradation is simulated by filtering the image with a low pass Gaussian filter and adding noise to the pixels at preestablished rates. Degraded image pixels make the input and nondegraded image pixels make the target output for the supervised learning process. The neural network performs an inverse operation by recovering a quasi-nondegraded image in terms of least squared. The main difference of the present approach to existing ones relies on the fact the space relations are taken from different scales, thus providing correlated space data to the neural network. The approach attempts to develop a simple method that provide good restored versions of degraded images, without the need of a priori knowledge or estimation of the possible image degradation causes. The multiscale operation is simulated by considering different window sizes around a pixel. In the generalization phase the neural network is exposed to indoor, outdoor, and satellite degraded images following the same steps used to degrade the artificial image of circles. The neural network restoration results show the proposed approach is promising and may be used in restoration processes with the advantage it does not need a priori knowledge of the degradation causes. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wyffels:2008:ijcnn, author = "Francis Wyffels and Benjamin Schrauwen and David Verstraeten and Dirk Stroobandt ", title = "Band-Pass Reservoir Computing", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0881.pdf}, url = {}, size = {}, abstract = {Many applications of Reservoir Computing (and other signal processing techniques) have to deal with information processing of signals with multiple time-scales. Classical Reservoir Computing approaches can only cope with multiple frequencies to a limited degree. In this work we investigate reservoirs build of band-pass filter neurons which can be made sensitive to a specified frequency band. We demonstrate that many currently difficult tasks for reservoirs can be handled much better by a band-pass filter reservoir. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Pina:2008:ijcnn, author = "Aloísio Carlos de Pina and Gerson Zaverucha", title = "Combining Attributes to Improve the Performance of Naive Bayes for Regression", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0882.pdf}, url = {}, size = {}, abstract = {Naive Bayes for Regression (NBR) uses the Naive Bayes methodology to numeric prediction tasks. The main reason for its poor performance is the independence assumption. Although many recent researches try to improve the performance of Naive Bayes by relaxing the independence assumption, none of them can be directly applied to the regression framework. The objective of this work is to present a new approach to improve the results of the NBR algorithm, by combining attributes by means of an auxiliary regression algorithm. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Kirk:2008:ijcnn, author = "James S. Kirk ", title = "Chinese Character Identification by Visual Features Using Self-Organizing Map Sets and Relevance Feedback", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0883.pdf}, url = {}, size = {}, abstract = {Because of its ability to condense a data set in a non-linear, dimension-reducing, topology-preseving way, the self-organizing map (SOM) has proven useful in a wide variety of applications. The Chinese Character Identifier (CCI) uses a set of SOMs along with other natural computation tools to address the problem of identifying an unknown Chinese character by its visual features. By repeatedly presenting small sets of Chinese characters to the user and analyzing which characters are chosen as visually similar to the target character, the system is intended to estimate the visual features upon which the user is presently basing his/her notion of visual similarity. An SOM is then chosen that organizes the universe of characters according to the user's feedback. A simple radial basis function network with basis functions defined in the output space of the selected SOM is used to select a set of characters to present to the user next. The result is a trajectory across the 10-dimensional feature space of the Chinese characters in the direction of the target character. The CCI illustrates the promises and the challenges of using a method of seaching high-dimensional data based on relevance feedback that may be termed ``piecewise topography preservation'' (PTP). This paper discusses the application of PTP to a set of 10-dimensional Chinese character data and explains why certain data sets, exemplified by the Chinese character data, pose a problem for the PTP approach. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Johansson:2008:ijcnn, author = "Ulf Johansson and Tuve Lofstrom and Henrik Bostrom", title = "The Problem with Ranking Ensembles Based on Training or Validation Performance", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0884.pdf}, url = {}, size = {}, abstract = {The main purpose of this study was to determine whether it is possible to somehow use results on training or validation data to estimate ensemble performance on novel data. With the specific setup evaluated; i.e. using ensembles built from a pool of independently trained neural networks and targeting diversity only implicitly, the answer is a resounding no. Experimentation, using 13 UCI datasets, shows that there is in general nothing to gain in performance on novel data by choosing an ensemble based on any of the training measures evaluated here. This is despite the fact that the measures evaluated include all the most frequently used; i.e. ensemble training and validation accuracy, base classifier training and validation accuracy, ensemble training and validation AUC and two diversity measures. The main reason is that all ensembles tend to have quite similar performance, unless we deliberately lower the accuracy of the base classifiers. The key consequence is, of course, that a data miner can do no better than picking an ensemble at random. In addition, the results indicate that it is futile to look for an algorithm aimed at optimizing ensemble performance by somehow selecting a subset of available base classifiers. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Nakano2:2008:ijcnn, author = "M. Nakano and S. Karungaru and S. Tsuge and T.Akashi and Y.Mitsukura and M. Fukumi", title = "Face Information Processing by Fast Statistical Learning Algorithm", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0885.pdf}, url = {}, size = {}, abstract = {In this paper, we propose a new statistical learning algorithm. This study quantitatively verifies the effectiveness of its feature extraction performance for face information processing. Simple-FLDA is an algorithm based on a geometrical analysis of the Fisher linear discriminant analysis. As a high-speed feature extraction method, the present algorithm in this paper is the improved version of Simple-FLDA. First of all, the approximated principal component analysis (learning by Simple-PCA) that uses the mean vector of each class is calculated. Next, in order to adjust within-class variance in each class to 0, the vectors in the class are removed. By this processing, it becomes high-speed feature extraction method than Simple-FLDA. The effectiveness is verified by computer simulations using face images. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Ventresca:2008:ijcnn, author = "Mario Ventresca and Hamid Reza Tizhoosh", title = "Numerical Condition of Feedforward Networks with Opposite Transfer Functions", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0886.pdf}, url = {}, size = {}, abstract = {Numerical condition affects the learning speed and accuracy of most artificial neural network learning algorithms. In this paper, we examine the influence of opposite transfer functions on the conditioning of feedforward neural network architectures. The goal is not to discuss a new training algorithm nor error surface geometry, but rather to present characteristics of opposite transfer functions which can be useful for improving existing or to develop new algorithms. Our investigation examines two situations: (1) network initialization, and (2) early stages of the learning process. We provide theoretical motivation for the consideration of opposite transfer functions as a means to improve conditioning during these situations. These theoretical results are validated by experiments on a subset of common benchmark problems. Our results also reveal the potential for opposite transfer functions in other areas of, and related to neural networks. }, keywords = { Numerical condition, ill-conditioning, opposite transfer functions, feedforward.}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Trentin:2008:ijcnn, author = "Edmondo Trentin and Ernesto Di Iorio", title = "Classification of Molecular Structures Made Easy", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0888.pdf}, url = {}, size = {}, abstract = {Several problems in bioinformatics and cheminformatics concern the classification of molecules. Relevant instances are automatic cancer detection/classification, machinelearning pathologic prediction, automatic predictive toxicology, etc. Molecules may be represented in terms of graphical structures in a natural way: each node in the graph can be used to represent an atom, whilst the edges of the graph represent the atom-atom bonds. Labels (in the form of real-valued vectors) are associated with nodes and edges in order to express physical and chemical properties of the corresponding atoms and bonds, respectively. These structured data are expected to contain more information than a traditional (flat) feature vector, information that may strengthen the classification capabilities of a machine learner. This paper investigates the application of a novel Bayesian/connectionist classifier to this graphical pattern recognition task. The approach is much simpler than stateof- the-art machine learning paradigms for graphical/relational learning. It relies on the idea of describing the graph in terms of a binary relation. The posterior probability of a class given the relation is estimated as a function of probabilistic quantities modeled with a neural network, trained over individual vertex pairs in the graph. The popular and challenging Mutagenesis dataset is considered for the experimental evaluation. Despite its simplicity, the technique turns out to yield the highest recognition accuracies to date on the complete (friendly + unfriendly) dataset, outperforming complex machines (relational and graph neural nets, kernels for graphs, inductive logic programming techniques, etc.). Some preliminary chemical/biological implications are eventually hypothesized in the light of the results obtained. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(R.:2008:ijcnn, author = "Diego G. Loyola R. ", title = "Climatology Databases using Neural Networks: Application to Global Temperature Profiles", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0889.pdf}, url = {}, size = {}, abstract = {Climatology databases containing geophysical parameters such as temperature, precipitation, ozone, surface albedo, cloud information, etc., are widely used in remote sensing, atmospheric, oceanographic, climate research, and operational environmental forecasting communities.Climatology databases are usually constructed as lookup tables with discrete regular latitude, longitude and time grids. The lookup table climatologies not only require large amount of memory, but also retrieving information from the climatology databases can be very time consuming as it usually requires search and interpolation on a multi-dimensional space.This paper presents a neural network approach for creating climatology databases that overcome the problems of the classical lookup tables. The neural networks provide in addition to the output parameters their first-order partial derivatives (Jacobian matrix) required for statistical analysis and retrieval algorithms. Moreover, the neural networks can use new proxies to efficiently fetch data from the climatology; the parameters obtained are therefore closer to the real state of the Earth's system. The proposed approach is applied to the development of a neural network based temperature profile climatology and the results are discussed in detail. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Tsonos:2008:ijcnn, author = "Dimitrios Tsonos and Kalliopi Ikospentaki and Georgios Kouroupetrolgou", title = "Towards Modeling of Readers' Emotional State Response for the Automated Annotation of Documents", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0890.pdf}, url = {}, size = {}, abstract = {This work presents an experimental study towards modeling the readers' emotional state as a response to font and typesetting elements of documents presented on a LCD display. Any content and/or domain dependent information was excluded from the document that was tested. An automated computer-based experimental procedure has been followed based on the paper-and-pencil Self Assessment Manikin Test. The typographic elements: font colour, size, type, background colour and the typesetting elements: bold, italics, bold-italics, along with their combinations are studied. The results indicate that the combination of text and background colour has the same impact across languages; the font size has a typical behavior. Readers' Emotional State, induced by the typesetting elements and the font type, probably depends on the current as well as on the previously displayed stimuli. A cognitive-based XML architecture for real-time extraction of readers' emotional state relatively to documents' typographic elements is also presented. The results of this paper can be considered when transforming emotionally annotated documents into acoustic modality. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Zhang12:2008:ijcnn, author = "Byoung-Tak Zhang ", title = "Cognitive Learning and the Multimodal Memory Game: Toward Human-Level Machine Learning", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0891.pdf}, url = {}, size = {}, abstract = {Machine learning has made great progress during the last decades and is being deployed in a wide range of applications. However, current machine learning techniques are far from sufficient for achieving human-level intelligence. Here we identify the properties of learners required for human-level intelligence and suggest a new direction of machine learning research, i.e. the cognitive learning approach, that takes into account the recent findings in brain and cognitive sciences. In particular, we suggest two fundamental principles to achieve human-level machine learning: continuity (forming a lifelong memory continuously) and glocality (organizing a plastic structure of localized micromodules connected globally). We then propose a multimodal memory game as a research platform to study cognitive learning architectures and algorithms, where the machine learner and two human players question and answer about the scenes and dialogues after watching the movies. Concrete experimental results are presented to illustrate the usefulness of the game and the cognitive learning framework for studying human-level learning and intelligence. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Chinellato:2008:ijcnn, author = "Eris Chinellato and Beata J. Grzyb and Angel P. del Pobil", title = "Brain Mechanisms for Robotic Object Pose Estimation", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0892.pdf}, url = {}, size = {}, abstract = {Integration of multiple visual cues provides natural systems with superior abilities in dealing with nearby objects. This research is aimed at verifying if robotic systems could also benefit from the merging of different visual cues of the same stimulus. A computational model of stereoscopic and perspective orientation estimators, merged according to different criteria, is implemented on a robotic setup and tested in different conditions. Experimental results suggest that the principle of cue integration can make robot sensory systems more reliable and robust. The same results compared with data from human studies show that the model is able to reproduce some well-known neuropsychological effects. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Gorgônio:2008:ijcnn, author = "Flavius L. Gorgônio and Jose Alfredo F. Costa", title = "Parallel Self-Organizing Maps with Application in Clustering Distributed Data", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0893.pdf}, url = {}, size = {}, abstract = {Clustering is the process of discovering groups within multidimensional data, based on similarities, with a minimal, if any, knowledge of their structure. Distributed data clustering is a recent approach to deal with geographically distributed databases, since traditional clustering methods require centering all databases in a single dataset. Moreover, current privacy requirements in distributed databases demand algorithms with the ability to process clustering securely. Among the unsupervised neural network models, the selforganizing map (SOM) plays a major role. SOM features include information compression while trying to preserve the topological and metric relationship of the primary data space. This paper presents a strategy for efficient cluster analysis in geographically distributed databases using SOM networks. Local datasets relative to database vertical partitions are applied to distinct maps in order to obtain partial views of the existing clusters. Units of each local map are chosen to represent original data and are sent to a central site, which performs a fusion of the partial results. Experimental results are presented for different datasets. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Songsiri:2008:ijcnn, author = "Patoomsiri Songsiri and Boonserm Kijsirikul and Thimaporn Phetkaew", title = "Information-Based Dichotomization: A Method for Multiclass Support Vector Machines", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0895.pdf}, url = {}, size = {}, abstract = {Approaches for solving a multiclass classification problem by Support Vector Machines (SVMs) are typically to consider the problem as combination of two-class classification problems. Previous approaches have some limitations in classification accuracy and evaluation time. This paper proposes a novel method that employs information-based dichotomization for constructing a binary classification tree. Each node of the tree is a binary SVM with the minimum entropy. Our method can reduce the number of binary SVMs used in the classification to the logarithm of the number of classes which is lower than previous methods. The experimental results show that the proposed method takes lower evaluation time while it maintains accuracy compared to other methods.}, keywords = { Information-Based Dichotomization, Multiclass Support Vector Machines, Entropy, }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Silva6:2008:ijcnn, author = "Luciana L. Silva and Mario L. Tronco and Henrique A. Vian and Giovana Pellinson and Arthur J. V. Porto", title = "Environment Mapping for Mobile Robots Navigation Using Hierarchical Neural Network and Omnivision", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0896.pdf}, url = {}, size = {}, abstract = {Autonomous robots must be able to learn and maintain models of their environments. In this context, the present work considers techniques for the classification and extraction of features from images in joined with artificial neural networks in order to use them in the system of mapping and localization of the mobile robot of Laboratory of Automation and Evolutive Computer (LACE). To do this, the robot uses a sensorial system composed for ultrasound sensors and a catadioptric vision system formed by a camera and a conical mirror. The mapping system is composed by three modules. Two of them will be presented in this paper: the classifier and the characterizer module. The first module uses a hierarchical neural network to do the classification; the second uses techiniques of extraction of attributes of images and recognition of invariant patterns extracted from the places images set. The neural network of the classifier module is structured in two layers, reason and intuition, and is trained to classify each place explored for the robot amongst four predefine classes. The final result of the exploration is the construction of a topological map of the explored environment. Results gotten through the simulation of the both modules of the mapping system will be presented in this paper. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Koufakou:2008:ijcnn, author = "Anna Koufakou and Jimmy Secretan and John Reeder and Michael Georgiopoulos", title = "Fast Parallel Outlier Detection for Categorical Datasets using MapReduce", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0897.pdf}, url = {}, size = {}, abstract = {Outlier detection has received considerable attention in many applications, such as detecting network attacks or credit card fraud. The massive datasets currently available for mining in some of these outlier detection applications require large parallel systems, and consequently parallelizable outlier detection methods. Most existing outlier detection methods assume that all of the attributes of a dataset are numerical, usually have a quadratic time complexity with respect to the number of points in the dataset, and quite often they require multiple dataset scans. In this paper, we propose a fast parallel outlier detection strategy based on the Attribute Value Frequency (AVF) approach, a high-speed, scalable outlier detection method for categorical data that is inherently easy to parallelize. Our proposed solution, MR-AVF, is based on the MapReduce paradigm for parallel programming, which offers load balancing and fault tolerance. MR-AVF is particularly simple to develop and it is shown to be highly scalable with respect to the number of cluster nodes. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Granger:2008:ijcnn, author = "Eric Granger and Jean-François Connolly and Robert Sabourin", title = "A Comparison of Fuzzy ARTMAP and Gaussian ARTMAP Neural Networks for Incremental Learning", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0900.pdf}, url = {}, size = {}, abstract = {Automatic pattern classifiers that allow for incremental learning can adapt internal class models efficiently in response to new information, without having to retrain from the start using all the cumulative training data. In this paper, the performance of two such classifiers - the fuzzy ARTMAP and Gaussian ARTMAP neural networks - are characterize and compared for supervised incremental learning in environments where class distributions are fixed. Their potential for incremental learning of new blocks of training data, after previously been trained, is assessed in terms of generalization error and resource requirements, for several synthetic pattern recognition problems. The advantages and drawbacks of these architectures are discussed for incremental learning with different data block sizes and data set structures. Overall results indicate that Gaussian ARTMAP is the more suitable for incremental learning as it usually provides an error rate that is comparable to that of batch learning for the data sets, and for a wide range of training block sizes. The better performance is a result of the representation of categories as Gaussian distributions, and of using categoryspecific learning rate that decreases during the training process. With all the data sets, the error rate obtained by training through incremental learning is usually significantly higher than through batch learning for fuzzy ARTMAP. Training fuzzy ARTMAP and Gaussian ARTMAP through incremental learning often requires fewer training epochs to converge, and leads to more compact networks. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Sabo:2008:ijcnn, author = "Devin Sabo and Xiao-Hua Yu", title = "A New Pruning Algorithm for Neural Network Dimension Analysis", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0901.pdf}, url = {}, size = {}, abstract = {The choice of network dimension is a fundamental issue in neural network applications. An optimal neural network topology not only reduces the computational complexity, but also improves its generalization capacity. In this research, a new pruning algorithm based on cross validation and sensitivity analysis is developed and compared with three existing pruning algorithms on various pattern classification problems. Computer simulation results show the network size can be significantly reduced using this new algorithm while the neural network still maintains satisfactory generalization accuracy. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Voichiţa:2008:ijcnn, author = "Călin Voichiţa and Purvesh Khatri and Sorin Drăghici", title = "Identifying Uncertainty Regions in Support Vector Machines using Geometric Margin and Convex Hulls", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0902.pdf}, url = {}, size = {}, abstract = {Like most classification techniques, the existing Support Vector Machines (SVM) approaches are challenged to correctly classify their input when the data points are either very close to the decision boundary or very dissimilar from the training data set. In both situations, most classifiers including SVMs will still give a prediction by assigning the test point to one of the classes. However, when a test instance is very close to the decision boundary, the side of the boundary on which the instance lies, and hence the predicted class, will depend in many instances more on the choices of the tuning or training parameters rather than a clear differences in features. Furthermore, if a test instance is substantially different from all instances used during the training, the classical SVM classifiers will still assign it to a class although there is little evidence to support such assignment. In both cases, it is very useful for a classifier to be able to assess its ability to classify a given instance by identifying those regions of the feature space in which the class assignments are less certain. In this paper, we propose two novel approaches based on: i) a geometric uncertainty margin and ii) the convex hulls of the training points in the feature space. Our proposed techniques improve upon the existing SVM-based approaches by adding the ability to identify ``uncertainty'' areas where the assignment of a test instance to a class cannot be guaranteed. We illustrate both the problems and our novel techniques on the Iris data set from the UCI machine learning repository. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Bernal-Urbina:2008:ijcnn, author = "M. Bernal-Urbina and A. Flores-Mendez", title = "Time Series Forecasting through Polynomial Artificial Neural Networks and Genetic Programming", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0903.pdf}, url = {}, size = {}, abstract = {The Polynomial Artificial Neural Network (PANN) has shown to be a powerful Network for time series forecasting. Moreover, the PANN has the advantage that it encodes the information about the nature of the time series in its architecture. However, the problem with this type of network is that the terms needed to be analysed grow exponentially depending on the degree selected for the polynomial approximation. In this paper, a novel optimisation algorithm that determines the architecture of the PANN through Genetic Programming is presented. Some examples of non linear time series are included and the results are compared with those obtained by PANN with Genetic Algorithm. }, keywords = {genetic algorithms, genetic programming}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Taylor:2008:ijcnn, author = "Dennis Taylor and Brett Bojduj and Franz Kurfess", title = "Towards Using Neural Networks to Perform Object-Oriented Function Approximation", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0905.pdf}, url = {}, size = {}, abstract = {Many computational methods are based on the manipulation of entities with internal structure, such as objects, records, or data structures. Most conventional approaches based on neural networks have problems dealing with such structured entities. The algorithms presented in this paper represent a novel approach to neural-symbolic integration that allows for symbolic data in the form of objects to be translated to a scalar representation that can then be used by connectionist systems. We present the implementation of two translation algorithms that aid in performing object-oriented function approximation. We argue that objects provide an abstract representation of data that is well suited for the input and output of neural networks, as well as other statistical learning techniques. By examining the results of a simple sorting example, we illustrate the efficacy of these techniques. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Potter:2008:ijcnn, author = "Chris Potter and Ganesh K. Venayagamoorthy", title = "MIMO Beam-Forming with Neural Network Channel Prediction Trained By a Novel PSO-EA-DEPSO Algorithm", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0906.pdf}, url = {}, size = {}, abstract = {A new hybrid algorithm based on particle swarm optimization (PSO), evolutionary algorithm (EA), and differential evolution (DE) is presented for training a recurrent neural network (RNN) for multiple-input multiple-output (MIMO) channel prediction. The hybrid algorithm is shown to be superior in performance to PSO and differential evolution PSO (DEPSO) for different channel environments. The received signal-to-noise ratio is derived for un-coded and beam-forming MIMO systems to see how the RNN error affects the performance. This error is shown to deteriorate the accuracy of the weak singular modes, making beam-forming more desirable. Bit error rate simulations are performed to validate these results. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Kumar2:2008:ijcnn, author = "Akhilesh Kumar and Finn Tseng and Yan Guo", title = "Hidden-Markov Model Based Sequential Clustering for Autonomous Diagnostics", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0907.pdf}, url = {}, size = {}, abstract = {Despite considerable advances over the last few decades in sensing instrumentation and information technology infrastructure, monitoring and diagnostics technology has not yet found its place in health management of mainstream machinery and equipment. The fundamental reason for this being the mismatch between the growing diversity and complexity of machinery and equipment employed in industry and the historical reliance on ``point-solution'' diagnostic systems that necessitate extensive characterization of the failure modes and mechanisms (something very expensive and tedious). While these point solutions have a role to play, in particular for monitoring highly-critical assets, generic yet adaptive solutions, meaning solutions that are flexible and able to learn on-line, could facilitate large-scale deployment of diagnostic and prognostic technology.We present a novel approach for autonomous diagnostics that employs model-based sequential clustering with hidden-Markov models as a means for measuring similarity of timeseries sensor signals. The proposed method has been tested on a CNC machining test-bed outfitted with thrust-force and torque sensors for monitoring drill-bits. Preliminary results revealed the competitive performance of the method. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Liu13:2008:ijcnn, author = "Qingzhong Liu and Andrew H. Sung", title = "Steganalysis of Multi-Class JPEG Images Based on Expanded Markov Features and Polynomial Fitting", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0909.pdf}, url = {}, size = {}, abstract = {In this article, based on the Markov approach proposed by shi et al. [1], we expand it to the inter-blocks of the DCT domain, calculate the difference of the expanded Markov features between the testing image and the calibrated version, and combine these difference features and the polynomial fitting features on the histogram of the DCT coefficients as detectors. We reasonably improve the detection performance in multi-class JPEG images. We also compare the steganalysis performance among the feature reduction/selection methods based on principal component analysis, singular value decomposition, and Fisher's linear discriminant. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(C:2008:ijcnn, author = "G. Jimenez de la C and Jose A. Ruz-Hernandez and R. Salazar-Mendoza", title = "Obtaining an Optimal Gas Injection Rate for an Oil Production System via Neural Networks", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0911.pdf}, url = {}, size = {}, abstract = {Using a model-based optimization, a neural network model is developed to calculate the optimal values of gas injection rate and oil rate of a gas lift production system. Two cases are analyzed: a) A single well production system and b) A production system composed by two gas lifted wells. For both cases minimizing the objective function the proposed strategy shows the ability of the neural networks to approximate the behavior of an oil production system and to solve optimization problems when a mathematical model is not available. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wei:2008:ijcnn, author = "Xunkai Wei and Rob Law and Lei Zhang and Yue Feng and Yan Dong and Yinghong Li", title = "A Fast Coreset Minimum Enclosing Ball Kernel Machines", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0913.pdf}, url = {}, size = {}, abstract = {A fast coreset minimum enclosing ball kernel algorithm was proposed. First, it transfers the kernel methods to a center-constrained minimum enclosing ball problem, and subsequently it trains the kernel methods using the proposed MEB algorithm, and the primal variables of the kernel methods are recovered via KKT conditions. Then, detailed theoretical analysis and rigid proofs of our new algorithm are given. After that, experiments are investigated via using several typical classification datasets from UCI machine learning benchmark datasets. Moreover, performances compared with standard support vector machines are seriously considered. It is concluded that our proposed algorithm owns comparable even superior performances yet with rather fast converging speed in the experiments studied in this paper. Finally, comments about the existing problems and future development directions are discussed. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Ji:2008:ijcnn, author = "Zhengping Ji and Xiao Huang and Juyang Weng", title = "Learning of Sensorimotor Behaviors by a SASE Agent for Vision-based Navigation", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0915.pdf}, url = {}, size = {}, abstract = {In this paper, we propose a model to develop robots' covert and overt behaviors by using reinforcement and supervised learning jointly. The covert behaviors are handled by a motivational system, which is achieved through reinforcement learning. The overt behaviors are directly selected by imposing supervised signals. Instead of dealing with problems in controlled environments with a low-dimensional state space, our model is applied for the learning in nonstationary environments. Locally Balanced Incremental Hierarchical Discriminant Regression (LBIHDR) Tree is introduce to be the engine of cognitive mapping. Its balanced coarse-to-fine tree structure guarantees real-time retrieval in self-generated high-dimensional state space. Furthermore, K-Nearest Neighbor strategy is adopted to reduce training time complexity. Visionbased outdoor navigation are used as challenging task examples. In the experiment, the mean square error of heading direction is 0° for re-substitution test and 1.1269° for disjoint test, which allows the robot to drive without a big deviation from the correct path we expected. Compared with IHDR [1], LBIHDR reduced the mean square error by 0.252° and 0.5052°, using re-substitution and disjoint test, respectively. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Asaduzzaman:2008:ijcnn, author = "Md. Asaduzzaman and Md. Shahjahan and Md. M. Kabir and M. Ohkura and K. Murase", title = "Generation of Equal Length Patterns from Heterogeneous Patterns for Using in Artificial Neural Networks", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0916.pdf}, url = {}, size = {}, abstract = {A challenging task is to classify Internet customers based on their heterogeneous search histories of shopping in the Internet. The problem is the data pattern itself. Each transition of a customer from one page to the next in purchasing a commodity is considered as an attribute and this is a pair of data. The purchase patterns consist of usually different length for different customers. We cannot classify customers using a neural network (NN) due to these two problems - pair of attribute and unequal lengths of data. Here, we have developed an algorithm that can automatically generate equal length data with non-pair attributes. Finally, we use an unsupervised competitive learning in order to classify them because we do not know how many classes are there. We found that most of the customers belong to single category or class. The results we obtained have a nice agreement with the customer's goal. The goal of all customers is to reach a common target page and to purchase a commodity. Therefore, we can consider that they may belong to the same category or class. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Xia:2008:ijcnn, author = "Bin Xia and He Sun and Bao-Liang Lu", title = "Multi-view Gender Classification based on Local Gabor Binary Mapping Pattern and Support Vector Machines", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0917.pdf}, url = {}, size = {}, abstract = {This paper proposes a novel face representation approach, local Gabor binary mapping pattern (LGBMP), for multi-view gender classification. In this approach, a face image is first represented as a series of Gabor magnitude pictures (GMP) by applying multi-scale and multi-orientation Gabor filters. Each GMP is then encoded as a LGBP image where a uniform local binary pattern (LBP) operator is used. After that, each LGBP image is divided into non-overlapping rectangular regions, from which spatial histograms are extracted. Although an LGBP feature vector can be obtained by fitting together the regional histograms, it can not be employed in pattern classification due to its high dimension. We propose that each regional LGBP feature be mapped onto a one-dimensional subspace independently before they are concatenated as a whole feature vector. This is attractive since we reduce the feature dimension and also preserve the spatial information of LGBP image. Two ways have been proposed to map the regional LGBP feature in this paper. One is so-called LGBMP-LDA using linear discriminant analysis (LDA) for dimensionality reduction while the other is to project the regional LGBP feature onto the class center connecting line, namely, LGBMP-CCL. As a result, despite several decades of Gabor filters, the final feature dimension is even less than that of the feature extracted by using LBP directly on gray-scale images. The classification tasks in our work are performed by support vector machines (SVM). The experimental results on the CAS-PEAL face database indicate that the proposed approach achieves higher accuracy than the others such as SVMs+Gray-scale pixel, SVMs+Gabor and SVMs+LBP approach, more particularly, it has the lowest dimension of feature vector. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Homma:2008:ijcnn, author = "Noriyasu Homma and Kazuhisa Saito and Tadashi Ishibashi and Zeng-Guang Hou and Ashu M. G. Solo", title = "Shape Features Extraction from Pulmonary Nodules in X-ray CT Images", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0919.pdf}, url = {}, size = {}, abstract = {In this paper, we propose a new computer aided diagnosis method of pulmonary nodules in X-ray CT images to reduce false positive (FP) rate under high true positive (TP) rate conditions. An essential core of the method is to extract and combine two novel and effective features from the raw CT images: One is orientation features of nodules in a region of interest (ROI) extracted by a Gabor filter, while the other is variation of CT values of the ROI in the direction along body axis. By using the extracted features, a principal component analysis technic and any pattern recognition technics such as neural network approaches can then used to discriminate between nodule and non-nodule images. Simulation results show that discrimination performance using the proposed features is extremely improved compared to that of the conventional method. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wen:2008:ijcnn, author = "Guihua Wen and Lijun Jiang and Jun Wen", title = "Kernel Relative Transformation with Applications to Enhancing Locally Linear Embedding", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0921.pdf}, url = {}, size = {}, abstract = {Locally linear embedding heavily depends on whether the neighborhood graph represents the underlying geometry structure of the data manifolds. Inspired from the cognitive law, the relative transformation(RT) and kernel relative transformation (KRT) are proposed. They can improve the distinction between data points and inhibit the impact of noise and sparsity of data, which can be then applied to construct the neighborhood graph so as to reduce the short circuit edges, while the embedding is still performed in the original space. Subsequently,another enhanced Hessian Locally Linear Embedding approach is developed with significantly increased performance. The conducted experiments on challenging benchmark data sets validate the proposed approaches. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Altamiranda:2008:ijcnn, author = "Junior Altamiranda and Jose Aguilar and Luís Hernandez", title = "Data Mining System for Biochemical Analysis in Experimental Physiology", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0923.pdf}, url = {}, size = {}, abstract = {We develop a Data Mining system to assist with the elucidation by graphical means of the biochemical changes in the brains of rodents. Manual analysis of such experiments is increasingly impractical because of the voluminous nature of the data that is generated, and the tedious nature of the analysis means that important information can be missed. For this purpose we are constructing an increasingly sophisticated Data Mining system which contains a number of pre-processing stages and classification via two steps of an Adaptive Resonance Theory Artificial Neural Network. In this paper we describe the system. The focus of our activity is the study of neurotransmitters: Glutamate and Aspartate and we present an example of how to use our Data Mining system for the automated classification of samples that are extracted from the brains of rodents. This methodology should prove equally valuable to other biochemical analysis problems in experimental Physiology. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Li11:2008:ijcnn, author = "Cuiran Li and Chengshu Li", title = "Opportunistic Spectrum Access in Cognitive Radio Networks", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0924.pdf}, url = {}, size = {}, abstract = {Radio spectrum is one of the most scare and valuable resources for wireless communications. Cognitive Radio has been considered as an efficient means to opportunistic spectrum sharing between primary (licensed) users and cognitive radio users. In this paper, based on the a two-phase channel and power allocation scheme proposed by A. T. Hoang etc., we present an opportunistic spectrum access approach for cognitive radio network. In the scheme proposed by A. T. Hoang etc., they consider a cognitive radio network that consists of multiple cells and the system throughput is defined as the total number of subscribers that can be simultaneously served. In this paper, we consider a cognitive radio network as self-organizing network. Furthermore, the throughput is defined as the average probability of success transmission. In our proposed approach, for each available channel, TDMA frame consists of N time slots, and each active cognitive user is assigned one transmission slot different from those of other active cognitive users in each frame. It allows an active cognitive user use the slots pre-assigned to the other active cognitive users under a range of values for accessing opportunity. We evaluate the performances of the opportunistic spectrum access approach in view of system throughput and energy efficiency. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Mainali:2008:ijcnn, author = "Manoj Kanta Mainali and Kaoru Shimada and Shingo Mabu and Kotaro Hirasawa", title = "Optimal Route of Road Networks by Dynamic Programming", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0925.pdf}, url = {}, size = {}, abstract = {This paper introduces an iterative Q value updating algorithm based on dynamic programming for searching the optimal route and its optimal traveling time for a given Origin-Destination (OD) pair of road networks. The proposed algorithm finds the optimal route based on the local traveling time information available at each adjacent intersection. For all the intersections of the road network, Q values are introduced for determining the optimal route. When the Q values converge, we can get the optimal route from multiple sources to single destination. If there exist multiple routes with the same traveling time, the proposed method can find all of it. When the traveling time of the road links change, an alternative optimal route is found starting with the already obtained Q values. The proposed method was applied to a grid like road network and the results show that the optimal route can be found in a small number of iterations. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Liu14:2008:ijcnn, author = "Wenxin Liu and Li Liu and David A. Cartes", title = "Neural Network Based Controller Design for Three-Phase PWM AC/DC Voltage Source Converters", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0927.pdf}, url = {}, size = {}, abstract = {Three-phase AC/DC converter is widely used in many industrial applications. To improve performance, this paper proposes an adaptive neural network based controller design for three-phase PWM AC/DC voltage source converters. The controller is designed based on a nonlinear multi-input multi-output model using Lyapunov's direct method. Since neural networks can approximate unknown nonlinear dynamics, there is no need to know the parameters of the system. In this way, the controller is robust to parameter drifting and changes of operating points. Additionally, the proposed control can be applied directly online after initialization. Thus, the time-consuming offline training process is avoided. Furthermore, the proposed controller design also avoids the singularity problem, which may exist in regular feedback linearization based controls. Co-simulation using Matlab /Simulink and PSIM demonstrates the effectiveness of the proposed controller design. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Matsumoto:2008:ijcnn, author = "Yuji Matsumoto and Motohide Umano and Masahiro Inuiguchi", title = "Visualization with Voronoi Tessellation and Moving Output Units in Self-Organizing Map of the Real-Number System", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0928.pdf}, url = {}, size = {}, abstract = {The Self-Organizing map (SOM) proposed by T. Kohonen is a method to produce a low-dimensional representation from high-dimensional input data automatically, where output units are restrictedly placed on grid points. We propose real-number SOM (RSOM), where output units are freely placed on the real-number coordinates plane and visualized as a Voronoi diagram. RSOM is a natural extension of the conventional SOM because Voronoi tessellation for the output units on the square grid generates square regions on the output plane, the same as the conventional SOM. We propose two methods of moving with preserving topology of the input data and several visualization method such as minimum spanning tree, variable boundary width and spherical RSOM. We illustrate moving methods decrease errors in results of simulation. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Hasegawa:2008:ijcnn, author = "Tomonari Hasegawa and Yusuke Matsuoka and", title = "Analysis of Inter-Spike Interval Characteristics of Piecewise Constant Chaotic Spiking Oscillators", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0930.pdf}, url = {}, size = {}, abstract = {This paper studies dynamics of a simple chaotic spiking oscillator having piecewise constant characteristics. The state variable can vibrate and is reset to the base level just after it reaches the threshold. Repeating this vibrate-and-fire behavior, rich chaotic spike-trains can be generated. Since the solution and return map are piecewise linear, precise analysis is possible. We have investigated characteristics of inter-spike intervals (ISIs) and have found interesting properties: ``The system can output chaotic spike-trains characterized by line-like spectrums of ISIs. Such phenomena and chaos with continuous spectrum appear alternately and make window-like structure in the parameter space. The continuous spectrum of chaos can have wider-band than other types of spiking oscillators.'' Presenting a simple electric circuit, typical phenomena are confirmed experimentally. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(He3:2008:ijcnn, author = "Wenwu He and Hui Jiang", title = "Explicit Update vs Implicit Update", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0932.pdf}, url = {}, size = {}, abstract = {In this paper, the problem of implicit online learning is considered. A tighter convergence bound is derived, which demonstrates theoretically the feasibility of implicit update for online learning. Then we combine SMD with implicit update technique and the resulting algorithm possesses the inherent stability. Theoretical result is well corroborated by the experiments we performed which also indicate that combining SMD with implicit update technique is another promising way for online learning. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Lin3:2008:ijcnn, author = "Chin-Teng Lin and Nikhil R. Pal and Chien-Yao Chuang and Tzyy-Ping Jung and Li-Wei Ko and Sheng-Fu Liang", title = "An EEG-based Subject- and Session-Independent Drowsiness Detection", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0933.pdf}, url = {}, size = {}, abstract = {Monitoring and predicting human cognitive state and performance using physiological signals such as Electroencephalogram (EEG) have recently gained increasing attention in the fields of brain-computer interface and cognitive neuroscience. Most previous psychophysiological studies of cognitive changes have attempted to use the same model for all subjects. However, the relatively large individual variability in EEG dynamics relating to loss of alertness suggests that for many operators, group statistics cannot be used to accurately predict changes in cognitive states. Attempts have also been made to build a subject-dependent model for each individual based on his/her pilot data to account for individual variability. However, such methods assume the cross-session variability in EEG dynamics to be negligible, which could be problematic due to electrode displacements, environmental noises, and skin-electrode impedance. Here first we show that the EEG power in the alpha and theta bands are strongly correlated with changes in the subject's cognitive state reflected through his driving performance and hence his departure from alertness. Then under very mild and realistic assumptions we derive a model for the alert state of the person using EEG power in the alpha and theta bands. We demonstrate that deviations (computed by Mahalanobis distance) of the EEG power in the alpha and theta bands from the corresponding alert models are correlated to the changes in the driving performance. Finally, for detection of drowsiness we use a linear combination of deviations of the EEG power in the alpha band and theta band from the respective alert models that best correlates with subject's changing level of alertness, indexed by subject's behavioral response in the driving task. This approach could lead to a practical system for noninvasive monitoring of the cognitive state of human operators in attention-critical settings. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Karnick:2008:ijcnn, author = "Matthew Karnick and Metin Ahiskali", title = "Learning Concept Drift in Nonstationary Environments Using an Ensemble of Classifiers Based Approach", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0934.pdf}, url = {}, size = {}, abstract = {We describe an ensemble of classifiers based approach for incrementally learning from new data drawn from a distribution that changes in time, i.e., data obtained from a nonstationary environment. Specifically, we generate a new classifier using each additional dataset that becomes available from the changing environment. The classifiers are combined by a modified weighted majority voting, where the weights are dynamically updated based on the classifiers' current and past performances, as well as their age. This mechanism allows the algorithm to track the changing environment by weighting the most recent and relevant classifiers higher. However, it also uses old classifiers by assigning them appropriate voting weights should a cyclical environment renders them relevant again. The algorithm learns incrementally, i.e., it does not need access to previously used data. The algorithm is also independent of a specific classifier model, and can be used with any classifier that fits the characteristics of the underlying problem. We describe the algorithm, and compare its performance using several classifier models, and on different environments as a function of time for several values of rate-of-change. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Karasuyama:2008:ijcnn, author = "Masayuki Karasuyama and Ryohei Nakano", title = "Optimizing Sparse Kernel Ridge Regression Hyperparameters Based on Leave-One-Out Cross-Validation", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0935.pdf}, url = {}, size = {}, abstract = {Kernel Ridge Regression (KRR) is a nonlinear extension of the ridge regression. The performance of the KRR depends on its hyperparameters such as a penalty factor C, and RBF kernel parameter σ.We employ a method called MCV-KRR which optimizes the KRR hyperparameters so that a cross-validation error is minimized. This method becomes equivalent to a predictive approach to Gaussian Process. Since the cost of KRR training is O(N3) where N is a data size, to reduce this complexity, some sparse approximation of the KRR is recently studied. In this paper, we apply the minimum crossvalidation (MCV) approach to such sparse approximation. Our experiments show the MCV with the sparse approximation of the KRR can achieve almost the same generalization performance as the MCV-KRR with much lower cost. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Shi:2008:ijcnn, author = "Xuelin Shi and Ying Zhao and Xiangjun Dong", title = "RDF Based Integrated Information Retrieval in Grid Computing Environment", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0936.pdf}, url = {}, size = {}, abstract = {The information explosion calls for adequate and efficient approaches to information retrieval. Integrated Information Retrieval (IIR) in grid computing environment is becoming more and more attractive for integration and share of heterogeneous resource to provide users integrated retrieval services. This paper proposes IIR service infrastructure on grid platform, GIIRS, which used Resource Description Framework (RDF) as data representation specification. And we designed a query mechanism to implement IIR of heterogeneous and semi- structured web data. The GIIRS can be easily deployed on grid platform and have feature of semantic interoperability. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Ohta:2008:ijcnn, author = "Masaya Ohta and Keiichi Mizutani and Naoki Fujita and Katsumi Yamashita ", title = "Complexity Suppression of Neural Networks for PAPR Reduction of OFDM Signal and its FPGA Implementation", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0938.pdf}, url = {}, size = {}, abstract = {In this paper, a neural network (NN) for peak power reduction of orthogonal frequency-division multiplexing (OFDM) signals is improved in order to suppress its computational complexity. Numerical experiments show that the proposed NN has less computational complexity than the conventional one. The number of IFFT in NN can be reduced to half, and the computational time can be suppressed by 32.7percent. From the HDL simulation for FPGA implementation, hardware resouces are approximately suppressed by about 30percent. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Kumar3:2008:ijcnn, author = "Sachin Kumar and Myra Torres and Y. C. Chan and Michael Pecht", title = "A Hybrid Prognostics Methodology for Electronic Products", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0939.pdf}, url = {}, size = {}, abstract = {Prognostics and health management enables in-situ assessment of a product's performance degradation and deviation from an expected normal operating condition. A unique hybrid prognostics and health management methodology combining both data-driven and physics-of-failure models is proposed for fault diagnosis and life prediction. The shortcomings of using data-driven and physics-of-failure methodologies independently are discussed. These approaches estimate future system health, based on a systems current health status, historical performance, and operating environmental conditions. Although these methodologies are applicable to legacy, current, and future electronics, and ranging from components to circuit assemblies and electronic products, the hybrid approach is preferred due to its capability to include potential failure precursor parameters with failure mechanism, thus improving accuracy in prognostic estimates. Various works on data-driven and physics-of-failure approaches to prognostics for electronics are summarized and a hybrid methodology case study is presented. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Alexandrino:2008:ijcnn, author = "Jose Lima Alexandrino and Cleber Zanchettin and Edson Costa de Barros Carvalho Filho", title = "A Hybrid Intelligent System Clonart for Short and Mid-term Forecasting for the Brazilian Energy Distribution System", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0940.pdf}, url = {}, size = {}, abstract = {The present work describes an application of Clonart (Clonal Adaptive Resonance Theory) for forecasting of amount of precipitation for the Brazilian Energy Distribution System. The effectiveness of the Brazilian electricity system directly depends on the difference between hydroelectric energy production and consumer use. Production depends upon the volume of water stored in the reservoirs. A forecasting system for the amount of rainfall throughout the year contributes significantly to the analysis. The plasticity of the Clonart ensures that a new piece of knowledge does not overshadow previous knowledge. This is especially important for forecast problems because this type of problem needs constants training. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Dominey:2008:ijcnn, author = "Peter F. Dominey and Isabelle Tapiero and Carol Madden and Emmanuel Reynaud and Michel Hoen and Olivier Koenig ", title = "A Hybrid Propositional-Embodied Cognitive Architecture for Human-Robot Cooperation", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0942.pdf}, url = {}, size = {}, abstract = {Robot platforms have now reached a level of technical development wherein they are becoming physically capable of useful interaction with humans, while ensuring safety and reasonable cost. The current challenge is for cognitive systems science to provide these robots with the necessary capabilities so that they can interact and cooperate with humans in a natural manner. We are addressing this problem by exploiting two central ideas derived from the human psychological sciences. The first idea is that the human conceptual system is based on situated simulations that are instantiated in the same systems that are used for perception and action, referred to as embodied cognition. The second idea is that human cooperation relies on the cooperating agents sharing a common representation of their shared plan, which involves the actions of both agents. This representation allows them to cooperate, to trade roles, and to help one another if necessary. We have implemented these concepts on multiple robot platforms including the HRP2 humanoid, and the Cooperator and Cooperator II visually guided robot manipulators. This paper will present the motivation for this system and results, and will then outline what we consider to be the crucial issues for human-like cognitive systems. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Taherkhani:2008:ijcnn, author = "A. Taherkhani and A. Mohammadi and S. A. Seyyedsalehi and H. Davande", title = "Design of a Chaotic Neural Network by Using Chaotic Nodes and NDRAM Network", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0944.pdf}, url = {}, size = {}, abstract = {Recent developments in nonlinear dynamics and the theory of chaos have shown deterministic chaotic property of EEGs. Such evidences made the researchers try to take advantage of the chaotic behavior in artificial neural networks. According to the natural selection theory a good problemsolver should have two main properties: The ability of emerging various solutions for problem and existence of a rule (or intelligence) to guide this evolution and variety to become close to the goal. In this paper we used a chaotic node with logistic map to make the ability of emerging various solutions. In order to intelligently control the evolution of chaotic nodes we designed a rule by using NDRAM. The performance of proposed chaotic neural network is about 80percent whereas the performance of NDRAM is about 40percent in the same condition. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Pereira:2008:ijcnn, author = "Cristiano de S. Pereira and George D. C. Cavalcanti", title = "Prototype Selection: Combining Self-Generating Prototypes and Gaussian Mixtures for Pattern Classification", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0945.pdf}, url = {}, size = {}, abstract = {This paper presents an investigation into prototype-based classifiers. Different methods have been proposed to deal with this problem. There are two main classes of prototype-selection algorithms. The first is merely selective, in which the resulting set of prototypes is formed by wellchosen samples from the training set. The second is known as the creative class of algorithms. This strategy creates new instances and performs adjustments of the prototypes during training. Two methods of the creative strategy are presented here: a self-generating prototype scheme and a fuzzy variation of Nearest Prototype Classification, which uses a Gaussian Mixture ansatz. The respective advantages and problems are discussed. A hybrid method is proposed to overcome difficulties and improve accuracy. The hybrid strategy obtained better results in the experiments when compared to each of two basic approaches and the classic K-Nearest Neighbor. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Puppala:2008:ijcnn, author = "Hima B. Puppala and Robert Kozma", title = "Identification of Phase Transitions in Simulated EEG Signals", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0946.pdf}, url = {}, size = {}, abstract = {The KIV model is a biologically inspired hierarchical model that describes non-linear dynamics found in brains. Previous animal and human EEG measurements indicated the presence of jumps in the spatio-temporal EEG patterns, which are relevant to cognitive processing. The present work introduces the KIV model to simulate phase transitions in EEG signals. Phase transitions have nonstationary and intermittent characteristics, which make automated detection a very difficult task. We analyze the simulated EEG signals using various statistical methods. We describe various classification methods to identify simulated phase transitions, which will be used to automate the detection process in actual EEG signals. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Barros:2008:ijcnn, author = "Adelia C. A. Barros and George D. C. Cavalcanti", title = "Combining Global Optimization Algorithms with a Simple Adaptive Distance for Feature Selection and Weighting", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0947.pdf}, url = {}, size = {}, abstract = {This work focuses on a study about hybrid optimization techniques for improving feature selection and weighting applications. For this purpose, two global optimization methods were used: Tabu Search (TS) and Simulated Annealing (SA). These methods were combined to k-Nearest Neighbor (k-NN) composing two hybrid approaches: SA/k-NN and TS/k-NN. Those approaches try to use the main advantage from the global optimization methods: they work efficiently in searching for solutions in the global space. In this study, the methodology is proposed by [4]. In the referred work, a hybrid TS/k-NN approach was suggested and successfully applied for feature selection and weighting problems. Based on the later, this analysis indicates a new SA/k-NN combination and compares their results using the classical Euclidean Distance and a Simple Adaptive Distance [8]. The results demonstrate that feature sets optimized by the studied models are very efficient when compared to the well-known k-NN. Both accuracy classification and number of features in the resultant set are considered in the conclusions. Furthermore, the combined use of the Simple Adaptive Distance improves even more the results for all datasets analyzed. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Kozma:2008:ijcnn, author = "Robert Kozma and Leonid Perlovsky and JaiSantosh Ankishetty", title = "Detection of Propagating Phase Gradients in EEG Signals using Model Field Theory of Non-Gaussian Mixtures", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0948.pdf}, url = {}, size = {}, abstract = {Model Field Theory (MFT) is a powerful tool of pattern recognition, which has been used successfully for various tasks involving noisy data and high level of clutter. Detection of spatio-temporal activity patterns in EEG experiments is a very challenging task and it is well-suited for MFT implementation. Previous work on applying MFT for EEG analysis used Gaussian assumption on the mixture components. The present work uses non-Gaussian components for the description of propagating phase-cones, which are more realistic models of the experimentally observed physiological processes. This work introduces MFT equations for non- Gaussian transient processes, and describes the identification algorithm. The method is demonstrated using simulated phase cone data. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Kovalerchuk:2008:ijcnn, author = "Boris Kovalerchuk and Leonid Perlovsky", title = "Dynamic Logic of Phenomena and Cognition", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0950.pdf}, url = {}, size = {}, abstract = {Modeling of complex phenomena such as the mind presents tremendous computational complexity challenges. The neural modeling fields theory (NMF) addresses these challenges in a non-traditional way. The main idea behind success of NMF is matching the levels of uncertainty of the problem/model and the levels of uncertainty of the evaluation criterion used to identify the model. When a model becomes more certain then the evaluation criterion is also adjusted dynamically to match the adjusted model. This process is called dynamic logic (DL) of model construction, which mimics processes of the mind and natural evolution. This paper provides a formal description of Phenomena Dynamic Logic (P-DL) and outlines its extension to the Cognitive Dynamic Logic (C-DL). P-DL is presented with its syntactic, reasoning, and semantic parts. Computational complexity issues that motivate this paper are presented using an example of polynomial models. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Resconi:2008:ijcnn, author = "Germano Resconi and Boris Kovalerchuk", title = "Fusion in Agent -Based Uncertainty Theory and Neural Image of Uncertainty", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0951.pdf}, url = {}, size = {}, abstract = {In neural network modeling, the goal often is to get a most specific crisp output (e.g., binary classification of objects) from neuron inputs that have multiple possible values. In this paper, we change the viewpoint and assume that the neuron is an operator that transforms binary classical logic input to the many valued logic output, e.g., changes crisp sets into fuzzy sets. In this interpretation, the neural network is composed of agents or neurons, which work to implement uncertainty calculus and many valued logics from crisp perceptual input. This idea is closely related to the Dynamic Logic approach and recent cognitive science experimental discoveries. According to this model having crisp perceptual input, brain (1) produces a less certain representation, (2) processes input at this uncertainty level of representation, (3) converts results to the next more certain level of information representation, (4) processes this information and (5) repeats these steps several times until the acceptable level of certainty is reached. To build such model we rely not on the binary logic but on the logic of the uncertainty to obtain the high flexibility and logic adaptation of the described process. This paper presents a concept of the Agent-based Uncertainty Theory (AUT) based on complex fusion of crisp conflicting judgments of agents Communication among agents is modeled by the fusion process in the neural elaboration. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Phienthrakul:2008:ijcnn, author = "Tanasanee Phienthrakul and Boonserm Kijsirikul ", title = "Adaptive Stabilized Multi-RBF Kernel for Support Vector Regression", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0953.pdf}, url = {}, size = {}, abstract = {In Support Vector Regression (SVR), kernel functions are used to deal with nonlinear problem by computing the inner product in a higher dimensional feature space. The performance of approximation depends on the chosen kernels. Although the radial basis function (RBF) kernel has been successfully used in many problems, it still has the restriction in some complex problems. In order to obtain a more flexible kernel function, the non-negative weighting linear combination of multiple RBF kernels is used. Then, the evolutionary strategy (ES) is applied for adjusting the parameters of SVR and kernel function. Moreover, the objective function of the ES is carefully designed, by involving a stability of bounded SVR. This leads to improved generalization performances and avoids the overfitting problem. The experimental results show the ability of the proposed method on symmetric mean absolute percentage error (SMAPE) that outperforms the other objective functions and grid search. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Yang9:2008:ijcnn, author = "Ming-Der Yang and Boris P.T. Chen and Chang-Shian Chen", title = "Using Artificial Neural Network for Outflow Estimation in an Ungauged Area", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0955.pdf}, url = {}, size = {}, abstract = {This research employs an artificial neural network with a variable mathematic structure that is capable of simulating a nonlinear structural system. A backpropagation neural network (BPN) is adopted to estimate outflow for an ungauged area by considering temporal distribution of rainfall-runoff and the spatial distribution of watershed environment. The nonlinear relationship among the physiographic factors, precipitation, and outflow of the specific watershed was established to estimate the outflow of the sub-watershed where no flow gauge has been settled. The model was tested at Bei-Shi watershed of Hou-Long River, Taiwan. Three typhoon occurrences were used for model calibration and verification that indicates the model validity and proves the model suitable for estimating the outflow of an ungauged area. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Alzate:2008:ijcnn, author = "Carlos Alzate and Johan A. K. Suykens ", title = "Sparse Kernel Models for Spectral Clustering Using the Incomplete Cholesky Decomposition", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0956.pdf}, url = {}, size = {}, abstract = {A new sparse kernel model for spectral clustering is presented. This method is based on the incomplete Cholesky decomposition and can be used to efficiently solve large-scale spectral clustering problems. The formulation arises from a weighted kernel principal component analysis (PCA) interpretation of spectral clustering. The interpretation is within a constrained optimization framework with primal and dual model representations allowing the clustering model to be extended to out-of-sample points. The incomplete Cholesky decomposition is used to compute low-rank approximations of a modified affinity matrix derived from the data which contains cluster information. A reduced set method is also presented to compute efficiently the cluster indicators for out-of-sample data. Simulation results with large-scale toy datasets and images show improved performance in terms of computational complexity }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Al-Mamory2:2008:ijcnn, author = "Safaa O. Al-Mamory and Zhang Hongli and Ayad R. Abbas", title = "IDS Alarms Reduction Using Data Mining", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0960.pdf}, url = {}, size = {}, abstract = {The Intrusion Detection Systems (IDSs) are one of robust systems which can effectively detect penetrations and attacks. However, they generate large number of alarms most of which are false positives. Fortunately, there are reasons for triggering alarms where most of these reasons are not attacks. In this paper, a new approximation algorithm has developed to group alarms and to produce clusters. Hereafter, each cluster abstracted as a generalized alarm; most of the generalized alarms are root causes. The proposed algorithm makes use of nearest neighboring and generalization concepts. As a clustering algorithm, the proposed algorithm uses a new measure to compute distances between alarms features values. This algorithm was verified with many datasets, and its reduction ratio was about 93percent of the total alarms. The resulting generalized alarms help the security analyst in writing filters. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Zhu2:2008:ijcnn, author = "Yingying Zhu and Zhong Ming and Jun Zhang", title = "Video Scene Classification and Segmentation Based on Support Vector Machine", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0961.pdf}, url = {}, size = {}, abstract = {Video scene classification and segmentation are fundamental steps for multimedia retrieval, indexing and browsing. In this paper, a robust scene classification and segmentation approach based on Support Vector Machine (SVM) is presented, which extracts both audio and visual features and analyzes their inter-relations to identify and classify video scenes. Our system works on content from a diverse range of genres by allowing sets of features to be combined and compared automatically without the use of thresholds. With the temporal behaviors of different scene classes, SVM classifier can effectively classify presegmented video clips into one of the predefined scene classes. After identifying scene classes, the scene change boundary can be easily detected. The experimental results show that the proposed system not only improves precision and recall, but also performs better than the other classification systems using the decision tree (DT), K Nearest Neighbor (K-NN) and Neural Network (NN). }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Mirza:2008:ijcnn, author = "Hanane H. Mirza and Hien D. Thai and Zensho Nakao", title = "A New Intelligent Digital Right Management Technique for E-Learning Content", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0962.pdf}, url = {}, size = {}, abstract = {The digitalization of E-learning sources makes it an easy target for frauds, conterfeiting and content stealing. In this paper we present a new technique to deal with the security problems of e-learning content, its authentication and Digital Right Management. The proposed technique is done by inserting a digital logo image, which serves as watermark signals, in the audio stream of E-learning material. This technique is based on Modulated Complex Lapped Transform that was selected for its audio reconstruction properties and the extraction of the watermark is performed using an Independent Component Analysis algorithm. To demonstrate the effectiveness of the proposed method, a real world implementation has been done and the algorithm shows quite good visual and audible quality in watermarked content, as well as a high robustness against common signal processing attacks. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Ferreira4:2008:ijcnn, author = "P. M. Ferreira and A. E. Ruano", title = "Application of Computational Intelligence Methods to Greenhouse Environmental Modelling", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0963.pdf}, url = {}, size = {}, abstract = {In order to implement a model-based predictive control methodology for a research greenhouse several predictive models are required. This paper presents the modelling framework and results about the models that were identified. RBF neural networks are used as non-linear auto-regressive and non-linear auto-regressive with exogenous inputs models. The networks parameters are determined using the Levenberg- Marquardt optimisation method and their structure is selected by means of multi-objective genetic algorithms. By network structure we refer to the number of neurons of the networks, the input variables and for each variable considered its lagged input terms. Two types of models were identified: process models (greenhouse climate) and external disturbances (external weather). Pseudo-random binary signals were employed to generate control input commands for the greenhouse actuators, in order to build input/output data sets suitable for the process models identification. The final model arrangement consists of four interconnected models, two of which are coupled, providing greenhouse climate and external weather long term predictions. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Xiao2:2008:ijcnn, author = "Yang Xiao and Zhiguo Cao and Yi Zheng and Ruicheng Yan", title = "Multi-sensor Data Fusion Based on Dynamic Fuzzy Neural Network", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0964.pdf}, url = {}, size = {}, abstract = {In this paper, a multi-sensor data fusion method based on dynamic fuzzy neural network (DFNN) for object recognition is proposed. DFNN is composed of two individual fuzzy neural networks. During the practical recognition process, one fuzzy neural network is used for recognition while the other is tracking trained. At the appropriate time the role of the two networks can be exchanged according to certain switching rule. The fusion recognition system is composed of two layers. At the first layer, the features extracted from middle wave and long wave infrared images are fused by DFNN to detect potential regions which may contain objects. And then the features extracted from visible image are used to make recognition in these potential regions based on DFNN at the second layer. The experiment demonstrates the efficiency of the proposed method. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Isawa:2008:ijcnn, author = "Haruka Isawa and Haruna Matsushita", title = "Fuzzy Adaptive Resonance Theory Combining Overlapped Category in Consideration of Connections", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0965.pdf}, url = {}, size = {}, abstract = {Adaptive Resonance Theory (ART) is an unsupervised neural network. Fuzzy ART (FART) is a variation of ART, allows both binary and continuous input patterns. However, Fuzzy ART has the category proliferation problem. In this study, to solve this problem, we propose a new Fuzzy ART algorithm: Fuzzy ART Combining Overlapped Category in consideration of connections (C-FART). C-FART has two important features. One is to make connections between similar categories. The other is to combine overlapping categories into with connections one category. We investigate the behavior of C-FART, and compare C-FART with the conventional FART. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Jankowski:2008:ijcnn, author = "Norbert Jankowski and Krzysztof Grabczewski", title = "Building Meta-Learning Algorithms Basing on Search Controlled by Machine Complexity", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0966.pdf}, url = {}, size = {}, abstract = {Meta-learning helps us find solutions to computational intelligence (CI) challenges in automated way. Metalearning algorithm presented in this paper is universal and may be applied to any type of CI problems. The novelty of our proposal lies in complexity controlled testing combined with very useful learning machines generators. The simplest and the best solutions are strongly preferred and are explored earlier. The learning algorithm is augmented by meta-knowledge repository which accumulates information about progress of the search through the space of candidate solutions. The approach facilitates using human experts knowledge to restrict the search space and provide goal definition, gaining meta-knowledge in an automated manner. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Vallejo:2008:ijcnn, author = "Jose Refugio Vallejo and Eduardo Bayro-Corrochano", title = "Clifford Hopfield Neural Networks", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0967.pdf}, url = {}, size = {}, abstract = {This paper presents the properties and the definition of Hopfield Neural Networks as a natural extension to Complex Hopfield Neural Networks and Quaternionic Hopfield Neural Networks. This extension allows us to generalize the concept of Hopfield Neural Networks to all type of Algebras and also to describe the main characteristics of this Networks. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Bayro-Corrochano:2008:ijcnn, author = "Eduardo Bayro-Corrochano and J. Refugio Vallejo-Gutierrez and Nancy Arana-Daniel", title = "Recurrent Clifford Support Machines", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0968.pdf}, url = {}, size = {}, abstract = {This paper introduces the Recurrent Clifford Support Vector Machines (RCSVM). First we explain the generalization of the real- and complex- valued Support Vector Machines using the Clifford geometric algebra. In this framework we handle the design of kernels involving the Clifford or geometric product and one redefines the optimization variables as multivectors. This allows us to have a multivector as output therefore we can represent multiple classes according to the dimension of the geometric algebra in which we work. We show that one can apply CSVM to build a recurrent CSVM.We study the performance of the recurrent CSVM with experiments using time series and tasks of visually guided robotics. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Monwar:2008:ijcnn, author = "M. M. Monwar and S. Rezaei", title = "Video Analysis for View-Based Painful Expression Recognition", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0969.pdf}, url = {}, size = {}, abstract = {In recent years, facial expressions of pain have been the focus of considerable behavioral research. Such work has documented that pain expressions, like other affective facial expressions, play an important role in social communication. Enabling computer systems to recognize pain expression from facial images is a challenging research topic. In this paper, we present two systems for pain recognition from video sequences. The first approach, eigenimage, projects the face images, detected from video sequences, onto a feature space, defined by eigenfaces, to produce the biometric template. Recognition is performed by projecting a new image onto that feature space and then classifying the face by comparing its position in the feature spaces with the positions of known individuals. To ensure better accuracy, the system is tested against two more feature spaces defined by eigeneyes and eigenlips. The second approach, neural network, extracts location and shape features of the detected faces and uses them as inputs to the artificial neural network which employs the standard error backpropagation algorithm for classification of faces. From experiments, we conclude that neural network based method is better in terms of speed and accuracy than eigenimage based method. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Qiu3:2008:ijcnn, author = "Hai Qiu and Neil Eklund and Xiao Hu and Weizhong Yan and Naresh Iyer ", title = "Anomaly Detection using Data Clustering and Neural Networks", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0971.pdf}, url = {}, size = {}, abstract = {Anomaly detection provides an early warning of unusual behavior in units in a fleet operating in a dynamic environment by learning system characteristics from normal operational data and flagging any unanticipated or unseen patterns. For a complex system such as an aircraft engine, normal operation might consist of multiple modes in a high dimensional space. Therefore, anomaly detection approaches based on single cluster data structure will not work. This paper investigates data clustering and neural network based approaches for anomaly detection, specifically addressing the situation which normal operation might exhibit multiple hidden modes. Results show detection accuracy can be improved by data clustering or learning the data structure using neural networks. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Peterson:2008:ijcnn, author = "Leif E. Peterson and Matthew A. Coleman", title = "Text-Mining Protein-Protein Interaction Corpus using Concept Clustering to Identify Intermittency", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0973.pdf}, url = {}, size = {}, abstract = {We used human protein-protein interaction (PPI) data transformed into documents to perform text-mining via concept clusters. The advantage of text-mining PPI data is that words (proteins) that are very sparse or over-abundant can be dropped, leaving the remaining bulk of data for clustering and rule mining. Libraries of tissue-specific binary PPIs were constructed from a list of 36,137 binary PPIs in the Human Protein Reference Database (HPRD). A randomization test for intermittency in the form of spikes and holes in frequency distributions of cluster-specific word frequencies was developed using scaled factorial moments. The test was based on a permutation form of a log-linear regression model to determine differences in slopes for ln(F2) vs. ln(M) in the intermittent and null distributions. Significant intermittency (p < 0.0005) in PPI was detected for prostate and testis tissue after a Bonferroni adjustment for multiple tests. The presence of intermittency reflects spikes and holes in histograms of cluster-specific word frequencies and possibly suggests identification of novel large signal transduction pathways or networks. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Ye2:2008:ijcnn, author = "Zhengmao Ye and Habib Mohamadian", title = "Independent Component Analysis for Spatial Object Recognition with Applications of Information Theory Synthesis", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0975.pdf}, url = {}, size = {}, abstract = {Each moving object contains particular unique signatures that can be used for pattern classification via object recognition and identification. Information extracted from the spatial object feature recognition can be provided by independent basis functions to represent actual physical attributes of the moving objects. Compared with principal component analysis, independent component analysis is a special feature extraction approach for blind signal separation, where an object is labeled to a special class. Some underlying factors or sources can be captured in a statistical sense. The true colour image is composed of red, green and blue components which are perpendicular to each other. These components may serve as a basis to be synthesized using independent component analysis. Each individual signature indicates unique information that can be evaluated using information theory. Thus, the quantitative measures of the colour component energy, discrete entropy and relative entropy have been introduced to independent component analysis issues for recognition of moving objects. }, keywords = { Independent Component Analysis, Discrete Entropy, , Relative Entropy, Colour Component Energy Recognition, Object}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Ye3:2008:ijcnn, author = "Zhengmao Ye and Habib Mohamadian and Yongmao Ye", title = "Sensing Data Discrete Wavelet Fusion for Pattern Recognition with Qualitative and Quantitative Measuring", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0976.pdf}, url = {}, size = {}, abstract = {Sensing data fusion has various types of real world applications in fields of weather forecasting, environmental surveillance, medical diagnosis, information assurance, space exploration and national security. Image fusion acts as a primary approach of data fusion. For similar images, some unique patterns occur within each individual one. There are some typical image fusion techniques, either area based or feature based. The feature-based approach is efficient and robust to handle multi-sensor image fusion with little rotation or translation, or the image has to be aligned beforehand. The area-based approach has no strict requirement on rotation or translation, but lack of robustness. A combination of two approaches is thus required. In this article, wavelet fusion is presented to analyze the effect of image fusion. Except for qualitative measures, quantitative measures are also proposed to evaluate image fusion. In particular, 2D discrete wavelet transform is used to both decompose images and reconstruct original images using the approximation, horizontal detail, vertical detail and diagonal detail components from the input images. At the same time, quantitative measures are used to evaluate the quality of the 2D wavelet transform and wavelet fusion, where gray level energy, discrete entropy and relative entropy and mutual information are applied. }, keywords = { Pattern Recognition, Gray Level Energy, Discrete Entropy, Relative Entropy, Wavelet Transform, Wavelet Fusion}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Hell:2008:ijcnn, author = "Michel Hell and Pyramo Costa and Fernando Gomide", title = "Hybrid Neurofuzzy Computing with Nullneurons", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0977.pdf}, url = {}, size = {}, abstract = {In this paper we address a new type of elementary neurofuzzy unit called nullneuron. A nullneuron is a generalization of and/or neurons based on the concept of nullnorm, a category of fuzzy sets operators that generalizes triangular norms and conorms. The nullneuron model is parametrized by an element u, called the absorbing element. If the absorbing element u = 0, then the nullneuron becomes a and neuron and if u = 1, then the nullneuron becomes a dual or neuron. Also, we introduce a new learning scheme for hybrid neurofuzzy networks based on nullneurons using reinforcement learning. This learning scheme adjusts the weights associated with the individual inputs of the nullneurons, and learns the role of the nullneuron in the network (and or or) by individually adjusting the parameter u of each nullneuron. Nullneuron-based neural networks and the associated learning scheme is more general than similar neurofuzzy networks because they allow different triangular norms in the same network structure. Experimental results show that nullneuron-based networks provide accurate results with low computational effort. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Lee5:2008:ijcnn, author = "Jie-Hung Lee and Chiu-Ching Tuan and Tzung-Pei Hong", title = "A Maximum Channel Reuse Scheme with Hopfield Neural Network-Based Static Cellular Radio Channel Allocation Systems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0979.pdf}, url = {}, size = {}, abstract = {In recent years, wireless and mobile communication systems become increasingly popular. The demand for mobile communication has thus made the industry put more efforts towards designing new-generation systems. One of the important issues in mobile-phone communications is about the static channel assignment problem (SCAP). Although many techniques have been proposed for SCAP, a challenge for the cellular radio communication system is how to enhance and maximize the frequency reuse. The general SCAP is known as an NP-hard problem. The static channel assignment scheme based on the Hopfield Neural Network was shown to perform well when compared to some other schemes such as graph colouring and genetic algorithm (GA). In this paper, we extend Kim et al.'s modified Hopfield Neural Network methods and focus on channel reusing to obtain a near-optimum solution for CAP. Several constraints are considered for obtaining the desired results. Eight-benchmark problems are simulated and the energy evolution process is discussed. Simulation results demonstrated that the proposed scheme could make higher channel reuse rate. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Jin4:2008:ijcnn, author = "Xu Jin and Habib Abdulrab and Mhamed Itmi", title = "A Multi-agent Based Model for Urban Demand-Responsive Passenger Transport Services", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0981.pdf}, url = {}, size = {}, abstract = {Multi-agent simulation has been looked as an efficient tool for urban dynamic traffic services. However, the main problem is how to build an agent-based model for it. This research presents a multi-agent based demand responsive transport (DRT) services model, which adopts a practical multi-agents planning approach for urban DRT services control that satisfies the main constraints: minimize total slack time, travel time, waiting time, client's special requests, and using minimum number of vehicle. In this paper, we propose an agent based multi-layer distributed hybrid planning model for the real-time problem which can solve this question. In the proposed method, an agent for each vehicle finds a set of routes by its local search, and selects a route by cooperation with other agents in its planning domain. By computational experiments, we examine the effectiveness of the proposed method. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Alippi:2008:ijcnn, author = "C. Alippi and M. Fuhrman and M. Roveri", title = "k-NN Classifiers: Investigating the k=k(n) Relationship", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0982.pdf}, url = {}, size = {}, abstract = {The paper proposes a theory-based method for estimating the optimal value of k in k-NN classifiers based on a n-sized training set. As expected, experiments show that the suggested k is such that k/n → 0 when both k and n tend to infinity, as required by the asymptotical consistency condition. Interestingly, it appears that the generalization error is robust w.r.t. to k when n becomes large (probably as a consequence of the k/n → 0 relationship); the immediate consequence is that there is no need to provide an accurate estimate for the optimal k and an approximated coarser value, e.g., provided with cross validation, l-fold cross validation or leave one out is more than adequate. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wang11:2008:ijcnn, author = "Wenwu Wang ", title = "Convolutive Non-Negative Sparse Coding", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0983.pdf}, url = {}, size = {}, abstract = {Non-negative sparse coding (NSC) is a powerful technique for low-rank data approximation, and has found several successful applications in signal processing. However, the temporal dependency, which is a vital clue for many realistic signals, has not been taken into account in its conventional model. In this paper, we propose a general framework, i.e., convolutive non-negative sparse coding (CNSC), by considering a convolutive model for the low-rank approximation of the original data. Using this model, we have developed an effective learning algorithm based on the multiplicative adaptation of the reconstruction error function defined by the squared Euclidean distance. The proposed algorithm is applied to the separation of music audio objects in the magnitude spectrum domain. Interesting numerical results are provided to demonstrate its advantages over both the conventional NSC and an existing convolutive coding method. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Herzog:2008:ijcnn, author = "Andreas Herzog and Karsten Kube and Bernd Michaelis and Thomas Baltz and Thomas Voigt ", title = "Transmission of Spatio-Temporal Patterns from Biological to Artificial Neural Networks by a Multi-Electrode Array", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0984.pdf}, url = {}, size = {}, abstract = {The monitoring of a set of individual neurons in cultured biological networks or in the brain has become feasible with the used/development of multi-electrode arrays (MEA). However, even with the huge mass of data, getting suitable information about the actual spatio-temporal context of the analyzed biological network is not easy. In this paper we present a new conception and first results of analyzing the measured data by a recurrent artificial neural network with similar parameters as the biological network. The signals of the biological network transfer into the artificial one and the balanced artificial network becomes a part of the dynamics of the biological network. The artificial network is more transparent for advanced methods to analyze synchronous firing patterns (i.e., polychronization) and may also generate adequate feedback signals to the biological network for using as a recurrent neurointerface. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Seiffertt:2008:ijcnn, author = "John Seiffertt and Donald C. Wunsch II", title = "A Quantum Calculus Formulation of Dynamic Programming and Ordered Derivatives", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0985.pdf}, url = {}, size = {}, abstract = {Much recent research activity has focused on the theory and application of quantum calculus. This branch of mathematics continues to find new and useful applications and there is much promise left for investigation into this field. We present a formulation of dynamic programming grounded in the quantum calculus. Our results include the standard dynamic programming induction algorithm which can be interpreted as the Hamilton-Jacobi-Bellman equation in the quantum calculus. Furthermore, we show that approximate dynamic programming in quantum calculus is tenable by laying the groundwork for the backpropagation algorithm common in neural network training. In particular, we prove that the chain rule for ordered derivatives, fundamental to backpropagation, is valid in quantum calculus. In doing this we have connected two major fields of research. }, keywords = { dynamic programming, quantum calculus, time scales, backpropagation, dynamic equations}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Coyle:2008:ijcnn, author = "Damien Coyle and Thomas M. McGinnity and irijesh Prasad ", title = "A Multi-Class Brain-Computer Interface with SOFNN-based Prediction Preprocessing", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0986.pdf}, url = {}, size = {}, abstract = {Recent research has shown that neural networks (NNs) or self-organizing fuzzy NNs (SOFNNs) can enhance the separability of motor imagery altered electroencephalogram (EEG) for brain-computer interface (BCI) systems. This is achieved via the neural-time-series-prediction-preprocessing (NTSPP) framework where SOFNN prediction models are trained to specialize in predicting the EEG time-series recorded from different EEG channels whilst subjects perform various mental tasks. Features are extracted from the predicted signals produced by the SOFNN and it has been shown that these features are easier to classify than those extracted from the original EEG. Previous work was based on a two class BCI. This paper presents an analysis of the NTSPP framework when extended to operate in a multiclass BCI system. In mutliclass systems normally multiple EEG channels are used and a significant amount of subject-specific parameters and EEG channels are investigated. This paper demonstrates how the SOFNN-based NTSPP, tested in conjunction with three different feature extraction procedures and different linear discriminant and support vector machine (SVM) classifiers, is effective in improving the performance of a multiclass BCI system, even with a low number of standardly positioned electrodes and no subject-specific parameter tuning. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Schwabe:2008:ijcnn, author = "Lars Schwabe and Olaf Blanke ", title = "Out-of-Body Experiences: False Climbs in a Supine Position?", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0987.pdf}, url = {}, size = {}, abstract = {Out-of-body experiences (OBEs) are illusions, where people experience themselves as being located outside their physical body and often flying or floating at an elevated location. Here, we propose that the flying and floating in OBEs can be explained as the result of a Bayesian inference, where ambiguous bottom-up signals from the otholiths in a supine position are integrated with a top-down prior for the upright position, which is not appropriate for the current supine position. We also measure these ecologically valid priors for the upright position as the empirical probabilities in natural head movements. Our results suggest a simple interpretation of some aspects of OBEs in terms of a mislead sensory inference and suggests new ways of experimentally inducing OBE-like experiences by manipulating sensory signals and top-down prior information. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Kuremoto:2008:ijcnn, author = "T. Kuremoto and M. Obayashi and K. Kobayashi and H. Adachi and K. Yoneda ", title = "A Reinforcement Learning System for Swarm Behaviors", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0990.pdf}, url = {}, size = {}, abstract = {This paper proposes a neuro-fuzzy system with a reinforcement learning algorithm to realize speedy acquisition of optimal swarm behaviors. The proposed system is constructed with a part of input states classification by the fuzzy net and a part of optimal behavior learning network adopting the actor-critic method. The membership functions and fuzzy rules in the fuzzy net are adaptively formed online by the change of environment states observed in trials of agent's behaviors. The weights of connections between the fuzzy net and the value functions of actor and critic are trained by temporal difference error (TD error). Computer simulations applied to a goal-directed navigation problem using multiple agents were performed. Effectiveness of the proposed learning system was confirmed by the simulation results. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Kim4:2008:ijcnn, author = "Jaekwang Kim and Jee-Hyong Lee", title = "A Methodology for Finding Source-level Vulnerabilities of the Linux Kernel Variables", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0991.pdf}, url = {}, size = {}, abstract = {Linux kernel provides several advantages to system developers and is widely used as an operating system in a variety of systems, including embedded systems, access routers and servers. These advantages are due to the fact that the Linux kernel is publicly available, however, this feature of openness can have negative impacts on system security. If an attacker wished to exploit Linux-based systems, the attacker could easily do so by finding and abusing the vulnerabilities of the systems' Linux kernel sources. There are several methods available that can find source-level vulnerabilities, but they are not always suitable for the Linux kernel. In this paper, we propose a two-step Onion mechanism as a methodology to find source-level vulnerabilities of the Linux kernel variables. The first step of the Onion mechanism is to select variables that may be vulnerable by exploiting their usage patterns. The second step is to inspect the vulnerabilities of the selected variables by making and analyzing system call trees. We also evaluate our proposed methodology by applying it to two well-known source-level vulnerabilities. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Mahdaviani:2008:ijcnn, author = "Kaveh Mahdaviani and Helga Mazyar and Saeed Majidi and Mohammad H. Saraee", title = "A Method to Resolve the Overfitting Problem in Recurrent Neural Networks for Prediction of Complex Systems' Behavior", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0992.pdf}, url = {}, size = {}, abstract = {In this paper a new method to resolve the overfitting problem for predicting complex systems' behavior has been proposed. This problem occurs when a neural network loses its generalization. The method is based on the training of recurrent neural networks and using simulated annealing for the optimization of their generalization. The major work is done based on the idea of ensemble neural networks. Finally the results of using this method on two sample datasets are presented and the effectiveness of this method is illustrated. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Souto3:2008:ijcnn, author = "Marcilio C. P. de Souto and Ricardo B. C. Prudêncio and Rodrigo G. F. Soares and Daniel S. A. de Araujo", title = "Ranking and Selecting Clustering Algorithms Using a Meta-Learning Approach", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0993.pdf}, url = {}, size = {}, abstract = {We present a novel framework that applies a metalearning approach to clustering algorithms. Given a dataset, our meta-learning approach provides a ranking for the candidate algorithms that could be used with that dataset. This ranking could, among other things, support non-expert users in the algorithm selection task. In order to evaluate the framework proposed, we implement a prototype that employs regression support vector machines as the meta-learner. Our case study is developed in the context of cancer gene expression microarray datasets. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Santos:2008:ijcnn, author = "Sergio P. Santos and Jose Alfredo F. Costa", title = "Application of Multiple Decision Trees for Condition Monitoring in Induction Motors", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0994.pdf}, url = {}, size = {}, abstract = {Induction machines (IMs) play a pivotal role in industry and there is a strong demand for their reliable and safe operation. IMs are susceptible to problems such as stator current imbalance and broken bars, usually detected when the equipment is already broken, and sometimes after irreversible damage has occurred. Condition monitoring can significantly reduce maintenance costs and the risk of unexpected failures through the early detection of potential risks. Several techniques are used to classify the condition of machines. This paper presents a new case study on the application of multiple decision trees in the on-line condition monitoring of induction motors. Some advantages can be seen, such as the improved performance of classification systems, in addition to the capacity to explain examples. The database was developed through a simplified mathematical model of the machine, considering the effects caused by asymmetries in the phase impedances of motors. A comparative analysis is performed for individual running (based on the neural networks, k-Nearest neighbor and Naïve Bayes) and a multi-classifier (based on the Bagging and Adaboost) approaches. Results demonstrate that the multi-classifier systems obtain better results than those of the individual experiments. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Frolov:2008:ijcnn, author = "Alexander Frolov and Dusan Husek and Hana Rezankova and Pavel Polyakov", title = "Clustering Variables by Classical Approaches and Neural Network Boolean Factor Analysis", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0995.pdf}, url = {}, size = {}, abstract = {In this paper, we compare three methods for grouping of binary variables: neural network Boolean factor analysis [3], hierarchical clustering, and a linear factor analysis on the mushroom dataset [9]. In contrast to the latter two traditional methods, the advantage of neural network Boolean factor analysis is its ability to reveal overlapping classes in the dataset. It is shown that the mushroom dataset provides a good demonstration of this advantage because it contains both disjunctive and overlapping classes. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Caiuta:2008:ijcnn, author = "Rafael Caiuta and Aurora Pozo and Leonardo Emmendorfer and Silvia Regina Vergilio", title = "Selecting Software Reliability Models with a Neural Network Meta Classifier", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0997.pdf}, url = {}, size = {}, abstract = {Software reliability is one of the most important quality characteristics for almost all systems. The use of a software reliability model to estimate and predict the system reliability level is fundamental to ensure software quality. However, the selection of an appropriate model for a specific case can be very difficult for project managers. This is because, there are several models that can be used and none has proved to perform well considering different projects and databases. Each model is valid only if its assumptions are satisfied. To aim at the task of choosing the best software reliability model for a dataset, this paper presents a meta-learning approach and describes experimental results from the use of a neural network meta classifier for selection among different kind of reliability models. The obtained results validate the idea and are very promising. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Murphey:2008:ijcnn, author = "Yi L. Murphey and ZhiHang Chen and Leo Kiliaris and Jungme Park and Abul Masrur and Anthony Phillips", title = "Neural Learning of Driving Environment Prediction for Vehicle Power Management", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0998.pdf}, url = {}, size = {}, abstract = {Vehicle power management has been an active research area in the past decade, and has intensified recently by the emergence of hybrid electric vehicle technologies. Research has shown that driving style and environment have strong influence over fuel consumption and emissions. In order to incorporate this type of knowledge into vehicle power management, an intelligent system has to be developed to predict the current traffic conditions. This paper presents our research in neural learning for predicting the driving environment such as road types and traffic congestions. We developed a prediction model, an effective set of features to characterize different types of roadways, and a neural network trained for online prediction of roadway types and traffic congestion levels. This prediction model was then used in conjunction with a power management strategy in a conventional (non-hybrid) vehicle. The benefits of having the predicted drive cycle available are demonstrated through simulation. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Barnes:2008:ijcnn, author = "Anna Barnes and Garry Honey and Alle-Meije Wink and John Suckling", title = "Modulation of the Fractal Properties of Low Frequency Endogenous Brain Oscillations in Functional MRI by a Working Memory Task", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN0999.pdf}, url = {}, size = {}, abstract = {Fractals - signals that display scaleinvariant behaviour - are ubiquitous in nature including a wide variety of physiological processes. Fractal analysis of blood oxygen level dependent (BOLD) time-series of fMRI acquisitions from the brain can be achieved by decomposing the data into a hierarchy of temporal scales so that although the signal may well be irregular and contain singularities, the properties of these singularities are constant in time and the entire series can be characterised by a single scaling exponent: the Hurst exponent, H. The observation that a signal has a noninteger fractal dimension suggests that the generating system is complex and has the potential to adapt to a wide variety of challenges. In contrast, the emergence of white noise or, alternatively, signal periodicity can be seen as degradation of fractal complexity and hence, maladaptivity. We tested the hypothesis that exogenous stimuli affects fractal signal properties in the context of brain function by manipulating the cognitive demand of a working memory task and using H as a summary measure of signal complexity. We show that this stimulus has a significant effect on H estimated from resting data acquired immediately before and after the task, and that the degree of change is related to cognitive load. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Li12:2008:ijcnn, author = "Jianwu Li and Zhanyong Xiao and Yao Lu ", title = "Adapting Radial Basis Function Neural Networks for One-Class Classification", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN1000.pdf}, url = {}, size = {}, abstract = {One-class classification (OCC) is to describe one class of objects, called target objects, and discriminate them from all other possible patterns. In this paper, we propose to adapt radial basis function neural networks (RBFNNs) for OCC. First, target objects are mapped into a feature space by using neurons in the hidden layer of the RBFNNs. Then, we perform support vector domain description (SVDD) with linear kernel functions in the feature space to realize OCC. In addition, we also model, in the feature space, the closed sphere centered on the mean of target objects for OCC. Compared to the SVDD with nonlinear kernel functions, our methods can use flexible nonlinear mappings, which do not necessarily satisfy Mercer's conditions. Moreover, we can also control the complexity of solutions easily by setting the number of neurons in the hidden layer of RBFNNs. Experimental results show that the classification accuracies of our methods can be close to, and even can reach those of the SVDD for most of results, but with typically much sparser models. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Teng:2008:ijcnn, author = "Teck-Hou Teng and Zhong-Ming Tan and Ah-Hwee Tan ", title = "Self-Organizing Neural Models Integrating Rules and Reinforcement Learning", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN1002.pdf}, url = {}, size = {}, abstract = {Traditional approaches to integrating knowledge into neural network are concerned mainly about supervised learning. This paper presents how a family of self-organizing neural models known as Fusion Architecture for Learning, COgnition and Navigation(FALCON) can incorporate a priori knowledge and perform knowledge refinement and expansion through reinforcement learning. Symbolic rules are formulated based on pre-existing know-how and inserted into FALCON as a priori knowledge. The availability of knowledge enables FALCON to start performing earlier in the initial learning trials. Through a temporal-difference (TD) learning method, the inserted rules can be refined and expanded according to the evaluative feedback signals received from the environment. Our experimental results based on a minefield navigation task have shown that FALCON is able to learn much faster and attain a higher level of performance earlier when inserted with the appropriate a priori knowledge. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Yang10:2008:ijcnn, author = "Sheng-Chih Yang and Yi-Jhen Lin and Pau-Choo Chung and Giu-Cheng Hsu and Chien-Shen Lo", title = "Mass Screening and Feature Reserved Compression in a Computer-aided System for Mammograms", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN1003.pdf}, url = {}, size = {}, abstract = {This paper presents a computer-aided prescreening and storage system, which automatically prescreens the mass regions from mammograms and based on the results, performs a progressive compression in the storage. This is performed in two subsystems called mass screening subsystem and mass feature reserved compression subsystem. In the first subsystem, breast region is firstly extracted from images, followed by Gradient Enhancement and Median Filtering. Then, 19 texture features are calculated from 32*32 pixel blocks on the extracted breast region, and suboptimal feature subset is extracted. Then SVM classifier is employed for classifying the regions into mass, breast without masses and background.In the second subsystem, Vector Quantization GHNN (Grey-based Competitive Hopfield neural network) is applied on the three regions with different compression rates according their importance factors so as to reserve important features and simultaneously reduce the size of mammograms for storage efficiency. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Yeh:2008:ijcnn, author = "Flora Yu-Hui Yeh and Marcus Gallagher", title = "An Empirical Study of the Sample Size Variability of Optimal Active Learning Using Gaussian Process Regression", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN1007.pdf}, url = {}, size = {}, abstract = {Optimal Active Learning refers to a framework where the learner actively selects data points to be added to its training set in a statistically optimal way. Under the assumption of log-loss, optimal active learning can be implemented in a relatively simple and efficient manner for regression problems using Gaussian Processes. However (to date), there has been little attempt to study the experimental behavior and performance of this technique.In this paper, we present a detailed empirical evaluation of optimal active learning using Gaussian Processes across a set of seven regression problems from the DELVE repository. In particular, we examine the evaluation of optimal active learning compared to making random queries and the impact of experimental factors such as the size and construction of the different sub-datasets used as part of training and testing the models. It is shown that the multiple sources of variability can be quite significant and suggests that more care needs to be taken in the evaluation of active learning algorithms. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Chen13:2008:ijcnn, author = "Zaiping Chen and Yueming Zhao and Yang Zheng and Rui Lou", title = "Neural Network Electrical Machine Faults Diagnosis Based on Multi-population GA", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN1009.pdf}, url = {}, size = {}, abstract = {A hybrid method combining artificial neural network (ANN) with genetic algorithm (GA) is discussed in this paper. A new strategy of optimization on ANN structure and weights based on multi-population GA is proposed, and the quantitative optimization of ANN is realized. The Levenberg-Marquardt(LM) algorithm is used for further training the neural network, which can avoid the weak local searching ability of GA and shows both of the merits of GA as well as ANN. In this paper, the algorithm proposed is employed in the electrical machine fault diagnosis, and the simulation results verified the correctness and effectiveness of the scheme proposed. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Miao2:2008:ijcnn, author = "Jun Miao and Lijuan Duan and Laiyun Qing and Xilin Chen and Wen Gao", title = "Visual Context Representation using a Combination of Feature-driven and Object-driven Mechanisms", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN1011.pdf}, url = {}, size = {}, abstract = {Visual context between objects is an important cue for object position perception. How to effectively represent the visual context is a key issue to study. Some past work introduced task-driven methods for object perception, which led a large coding quantity. This paper proposes an approach that incorporates feature-driven mechanism into object-driven context representation for object locating. As an example, the paper discusses how a neuronal network encodes the visual context between feature salient regions and human eye centers with as little coding quantity as possible. A group of experiments on efficiency of visual context coding and object searching are analyzed and discussed, which show that the proposed method decreases the coding quantity and improve the object searching accuracy effectively. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Graham:2008:ijcnn, author = "James Graham and Janusz A. Starzyk", title = "A Hybrid Self-Organizing Neural Gas Based Network", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN1013.pdf}, url = {}, size = {}, abstract = {This paper examines the neural gas networks proposed by Martinetz and Schulten [1] and Fritzke [2] in an effort to create a more biologically plausible hybrid version. The hybrid algorithm proposed in this work retains most of the advantages of the Growing Neural Gas (GNG) algorithm while adapting a reduced parameter and more biologically plausible design. It retains the ability to place nodes where needed, as in the GNG algorithm, without actually having to introduce new nodes. Also, by removing the weight and error adjusting parameters, the guesswork required to determine parameters is eliminated. When compared to Fritzke's algorithm, the hybrid algorithm performs admirably in terms of the quality of results it is slightly slower due to the greater computational overhead. However, it is more biologically feasible and somewhat more flexible due to its hybrid nature and lack of reliance on adjustment parameters. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(An:2008:ijcnn, author = "Jing An and Qi Kang and Lei Wang and Qidi Wu", title = "A Turbo Codes Optimization Method Using Particle Swarm Algorithm", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN1014.pdf}, url = {}, size = {}, abstract = {Turbo Codes present a new direction for the channel encoding, especially since they were adopted for multiple norms of telecommunications, such as deeper communication, etc. To obtain an excellent performance, it is necessary to design robust turbo code interleaver and decoding algorithms. In this paper, we are investigating particle swarm algorithm as a promising optimization method to find good interleaver for the large frame sizes, as well as design the decoding optimization mode (PSO-Turbo); and apply the proposed PSO-Turbo codes mode to the security radio data transmission; in which, a kind of transport control proposal based on PSO-Turbo optimizer for CBTC wireless channel is designed and simulated to validate our method. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Shi2:2008:ijcnn, author = "Min Shi and Haifeng Wu and Hasan Fleyeh", title = "Support Vector Machines for Traffic Signs Recognition", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN1015.pdf}, url = {}, size = {}, abstract = {In many traffic sign recognition system, one of the main tasks is to classify the shapes of traffic sign. In this paper, we have developed a shape-based classification model by using support vector machines. We focused on recognizing seven categories of traffic sign shapes and five categories of speed limit signs. Two kinds of features, binary image and Zernike moments, were used for representing the data to the SVM for training and test. We compared and analyzed the performances of the SVM recognition model using different feature representations and different kernels and SVM types. Our experimental data sets consisted of 350 traffic sign shapes and 250 speed limit signs. Experimental results have shown excellent results, which have achieved 100percent accuracy on sign shapes classification and 99percent accuracy on speed limit signs classification. The performance of SVM model highly depends on the choice of model parameters. Two search algorithms, grid search and simulated annealing search have been implemented to improve the performances of our classification model. The SVM model were also shown to be more effective than Fuzzy ARTMAP model. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Parker:2008:ijcnn, author = "Matt Parker and Bobby D. Bryant", title = "Neuro-visual Control in the Quake II Game Engine", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN1016.pdf}, url = {}, size = {}, abstract = {The first-person-shooter Quake II is used as a platform to test neuro-visual control and retina input layouts. Agents are trained to shoot a moving enemy as quickly as possible in a visually simple environment, using a neural network controller with evolved weights. Two retina layouts are tested, each with the same number of inputs: first, a graduated density retina which focuses near the center of the screen and blurs outward; second, a uniform retina which focuses evenly across the screen. Results show that the graduated density retina learns more successfully than the uniform retina. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Marquez:2008:ijcnn, author = "Jose Manuel Marquez and Juan Antonio Ortega", title = "Creating Adaptive Learning Paths using Ant Colony Optimization and Bayesian Networks", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN1017.pdf}, url = {}, size = {}, abstract = {This paper presents a new way to combine two different approaches of artificial intelligence looking for the best path in a graph, Ant Colony Optimization and Bayesian Networks. The main objective is to develop a learning management system which will have the capability of adapting the learning path to the learner's needs in execution time, taking into account the pedagogical weight of each learning unit and the system's social behavior. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Huang7:2008:ijcnn, author = "Jian Huang and Xiaoming Chen and P C Yuen and Jun Zhang and W S Chen and J H Lai", title = "Kernel Parameter Optimization for Kernel-based LDA Methods", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN1018.pdf}, url = {}, size = {}, abstract = {Kernal approach has been employed to solve classification problem with complex distribution by mapping the input space to higher dimensional feature space. However, one of the crucial factors in the Kernel approach is the choosing of kernel parameters which highly affect the performance and stability of the kernel-based learning methods. In view of this limitation, this paper adopts the Eigenvalue Stability Bounded Margin Maximization (ESBMM) algorithm to automatically tune the multiple kernel parameters for Kernel-based LDA methods. To demonstrate its effectiveness, the ESBMM algorithm has been extended and applied on two existing kernel-based LDA methods. Experimental results show that after applying the ESBMM algorithm, the performance of these two methods are both improved. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Okada:2008:ijcnn, author = "Shogo Okada and Osamu Hasegawa", title = "On-line Learning of Sequence Data Based on Self-Organizing Incremental Neural Network", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN1020.pdf}, url = {}, size = {}, abstract = {This paper presents an on-line, continuously learning mechanism for sequence data. The proposed approach is based on SOINN-DTW method (Okada and Hasegawa, 2007), which is designed for learning of sequence data. It is based on Self-Organizing Incremental Neural Network (SOINN) and Dynamic Time Warping (DTW). Using SOINN's function represents the topological structure of online input data, the output distribution of each states is represented and adapted in a self-organizing manner corresponding to online input data. Consequently, this method can train a network and estimate parameters of the output distribution using new (on-line) data continuously, based on scarce batch-training data. Through online learning, the recognition accuracy is improved continuously. To confirm the effectiveness of the on-line learning mechanism of SOINN-DTW, we present an extensive set of experiments that demonstrate how our method outperforms the online learning method of HMM in classifying phoneme data. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Uchitani:2008:ijcnn, author = "Yumiko Uchitani and Yoshifumi Nishio", title = "Synchronization Patterns Generated in a Ring of Cross-Coupled Chaotic Circuits", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN1021.pdf}, url = {}, size = {}, abstract = {Studies on chaos synchronization in coupled chaotic circuits are extensively carried out in various fields. In this study, synchronization patterns generated in a ring of crosscoupled chaotic circuits are investigated. Computer simulations show that this coupled system produces several phase patterns. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Altahhan:2008:ijcnn, author = "Abdulrahman Altahhan and Kevin Burn", title = "Visual Robot Homing Using Sarsa(λ), Whole Image Measure, and Radial Basis Function", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN1022.pdf}, url = {}, size = {}, abstract = {This paper describes a model for visual homing. It uses Sarsa(λ) as its learning algorithm, combined with the Jeffery Divergence Measure (JDM) as a way of terminating the task and augmenting the reward signal. The visual features are taken to be the histograms difference of the current view and the stored views of the goal location, taken for all RGB channels. A radial basis function layer acts on those histograms to provide input for the linear function approximator. An on-policy on-line Sarsa(λ) method was used to train three linear neural networks one for each action to approximate the action-value function with the aid of eligibility traces. The resultant networks are trained to perform visual robot homing, where they achieved good results in finding a goal location. This work demonstrates that visual homing based on reinforcement learning and radial basis function has a high potential for learning local navigation tasks. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Acampora:2008:ijcnn, author = "Giovanni Acampora and Matteo Gaeta", title = "Optimizing Learning Path Selection Through Memetic Algorithms", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN1023.pdf}, url = {}, size = {}, abstract = {e-Learning is a critical support mechanism for industrial and academic organizations to enhance the skills of employees and students and, consequently, the overall competitiveness in the new economy. The remarkable velocity and volatility of modern knowledge require novel learning methods offering additional features as efficiency, task relevance and personalization. The main aim of adaptive eLearning is to support content and activities, personalized to specific needs and influenced by specific preferences of the learner. This paper describes a collection of models and processes for adapting an e-Learning system to the learner expectations and to formulate objectives in a dynamic intelligent way. Precisely, our proposal exploits ontological representations of learning environment and a memetic optimization algorithm capable of generating the best learning presentation in an efficient and qualitative way. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Perfilieva:2008:ijcnn, author = "Irina Perfilieva and Vilem Novak and Viktor Pavliska and Antonín Dvořak and Martin Štěpnička", title = "Analysis and Prediction of Time Series Using Fuzzy Transform", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN1024.pdf}, url = {}, size = {}, abstract = {A new methodology for forecasting of time series is proposed. It is based on combination of two techniques: fuzzy transform and perception-based logical deduction on the basis of learned linguistic description. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Chen14:2008:ijcnn, author = "Cunjian Chen ", title = "Information Fusion of Wavelet Projection Features for Face Recognition", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN1025.pdf}, url = {}, size = {}, abstract = {This paper proposes a novel feature extraction method for face recognition in the wavelet domain called wavelet projection entropy (WPE). First, the projection entropy features from each wavelet subband are computed along the vertical and horizontal direction after the division. Then information fusion scheme is applied to integrate results obtained from each subband. Experiments show that WPE can extract the meaningful information from the wavelet domain. Meanwhile the decision level fusion achieves the best recognition rate among the three common information fusion methods. The proposed algorithms are validated on ORL and Yale face database for different pose and expression changes analysis. Detailed comparisons with previous published results are provided and it shows that our proposed algorithm performs very well. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Mehboob:2008:ijcnn, author = "Zareen Mehboob and Stefano Panzeri and Mathew E. Diamond and Hujun Yin", title = "Topological Clustering of Synchronous Spike Trains", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN1028.pdf}, url = {}, size = {}, abstract = {This paper describes a topological clustering of synchronous spike trains recorded in rat somatosensory cortex in response to sinusoidal vibrissal stimulations characterized by different frequencies and amplitudes. Discrete spike trains are first interpreted as continuous synchronous activities by a smoothing filter such as causal exponential function. Then clustering is performed using the self-organizing map, which yields topologically ordered clusters of responses with respect to the stimuli. The grouping is formed mainly along the product of amplitude and frequency of the stimuli. This result coincides with the result obtained previously using mutual information analysis on the same data set. That is, the response is proportional in logarithm to the energy of the vibration. It suggests that such clustering can naturally find underlying stimulus-response patterns and it also seems to associate the spike-count based mutual information decoding with temporal patterns of the neuronal activities. The study also shows that causal decaying exponential kernel is better than noncausal Gaussian kernel in interpreting the discrete spike trains into continues ones and produces better clusters. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Lo:2008:ijcnn, author = "James Ting-Ho Lo ", title = "Probabilistic Associative Memories", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN1030.pdf}, url = {}, size = {}, abstract = {Recurrent multilayer network structures and Hebbian learning are two of the research results on thebrain that are widely accepted by neuroscientists. The for- mer led to multilayer perceptrons (MLPs) and recurrent MLPs, and the latter to associative memories. This pa- per presents recurrent and/or multilayer networks of novel associative memories, each being a new functional model of the neuron with its dendritic weights. The recurrent and/or multilayer networks are called probabilistic asso- ciative memory (PAMs) and the functional model of the neuron is called processing element. Each processing el- ement with its weights learns by the Hebbian rule and computes a subjective conditional probability as well as a point estimate of the class label of the cause(s) within its receptive ?eld. Detected and recognized causes are in- tegrated by the processing elements, aided by feedbacks, from layer to layer and from time to time into a spatial and/or temporal hierarchy of causes to facilitate under- standing of the pattern or sequence of patterns presented to the PAM. Mainly due to multilayer and recurrent struc- tures and Hebbian learning, PAMs have many such desir- able properties of a pattern recognizer or learning machine as (1) fast learning and responding to large temporal and spatial patterns; (2) detecting and recognizing multiple causes associatively and hierarchically; (3) having good generalization capabilities; (4) representing and resolving ambiguity and uncertainty with conditional probabilities }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Li13:2008:ijcnn, author = "Yanyan Li and Mingkai Dong and Ronghuai Huang", title = "Special Interest Groups Discovery and Semantic Navigation Support within Online Discussion Forums", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN1032.pdf}, url = {}, size = {}, abstract = {Online discussion forums provide open workspace allowing learners to share information, exchange ideas, address problems and discuss on specific themes. But the substantial impediment to its promotion as effective e-learning facility lies in the continuously increasing postings but with discrete and incoherent structure as well as the loosely-tied learners with response-freeness. This paper proposes a hybrid approach to automatically discover special interest groups within discussion forums. Once a learner becomes a member of a special interest group, he will be informed of other learning companions to enhance their in-depth communication and learning, and the newly-emerged related information will be proactively pushed to him as well. Furthermore, by identifying the posting themes and types, this paper presents a semantic search to assist learners navigating through well-structured and coherent postings to meet their learning demands. The proposed approach has been integrated into a discussion forum, and the experimental results show that the approach is feasible and efficient, enabling the effective discovering of interest groups and proper demand-driven navigational guidance. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wan:2008:ijcnn, author = "Xin Wan and Toshie Ninomiya and Toshio Okamoto", title = "A Learner's Role-based Multi Dimensional Collaborative Recommendation (LRMDCR) for Group Learning Support", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN1033.pdf}, url = {}, size = {}, abstract = {This article argues for the new solution of personal recommender systems that can provide learners with suitable learning objects to learn in group learning. In order to improve the ``educational provision'' to implement the e-learning recommender system, we propose a new recommendation approach which has been proven to be more suitable to realize personalized recommendation based on not only learning histories but also learning activities and learning processes which is defined as LRMDCR (a Learner's Role-based Multi-dimensional Collaborative Recommendation) by us. In the approach, firstly we use the Markov Chain Model to divide the group learners into advanced learners and beginner learners by using the learners' learning activities and learning processes. Secondly we use the multidimensional collaborative filtering to decide the recommendation learning objects to every learner of the group. We believe our approach is more effective and efficient to group learning. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Lin4:2008:ijcnn, author = "Hsio-Yi Lin and An-Pin Chen", title = "Application of Dynamic Financial Time-Series Prediction on the Interval Artificial Neural Network Approach with Value-at-Risk Model", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN1037.pdf}, url = {}, size = {}, abstract = {Artificial Neural Networks (ANNs) are promising approaches for financial time-series prediction. This study adopts a hybrid approach, called a Fuzzy BPN, consisting of a Back-Propagation Neural Network (BPN) and a fuzzy membership function which takes advantage of the ANNs' nonlinear features and interval values instead of the shortcoming of ANNs' single-point estimation. To employ the two characteristics mentioned above, a dynamic intelligent time-series forecasting system will be built more efficiently for practical financial predictions. Additionally, with the liberalization and opening of financial markets, the relationships among financial commodities became much closer and complicated. Hence, establishing a perfect measure approach to evaluate investment risk has become a critical issue. The objective of this study is not only to achieve higher efficiency in dynamic financial time-series predictions but also a more effective financial risk control with Value-at-Risk methodology, which is called Fuzzy-VaR BPN model in this study. By extending to the financial market environment, it is expected that wider and more suitable applications in financial time-series and risk management problems would be covered. Moreover, the Fuzzy-VaR BPN model would be applied to the Taiwan Top50 Tracker Fund to demonstrate the capability of our study. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Kouzani:2008:ijcnn, author = "A. Z. Kouzani ", title = "Subcellular Localisation of Proteins in Fluorescent Microscope Images Using a Random Forest", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN1038.pdf}, url = {}, size = {}, abstract = {This paper presents a system that employs random forests to formulate a method for subcellular localisation of proteins. A random forest is aan ensemble learner that grows classification trees. Each tree produces a classification decision, and an integrated output is calculated. The system classifies the protein-localisation patterns witjin fluorescent microscope images. 2D images of HeLa cells that include all major classes of subcellular structures, and the associated feature set are used. The performance of the developed system is compared against that of the support vector machine and decision tree approaches. Three experiments are performed to study the influence of the training and test set size on the performance of the examined methods. The calculated classification errors and execution times are presented and discussed. The lowest classification error (2.9percent) has been produced by the developed system. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(He4:2008:ijcnn, author = "Fei He and Martin Brown and Lam Fat Yeung", title = "On the Complexity — Sensitivity Trade-Off for the NF-κB Pathway Modeling", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN1039.pdf}, url = {}, size = {}, abstract = {An important aspect of systems biology research is the so-called ''reverse engineering'' of cellular metabolic dynamics from measured input-output data. This allows researchers to estimate and validate both the pathway's structure as well as the kinetic constants. In this paper, a regularization based method which performs model structure selection is developed and applied to the problem of analyzing how existing pathway knowledge can be used as a prior investigate the model change complexity/sensitivity trade-off. Specifically, a 1-norm prior on parameter deviations from an existing model of the IκB-NF-κB pathway is combined with new experimental data and an analysis is performed to determine which are the most relevant components to alter. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Daqi:2008:ijcnn, author = "Gao Daqi and Yang Zeping and Sun Jianli", title = "Modular Neural Networks for Estimating Odor Concentrations", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN1042.pdf}, url = {}, size = {}, abstract = {The concentration estimation for multiple kinds of odors is regarded first as multiple two-class classification and then as multiple approximation problems, and solved by multiple single-output multi-layer perceptrons (MLPs) lined up in two parallel rows. A pair of MLPs in cascade is on behalf of a specified odor. n pairs of MLPs represent n kinds of odors, one for one. An MLP in the first row separates its represented odor from the others. Because the two-class training subsets are often unbalanced, the samples from the minority sides are virtually reinforced. The generalization of an MLP is limited in local regions with respect to the distribution of the represented odor. An MLP in the second row approximates the relationship between the responses of the sensor array and the concentrations of the represented odor. A sample is assigned to a kind of odor by the MLP with the maximum output in the first row, and then its concentration is estimated by another MLP in the corresponding pair. The effectiveness of the proposed MLP models is verified by the experiments for 4 kinds of fragrant materials as well as their extended dataset. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Zhang13:2008:ijcnn, author = "Wenle Zhang and Rutao Luo", title = "An Adaptive Feedback Neural Network Approach to Job-shop Scheduling Problem", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN1045.pdf}, url = {}, size = {}, abstract = {Job-shop scheduling problem is a typical representative of NP-complete problems and it is also a popular topic for the researchers during the recent decades. Lots of artificial intelligence techniques were used to solve this kind of problems, such as: Genetic Algorithm, Tabu Searching Method, Simulated Annealing and Neural Network. Based on the previous research of Zhou [2] and Willems [9], this paper proposes a neuro-dynamic model with two heuristics to solve job-shop scheduling problems. The stability of this neural network is proven by using Lyapunov Stability Theorem. Both small-size and big-size problems are used to test this neural network. Simulation results of some tested samples are given. And the performance of this neural network is compared with several other neural works under experimental conditions. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wu3:2008:ijcnn, author = "Yunfeng Wu and Yachao Zhou and Sin-Chun Ng and Yixin Zhong", title = "Combining Neural-Based Regression Predictors Using an Unbiased and Normalized Linear Ensemble Model", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN1046.pdf}, url = {}, size = {}, abstract = {In this paper, we combined a group of local regression predictors using a novel unbiased and normalized linear ensemble model (UNLEM) for the design of multiple predictor systems. In the UNLEM, the optimization of the ensemble weights is formulated equivalently to a constrained quadratic programming problem, which can be solved with the Lagrange multiplier. In our simulation experiments of data regression, the proposed multiple predictor system is composed of three different types of local regression predictors, and the effectiveness evaluation of the UNLEM was carried out on eight synthetic and four benchmark data sets. Results of the UNLEM's performance in terms of mean-squared error are significantly lower, in comparison with the popular simple average ensemble method. Moreover, the UNLEM is able to provide the regression predictions with a relatively higher normalized correlation coefficient than the results obtained with the simple average approach. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Pasila:2008:ijcnn, author = "Felix Pasila and Sautma Ronni and Thiang and Lie Handra Wijaya", title = "Long-term Forecasting in Financial Stock Market using Accelerated LMA on Neuro-Fuzzy Structure and Additional Fuzzy C-Means Clustering for Optimizing the GMFs", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN1047.pdf}, url = {}, size = {}, abstract = {The paper describes the combination of two modeling strategies between the accelerated Levenberg- Marquardt algorithm (accelerated LMA) on neuro-fuzzy approach and fuzzy clustering algorithm C-Means that can be used to forecast financial stock market such as Jakarta Stock Indices (JCI) using the Takagi-Sugeno (TS) type multi-input single-output (MISO) neuro-fuzzy network efficiently. The accelerated LMA algorithm is efficient in the common sense that it can bring the performance index of the network, such as the root mean squared error (RMSE), down to the desired error goal much faster than the simple Levenberg-Marquardt algorithm (LMA). The C-Means fuzzy clustering algorithm allows the selection of initial parameters of fuzzy membership functions, e.g. mean and variance parameters of Gaussian membership functions of neuro-fuzzy networks, which are otherwise selected randomly. The initial parameters of fuzzy membership functions, which result in low Sum Squared Error (SSE) value with given training data of neuro-fuzzy network, are further fine tuned during the network training. As a final point, the above training algorithm is tested on TS type MISO neuro-fuzzy structure for long-term forecasting application of Stock Market in Indonesia. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Liu15:2008:ijcnn, author = "Nan Liu and Han Wang", title = "Feature Selection in Frequency Domain and Its Application to Face Recognition", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN1050.pdf}, url = {}, size = {}, abstract = {Face recognition system usually consists of components of feature extraction and pattern classification. However, not all of extracted facial features contribute to the classification phase positively because of the variations of illumination and poses in face images. In this paper, a three-step feature selection algorithm is proposed in which discrete cosine transform (DCT) and genetic algorithms (GAs) as well as dimensionality reduction methods are used to create a combined framework of feature acquisition. In details, the face images are first transformed to frequency domain through DCT. Then GAs are used to seek for optimal features in the redundant DCT coefficients where the generalization performance guides the searching process. The last step is to reduce the dimension of selected features. In experiments, two face databases are used to evaluate the effectiveness of the proposed method. In addition, an entropy-based improvement is also proposed. The experimental results present the superiority of selected frequency features. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Chu:2008:ijcnn, author = "Xiao-Lei Chu and Chao Ma and Jing Li and Bao-Liang Lu and Masao Utiyama and Hitoshi Isahara", title = "Large-Scale Patent Classification with Min-Max Modular Support Vector Machines", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN1054.pdf}, url = {}, size = {}, abstract = {Patent classification is a large-scale, hierarchical, imbalanced, multi-label problem. The number of samples in a real-world patent classification typically exceeds one million, and this number increases every year. An effective patent classifier must be able to deal with this situation. This paper discusses the use of min-max modular support vector machine (M3-SVM) to deal with large-scale patent classification problems. The method includes three steps: decomposing a large-scale and imbalanced patent classification problem into a group of relatively smaller and more balanced two-class subproblems which are independent of each other, learning these subproblems using support vector machines (SVMs) in parallel, and combining all of the trained SVMs according to the minimization and the maximization rules. M3-SVM has two attractive features which are urgently needed to deal with largescale patent classification problems. First, it can be realized in a massively parallel form. Second, it can be built up incrementally. Results from experiments using the NTCIR-5 patent data set, which contains more than two million patents, have confirmed these two attractive features, and demonstrate that M3-SVM outperforms conventional SVMs in terms of both training time and generalization performance. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Yang11:2008:ijcnn, author = "Hao-Yung Yang and Jiin-Chyr Hsu and Yung-Fu Chen and Xiaoyi Jiang and Tainsong Chen", title = "Using Support Vector Machine to Construct a Predictive Model for Clinical Decision-Making of Ventilation Weaning", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN1055.pdf}, url = {}, size = {}, abstract = {Ventilator weaning is the process of discontinuing mechanical ventilation from patients with respiratory failure. Ventilator support should be withdrawn as soon as possible when it is no longer necessary in order to reduce the likelihood of known nosocomial complications and costs. Previous investigation indicated that clinicians were often wrong when predicting weaning outcome. The motivation of this study is that although successful ventilator weaning of ICU patients has been widely studied, indicators for accurate prediction are still under investigation. The goal of this study is to find a prediction model for successful ventilator weaning using variables such physiological variables, clinical syndromes, demographic variables, and other useful information. The data obtained from 231 patients who had been supported by mechanical ventilator for longer than 21 days within the period from Nov. 2002 to Dec. 2005 were studied retrospectively. Among them, 188 patients were recruited from the period within Nov. 2002 to Dec. 2004 and the other 43 patients from Jan. 2004 to Dec. 2005. All the patients were clinically stable before being considered to undergo a weaning trial. Twenty-seven variables in total were collected with only 6 variables reaching significant level (p < 0.05) were used for support vector machine (SVM) classification after statistical analysis. The results show that the constructed model is valuable in assisting clinical doctors to decide if a patient is ready to wean from the ventilator with the sensitivity, specificity, and accuracy as high as 94.74percent, 95.83percent, and 95.35percent, respectively. Further prospective bed side test is needed to verify the efficacy of the model. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Luciw:2008:ijcnn, author = "Matthew D. Luciw and Juyang Weng", title = "Topographic Class Grouping with Applications to 3D Object Recognition", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN1056.pdf}, url = {}, size = {}, abstract = {The cerebral cortex uses a large number of topdown connections, but the roles of the top-down connections remain unclear. Through end-to-end (sensor-to-motor) multilayered networks that use three types of connections (bottom-up, lateral, and top-down), the new Topographic Class Grouping (TCG) mechanism shown in this paper explains how the topdown connections influence (1) the type of feature detectors (neurons) developed and (2) their placement in the neuronal plane. The top-down connections boost the variations in the neuronal between class directions during the training phase. The first outcome of this top-down boosted input space is the facilitation of the emergence of feature detectors that are purer, measured statistically by the average entropy of the neurons' development. The relatively purer neurons are more ''abstract,'' i.e., characterizing class-specific (or motorspecific) input information, resulting in better classification rates. The second outcome of this top-down boosted input space is the increase of the distance between input samples that belong to different classes, resulting in a farther separation of neurons according to their class. Therefore, neurons that respond to the same class become relatively nearer. This results in TCG, measured statistically by a smaller within-class scatter of responses when the neuronal plane has a fixed size. Although these mechanisms are potentially applicable to any pattern recognition applications, we report quantitative effects of these mechanisms for 3D object recognition of center-normalized, background-controlled objects. TCG has enabled a significant reduction of the recognition errors. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Huang8:2008:ijcnn, author = "Sue-Fn Huang and Liang-Ying Wei and Jr-Shian Chen and Ching-Hsue Cheng", title = "RBF-NN Based Fusion Model for E-learning Achievement Evaluation", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN1057.pdf}, url = {}, size = {}, abstract = {The trend of using e-learning as a learning and teaching tool is widely adopted by numerous organizations. In order to enhance the e-learning efficiency, there are some advantages in e-learning system: (1) repeatable (learning), (2) timeless, (3) distanceless and (4) spaceless. Because ``student-centered'' instruction is likely to become the primary trend in education, the e-learning system should consider both of personalization and adaptability. By using the online examination, we can obtain the learning levels of students to adjust the learning schedule instantly for each one and build more adaptive e-learning system. But, the biases of assessments are assigned by teacher under un-controllable condition (i.e. tiredness, preference). To overcome the drawback, this paper proposes a fusion model to assign learning achievements based on RBF-NN (radial basis function-neural networks) for assisting teachers. Proposed model uses similarity threshold to remove inconsistent data and make our achievements evaluation more reliable. To verify our model, this paper collects e-learning online examination data to illustrate and compare the performance of proposed model with conventional RBF-NN model. The performance comparison results show that the proposed model outperforms the conventional RBF model. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Mańndziuk:2008:ijcnn, author = "Jacek Mańndziuk ", title = "Some Thoughts on Using Computational Intelligence Methods in Classical Mind Board Games", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN1061.pdf}, url = {}, size = {}, abstract = {In the last two decades the advancement of AI/CI methods in classical board and card games (such as Chess, Checkers, Othello, Go, Poker, Bridge, ...) has been enormous. In nearly all ''world famous'' board games humans have been decisively conquered by machines (actually Go remains almost the last redoubt of human supremacy). In the above perspective the natural question is whether there is still any need for further development of CI methods in this area. What kind of goals can be achieved on this path? What are (if any) the challenging problems in the field? The paper tries to discuss these issues with respect to classical board mind games and provides (highly subjective) partial answers to some of the open questions. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Chen15:2008:ijcnn, author = "Yeou-Jiunn Chen and Jiunn-Liang Wu and Hui-Mei Yang", title = "Automatic Speech Recognition and Dependency Network to Identification of Articulation Error Patterns", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN1065.pdf}, url = {}, size = {}, abstract = {Articulation errors will seriously reduce speech intelligibility and the ease of spoken communication. Typically, a language therapist uses his or her clinical experience to identify articulation error patterns, a time-consuming and expensive process. This paper presents a novel automatic approach to identifying articulation error patterns and providing error information of pronunciation to assist the linguistic therapist. A photo naming task is used to capture examples of an individual's articulation patterns. The collected speech is automatically segmented and labeled by a speech recognizer. The recognizer's pronunciation confusion network is adapted to improve the accuracy of the speech recognizer. The modified dependency network and a multiattribute decision model are applied to identify articulation error patterns. Experimental results reveal the usefulness of the proposed method and system. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Yang12:2008:ijcnn, author = "Haiqin Yang and Kaizhu Huang and Irwin King and Michael R. Lyu", title = "Efficient Minimax Clustering Probability Machine by Generalized Probability Product Kernel", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN1068.pdf}, url = {}, size = {}, abstract = {Minimax Probability Machine (MPM), learning a decision function by minimizing the maximum probability of misclassification, has demonstrated very promising performance in classification and regression. However, MPM is often challenged for its slow training and test procedures. Aiming to solve this problem, we propose an efficient model named Minimax Clustering Probability Machine (MCPM). Following many traditional methods, we represent training data points by several clusters. Different from these methods, a Generalized Probability Product Kernel is appropriately defined to grasp the inner distributional information over the clusters. Incorporating clustering information via a non-linear kernel, MCPM can fast train and test in classification problem with promising performance. Another appealing property of the proposed approach is that MCPM can still derive an explicit worst-case accuracy bound for the decision boundary. Experimental results on synthetic and real data validate the effectiveness of MCPM for classification while attaining high accuracy. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Del-Moral-Hernandez:2008:ijcnn, author = "Emilio Del-Moral-Hernandez ", title = "RPE-BAM Networks: Bidirectional Heteroassociation in Neural Networks of Recursive Nodes with Rich Dynamics", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN1072.pdf}, url = {}, size = {}, abstract = {This paper addresses networks of recursive processing elements (RPEs) that exhibit, even at the single node level, rich dynamics and switching between ordered, erratic (chaotic) and diverse periodic trajectories. These networks are considered here for the implementation of bidirectional heteroassociation. These newly proposed architectures are named RPE-BAM. Dynamic mixture of erratic and ordered dynamics is explored, during the episodes of: a) input prompting; b) search for the embedded heteroassociations compatible with the input pattern; c) the production of an heteroassociation pair. Concepts and design methods on RPEBAMs and on parametric coupling of recursive nodes are discussed, and numerical experiments are analyzed, showing robust operation of the RPE-BAM architecture. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Hussin:2008:ijcnn, author = "Mahmoud F. Hussin and Mahmoud R. farra and Yasser El-Sonbaty ", title = "Extending the Growing Hierarchal SOM for Clustering Documents in Graphs Domain", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN1073.pdf}, url = {}, size = {}, abstract = {The Growing Hierarchal Self-Organizing Map (GHSOM) is the most efficient model among the variants of SOM. It is used successfully in document clustering and in various pattern recognition applications effectively. The main constraint that limits the implementation of this model and all the other variants of SOM models is that they work only with Vector Space Model (VSM). In this paper, we extend the GHSOM to work in the graph domain to enhance the quality of clusters. Specifically, we represent the documents by graphs and then cluster those documents by using a new algorithm GGHSOM: Graph-based Growing Hierarchal SOM after modifying its operations to work with the graph instead of vector space. We have tested the G-GHSOM on two different document collections using three different measures for evaluating clustering quality. The experimental results of the proposed G-GHSOM show an improvement in terms of clustering quality compared to classical GHSOM. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Pacheco:2008:ijcnn, author = "Diogo F. Pacheco and Flavio R. S. Oliveira and Fernando B. Lima Neto", title = "Including Multi-Objective Abilities in the Hybrid Intelligent Suite for Decision Support", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN1074.pdf}, url = {}, size = {}, abstract = {Hybrid intelligent systems (HIS) are very successful in tackling problems comprising of more than one distinct computational subtask. For instance, decision-making problems are good candidates for HIS because of their frequent dual nature. This is because supporting decision-making most often involves two phases: (i) forecasting decision scenarios and (ii) searching in those scenarios. In addition to reducing the inherent uncertainty and effort in decision making, previous works in the area of decision support have shown that some of the inconveniences of the `Inverse Problem' can be overcome by the use of Hybrid Intelligent Decision Suites (HIDS). This paper extends HIDS by including a third module that deals with multi-objective (MO) tasks through Evolutionary Multi- Objective Optimization (EMOO). This EMOO module helps by creating the Pareto front for each forecast scenario produced by Artificial Neural Networks (ANN), acting here as the predictive engine of the decision support system. In order to interface better with decision makers, we use a fuzzy-heuristic module of the original HIDS. To test this concept we have applied our new approach to two distinct problems: (1) diagnosis of heart diseases (of the proben-1 data-set) and (2) automobile feature selection (of UCI data-set). Results have indicated that this new ensemble of intelligent techniques enhances the quality of decision making. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Koyama:2008:ijcnn, author = "Jumpei Koyama and Masahiro Kato and Akira Hirose", title = "Distinction Between Handwritten and Machine-Printed Characters with no Need to Locate Character or Text Line Position", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN1075.pdf}, url = {}, size = {}, abstract = {In this paper, we propose a method for distinction between handwritten and machine-printed characters with no need to locate positions of characters or text lines. We call the proposed method `spectrum-based local fluctuation detection method. The method transforms local regions in document images into power spectrum to extract feature values which represent fluctuations caused by handwriting. We employ a multilayer perceptron for the distinction. We feed the obtained feature values to a preliminarily optimized multilayer perceptron (MLP), and the MLP yields likelihood of handwriting. We prepare a document image which has randomly aligned characters for an experiment. The experimental result shows that our method can distinguish handwritten and machine-printed characters with no need to locate positions of characters or text lines. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Liu16:2008:ijcnn, author = "Xiaoming Liu and Zhaohui Wang and Jun Liu and Zhilin Feng", title = "Face Recognition with Locality Sensitive Discriminant Analysis Based on Matrix Representation", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN1076.pdf}, url = {}, size = {}, abstract = {Locality Sensitive Discriminant Analysis (LSDA) algorithm is a new data analysis tool for studying the class relationship between data points, which can use local geometry structure of the data manifold and discriminant information at the same time. A major disadvantage of LSDA is it that can only deal with vector data, and thus is often confronted with singularity problem. In this paper, an extension of LSDA is proposed, called two-dimensional locality sensitive discriminant analysis (2DLSDA), which is directly based on 2D image matrices for face recognition, can overcome the singularity problem and use the spatial information among pixels more effectively. Besides, based on the Schur decomposition, the projection matrices can be obtained efficiently with high numerical stablity, and orthogonality of projection matrix is guaranteed. Experiments on both ORL and Yale datasets demonstrate that the proposed method can achieve better performance than PCA, LDA and LSDA methods. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Feng2:2008:ijcnn, author = "Du Feng and Qian Qingquan ", title = "Heterogeneous Wireless Networked Control Systems Based on Modify Smith Predictor and CMAC-PID Control", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN1078.pdf}, url = {}, size = {}, abstract = {The cerebellar model articulation controller (CMAC) neural network is a practical tool for improving existing nonlinear control systems, and it can effectively reduce tracking error of control system. In order to effectively restrain the impact of network delays for wireless networked control systems (WNCS), a novel approach is proposed that modified Smith predictor combined with CMAC-PID control for the heterogeneous wireless networked control systems (HWNCS). The HWNCS adopts cascade control system structure, use P control and CMAC-PID control, and data communications adopt heterogeneous wireless networks in the inner and outer loops. Based on modified Smith predictor, achieve complete compensations for the delays of networks and controlled plants. Because modified Smith predictor does not include network delay models, it is no need for measuring, identifying or estimating network delays on line. Therefore it is applicable to some occasions that network delays are larger than one, even tens of sampling periods. Based on IEEE 802.15.4 (ZigBee) in the inner loop and IEEE 802.11b/g (WLAN) in the outer loop, and there are data packets loss in the loops. The results of simulation show validity of the control scheme, and can improve dynamic performance, enhance robustness, self-adaptability and anti-jamming ability. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Ergüt:2008:ijcnn, author = "Salih Ergüt and Ramesh R. Rao and Özgür Dural", title = "Localization via Multipath Strengths in a CDMA2000 Cellular Network Using Neural Networks", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN1079.pdf}, url = {}, size = {}, abstract = {Localization is becoming more important with increasing number of cellular phone users. Due to safety aspects with increased emergency calls from mobile phones, new applications related to location based services, and the network optimization with increasing load, localization draws interest from both the academia and the industry. In this study, we propose a neural network based algorithm that uses multipath strengths to locate a mobile user without a GPS receiver. We validated our algorithm in a commercial network. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Pham:2008:ijcnn, author = "Minh Tuan Pham and Kanta Tachibana and Eckhard Hitzer and Sven Buchholz and Takeshi Furuhashi", title = "Feature Extractions with Geometric Algebra for Classification of Objects", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN1080.pdf}, url = {}, size = {}, abstract = {Most conventional methods of feature extraction do not pay much attention to the geometric properties of data, even in cases where the data have spatial features. In this study we introduce geometric algebra to undertake various kinds of feature extraction from spatial data. Geometric algebra is a generalization of complex numbers and of quaternions, and it is able to describe spatial objects and relations between them. This paper proposes to use geometric algebra to systematically extract geometric features from data given in a vector space. We show the results of classification of hand-written digits, which were classified by feature extraction with the proposed method. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Vasudevan:2008:ijcnn, author = "Bintu G. Vasudevan and Sorawish Dhanapanichkul and Rajesh Balakrishnan", title = "Flowchart Knowledge Extraction on Image Processing", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN1081.pdf}, url = {}, size = {}, abstract = {In the paper, we present an approach of image processing analysis to extract flowchart information from digital imagery. Firstly, flowchart imagery is processed to extract the text components and then extract the geometrical shapes components. We analyze text, and various geometrical shapes present in flowchart and carry out a variety of processes such as image segmentation, shape description, text and geometric components extraction, recognition and linking. The text components are extracted and then geometrical components are extracted. we also proposed a auto directional transformation of contour chain method for shape description. The internal relationship between the components is set up by tracing the flow lines which connect different components. Thus a flowchart is correctly extracted. The extracted components are output to metadata (XML format) which is machine readable. These metadata can be archived, store as knowledge base or shared with others. Finally, an example is presented and the results show that the proposed technique is efficient. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Ma2:2008:ijcnn, author = "Liying Ma ", title = "Facial Expression Recognition Using 2-D DCT of Binarized Edge Images and Constructive Feedforward Neural Networks", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN1082.pdf}, url = {}, size = {}, abstract = {Computer-based automatic human facial expression recognition (FER) is fundamental and indispensable in realizing truly intelligent human-machine interfaces. In this paper, a new FER technique is proposed, which uses lowerfrequency 2-D DCT coefficients of binarized edge images and constructive one-hidden-layer (OHL) feedforward neural networks (NNs). The 2-D DCT is thereby used to compress the binarized edge images to capture the important features for recognition. Constructive OHL NNs are then used to realize the mapping from the feature space to facial expression space. Facial expression ''neutral'' is regarded as a subject of recognition in addition to two other expressions, ''smile'' and ''surprise''. The proposed recognition technique is applied to two databases which contain 2-D front face images of 60 men (database (a)) and 60 women (database (b)), respectively. Experimental results reveal that our proposed technique provides in general improved performance when compared to two other recognition methods that use vector matching and fixed-size BP-based NNs. Our method yields testing recognition rates as high as 100percent and 95percent for databases (a) and (b), respectively, which clearly demonstrates its promising capabilities. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Hamidian:2008:ijcnn, author = "Hajar Hamidian and Hamid Soltanian-Zadeh and Reza Faraji-Dana and Masoumeh Gity", title = "Comparison of Two Linear Models for Estimating Brain Deformation during Surgery Using Finite Element Method", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN1085.pdf}, url = {}, size = {}, abstract = {This paper presents finite element computation of brain deformation during craniotomy. Two mechanical models are compared for this purpose: linear solid-mechanic model and linear elastic model. Both models assume finite deformation of the brain after opening the skull. We use a test sphere as a model of the brain, tetrahedral finite element mesh, and function optimization that optimizes the models' parameters by minimizing the distance between the resulting deformation and the supposed deformation. Based on the final value of the objective function, we conclude that the accuracy of the solid mechanic model is higher than that of the elastic model. Applications of the methods to the MR images of the brain confirm this finding. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wang12:2008:ijcnn, author = "X. Wang and S. N. Balakrishnan", title = "Optimal Controller Synthesis of Variable-Time Impulsive Problems Using Single Network Adaptive Critics", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN1086.pdf}, url = {}, size = {}, abstract = {This paper presents a systematic approach to solve for the optimal control of a variable-time impulsive system. First, optimality condition for a variable-time impulsive system is derived using the calculus of variations method. Next, a single network adaptive critic technique is proposed to numerically solve for the optimal control and the detailed algorithm is presented. Finally, two examples-one linear and one nonlinear-are solved applying the conditions derived and the algorithm proposed. Numerical results demonstrate the power of the neural network based adaptive critic method in solving this class of problems. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Meng:2008:ijcnn, author = "Fei Meng and Kai-yu Tong and Suk-tak Chan", title = "BCI-FES Training System Design and Implementation for Rehabilitation of Stroke Patients", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN1092.pdf}, url = {}, size = {}, abstract = {A BCI-FES training platform has been designed for rehabilitation on chronic stroke patients to train their upper limb motor functions. The conventional functional electrical stimulation (FES) was driven by users' intention through EEG signals to move their wrist and hand. Such active participation was expected to be important for motor rehabilitation according to motor relearning theory. The common spatial pattern (CSP) algorithm was applied as one pre-processing step in brain-computer interface (BCI) module to search for the optimal spatial projection direction after brain reorganization. The pre- and post- clinical assessment was conducted to identify the possible functional improvement after the training. Two chronic stroke subjects attended this pilot study and the error rate of the BCI control was less than 20percent after training of 10 sessions. This implementation showed the feasibility for stroke patients to accomplish the BCI triggered FES rehabilitation training. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Park3:2008:ijcnn, author = "Hogun Park and Yoonjung Choi and Yuchul Jung and Sung-Hyon Myaeng", title = "Supporting Mixed Initiative Human-Robot Interaction: A Script-Based Cognitive Architecture Approach", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN1093.pdf}, url = {}, size = {}, abstract = {As complex indoor-robot systems are developed and deployed into the real-world, the demand for human-robot interaction is increasing. Mixed-initiative human-robot interaction is a good method to coordinate actions of a human and a robot in a complementary fashion. In order to support such interactions, we employ scripts that are rich, flexible, and extensible for a robot's interactions in a variety of situations. Scripts are amenable for expressing knowledge in an applicable form, especially describing a sequence of actions in organizing tasks. In this paper, we propose a script-based cognitive architecture for collaboration, which is based on three-level cognitive models. It incorporates Dynamic Bayesian Network (DBN) to automatically govern action sequences in the scripts and detect user's intention or goal. Starting from an understanding of user initiatives, our intelligent task manager suggests the most relevant initiatives for an efficient collaboration. DBN has been evaluated in real indoor task scenarios for its efficacy in interaction reduction, error minimization, and task satisfaction. }, keywords = { Mixed-Initiative Interaction, Dynamic Bayesian Network, Human-Robot Interaction, Script, Robot-Task Script}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Jung2:2008:ijcnn, author = "Jae-Yoon Jung and Janice I. Glasgow and Stephen H. Scott", title = "Trial Map: A Visualization Approach for Verification of Stroke Impairment Assessment Database", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN1095.pdf}, url = {}, size = {}, abstract = {Robotic/mechanic devices have become widely used for various medical assessments recently. While using these devices are beneficial in terms of accuracy and objectiveness, validation and consistency problem may occur when combining these data with traditional clinical information. Here we propose a visualization tool that can summarize the experimental data and compare them with the clinical data, in the stroke impairment assessment domain. This visual tool is based on a neural network ensemble that is trained to match the experimental data with Chedoke-McMaster scale, one of the major outcome measure for stroke impairment and recovery assessment. We compare our ensemble model with ten combinations of different classifiers and ensemble schemes, showing that it outperforms competitors. We also demonstrate that our visualization approach is consistent with clinical information, and reliable in a sense that output of our ensemble can be an estimator for the corresponding clinical data when Chedoke-McMaster scores are missing. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Smith-Miles:2008:ijcnn, author = "Kate A. Smith-Miles ", title = "Towards Insightful Algorithm Selection For Optimisation Using Meta-Learning Concepts", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN1096.pdf}, url = {}, size = {}, abstract = {In this paper we propose a meta-learning inspired framework for analysing the performance of meta-heuristics for optimization problems, and developing insights into the relationships between search space characteristics of the problem instances and algorithm performance. Preliminary results based on several metaheuristics for well-known instances of the Quadratic Assignment Problem are presented to illustrate the approach using both supervised and unsupervised learning methods. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Inoue:2008:ijcnn, author = "Takashi Inoue and Masaru Nakano and Yoshifumi Nishio", title = "Output Characteristics of Cellular Neural Networks Using Mixture Template", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN1101.pdf}, url = {}, size = {}, abstract = {In this research, we propose cellular neural networks using mixture template as an example of space-varying cellular neural networks. As the first step of the investigation of such complex nonlinear circuit networks, we propose two mixing methods of the templates and investigate the output characteristics of the simple image processing with a binary image and a grayscale image by computer simulations. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Choi2:2008:ijcnn, author = "Hyun-Chul Choi and Sam-Yong Kim and Sang-Hoon Oh and Se-Young Oh and Sun-Young Cho", title = "Pose Invariant Face Recognition with 3D Morphable Model and Neural Network", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN1102.pdf}, url = {}, size = {}, abstract = {This paper introduces a pose invariant face recognition method with a training image and a query image using 3D morphable model and neural network. Our system uses 3D morphable model to get the reconstructed 3D face from the training image and obtains 2D image patches of facial components from the 3D face under varying head pose. The 2D image patches are used to train a neural network for pose invariant face recognition. Because those patches are obtained from the varying head pose, the neural network has robustness in the query image under the different head pose form the training image. Our pose invariant face recognition system has the performance of correct recognition higher than 98percent with BJUT 3D scan database. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Adhyaru:2008:ijcnn, author = "Dipak M. Adhyaru and I. N. Kar and M. Gopal", title = "Constrained Optimal Control of Bilinear Systems using Neural Network Based HJB Solution", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN1105.pdf}, url = {}, size = {}, abstract = {In this paper, a Hamilton-Jacobi-Bellman (HJB) equation based optimal control algorithm is proposed for a bilinear system. Using the Lyapunov direct method, the controller is shown to be optimal with respect to a cost functional, which includes penalty on the control effort and the system states. In the proposed algorithm, Neural Network (NN) is used to find approximate solution of HJB equation using least squares method. Proposed algorithm has been applied on bilinear systems. Necessary theoretical and simulation results are presented to validate proposed algorithm. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Kuroe:2008:ijcnn, author = "Yasuaki Kuroe and Yuriko Taniguchi", title = "Models of Complex-Valued Dynamic Associative Memories and Analysis of Their Dynamics -Analytic and Non-analytic Activation Functions-", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN1107.pdf}, url = {}, size = {}, abstract = {Associative memories are one of the popular applications of neural networks and several studies on their extension to the complex domain have been done. Associative memories should recall memory patterns, and their dynamics are greatly affected by activation functions and connection weights. The theoretical analysis on qualitative properties of neural networks is very important to associative memories. We already proposed some models of complex valued associative memory using nonlinear bounded complex functions, which are not analytic. In this paper, we present several models of orthogonal type and auto-correlation type associative memories using several nonlinear complex functions which include analytic and nonanalytic functions, and investigate their behavior as associative memories theoretically. Comparisons are made among these models in terms of dynamics. Simulation studies are also done to investigate dynamics of an associative memory with singular points. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Kuznetsov:2008:ijcnn, author = "V. A. Kuznetsov and E. Motakis and A. V. Ivshina", title = "Low- and High-Agressive Genetic Breast Cancer Subtypes and Significant Survival Gene Signatures", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN1113.pdf}, url = {}, size = {}, abstract = {We characterize three small gene signatures derived consequently from the original 232-gene breast cancer aggressiveness signature which could improve biological classification and clinical assignment of ~ 50percent of breast cancer patients having histologic grade 2 tumors [3]. Here, we develop a novel approach to identify small gene signatures providing statistically reliable, biological important and clinical significant molecular markers. We consider three small molecular signatures which strongly represent three specific groups of genes related to (i) cell cycle/mitosis, (2) chromosome segregation and microtubular formation, (3) cell-cell communication, extracellular/immune signaling, and RNA binding. These results shed light on underlined biological mechanisms of lowaggressive and high-aggressive human breast cancer phenotypes and support our suggestion that re-classification of grade 2 breast tumors onto tumor grade 1-like and tumor grade 3-like subtypes can be related to two genetically and clinically distinct cancer types. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Mohan:2008:ijcnn, author = "Permanand Mohan ", title = "A Teacher for Every Learner: Rising to the Challenge with Computational Intelligence", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN1114.pdf}, url = {}, size = {}, abstract = {A few years ago, providing a teacher for every learner was proposed as one of five Grand Research Challenges in Computer Science and Engineering. Although current research interest with learning objects is on the decline, this paper argues that they can still play a major role in meeting the Grand Challenge. In particular, the paper discusses the granularity, sequencing, and context aspects of learning objects, showing how these aspects are at the heart of personalization in an e-learning system. However, catering for granularity, sequencing, and context in an instructionally principled fashion are difficult computational problems. The paper discusses and proposes a range of computational intelligence techniques that can address these problems and thus contribute to achieving the vision of a teacher for every learner. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Garcia:2008:ijcnn, author = "Francis Garcia and Ernesto Araujo", title = "Visual Multi-Target Tracking by using Modified Kohonen Neural Networks", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN1115.pdf}, url = {}, size = {}, abstract = {A visual target tracking identification by employing using a ``Winner-takes-all'' artificial neural network is proposed in this paper. In this approach a modified Kohonen Neural Network is the mechanism used both to determine the position as to represent the target trajectory given a sequence of images. Some of the advantages employing this technique is that the initial condition are supplied randomly and that the performance of the algorithm is independent of the initial condition as well as of the number of them. Besides, this algorithm converge for the center of mass of the target. This methodology is useful in remote and local systems when information is given by images be it related to aerospace applications, robotics, radar systems, or industrial applications. The proposed algorithm is here used in the identification of airplane trajectory by using digital images. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Júnior:2008:ijcnn, author = "Francisco Chagas de Lima Júnior and Jorge Dantas de Melo and Adrião Duarte Doria Neto", title = "", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1821-3", file = {NN1116.pdf}, url = {}, size = {}, abstract = {Currently many non-tractable considered problemshave been solved satisfactorily through methods of approximateoptimization called metaheuristic. These methods usenon-deterministic approaches that find good solutions which,however, do not guarantee the determination of the global optimum.The success of a metaheuristic is conditioned by capacityto adequately alternate between exploration and exploitationof the solution space. A way to guide such algorithms whilesearching for better solutions is supplying them with moreknowledge of the solution space (environment of the problem).This can to be made in terms of a mapping of such environmentin states and actions using Reinforcement Learning. This paperproposes the use of a technique of Reinforcement Learning -Q-Learning Algorithm - for the constructive phase of GRASPand Reactive GRASP metaheuristic. The proposed methods willbe applied to the symmetrical traveling salesman problem. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Kwok:2008:fuzz, author = "Antares San-Chin Kwok and Wai-Chuen Gan and Norbert C. Cheung", title = "Improvements in the Motion Accuracy of Linear Switched
Reluctance Motors", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0002.pdf}, url = {}, size = {}, abstract = {During the last decade, the Linear Switched Reluctance Motor (LSRM) has become popular due to its structural simplicity, robustness and high power density. However, its significant torque ripple creates difficulty on precision motion control. This paper aims to develop a robust control system to improve the motion accuracy of LSRMs. The LSRM prototype is firstly investigated to study its force and current relationship. With the help of software, LSRM motion tests are simulated before real experiment. The significant improvement on position control strongly proves the success of the proposal. After that, the experimental result applying on the real prototype closely matches the simulation result. In order to enhance the LSRM robustness and the position tracking responses, another fuzzy logic controller is newly designed and implemented to supervise the traditional Proportional-Differential (PD) control parameters. Combining the inner control loop on current force relationship and the outer control loop on PD parameter supervision, the LSRM system in this project is very robust and capable to provide a high precision motion performance. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Du:2008:fuzz, author = "Lixia Du and Xu Xu and Yan Cao and Jiying Li", title = "A Novel Approach to Find the Satisfaction Pattern of Customers in Hotel Management", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0004.pdf}, url = {}, size = {}, abstract = {Nowadays, many studies of the discovery of needs and feelings of the hotel customers are not only around before-booking period, but also do not consider the privacy of customers completely. While the best period of studies of this knowledge are after the booking took place, there are two major problems for its unpopular: one is personal privacy, the other is not having a scientific and valuable approach. In this paper, we propose a novel approach to deal with the above existing problems. We employ intuitionistic fuzzy set, α-cuts, and Apriori algorithm to discovery the knowledge of needs and feelings of customers under an anonymous way. The approach is expatiated under different α by an example. And The yielded pattern and association rules have taken to the cooperative hotel more effects than before. So the approach is provable and valuable. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Shirvanian:2008:fuzz, author = "Marcel Shirvanian and Wolfram Lippe ", title = "Optimization of the Normalization of Fuzzy Relational Databases by Using Alternative Methods of Calculation for the Fuzzy Functional Dependency", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0009.pdf}, url = {}, size = {}, abstract = {Although, in comparison to standard databases, a tremendous benefit is often derived by using fuzzy databases, their distribution is very low. A reason for the relatively poor acceptance of fuzzy relational databases is to be seen in the difficulty to carry out an adequate normalization. The various procedures discussed in the literature possess miscellaneous weaknesses. In this work a normalization method is regarded whose most significant deficit lies in the Fuzzy Functional Dependency (FFD) because thereby comprehensible results are not produced. Therefore, it is registered which alternatives for the determination of the degree of FFD exist. Furthermore, it is examined with which of these methods the just addressed disadvantage can be eliminated. For this purpose, the presented methods are applied to several examples in order to identify their characteristics. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Chen:2008:fuzz, author = "Gang Chen ", title = "Discussion of Approximation Error Bounds to the Class of Fuzzy System", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0011.pdf}, url = {}, size = {}, abstract = {The standard fuzzy systems are established with partition of normal quadratic polynomial membership functions and normal trigonometric membership functions. Based on the systems established and the standard fuzzy systems with partition of normal triangle functions, approximation error bounds problems are discussed by interpolation theory. Universal approximation error bounds of these fuzzy systems from SISO to MISO are given and their relations are founded. The error remainder term and auxiliary function are employed for the first time in proving process. Moreover, advantage and shortcoming of the three fuzzy systems are compared and correlative conclusions are obtained. Finally, computing examples are given and the validity of the conclusions is confirmed. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Cheng:2008:fuzz, author = "Ba-yi Cheng and Hua-ping Chen and Shuan-shi Wang ", title = "Fuzzy Scheduling for Single Batch-processing Machine with Non-identical Job Sizes", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0016.pdf}, url = {}, size = {}, abstract = {In this paper, we introduce the fuzzy model of the makespan on a single batch-processing machine with non-identical job sizes and propose an improved DNA evolutionary algorithm (IDEA) solution approach. The model is based on fuzzy batch processing time and fuzzy intervals between batches. DEA is improved by integrating the crossover operator to overcome the immature convergence caused by the determinate selection of vertical operator in DEA. To decode the permutations of jobs searched by IDEA, the heuristic first fit decreasing (FFD) is applied to produce batches. In the experiment, the results of the fuzzy makespan demonstrate the proposed algorithm outperforms GA and SA on all instances. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Yamada:2008:fuzz, author = "Koichi Yamada and Osamu Onosawa and Muneyuki Unehara", title = "Simulating Associations and Interactions Among Multiple Pieces of Brand Image Using Fuzzy Bidirectional Associative Memory", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0017.pdf}, url = {}, size = {}, abstract = {The paper discusses an idea of representing brand image on a computer and simulating associations and interactions among multiple pieces of brand image. Brand image is represented using a fuzzy set based on the theory of brand personality, which is a theory to represent brand image indirectly by a set of human characteristics associated with a brand. An convenient feature of the representation is generality that image of any kind of brands could be defined on the same universal set. The interactions among multiple pieces of image are simulated using the framework of Conceptual Fuzzy Set which is realized as combination of two Fuzzy Bidirectional Associative Memories. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Chang:2008:fuzz, author = "Tsung-Han Chang and Tien-Chin Wang", title = "Fuzzy Preference Relation Based Multi-Criteria Decision Making Approach for WiMAX License Award", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0019.pdf}, url = {}, size = {}, abstract = {This paper develops a multi-criteria evaluation approach based on the preference relation to help the National Communication Commission (NCC) in Taiwan award a WiMAX license under fuzzy environment, where the vagueness and subjectivity are handled with linguistic variables parameterized by triangular fuzzy numbers. This study applies the fuzzy multi-criteria decision making (MCDM) method to determine the importance weights of evaluation criteria and synthesize the ratings of possible alternatives. Aggregated the evaluators' attitude toward possible alternatives; then the non-dominated degree is employed to obtain a crisp overall performance value for each contender to make a final decision. This approach is demonstrated with a real case study involving seven evaluation criteria, eight mobile companies assessed by four evaluators from academia and telecommunication arena. }, keywords = {Multiple criteria decision making, fuzzy sets theory, preference relation, }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Xu:2008:fuzz, author = "Jianhua Xu and Yaning Chen and Weihong Li", title = "Grey Modelling the Groundwater Level Dynamic in the Lower Reaches of Tarim River Affected by Water Delivery from Upper Reaches: A Demonstration from Yingsu Section", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0020.pdf}, url = {}, size = {}, abstract = {Using the grey system theory and the monitored data from the monitoring section of Yingsu, this paper models the groundwater level dynamic in the lower reaches of Tarim River affected by water delivery from upper reaches. The main conclusions are: (1) Discharging volume, running days for water delivery and daily discharging volume, which related with water delivery from the upper reaches of Tarim River, are three main factors that markedly control and affect the groundwater level. (2) The sensitivity of groundwater level changing respond to itself becomes more and more lower versus the distance apart from river center, and the affection from discharging volume and running days for water delivery to the change rate of groundwater level becomes more and more significant with increase of the distance apart from river center. Water delivery not only markedly controls and raises the groundwater level near river, but also affects the groundwater level as far as the range in the distance of 1050 m apart from river center. }, keywords = { groundwater level, dynamic, the lower reaches of Tarim River, water delivery, grey system, modelling}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Shiwang:2008:fuzz, author = "Hou Shiwang and Tong Shurong", title = "Fuzzy Logic Based Assignable Causes Ranking System for Control Chart Abnormity Diagnosis", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0022.pdf}, url = {}, size = {}, abstract = {When using control chart patterns as signals to identify the cause for faster and easier process diagnosis, tradition method is hard to handle with the uncertainties, ambiguities and vagueness associated with the problem. Based on fuzzy logic, this paper develops a fuzzy inference system (FIS), composed by six sub modules. Each determines the intensity of corresponding causes based on degree of presence of each pattern. All the evidence supporting each cause from the unnatural patterns are aggregated using fuzzy connective operators and causes are prioritized according to the final aggregating results. The search can be done from the cause having highest priority when process goes out of control. }, keywords = {fuzzy inference system, process abnormity diagnosis, assignable causes ranking }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Hao:2008:fuzz, author = "Fei Hao and Zheng Pei and Shengtong Zhong", title = "Searching Minimal Attribute Reduction Sets Based on Combination of the Binary Discernibility Matrix and Graph Theory", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0023.pdf}, url = {}, size = {}, abstract = {Attribute reduction plays an important role in rough set theory. It is an important application in data mining. In this paper, we focus on discussing the relation between set covering and attribute reduction in rough set theory. Based on the equivalence between minimal set covering and minimal attribute reduction sets, attribute reduction graph (ARG) is constructed. A novel algorithm to find the minimal attribute reduction sets, which is based on combination of binary discernibility matrix and graph theory is proposed in this paper. This algorithm demonstrates its efficiency and feasibility by an example. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Seki:2008:fuzz, author = "Hirosato Seki and Hiroaki Ishii ", title = "On the Monotonicity of Functional Type SIRMs Connected Fuzzy Reasoning Method and T-S Reasoning Method", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0024.pdf}, url = {}, size = {}, abstract = {Yubazaki et al. have proposed "single input rule modules connected type fuzzy reasoning method" (SIRMs method, for short) whose final output is obtained by summarizing the product of the importance degrees and the inference results from single input fuzzy rule module. Moreover, Seki et al. have proposed "functional type single input rule modules connected fuzzy reasoning method" (functional type SIRMs method, for short) whose consequent parts are generalized to functions from real numbers. It is expect that inference results of functional type SIRMs method have monotonicity if the antecedent parts and consequent parts of fuzzy rules in the functional type SIRMs rule modules have monotonicity. However, this paper points out that even if fuzzy rules in functional type SIRMs rule modules have monotonicity, the inference results do not necessarily have monotonicity. Moreover, it clarifies the conditions for the monotonicity of inference results by functional type SIRMs method, Takagi-Sugeno reasoning method (T-S reasoning method, for short), and simplified fuzzy reasoning method. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wang:2008:fuzz, author = "Ning Wang and Xianyao Meng", title = "Analytical Structures and Stability Analysis of Three-Dimensional Fuzzy Controllers", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0026.pdf}, url = {}, size = {}, abstract = {We have revealed the analytical structures and stability analysis of the three-dimensional fuzzy controllers involving trapezoidal input fuzzy sets, singleton output fuzzy sets, Zadeh fuzzy AND triangular norm, Zadeh fuzzy OR triangular co-norm, Mamdani inference method and centroid defuzzification algorithm. This class of fuzzy controllers is a combination of a nonlinear PID controller with dynamic proportional gain, dynamic integral gain and dynamic derivative gain plus a piecewise constant term. Based on the mathematical structures, the bounded-input bounded-output (BIBO) stability conditions for fuzzy control systems have been obtained by the well-known Small Gain Theorem. A computer simulation is provided to illustrate that the new fuzzy controller is effective and superior to the conventional PID controller. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wang2:2008:fuzz, author = "Ning Wang and Xianyao Meng", title = "Analysis of Structure and Stability for The Simplest Two-Dimensional Fuzzy Controller Using Generalized Trapezoid-Shaped Input Fuzzy Sets", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0027.pdf}, url = {}, size = {}, abstract = {By summarizing the common characteristics of popular triangular and trapezoidal fuzzy sets, the more extensive generalized trapezoid-shaped (GTS) fuzzy set has been proposed. We have contributed to the analytical structures and stability analysis of the simplest two-dimensional fuzzy controllers using GTS input fuzzy sets. This class of fuzzy controllers is a combination of a piecewise linear PI controller plus a piecewise constant term. Based on the mathematical structures, the bounded-input bounded-output (BIBO) stability conditions for fuzzy control systems have been obtained by the well-known Small Gain Theorem. Two computer simulations are provided to demonstrate that the new fuzzy controller is effective and superior to the conventional PID controller. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Chang2:2008:fuzz, author = "Pei-Chann Chang and Chin-Yuan Fan and Chia-Hsuan Yeh and Wan-Ling Pan", title = "A Hybrid System by Integrating Case Based Reasoning and Fuzzy Decision Tree for Financial Time Series Data", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0028.pdf}, url = {}, size = {}, abstract = {Stock price predictions suffer from two well known difficulties, i.e., complicated and non-stationary variations within the large historic data. This paper establishes a novel financial time series-forecasting model by a case based fuzzy decision tree induction for stock price movement predictions in Taiwan Stock Exchange Corporation (TSEC). This forecasting model integrates a case based reasoning technique, a Fuzzy Decision Tree (FDT), and Genetic Algorithms (GA) to construct a decision-making system based on historical data and technical indexes. The model is major based on the idea that the historic price data base can be transformed into a smaller case-base together with a group of fuzzy decision rules. As a result, the model can be more accurately react to the current tendency of the stock price movement from these smaller case based fuzzy decision tree inductions. Hit rate is applied as a performance measure and the effectiveness of our proposed CBFDT model is demonstrated by experimentally compared with other approaches on various stocks from TSEC. The average hit rate of CBFDT model is 91percent the highest among others. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Cao:2008:fuzz, author = "Jiangtao Cao and Honghai Liu and Ping Li and David Brown", title = "Adaptive Fuzzy Logic Controller for Vehicle Active Suspensions with Interval Type-2 Fuzzy Membership Functions", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0029.pdf}, url = {}, size = {}, abstract = {Elicited from the Least Means Squares optimal algorithm (LMS), an adaptive fuzzy logic controller (AFC) based on interval type-2 fuzzy sets is proposed for vehicle non-linear active suspension systems. The interval membership functions (IMF2s) are used in the AFC design to deal with not only non-linearity and uncertainty caused from irregular road inputs and immeasurable disturbance, but also the potential uncertainty of expert's knowledge and experience. The adaptive strategy is designed to self-tune the active force between the lower bounds and upper bounds of interval fuzzy outputs. A case study based on a quarter active suspension model has demonstrated that the proposed type-2 fuzzy controller significantly outperforms conventional fuzzy controllers of an active suspension and a passive suspension. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wai:2008:fuzz, author = "Rong-Jong Wai and Zhi-Wei Yang", title = "Adaptive Fuzzy-Neural-Network Control of Robot Manipulator Using T-S Fuzzy Model Design", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0031.pdf}, url = {}, size = {}, abstract = {This study focuses on the development of an adaptive fuzzy-neural-network control (AFNNC) scheme for an n-link robot manipulator to achieve high-precision position tracking. In general, it is difficult to adopt a model-based design to achieve this control objective due to the uncertainties in practical applications, such as friction forces, external disturbances and parameter variations. In order to cope with this problem, an AFNNC system is investigated without the requirement of prior system information. In this model-free control scheme, a continuous-time Takagi-Sugeno (T-S) dynamic fuzzy model with on-line learning ability is constructed for representing the system dynamics of an n-link robot manipulator. Then, a four-layer fuzzy-neural-network (FNN) is used for estimating nonlinear dynamic functions in this fuzzy model. Moreover, the AFNNC law and adaptive tuning algorithms for FNN weights are established in the sense of Lyapunov stability analyses to ensure the network convergence as well as stable control performance. Numerical simulations of a two-link robot manipulator actuated by DC servomotors are given to verify the effectiveness and robustness of the proposed AFNNC methodology. In addition, the superiority of the proposed control scheme is indicated in comparison with proportional-differential control (PDC), Takagi-Sugeno-Kang (TSK) type fuzzy-neural-network control (T-FNNC), robust-neural-fuzzy-network control (RNFNC), and fuzzy-model-based control (FMBC) systems. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(M.:2008:fuzz, author = "Mario I. Chacón M. and Juan I. Nevarez S. ", title = "A Fuzzy Clustering Approach on the Classification of Non Uniform Cosmetic Defects", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0032.pdf}, url = {}, size = {}, abstract = {In this paper a fuzzy clustering approach for the classification of cosmetic defects is presented. The paper investigates the solution of this classification problem with the Gustafson-Kessel (GK), and Geth-Geva (GG) with Abonyi-Szeifert (AS) fuzzy algorithms. The clustering process is achieved on multidimensional feature vectors that represent the cosmetic defects. The performance of the GK algorithm may be considered similar to a human inspector which is between 85percent and 90percent approximately. However, the fuzzy clustering technique has the advantage to be very consistent, contrary to a human inspector that can change her/his mind due to subjective influences. The paper also presents the comparison between the fuzzy approach and the artificial neural network approach. The problem faced in this work also helped to compare the performance of FC algorithms with ANN in real world applications. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Havens:2008:fuzz, author = "Timothy C. Havens and James M. Keller and Mihail Popescu and James C. Bezdek", title = "Ontological Self-Organizing Maps for Cluster Visualization and Functional Summarization of Gene Products using Gene Ontology Similarity Measures", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0033.pdf}, url = {}, size = {}, abstract = {This paper presents an ontological self-organizing map (OSOM), which is used to produce visualization and functional summarization information about gene products using Gene Ontology (GO) similarity measures. The OSOM is an extension of the self-organizing map as initially developed by Kohonen, which trains on data composed of sets of terms. Term-based similarity measures are used as a distance metric as well as in the update of the OSOM training procedure. We present an OSOM-based visualization method that shows the cluster tendency of the gene products. Also demonstrated is an OSOM-based functional summarization which produces the most representative term(s) (MRT) from the GO for each OSOM prototype and, subsequently, each gene product cluster. We validated the results of our method by applying the OSOM to a well-studied set of gene products. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Bosc:2008:fuzz, author = "Patrick Bosc and Olivier Pivert", title = "On the Division Operator for Probabilistic and Possibilistic Relational Databases", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0037.pdf}, url = {}, size = {}, abstract = {This paper is situated in the area of imprecise (probabilistic and possibilistic) databases. Any imprecise database has a canonical interpretation as a set of more or less possible regular databases, also called worlds. In order to manipulate such databases in a safe and efficient way, a constrained framework has been previously proposed, where a restricted number of querying operations are permitted (selection, union, projection and foreign-key join which can handle attributes taking imprecise values). The key for efficiency resides in the fact that these operators do not require to make computations explicitly over all the more or less possible worlds. The division operation is dealt with in this paper and the impact of the uncertainty model on the processing technique is particularly studied. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Li:2008:fuzz, author = "Yongming Li ", title = "Fuzzy Finite Automata and Fuzzy Monadic Second-Order Logic", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0040.pdf}, url = {}, size = {}, abstract = {We introduce fuzzy monadic second-order (LMSO-) logic and prove that the behaviours of fuzzy finite automata with membership values in an MV-algebra are precisely the fuzzy languages definable with sentences of our LMSO logic. This generalizes Büchi's and Elgot's fundamental theorems to fuzzy logic setting. We also consider fuzzy first-order logic and show that star-free fuzzy languages and aperiodic fuzzy languages introduced here coincide with the fuzzy first-order definable ones. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Honda:2008:fuzz, author = " Katsuhiro Honda and Takahiro Ohyama and Hidetomo Ichihashi and Akira Notsu", title = " FCM-Type Switching Regression with Alternating Least Squares Method", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0043.pdf}, url = {}, size = {}, abstract = {Fuzzy c-Regression Models (FCRM) performs switching regression based on a Fuzzy c-Means (FCM)-like iterative optimization procedure, in which regression errors are also used for clustering criteria. In data mining applications, we often deal with databases consisting of mixed measurement levels. The alternating least squares method is a technique for mixed measurement situations, in which nominal variables (categorical observations) are quantified so that they suit the current model, and has been applied to FCM-type fuzzy clustering in order to characterize each cluster considering mutual relation among categories. This paper proposes two new algorithms for handling mixed measurement situations in FCM-type switching regression based on the alternating least squares method. The iterative algorithms include additional optimal scaling steps for calculating numerical scores of categorical variables. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wu:2008:fuzz, author = "Kewei Wu and Zhao Xie and Jun Gao and Wengang Feng", title = "FCM in Novel Application of Science and Technology Progress Monitor System", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0044.pdf}, url = {}, size = {}, abstract = {This paper focuses on the issues about the complex relations in large-scale FCM, and then proposes a promising method for weight global optimization with local inference to analyze and predict indexes in Anhui sci-tech progress monitor system. Firstly, a new concept, unbalanced degree, is introduced for standard evaluation in FCM model to modify the weight assessment factors and result in the satisfied convergence rate. Secondly, relations between unbalanced degree and convergence error are also presented for further analysis with training error and guarantee on perfect condition in model. Thirdly, local inference in FCM is discussed to enhance prediction accuracy rate. Finally, experimental result reveals successful application of FCM in large-scale complex sci-tech systems. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wan:2008:fuzz, author = "Jia-Ren Wan and Ji-Chang Lo", title = "LMI Relaxations for Nonlinear Fuzzy Control Systems Via Homogeneous Polynomials", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0045.pdf}, url = {}, size = {}, abstract = {Based on recent results on homogeneous polynomially parameter-dependent (HPPD) solutions to parameter-dependent LMIs (PD-LMIs) that arise from robust stability of linear parameter varying (LPV) systems, we investigate the relaxed conditions characterized by parameter-dependent LMIs (PD-LMIs) in terms of firing strength belonging to the unit simplex, exploiting the algebraic property of Pólya's Theorem to construct a family of finite-dimensional LMI relaxations. The main contribution of this paper is that sets of relaxed LMIs are parameterized in term of the polynomial degree d. As d increases, progressively less conservative LMI conditions are generated, being easier satisfied due to more freedom provided by new variables involved. An example to illustrate the relaxation is provided. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Qiu:2008:fuzz, author = "Jianbin Qiu and Gang Feng and Jie Yang", title = "Delay-Dependent Robust H∞ Filtering Design for Uncertain Discrete-Time T-S Fuzzy Systems with Interval Time-Varying Delay", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0046.pdf}, url = {}, size = {}, abstract = {This paper investigates the problem of delay-dependent robust H∞ filtering design for a class of uncertain discrete-time state-delayed T-S fuzzy systems. The state delay is assumed to be time-varying and of an interval-like type, which means that both the lower and upper bounds of the time-varying delay are available. The parameter uncertainties are assumed to have a structured linear fractional form. Based on a novel delay and fuzzy-basis-dependent Lyapunov-Krasovskii functional combined with Finsler's Lemma, a new sufficient condition for robust H∞ performance analysis is firstly derived and then the filter synthesis is developed. It is shown that by using a new linearization technique incorporating a bounding inequality, a unified framework can be developed such that both the full-order and reduced-order filters can be obtained by solving a set of linear matrix inequalities, which are numerically efficient with commercially available software. Finally, a numerical example is provided to illustrate the advantages and less conservatism of the proposed approach. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Hagras:2008:fuzz, author = "Hani Hagras ", title = " Developing a Type-2 FLC Through Embedded Type-1 FLCs", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0048.pdf}, url = {}, size = {}, abstract = {Type-1 Fuzzy Logic Controllers (FLCs) have been widely employed in many control applications as they give a good performance and it is relatively easy to extract the type-1 FLC parameters from experts. However, type-1 FLCs cannot fully handle the encountered uncertainties in changing unstructured environments as they use crisp type-1 fuzzy sets. Consequently, in order for type-1 FLCs to provide a satisfactory performance in face of high levels of uncertainties, some common practices are followed including continuously tuning the type-1 FLC or providing a set of type-1 FLCs where each FLC handles specific operation conditions. Alternatively, type-2 FLCs can handle uncertainties to give a better control performance. However, it is relatively challenging to extract from experts the Footprint of Uncertainty (FOU) information and consequently the type-2 fuzzy sets for type-2 FLCs. In this paper, we will present a novel method for generating the input and output type-2 fuzzy sets so that their FOUs can capture the faced uncertainties. The proposed method will generate a type-2 FLC that will try to embed the type-1 FLCs corresponding to the various operation conditions faced so far besides embedding a large number of other embedded type-1 FLCs. This will allow the type-2 FLC to handle the uncertainties trough a big number of embedded type-1 FLCs to produce a smooth and robust control performance. We will show through real world experiments how the developed type-2 FLC will handle the uncertainties and give a smooth control response that outperforms the individual and aggregated type-1 FLCs. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Hagras2:2008:fuzz, author = "Hani Hagras and Ian Packham and Yann Vanderstockt and Nicholas McNulty and Abhay Vadher and Faiyaz Doctor", title = " An Intelligent Agent Based Approach for Energy Management in Commercial Buildings", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0049.pdf}, url = {}, size = {}, abstract = {Global warming is becoming one of the serious issues facing humanity. Several initiatives have been introduced to deal with global warming including the Kyoto Protocol which assigned mandatory targets for the reduction of greenhouse gas emissions to signatory nations. However, over the last decade, commercial buildings worldwide have experienced massive growth in energy costs. This was caused by the expansion in the use of air conditioning and artificial lighting as well as an ever increasing energy demand for computing services. Existing Building Management Systems (BMSs) have, generally, failed to fully optimize energy consumption in commercial buildings. This is because they lack control systems that can react intelligently and automatically to anticipated changes in ambient weather conditions and the many other environmental variables typically associated with large buildings.In this paper, we present a novel agent based system entitled Intelligent Control of Energy (ICE) for energy management in commercial buildings. ICE uses different Computational Intelligence (CI) techniques (including fuzzy systems, neural networks and genetic algorithms) to 'learn' a buildings thermal response to many variables including the outside weather conditions, internal occupancy requirements and building plant responses. ICE then uses CI based algorithms which work in real-time with the building's existing BMS to minimize the building's energy demand. We will show how the use of ICE will allow significant energy cost savings, while still maintaining customer-defined comfort levels. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Li2:2008:fuzz, author = "Qiaoxing Li and Jianmei Yang ", title = "Aggregation of Fuzzy Opinions with an Area Between the Centroid Point and the Original Point Under Group Decision Making", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0050.pdf}, url = {}, size = {}, abstract = {It is an important issue to obtain the consensus opinion under group decision making. In this paper, we propose a new approach to aggregate the experts' opinions which is based on the area between the centroid points of fuzzy numbers and the original point. The opinions of experts are represented by positive fuzzy numbers which include the normal and the non-normal trapezoidal fuzzy numbers as well as interval numbers. A new index that the consensus of each expert to others is constructed by using a similarity measure under the area. We also take into consideration the importance of each expert in the process of aggregation. The operational procedure is simple and the numerical examples show its reliability. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Hwang:2008:fuzz, author = "Chih-Lyang Hwang and Ching-Chang Wong", title = "Fuzzy Mixed H2/H∞ Optimized Design of Decentralized Control for Nonlinear Interconnected Dynamic Delay Systems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0053.pdf}, url = {}, size = {}, abstract = {Each subsystem of a nonlinear interconnected dynamic delay system (NIDDS) is first approximated by a weighted combination of L transfer function delay systems (TFDSs). The H2-norm of the difference between the transfer function of the reference model and the closed-loop transfer function of the kth TFDS of subsystem i is then minimized to obtain a suitable frequency response. Because the output disturbance of the kth TFDS, including the interconnections coming from the other subsystems, the approximation error of the ith subsystem, and the interactions resulting from the other TFDSs, is not small and includes various frequencies, the H∞-norm of the weighted sensitivity function between the output disturbance and its corresponding output of the kth TFDS is simultaneously minimized to attenuate its effect. In addition, an appropriate selection of the weighted function for the sensitivity can reject the specific mode of the output disturbance. Finally, the stability of the overall system is verified by the concept of Ln2-stable with finite gain. }, keywords = {Decentralized control, Fuzzy linear model, Nonlinear interconnected dynamic delay system, H2 -optimization, H∞, }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Xu2:2008:fuzz, author = "Jian-Xin Xu and Chao Xue and Chang-Chieh Hang and Krishna V. Palem", title = "A Fuzzy Control Chip Based on Probabilistic CMOS Technology", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0055.pdf}, url = {}, size = {}, abstract = {In this work, a novel approach using Probabilistic CMOS (PCOMS) technology is used to reduce the energy consumption of a fuzzy PID (proportional-integral-derivative) controller. Energy saving is achieved through designing a probabilistic circuit which deliberately reduces the supply voltage of some less significant bits. The fuzzy PID controller consists of 15 bits with floating point representation. Through numerical simulations and VHDL validation, the fuzzy PID can obtain a satisfactory tradeoff with about 4percent deviation while achieving a total energy saving of about 32percent. Through error analysis, a fuzzy PID is redesigned to tolerate more of randomness in the control signals, hence obtain a better steady state performance while achieving an energy saving of about 51percent. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Yu:2008:fuzz, author = "Yongguang Yu and Han-Xiong Li", title = "Stable Flocking of Mobile Formation in 3-Dimensional Space", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0056.pdf}, url = {}, size = {}, abstract = {The paper investigates the flocking behaviors of multi-agent formation in 3-dimensional space which are based on leader following. A class of decentralized control laws for a group of mobile agents are proposed under the conditions that the topology of the control interconnections is fixed and dynamically time-variant, respectively. These control laws are a combination of attractive/repulsive and alignments forces which can guarantee the collision avoidance and cohesion of the formation and an aggregate motion along the same heading direction of the leader. According to the algebraic graph theory, differential inclusions and non-smooth analysis, we model the interconnection relationship of multi-agent formation, and achieve the stability analysis of the system by Lyapunov theory. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Chiang:2008:fuzz, author = "Chiang-Cheng Chiang and Shih-Wei Wang ", title = "Observer-Based Robust Adaptive Fuzzy Control of Uncertain Nonlinear Systems with Delayed Output", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0058.pdf}, url = {}, size = {}, abstract = {In this paper, an observer-based robust adaptive fuzzy controller is proposed to deal with the output tracking control problem for a class of uncertain single-input single-output (SISO) nonlinear systems with output delay and unmatched uncertainties. Within this scheme, the state observer is applied for estimating all states which are not available for measurement in the system, and then fuzzy logic systems and some adaptive laws are used to approximate the unknown nonlinear functions and the unknown upper bounds of unmatched uncertainties. By constructing an appropriate Lyapunov function and solving Lyapunov equations, the proposed robust adaptive fuzzy controller can guarantee that the asymptotic stabilization and the output tracking performance of the whole closed-loop system can be achieved. Finally, an example is given to illustrate the effectiveness of the proposed approach. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Ludwig:2008:fuzz, author = "Simone A. Ludwig ", title = "Fuzzy Match Score of Semantic Service Match", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0060.pdf}, url = {}, size = {}, abstract = {Automatic discovery of services is a crucial task for the e-Science and e-Business communities. Finding a suitable way to address this issue has become one of the key points to convert the Web into a distributed source of computation, as it enables the location of distributed services to perform a required functionality. To provide such an automatic location, the discovery process should be based on the semantic match between a declarative description of the service being sought and a description being offered. This problem requires not only an algorithm to match these descriptions, but also a language to declaratively express the capabilities of services. The proposed matchmaking approach is based on semantic descriptions for service attributes, descriptions and metadata. For the ranking of service matches a match score is calculated whereby the weight values are either given by the user or estimated using a fuzzy approach. An evaluation of both weight assignment approaches is conducted identifying in which scenario one works better than the other. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Ichihashi:2008:fuzz, author = "Hidetomo Ichihashi and Katsuhiro Honda and Akira Notsu and Eri Miyamoto ", title = "FCM Classifier for High-Dimensional Data", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0061.pdf}, url = {}, size = {}, abstract = {A fuzzy classifier based on the fuzzy c-means (FCM) clustering has shown a decisive generalization ability in classification. The FCM classifier uses covariance structures to represent flexible shapes of clusters. Despite its effectiveness, the intense computation of covariance matrices is an impediment for classifying a set of high-dimensional data. This paper proposes a way of directly handling high-dimensional data in the FCM clustering and classification. The proposed classifier without any preprocessing outperforms the k-nearest neighbor (k-NN) classifier with PCA on the benchmark set of COREL image collection. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Ichihashi2:2008:fuzz, author = "Hidetomo Ichihashi and Katsuhiro Honda and Akira Notsu and Keichi Ohta ", title = "Fuzzy c-Means Classifier with Particle Swarm Optimization", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0062.pdf}, url = {}, size = {}, abstract = {Fuzzy c-means-based classifier derived from a generalized fuzzy c-means (FCM) partition and optimized by particle swarm optimization (PSO) is proposed. The procedure consists of two phases. The first phase is an unsupervised clustering, which is not initialized with random numbers, hence being deterministic. The second phase is a supervised classification. The parameters of membership functions and the location of cluster centers are optimized by the PSO and cross validation (CV) procedures.Since different types of classifiers work best for different types of data, our strategy is to parameterize the classifier and tailor it to individual data set. The FCM classifier outperforms well established methods such as k-nearest neighbor classifier (k-NN), support vector machine (SVM) and Gaussian mixture classifier (GMC) in terms of 10-fold CV and three-way data splits. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Ichihashi3:2008:fuzz, author = "Hidetomo Ichihashi and Katsuhiro Honda and Akira Notsu and Takao Hattori ", title = "Classifier of BOLD Signals from Active and Inactive Brain States Using FCM Clustering and Evolutionary Algorithms", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0063.pdf}, url = {}, size = {}, abstract = {A fuzzy classifier based on the fuzzy c-means (FCM) clustering has shown a decisive generalization ability in classification. This paper reports a result of test on a data set with high-dimensional feature values. For classifying the blood oxygen level dependent (BOLD) responses of the brain, a way of directly handling high-dimensional fMRI signals is applied. Our goal is to distinguish the BOLD responses to recalling tasks from those to resting (i.e., a binary classification problem). We use the signals from wide areas of the brain, which forms a set of high dimensional data vectors. The FCM classifier is compared with support vector machine (SVM). SVM is a high performance classifier and well suited for binary classification problems, since the size of the kernel matrix of SVM depends only on the number of instances. The error rate on the test set by the FCM classifier surpassed the SVM, though the SVM can easily handle sets of high dimensional feature vectors. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Xu3:2008:fuzz, author = "Sheng Xu and Huifang Zhao and Xuanli Lv", title = "A Grey SVM Based Model for Patent Application Filings Forecasting", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0064.pdf}, url = {}, size = {}, abstract = {Tracking historical levels as well as estimating future levels of patent applications is an ongoing activity of considerable significance. The patent applications filings (PAF) are complex to conduct due to its nonlinearity of influenced factors. Support vector machines (SVM) have been successfully employed to solve nonlinear regression and time series problems. Grey theory is a truly multidisciplinary and generic theory that deals with systems that are characterized by poor information and/or for which information is lacking. Grey system theory successfully uses accumulated generating data instead of original data to build forecasting model, which makes raw data stochastic weak, or reduces noise influence in a certain extent. However, the application combining grey system theory and SVM for PAF forecasting is rare. In this study, a grey support vector machines with genetic algorithms (GSVMG) is proposed to forecast PAF. In addition, Grey system is used to add a grey layer before neural input layer and white layer after SVM layer. Genetic algorithms (GAs) are used to determine free parameters of support vector machines. Evaluation method has been used for comparing the performance of forecasting techniques. The experiments show that the GSVMG model is outperformed grey model and SVM with genetic algorithms (SVMG) model and PAF forecasting based on GSVMG is of validity and feasibility. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Zhang:2008:fuzz, author = "Tiejun Zhang and Gang Feng and Wenguo Xiang", title = "Fuzzy Dynamic Modeling and Predictive Load Following Control of a Solid Oxide Fuel Cell Power System", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0065.pdf}, url = {}, size = {}, abstract = {Solid oxide fuel cell (SOFC) is widely accepted for clean and distributed power generation use, but critical operation problems often occur when stand-alone fuel cell is directly connected to the electricity grid or the DC electric user. In order to address these problems, in this paper a data-driven fuzzy identification method is applied to the dynamic modeling of an integrated SOFC and capacitor system. And the identified fuzzy SOFC model is employed to develop a novel constrained feedforward generalized predictive controller. Both the rapid power load following and safe SOFC operation requirements are taken into account in the design of the closed-loop control system. Simulations are also given to demonstrate the load following control performance of the proposed fuzzy predictive control strategy for the SOFC/Capacitor power system. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Chen2:2008:fuzz, author = "Cheng-Hung Chen and Yong-Cheng Liu and Cheng-Jian Lin and Chin-Teng Lin", title = "A Hybrid of Cooperative Particle Swarm Optimization and Cultural Algorithm for Neural Fuzzy Networks", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0066.pdf}, url = {}, size = {}, abstract = {This study presents an evolutionary neural fuzzy network, designed using the functional-link-based neural fuzzy network (FLNFN) and a new evolutionary learning algorithm. This new evolutionary learning algorithm is based on a hybrid of cooperative particle swarm optimization and cultural algorithm. It is thus called cultural cooperative particle swarm optimization (CCPSO). The proposed CCPSO method, which uses cooperative behavior among multiple swarms, can increase the global search capacity using the belief space. Cooperative behavior involves a collection of multiple swarms that interact by exchanging information to solve a problem. The belief space is the information repository in which the individuals can store their experiences such that other individuals can learn from them indirectly. The proposed FLNFN model uses functional link neural networks as the consequent part of the fuzzy rules. Finally, the proposed functional-link-based neural fuzzy network with cultural cooperative particle swarm optimization (FLNFN-CCPSO) is adopted in several predictive applications. Experimental results have demonstrated that the proposed CCPSO method performs well in predicting the time series problems. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Kazemian:2008:fuzz, author = "H. B. Kazemian and S. Chantaraskul ", title = "Intelligent Approach to Video Transmission Over 2.4 GHz Wireless Technology", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0067.pdf}, url = {}, size = {}, abstract = {Video transmission over dynamic channels like 2.4 GHz wireless technology is unpredictable, because of interferences caused by other wireless devices in the same ISM (Industrial, Scientific and Medicine) frequency band and/or general channel noises. Transmitting Moving Picture Expert Group (MPEG) video stream over wireless technology also presents a challenge, as MPEG demands large bandwidth. Considering these issues, an intelligent transmission is introduced in this research so that the controller may adjust itself to the current state of the wireless channel in order to sustain MPEG video quality during the communication process. A neural-fuzzy controller and a rule-based fuzzy controller are implemented in the design to monitor input – output of a traffic shaping buffer and offer suitable parameters for the MPEG encoder for video transmission over the network. Matlab computer simulation results show that the proposed method reduces data loss and improves image quality as compared with traditional MPEG video transmission over 2.4 GHz wireless technology. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Hwang2:2008:fuzz, author = "Chih-Lyang Hwang and Hsiu-Ming Wu and Ching-Long Shih", title = "Fuzzy Sliding-Mode Under-Actuated Control for Autonomous Dynamic Balance of an Electrical Bicycle", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0068.pdf}, url = {}, size = {}, abstract = {The purpose of this paper is to stabilize the running motion of an electrical bicycle. In order to do so, two strategies are employed in this paper. One is to control the bike's center of gravity (CG), and the other is to control the angle of the bike's steering handle. In addition, the proposed system produces three outputs that will affect the dynamic balance of an electrical bicycle: the bike's pendulum angle, lean angle, and steering angle. Based on the data of input-output, two scaling factors are employed to normalize the sliding surface and its derivative. According to the concept of if-then rule, an appropriate rule table for the ith subsystem is obtained. Then the output scaling factor based on Lyapunov stability is determined. The proposed control method used to generate the handle torque and pendulum toque is called fuzzy sliding-mode under-actuated control (FSMUAC). The purpose of using the FSMUAC is the huge uncertainties of a bicycle system often caused by different ground conditions and gusts of wind; merely ordinary proportional-derivative-integral (PID) control method or other linear control methods usually do not show good robust performance in such situations. }, keywords = {Electrical bicycle, Dynamic balance, Variable structure under-actuated control, Modified proportional -derivative control, Lyapunov stability.}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Jeon:2008:fuzz, author = "Gwanggil Jeon and Rafael Falcón and Joohyun Lee and Jechang Jeong", title = "Spatio-Temporal Edge-Based Weighted Fuzzy Filtering for Providing Interlaced Video on a Progressive Display", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0071.pdf}, url = {}, size = {}, abstract = {In this paper, we propose a new weighted fuzzy filter, which selectively uses spatial and temporal information. The accuracy of the edge direction detection, motion detection, and interpolation is crucial key factors to obtain excellent visual quality in deinterlaced images. The adopted fuzzy concepts are used to design a weight-evaluating technique. The weights were considered to be multiplied by the candidate deinterlaced pixels. Experimental results demonstrate that the proposed deinterlacing method performs better than previous techniques. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Liu:2008:fuzz, author = "Xinwang Liu and Xiaoguang Yang and Yong Fang", title = "The Relationships Between Two Kinds of OWA Operator Determination Methods", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0072.pdf}, url = {}, size = {}, abstract = {The paper propose a general model that can combine the two kinds of OWA determination methods together: the optimization based methods and sample learning methods, that the relationship between these two kinds of problems is discussed. The analytical solution of this general model is proposed. Some properties of the problem are discussed. With this general model, we can observe the influences of the optimization criteria and the sample data on the OWA operator weight solution changes with the number of data examples. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Duan:2008:fuzz, author = "Xiao-Gang Duan and Han-Xiong Li and Hua Deng", title = "A Simple Tuning Method for Fuzzy PID Control", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0073.pdf}, url = {}, size = {}, abstract = {A new tuning method is proposed for fuzzy PID controller based on internal model control theory. First, the analytical model of the fuzzy PID controller is expressed as a linear PID controller plus a nonlinear compensation item. Then, the internal model control method can be used to approximately design the parameters of fuzzy PID controller analytically. Finally, stability of the fuzzy PID control system is analyzed, and the validity of the tuning methodology is demonstrated by simulation. }, keywords = {Tuning, Fuzzy PID, Internal model control}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Hung:2008:fuzz, author = "Wen-Liang Hung and Miin-Shen Yang and De-Hua Chen", title = "Variation Approaches to Feature-Weight Selection and Application to Fuzzy Clustering", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0074.pdf}, url = {}, size = {}, abstract = {In statistics field, variation plays an important role. This is because greater variations in some features of data can provide more important information. Therefore, in this paper, we use this idea to select feature-weights in data. The proposed approach is simple to compute and interpret for feature-weights selection. Compared with the feature-weights proposed by Wang et al. [10], Modha and Spangler [7], Pal et al. [8] & Basak et al. [1], we find that the proposed method provides a better clustering performance for the Iris data and colour image segmentation and also has lower computational complexity. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Vychodil:2008:fuzz, author = "Vilem Vychodil ", title = "On the Importance of Fuzzy Attribute Implications", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0076.pdf}, url = {}, size = {}, abstract = {Our paper deals with the expressive power of fuzzy attribute implications which are if-then rules describing dependencies among graded attributes. In our previous work, we have shown that fuzzy attribute implications are important in data mining because they can be used as a concise description of all if-then dependencies which are hidden in object-attribute fuzzy relational data. Fuzzy attribute implications can be seen as formulas (in the narrow sense) of the form A ⇒ B where both A and B are conjunctions of subformulas containing constants for truth degrees acting as (constants for) threshold truth degrees. This paper investigates possibility to replace sets of fuzzy attribute implications with fuzzy sets of ordinary if-then formulas in the sense of Pavelka's abstract logic. We reveal the impossibility to replace fuzzy attribute implications by the ordinary formulas without losing their expressive power. From the technical point of view, we present counterexamples demonstrating that fuzzy sets of the ordinary attribute implications cannot be used to describe sets of fixed points of arbitrary fuzzy closure operators. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wang3:2008:fuzz, author = "Zhudeng Wang and Jin-xuan Fang", title = "Residual Operations of Left and Right Uninorms", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0078.pdf}, url = {}, size = {}, abstract = {Uninorms are an important generalization of triangular norms and conorms, having a neutral element lying anywhere in the unit interval. In this paper, we extend the notion of uninorm to a complete lattice, introduce the concepts of left and right uninorms on a complete lattice, discuss the residual operations of left and right uninorms, and study some basic properties of the residual operations of infinitely V-distributive left (right) uninorms and pseudo-uninorms. }, keywords = {Uninorm, Left (right) uninorm, Pseudouninorm, Infinitely V-distributive, Residual operation}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Okamoto:2008:fuzz, author = "Wataru Okamoto and Shun'ichi Tano and Toshiharu Iwatani and Atsushi Inoue", title = "An Inference Method for Fuzzy Quantified Natural Language Propositions Based on New Interpretation of Truth Qualification", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0079.pdf}, url = {}, size = {}, abstract = {In this paper, we propose a method that affects inference results leading to a new interpretation of a truth qualification by adding a weight attribute to truth qualified fuzzy sets. With this method, we can obtain different inference results depending on the truth qualifiers by transforming a statement with fuzzy quantified and truth qualified natural language propositions. We applied our method to two examples transforming a fuzzy predicate of the natural language propositions and showed an effectiveness of the method. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Hao2:2008:fuzz, author = "Pei-Yi Hao ", title = "A Fuzzy Model of Support Vector Classification Algorithm", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0081.pdf}, url = {}, size = {}, abstract = {In this paper, we incorporate the concept of fuzzy set theory into the support vector machine (SVM) methodology. We apply a fuzzy membership to each input point and reformulate the optimization problem of SVM such that different input points can make different contributions to the learning of decision surface. Besides, the parameters to be identified in the SVM, such as the components within the weight vector and the bias term, are fuzzy numbers. This integration preserves the benefits of SVM learning theory and fuzzy set theory, where the SVM learning theory characterizes the properties of learning machines which enable them to effectively generalize the unseen data and the fuzzy set theory might be very useful for finding a fuzzy structure in an evaluation system. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Hadjahmadi:2008:fuzz, author = "A. H. Hadjahmadi and M. M. Homayounpour and S. M. Ahadi", title = "Robust Weighted Fuzzy C-Means Clustering", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0082.pdf}, url = {}, size = {}, abstract = {Nowadays, the fuzzy c-means method (FCM) became one of the most popular clustering methods based on minimization of a criterion function. However, the performance of this clustering algorithm may be significantly degraded in the presence of noise. This paper presents a robust clustering algorithm called robust weighted fuzzy c-means (RWFCM). We used a new objective function that uses some kinds of weights for reducing the infection of noises in clustering. Experimental results show that compared to three well-known clustering algorithms, namely, the fuzzy possibilistic c-means (FPCM), credibilistic fuzzy c-means (CFCM) and density weighted fuzzy c-means (DWFCM), RWFCM is less sensitive to outlier and noise and has an acceptable computational complexity. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Chuang:2008:fuzz, author = "Chen-Chia Chuang and Jin-Tsong Jeng and Mei-Lang Chan", title = "Robust Least Squares-Support Vector Machines for Regression with Outliers", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0083.pdf}, url = {}, size = {}, abstract = {In this study, the robust least square support vector machines for regression (RLS-SVMR) is proposed to deal with training data set with outliers. There are two-stage strategies in the proposed approach. In the stage I, called as data preprocessing, the support vector regression (SVR) approach is used to filter out the outliers in the training data set. Due to the outliers in the training data set are removed, the concepts of robust statistic theory have no need to reduce the outlier's effect. Then, the training data set except for outliers, called as the reduced training data set, is directly used to training the non-robust least squares support vector machines for regression (LS-SVMR) in the stage II. Consequently, the learning mechanism of the proposed approach is much easier than the weighted LS-SVMR approach. Based on the simulation results, the performance of the proposed approach is superior to the weighted LS-SVMR approach when the outliers are existed. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Kasperski:2008:fuzz, author = "Adam Kasperski and Paweł Zieliński", title = "Solving Combinatorial Optimization Problems with Fuzzy Weights", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0084.pdf}, url = {}, size = {}, abstract = {In this paper a combinatorial optimization problem with fuzzy weights is discussed. In order to choose a solution the concept of a necessary soft optimality is adopted. It is shown that the fuzzy problem can be reduced to solving a family of interval problems with the maximal regret criterion. Two general methods of solving the interval problems are presented. The first one is based on a branch and bound technique and the second method is based on a mixed integer programming formulation. Both techniques are general and can be applied if the underlying interval problem is NP-hard. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Kasperski2:2008:fuzz, author = "Adam Kasperski and Paweł Zieliński", title = "On Possibilistic Combinatorial Optimization Problems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0085.pdf}, url = {}, size = {}, abstract = {This paper deals with a general combinatorial optimization problem with uncertain element weights modeled by fuzzy intervals. A fuzzy interval is regarded as a possibility distribution describing the set of more or less plausible values of an element weight. In order to choose a “best” solution the concept of a necessary optimality and the concept of a necessary soft optimality are adopted. It is shown that the use of possibility theory leads to finding robust solutions under fuzzy weights. Some general algorithms that compute the degrees of necessary and necessary soft optimality of a given solution and find an optimal solution according to the introduced concepts are provided. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Couto:2008:fuzz, author = "Pedro Couto and Miguel Pagola and Humberto Bustince and Edurne Barrenechea and Pedro Melo-Pinto", title = "Uncertainty in Multilevel Image Thresholding Using Atanassov's Intuitionistic Fuzzy Sets", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0086.pdf}, url = {}, size = {}, abstract = {The problem of segmentation in spite of all the work over the last decades is still an important research field. Moreover, during the past years, fuzzy logic theory has been successfully applied to image thresholding. Considering that for segmentation purposes, in most cases, image pixels have an inherent ambiguity in the predicate that they must fulfill to belong to an object, which results in the experts uncertainty in assigning the pixel to that object, Atanassov's Intuitionistic fuzzy sets (A-IFSs) are a relevant and interesting extension since, uncertainty is one of the underlying ideas behind this theory.In this paper we describe a thresholding technique using A-IFSs. This approach uses Atanassov's intuitionistic index values for representing the uncertainty of the expert when assigning the pixel to the background or to the object. The general framework of this approach and its natural extension to multilevel thresholding are presented.Segmentation results and their performance evaluation are presented. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Lee:2008:fuzz, author = "Mahnhoon Lee ", title = "Fuzzy Cluster Validity Index Based on Object Proximities Defined Over Fuzzy Partition Matrices", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0087.pdf}, url = {}, size = {}, abstract = {Cluster validity index algorithms, which find the number of clusters in a given object set, play an important role in clustering. There have been many proposals of cluster validity index, especially for fuzzy clustering, and many of them are dependent on clustering algorithms that can use the different interpretations of similarities between objects, usually in the geometric interpretation of objects. We present a new fuzzy cluster validity index that is independent of clustering algorithms. The index uses the concept of distinguishableness of clusters, which is measured based on the object proximities defined over a given fuzzy partition matrix. We show the effectiveness of the proposed index by comparing it to other indices, with the fuzzy partition matrices obtained using the Fuzzy C-Means algorithm over various synthetic object sets. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Chen3:2008:fuzz, author = "Mao-Lin Chen and Hung-Yan Gu and Ching-Long Shih", title = "A New Speech Enhancing Scheme Combining NLMS, Fuzzy Logic and Kalman Filtering", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0088.pdf}, url = {}, size = {}, abstract = {This paper presents an improvement to ordinary voice filters that are unable to remove low frequency noise. This problem occurs because their filter parameters are over tuned, thereby destroying the speech characteristics. The proposed scheme combines NLMS, Fuzzy logic and Kalman filtering to restrain the background noise and keep the speech characteristics. This scheme is called the Normalized Fuzzy Logic Kalman Filter (NFLKF). It is especially effective when speech signals are collected in a noisy environment. Here, the output signal of the Kalman filtering is analyzed with the normalized LMS to obtain the coefficient, σk, and is also analyzed with the fuzzy logic to obtain the threshold, fk. Then, σk and fk are used together to adjust the Kalman filter parameters. This scheme can restrain the noise and improve the signal-to-noise ratio. The empirical validation is done by comparing the spectrogram from our scheme with the spectrograms from other filtering schemes, including the Normalized Least Mean Square (NLMS) Filter, the Kalman Filter and the Recursive Least Square Filter (RLS). The results show that the filtering schemes proposed can indeed restrain medium and low frequency noises which are usually difficult to handle, and does not compromise the speech characteristic. Therefore, a better signal-noise ratio is obtained and the speech quality is enhanced. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Tanaka:2008:fuzz, author = "Kazuo Tanaka and Takamichi Komatsu and Hiroshi Ohtake and Hua O. Wang", title = "Micro Helicopter Control: LMI Approach vs SOS Approach", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0089.pdf}, url = {}, size = {}, abstract = {This paper presents a comparison result of micro helicopter control via a typical linear matrix inequality (LMI) approach and a sum of squares (SOS) approach. First we construct a dynamical model of a micro helicopter and convert it to a Takagi-Sugeno fuzzy model. Next, this paper summarizes a recent developed SOS design approach for polynomial fuzzy control systems based on polynomial Lyapunov functions. We emphasize that the SOS design approach to polynomial fuzzy control systems is more general than that based on the existing LMI design approaches to T-S fuzzy control systems. The control results of a micro helicopter show that the SOS design approach provides better control results than the LMI design approach. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Mendel:2008:fuzz, author = "Jerry M. Mendel and Feilong Liu", title = "On New Quasi-Type-2 Fuzzy Logic Systems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0090.pdf}, url = {}, size = {}, abstract = {This paper provides an answer to the question that the type-2 fuzzy logic community is now asking: “What comes after interval type-2 fuzzy logic systems (IT2 FLSs)?” It demonstrates, through a geometrical understanding of the type-reduced set, that logical next steps in the progression from type-1 to interval type-2 to type-2 FLSs are quasi-T2 FLSs, either an interconnection of a T1 FLS and an IT2 FLS, or an interconnection of two IT2 FLSs, in which both FLSs are designed simultaneously. The quasi-T2 FLSs overcome the computational difficulties that are associated with set theoretic operations and type-reduction (TR) for general T2 FSs and FLSs, because all set theoretic operations can be performed as in existing T1 or IT2 FLSs, and because TR for an IT2 FLS can be performed using existing KM Algorithms. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Lo:2008:fuzz, author = "Ji-Chang Lo and Dong-Lin Wu ", title = "Dissipative Filtering for Discrete Fuzzy Systems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0091.pdf}, url = {}, size = {}, abstract = {The goal of this paper is, via dissipative theory, to design a dissipative filter for a class of nonlinear systems described by Takagi-Sugeno (TS) fuzzy model. We first reformulate the problems in an estimation/filtering error system such that the error system is dissipatively asymptotically stable with respect to a specifically chosen quadratic supply rate. Then we derive the synthesis conditions based on Lyapunov method, leading to an LMI representation that can be numerically solved by existing LMI solvers. We demonstrate that a class of filtering problems can be formulated into a general quadratic dissipative framework. Finally the utility of the proposed method is illuminated by an example. }, keywords = {Dissipative filter, Sector-bounded nonlinearity, TS fuzzy models, Linear matrix inequality (LMI)}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Haga:2008:fuzz, author = "Naoki Haga and Katsuhiro Honda and Hidetomo Ichihashi and Akira Notsu", title = "Linear Fuzzy Clustering of Relational Data Based on Extended Fuzzy c-Medoids", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0092.pdf}, url = {}, size = {}, abstract = {Linear fuzzy clustering is a fuzzy clustering-based local PCA technique, in which the Fuzzy c-Means (FCM)-like iterative procedure is performed by using linear varieties as the prototypes of clusters. Fuzzy c-Medoids (FCMdd) is a modified FCM algorithm, in which the representative objects “medoids” are selected from data samples, and is useful for handling relational data. This paper proposes an extended linear fuzzy clustering algorithm that can capture local linear sub-structures in relational data by estimating linear prototypes spanned by representative objects “medoids.” In the proposed algorithm, the clustering criterion is calculated using only the mutual distances among objects under the assumption of metric relational data, then estimation of linear prototypes is reduced to combinatorial optimization problems. In order to decrease the complexity of the prototype estimation step, a modified algorithm is also considered, in which the “medoids” are selected only from a subset of objects having large membership values. The clustering result of the proposed method is also comparative with multi-dimensional scaling and characteristic features are demonstrated in numerical experiments. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wu2:2008:fuzz, author = "Kuo-Lung Wu ", title = "An Analysis of Robustness of Partition Coefficient Index", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0093.pdf}, url = {}, size = {}, abstract = {We know that the partition coefficient index has the monotonic tendency with cluster number c. Moreover, they always select the smallest cluster number c = 2 as a optimal cluster number estimate when data contains some noise points. In this paper, we will discuss this problem by defining the validity measure of each single data point. We then define the singular point that has equal memberships to each cluster. By analyzing the influence of the singular point on the validity index, we can then give some guidelines for designing the fuzzy c-partitions based validity indexes that can avoid the influence of the noise. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Pham:2008:fuzz, author = "Tuan D. Pham ", title = "Cancer Classification by Minimizing Fuzzy Scattering Effect", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0095.pdf}, url = {}, size = {}, abstract = {Proteomic technology has been found promising for classifying complex diseases that leads to early prediction. However, for effective classification, the extraction of good features that can represent the identities of different classes plays the frontal critical factor for any classification problems. In addition, another major problem associated with pattern recognition is how to effectively handle a large feature space. This paper addresses these two frontal issues for mass spectrometry (MS) classification. We apply the theory of linear predictive coding to extract features and fuzzy vector quantization to reduce the large feature space of MS data. The minimization of the fuzzy scattering matrix in the setting of the fuzzy c-means algorithm provides better grouping for feature classification. The proposed methodology was tested using two MS-based cancer datasets and the results are promising. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Chiang2:2008:fuzz, author = "Chiang-Cheng Chiang and Mon-Han Chen ", title = "Robust Adaptive Fuzzy Control of Uncertain Nonlinear Time-Delay Systems with an Unknown Dead-Zone", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0097.pdf}, url = {}, size = {}, abstract = {This paper proposes a robust adaptive fuzzy control scheme for the uncertain nonlinear time-delay systems preceded by an unknown dead-zone. It is well known that time-delay and dead-zone characteristics are frequently encountered in various engineering systems. The dead-zone phenomena may occur in various components of control systems including sensors, amplifiers and actuators, especially in pneumatic valves, in electric servo motors and in hydraulic components. On the other hand, the time-delay phenomena are quite commonly found in chemical process, hydraulic systems and pneumatic systems. In most practice motion systems, the dead-zone characteristics are usually poorly known and may vary with time. Therefore, by using a description of a dead-zone and exploring the properties of this dead-zone model intuitively and mathematically, a robust adaptive fuzzy control method is presented without constructing the dead-zone inverse. According to some adaptive laws, the unknown nonlinear functions of the plant are approximated by the fuzzy logic system. Based on Lyapunov stability theorem, the proposed robust adaptive fuzzy control scheme can guarantee the robust stability of the whole closed-loop nonlinear time-delay system with an unknown dead-zone in the actuator and obtain good tracking performance as well. Finally, an example and simulation results are provided to show the efficiency of the proposed control method. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wang4:2008:fuzz, author = "Ying Wang and Sifeng Liu and Chuanmin Mi", title = "The Influence of OFDI on Chinese Industrial Structure — An Analysis Based on the Grey Incidence Theory", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0098.pdf}, url = {}, size = {}, abstract = {According to the data of Chinese OFDI industry structure and its domestic industrial output structure during 2003 and 2006, the absolute degree of grey incidence, the relative degree of grey incidence and the synthetic degree of grey incidence are calculated, so as to analyze the influence of OFDI on domestic industrial structure adjustment. It is shown that there is a close relationship between the OFDI industry structure and the domestic industrial output structure, with OFDI promoting the upgrade of domestic industrial structure. Furthermore, the OFDI in mining industry and manufacturing industry has a more important influence on domestic industrial structure optimization. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Ling:2008:fuzz, author = "Wang Ling and Mu Zhi-Chun and Guo Hui", title = "Prior Knowledge-Based Fuzzy Support Vector Regression", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0099.pdf}, url = {}, size = {}, abstract = {A new method was proposed for incorporating prior knowledge in the form of fuzzy knowledge sets into Support Vector Machine for regression problem. The prior knowledge of Fuzzy IF-THEN rules can be transformed into fuzzy information to generate fuzzy kernel, based on which FSVR (Fuzzy Support Vector Regression) is introduced. The merit of FSVR is that it can incorporate with prior knowledge represented by fuzzy IF-THEN rules to improve the performance of the conventional SVR in incomplete numeral dataset for training. The simulation results are feasible. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Jeng:2008:fuzz, author = "Jin-Tsong Jeng and Chen-Chia Chuang and Mei-Lang Chan", title = "Hybrid Support Vector Machines Learning for Fuzzy Neural Networks with Outliers", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0100.pdf}, url = {}, size = {}, abstract = {In this study, the hybrid support vector machines for regression (HSVMR) is proposed to deal with training data set with outliers for fuzzy neural networks (FNNs). There are two-stage strategies in the proposed approach. In the stage I, called as data preprocessing, the support vector machines for regression (SVMR) approach is used to filter out the outliers in the training data set. Due to the outliers in the training data set are removed, the concept of robust statistic theory have no need to reduce the outlier's effect. Then, the training data set except for outliers, called as the reduced training data set, is directly used to training the sparse least squares support vector machines for regression (LS-SVMR) in the stage II. Consequently, the learning mechanism of the proposed approach for fuzzy neural network does not need iterated learning for simplified fuzzy inference systems. Based on the simulation results, the performance of the proposed approach is superior to the robust LS-SVMR approach when the outliers are existed. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Kruszewski:2008:fuzz, author = "A. Kruszewski and T. M. Guerra and R. Wang", title = "New Approaches for Stability and Stabilization Analysis of a Class of Nonlinear Discrete Time-Delay Models", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0101.pdf}, url = {}, size = {}, abstract = {In this paper the relaxed stability conditions and the design of stabilization controller for discrete Takagi-Sugeno fuzzy time-delay model are considered through a new approach using specific Lyapunov's functions. Firstly, we give some relaxed sufficient conditions for stability of discrete T-S time-delay model via LMIs. Secondly, we consider the problems of stabilization for such models in two different cases, whether the time-delay is known or unknown. All the presented results outperform previous ones found in the literature and several examples are presented to illustrate the effectiveness of the method. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Tokumaru:2008:fuzz, author = "Masataka Tokumaru and Noriaki Muranaka", title = "An Evolutionary Fuzzy Colour Emotion Model for Colouring Support Systems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0103.pdf}, url = {}, size = {}, abstract = {In this study, we improved the colouring support system that was proposed in our previous research. The previous system could advise colour combinations for clothing. The user enters some emotional keywords, such as “casual“ and “pretty,” which are impressions put forth by colours, and the system retrieves colour combinations that seem to suit the keyword from a colour database. The proposed system has many fuzzy colour emotion models, which are controlled by parameters for defining membership functions or fuzzy rules. These models are evolved using interactive genetic algorithms, to adapt them to the user's particular emotional responses, because human reactions vary for each individual. This paper proposes a method for the advanced evolution of the fuzzy colour emotion model. This method supports co-evaluation of colour combinations, which exhibits a better performance in evolving models compared to the previous method. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Lam:2008:fuzz, author = "Toby H. W. Lam and James N. K. Liu", title = "Modeling a Personal Profile by Fuzzy Ontology Map and Its Applications", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0106.pdf}, url = {}, size = {}, abstract = {Current ontology markup language, such as Resource Description Framework (RDF) and Web Ontology Language (OWL), supports modeling information with hard semantics. However, it is not possible for us to represent some uncertain information by using these standard modeling languages. We proposed an extension called Fuzzy Ontology Map (FOM) which can be used to extend the current crisp ontology representations to model uncertain information. FOM was developed with strong mathematical basis. It is based on fuzzy theory and graph theory. In this paper, we presented the details about this fuzzy extension. In addition, we also showed how to adopt FOM for storing the personal profile information. This profile can provide enhanced functionalities for applications such as information filtering and personalization. At the end of this paper, we also presented an example application, CompBook, to show the idea how FOM profile works. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wei-Gen:2008:fuzz, author = "Qiu Wei-Gen ", title = "Extension of Rough Sets Model of Generalized Information System", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0107.pdf}, url = {}, size = {}, abstract = {In order to improve the ability of dealing with imprecise linguistic terms in information system, the paper study the extended rough set model of generalized information systems. The paper defines a kind of generalized information system and extends almost all basic concepts of rough set theory to fit this case. The paper has also considered the construction of homogeneous granules by introducing the TL-similarity property and the principle of Ziarko's Variable Precision to the fuzzy case. The reduction approach proposed in the paper can ensure all important information and only efficiently eliminate that not essential ones, which is superior to the Pawlak's typical approach in the simplification of decision tables. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Chen4:2008:fuzz, author = "Long Chen and C. L. Philip Chen", title = "Gradient Pre-Shaped Fuzzy C-Means Algorithm (GradPFCM) for Transparent Membership Function Generation", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0109.pdf}, url = {}, size = {}, abstract = {Linguistic terms are widely used in fuzzy modeling. The generation of membership functions for the linguistic terms is usually done by fuzzy C-means algorithm (FCM). However, most of FCM-based membership function generation algorithms consider little on the transparency or the understandability of the resulting membership functions. This paper proposes a gradient pre-shaped fuzzy C-means (GradPFCM) algorithm to generate better transparent membership functions. GradPFCM will preserve the predefined transparent shapes of membership functions during the process of the gradient descent based optimization of the clustering algorithm. Numeric experiments based on data collected in a civil project demonstrate the feasibility and superiority of the proposed new algorithm. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Minghui:2008:fuzz, author = "Wang Minghui and Yu Yongquan and Zhang Yun and Wang Fei", title = "Optimization of Fuzzy Control System Based on Extension Method for Ship Course-Changing/Keeping", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0113.pdf}, url = {}, size = {}, abstract = {This paper presents a new method to optimize the fuzzy control system for ship course-changing and course-keeping. In fuzzy control system, Fuzzy rules optimization is a crucial step in the development of a fuzzy model. This work studies an new approach for fuzzy rule base optimization by means of extension method. The optimization solution especially solves the non-compatible problem in the generation process of fuzzy control rules. For the design study, the optimization model has been carried out in the MATILAB/Simulink environment. Simulation results show the effectiveness of the proposed control algorithm. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Zhou:2008:fuzz, author = "Juan Zhou and Zuhua Liao", title = "Generalized (α, β)-Convex Fuzzy Cones", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0115.pdf}, url = {}, size = {}, abstract = {In this paper, we give the definition of generalized (α, β)-convex fuzzy cone by fuzzy points and generalized “quasi-coincident with” relationship, and discuss some of the fundamental properties of such convex fuzzy cone. Generalized (α, β)-convex fuzzy cone is the union and expansion of convex fuzzy cone defined by Yuan Xue-hai and ordinary convex fuzzy cone. }, keywords = {Fuzzy point, Generalized quasi-coincident, Belong to, , Convex fuzzy cone, (α, , β, )-convex fuzzy cone}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wang5:2008:fuzz, author = "Mengling Wang and Ning Li and Shaoyuan Li ", title = "Type-2 T-S Fuzzy Modeling for the Dynamic Systems with Measurement Noise", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0116.pdf}, url = {}, size = {}, abstract = {In actual industrial processes, the measurement data always contain noise. Therefore, it will affect the accuracy of modeling. Compare to type-1 fuzzy sets, the membership functions in type-2 fuzzy sets include primary membership function and secondary membership function. It provides additional degrees of freedom that make it possible to model uncertainties brought by the noise. In this paper, a type-2 T-S fuzzy model is presented to minimize the effect of measurement noise. Furthermore, the influence of the initial conditions is considered in the algorithm. The primary membership function is gained through an improved nearest-neighborhood clustering algorithm, and the secondary membership function is determined through GMM based on the sufficient statistics. The orthogonal least-squared algorithm is used to identify the consequent of the fuzzy rules. Finally, the simulation results are compared with those obtained from a type-1 T-S fuzzy modeling results and the superiority of the proposed approach is highlighted. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Fu:2008:fuzz, author = "Jinping Fu and Shoumei Li", title = "Convergences of Set-Valued Stochastic Series", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0117.pdf}, url = {}, size = {}, abstract = {In this paper, we shall study convergences of set-valued stochastic series. After reviewing necessary concepts and basic results about set-valued random variables, we shall introduce definitions of set-valued stochastic series. We shall mainly give the sufficient and necessary conditions of convergences of set-valued stochastic series, and prove three-series theorems of set-valued random variables in the senses of two different metrics. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Zhang2:2008:fuzz, author = "Yunong Zhang and Wei Li and Chenfu Yi and Ke Chen", title = "A Weights-Directly-Determined Simple Neural Network for Nonlinear System Identification", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0120.pdf}, url = {}, size = {}, abstract = {Based on polynomial interpolation and approximation theory, a special feed-forward neural network using power activation functions is constructed in this paper. The neural model employs a three-layer structure with the hidden-layer neurons activated by a group of order-increasing power functions (while other layers' neurons use linear activation functions). In addition, the weights-updating formula for such a neural network could be derived from the standard BP training method. A pseudoinverse-based method (or termed, weights-direct-determination/one-step-weights-determination method) is then established to determine immediately the neural-network weights without lengthy iterative BP-training. It is shown that such a power-activated feed-forward neural network could perform effectively and efficiently for nonlinear system identification. Computer-simulation results further substantiate the benefits of its weights-direct-determination method. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Dietze:2008:fuzz, author = "Stefan Dietze and Alessio Gugliotta and John Domingue", title = "Fuzzy Context Adaptation Through Conceptual Situation Spaces", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0121.pdf}, url = {}, size = {}, abstract = {Context-adaptive information systems (IS) are highly desired across several application domains and usually rely on matching a particular real-world situation to a finite set of predefined situation parameters. To represent context parameters, semantic and non-semantic representation standards are widely used. However, describing the complex and diverse notion of specific situations is costly and may never reach semantic completeness. Whereas not any situation parameter completely equals another, the number of (predefined) representations of situation parameters is finite. Moreover, following symbolic representation approaches leads to ambiguity issues and does not entail semantic meaningfulness. Consequently, the challenge is to enable fuzzy matchmaking methodologies to match real-world situation characteristics to a finite set of predefined situation descriptions. In this paper, we propose Conceptual Situation Spaces (CSS) which enable the description of situation characteristics as members in geometrical vector spaces following the idea of Conceptual Spaces. Consequently, fuzzy matchmaking is supported by calculating the semantic similarity between the current situation and prototypical situation descriptions in terms of their Euclidean distance within a CSS. Aligning CSS to existing symbolic representation standards, enables the automatic matchmaking between real-world situation characteristics and symbolic parameter representations. To prove the feasibility, we apply our approach to the domain of e-Learning. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Er:2008:fuzz, author = "Meng Joo Er and Linn San", title = "Automatic Generation of Fuzzy Inference Systems Using Incremental-Topological-Preserving-Map-Based Fuzzy Q-Learning", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0122.pdf}, url = {}, size = {}, abstract = {This paper represents a new approach for automatically generating Fuzzy Inference System (FIS) using Incremental Topological Preserving Map Fuzzy Q-Learning (ITPM-FQL). The ITPM-FQL can create and tune the fuzzy rules automatically without any priori knowledge. The online self organizing ITPM approach is used to achieve automatic structure identification while the Fuzzy Q-Learning approach is used for parameter identification to deal with continuous states and actions. Compared with the first author's previous works in Dynamic Fuzzy Q-Learning (DFQL), this proposed approach is able to achieve fewer numbers of fuzzy rules. Similar to the DFQL, ∈-completeness criterion is used to generate fuzzy rules but the convergence capability of the ITPM is added to provide flexible fuzzy clustering. Experimental results and comparative studies with conventional Fuzzy Q-Learning (FQL), Continuous-Action Q-Learning (CAQL), DFQL and its related developments, Dynamic Self Generated Fuzzy Q-Learning (DSGFQL) and Enhanced Dynamic Self Generated Fuzzy Q-Learning (EDSGFQL), in wall-following task of a mobile robot are presented to demonstrate the superiority of the proposed approach. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Hsu:2008:fuzz, author = "Ker-Tah Hsu and Tzung-Ming Yan and Ting-Hui Hsu", title = "Applying Grey Models to Forecast Financial Ratios of National Health Insurance in Taiwan", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0123.pdf}, url = {}, size = {}, abstract = {The main purpose of this study is to predict and analyze the financial ratios of Bureau of National Health Insurance in the near future. We apply GM(1,1) model and Tan's modified GM(1,1) model to forecast 4 financial ratios of Bureau of National Health Insurance, i.e. debt ratio, receivables turnover, current ratio and quick ratio, to analyze its financial performance. The original GM(1,1) model performs better in all these four cases. The results reveal that BNHI has no instant risk of insolvency, its credit policy will be less liberal, but the risk illiquidity emerge since 2004 and getting worse in the future. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Doctor:2008:fuzz, author = "Faiyaz Doctor and Hani Hagras and Dewi Roberts and Victor Callaghan", title = "A Type-2 Fuzzy Based System for Handling the Uncertainties in Group Decisions for Ranking Job Applicants within Human Resources Systems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0125.pdf}, url = {}, size = {}, abstract = {Ranking applicants for a given job is one of the most important processes for Human Resources (HR) systems. The ranking of job applicants involves two main processes which are the specification of the requirements criteria for a given job (experience, skills, etc) and the matching between the applicants' profiles and the job requirements. There is currently a strong move towards automating these two processes to generate an applicants' ranking system that gives consistent and fair results. However there is a high level of uncertainty involved in these two processes as they involve the input of several experts. These experts will have different opinions, expectations the interpretations for the requirements specification as well as for the applicants matching and ranking. This paper presents a novel approach for ranking job applicants by employing type-2 fuzzy sets for handling the uncertainties in group decisions in a panel of experts. Hence the presented system will enable automating the processes of requirements specification and applicants matching/ranking. We have performed real world experiments in the care domain where our system handled the uncertainties and produced ranking decisions that were consistent with those of the human experts. To the authors' knowledge, this will be the first type-2 based commercial software system. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wagner:2008:fuzz, author = "Christian Wagner and Hani Hagras ", title = "zSlices — Towards Bridging the Gap Between Interval and General Type-2 Fuzzy Logic", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0126.pdf}, url = {}, size = {}, abstract = {Higher order fuzzy logic systems such as interval type-2 fuzzy logic systems have been shown to be very well suited to dealing with the large amounts of uncertainties present in the majority of real world applications. General type-2 fuzzy logic systems are expected to further extend this capability. However, the complexity as well as the immense computational requirements have generally prevented a foray into general type-2 fuzzy logic research. This paper introduces an alternative approach termed zSlices for representing general type-2 sets based on interval type-2 sets. Thus, this will lead to a smooth transition from interval to general type-2 fuzzy systems. The proposed approach will lead to a significant reduction in both the complexity and the computational requirements for general type-2 fuzzy logic systems. Hence, this will lead to facilitating the application of general type-2 fuzzy logic to many real world applications. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Seising:2008:fuzz, author = "Rudolf Seising ", title = "Is there a Concept of Fuzziness in the Epistemological Systems of Heinrich Hertz and Ludwig Wittgenstein?", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0127.pdf}, url = {}, size = {}, abstract = {“A picture is a model of reality”, “A picture is a fact”, and “We picture facts to ourselves” asserted Ludwig Wittgenstein in his Tractatus logico-philosophicus, thereby confirming the influence on his thinking — which he himself acknowledged — of Heinrich Hertz's Prinzipien der Mechanik (Principles of Mechanics). In this contribution the “picture” concept, which has a long tradition in philosophy, serves as the starting point of an interpretation of the relationship between real systems and theoretical structures of modern science. In addition, the approach dubbed as the “structuralist” approach of scientific theories in the 20th century will be extended and enhanced by the concept of “fuzzy sets” and “fuzzy relations”. This fuzzy structuralist view on scientific theories enables us to combine philosophy of science with the methodologies of Computing with words (CW) and the Computational theory of perceptions (CTP). Finally we give future prospects of this work. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Lietard:2008:fuzz, author = "Ludovic Lietard ", title = "A New Definition for Linguistic Summaries of Data", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0128.pdf}, url = {}, size = {}, abstract = {The linguistic summaries of data proposed by Yager are made of a quantified statement associated to a degree of validity. Such a summary expresses that a quantity of information (quantity described by the linguistic quantifier) satisfies a constraint (defined by a fuzzy predicate). This article shows that Yager's proposition does not sufficiently stress the relationship between these two aspects. As a consequence, we propose a new kind of linguistic summaries made of a linguistic statement (where the universal quantifier is implicit) associated to a degree of validity with a clear meaning in terms of quantity and quality. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Sala:2008:fuzz, author = "Antonio Sala ", title = "Introducing Shape-Dependent Relaxed Conditions in Fuzzy Control of Nonlinear Systems in Takagi-Sugeno Form", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0129.pdf}, url = {}, size = {}, abstract = {Most of the fuzzy literature presents stability and performance results in fuzzy control of Takagi-Sugeno models via LMI conditions; such conditions are independent of the particular shape of the LMIs. Shape-dependent conditions may be used to relax the results: some of them are only conditions on the memberships themselves; others include relations between state variables and memberships. The latter approach, then, enters into the realm of actual nonlinear control, departing from the conventional analysis which proves stability of nonlinear systems via proving stability of some linear time-variant convexhull models (the Takagi-sugeno systems). The conditions may be made polynomial with the sum-of-squares approach. With the ideas in this paper, the gap between fuzzy and nonlinear control gets smaller. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Busoniu:2008:fuzz, author = "Lucian Busoniu and Damien Ernst and Bart De Schutter and Robert Babuska", title = "Consistency of Fuzzy Model-Based Reinforcement Learning", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0130.pdf}, url = {}, size = {}, abstract = {Reinforcement learning (RL) is a widely used paradigm for learning control. Computing exact RL solutions is generally only possible when process states and control actions take values in a small discrete set. In practice, approximate algorithms are necessary. In this paper, we propose an approximate, model-based Q-iteration algorithm that relies on a fuzzy partition of the state space, and on a discretization of the action space. Using assumptions on the continuity of the dynamics and of the reward function, we show that the resulting algorithm is consistent, i.e., that the optimal solution is obtained asymptotically as the approximation accuracy increases. An experimental study indicates that a continuous reward function is also important for a predictable improvement in performance as the approximation accuracy increases. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Jun-fei:2008:fuzz, author = "Qiao Jun-fei and Han Hong-gui", title = "A New Growing Self-Organizing Neuron-Fuzzy Network with Application to Wastewater Treatment", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0132.pdf}, url = {}, size = {}, abstract = {A new neural fuzzy algorithm based on the method of growing self-organizing network is proposed in this paper. Then the fuzzy rules can be changed on-line, it takes the experience out of the necessary side for the number of the fuzzy rules. A novel learning algorithm based on dynamic descent gradient is also presented. The main salient characters of the algorithm in this paper are: (1) a new method resolves the problem of the conventional neural network which can't change the structure of the network; (2) the neurons of the neural network can be changed on-line; (3) a new method for the fast learning speed can be own. This new algorithm can be used to control the dissolved oxygenic in the wastewater treatment process. The results of the experiments prove the superiority of this algorithm compared with the conventional neural fuzzy algorithm. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Zhou2:2008:fuzz, author = "Ning-ning Zhou and Long Hong", title = "Medium Mathematics Systems: Review and Prospect", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0134.pdf}, url = {}, size = {}, abstract = {Medium mathematics systems (MMS) are the new systems that extend precise quantity objects to fuzzy ones in mathematical foundation. MMS have had important influence to the researches on the logical foundation of mathematics and set theory. Most of the contents in this paper fall into one of the following three aspects: research background of MMS; fundamental contents in MMS and the relation between MMS and classical mathematics systems, in which medium concepts, medium principle, medium logic systems (ML), medium axiom set theory (MS) and current situation of MMS are included; the academic disputation about ML, especially quoting the pros and cons whether or not ML is a new logic. In addition, the application prospect of MMS has been viewed. }, keywords = {Medium mathematics systems, Logics, Axiom set theory, Academic disputation, Application}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Kung:2008:fuzz, author = "Chaang-Yung Kung and Huei-Shr Chen and Chih-Cheng Huang and Kun-Li Wen ", title = "Using GM(1,1) Method to Forecast the Development of Cell Phone Market in Taiwan", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0135.pdf}, url = {}, size = {}, abstract = {This research aims to predict the sale amount of 3G mobile phone market of Chunghwa Telecom by GM(1,1) 4 terms prediction model and regression of 3G developing trend and attempts to formulate the suitable countermeasures for 3G development. The result of this research is that the grey prediction theory can fit the four-term development precisely in the 3G market. The accuracy of the prediction result is above 90percent and corresponds with a distinction, which the grey theory can meet expectations with small samples or data. In addition, the accuracy of GM(1,1) 4 terms prediction model is higher than that of the regression model. For example, along with the 1.5 million users from December in in 2005 to June in 2007, the results not only show that Chunghwa Telecom will reach the line of 7.5 million users in 2009, but also indicate that 3G market will become mature in a long run and keep growing up in the near future. As such, the importance of 3G mobile market is foreseable; therefore, the operators ought to make a great effort to allocate the marketing resources as early as possible. The suggestion of this research is that under the circumstances of fieree competition, Chunghwa Telecom as a leader can cooperate with the other operators to further the interests of the 3G market and in turu paves a way to the future 4G market. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Meng:2008:fuzz, author = "Xia Meng and Yongguang Yu and Guoguang Wen and Rongguang Chen", title = "Chaos Synchronization of Unified Chaotic System Using Fuzzy Logic Controller", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0136.pdf}, url = {}, size = {}, abstract = {The fuzzy logic controller is used to synchronize the master-slave unified chaotic systems with uncertainties. The simulation results show that the error dynamics of the unified chaotic synchronization systems are regulated to zero asymptotically in shorter time in spite of the overall system is undergoing uncertainty and disturbance. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Hasuike:2008:fuzz, author = "Takashi Hasuike and Hideki Katagiri and Hiroaki Ishii", title = "Probability Maximization Model of 0-1 Knapsack Problem with Random Fuzzy Variables", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0137.pdf}, url = {}, size = {}, abstract = {This paper considers a new model of 0-1 knapsack problem including probabilistic coefficients with ambiguous expected returns assumed as random fuzzy variables. Since the random fuzzy 0-1 knapsack problem is not well-defined integer programming problem due to involve random fuzzy variables, it is hard to construct the efficient solution method to solve this problem directly. In this paper, using chance constraints, possibility measure and fuzzy goal based on both stochastic and fuzzy programming approaches, the main problem is transformed into a deterministic equivalent quadratic integer programming. Then, the efficient solution method to find a strict optimal solution based on dynamic programming is constructed. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Montiel:2008:fuzz, author = "Oscar Montiel and Jose Olivas and Roberto Sepúlveda and Oscar Castillo", title = "Development of an Embedded Simple Tuned Fuzzy Controller", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0139.pdf}, url = {}, size = {}, abstract = {This work focuses in the design and implementation of a digital fuzzy controller, that uses a novel tuning technique called Simple Tuning Algorithm (STA) to achieve the desired controller's response. Improvements over exciting architectures to implement the Fuzzy Inference Systems (FIS) in a Field Programmable Gate Array (FPGA) are presented. A methodology that minimizes the test and validation process is given. Experiments were done using a geared Direct Current (DC) motor; the control goal was to maintain the speed at a given value. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Lur:2008:fuzz, author = "Yung-Yih Lur and Yan-Kuen Wu and Sy-Ming Guu", title = "Convergence of Powers of a Max-Convex Mean Fuzzy Matrix", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0140.pdf}, url = {}, size = {}, abstract = {Fuzzy matrices provide convenient representations for fuzzy relations on finite universes. In the literature, the behavior of powers of a fuzzy matrix with max-min/max-product/max-Archimedean t-norm/max-t-norm compositions have been studied. Conventionally, the algebraic operations involved in the study of powers of a fuzzy matrix usually belong to the max-t-norms. Recently the powers of a max-arithmetic mean fuzzy matrix have been studied. Typically, the max-arithmetic mean operation is not a max-t-norm. Since the max-arithmetic mean is a special example of the max-convex mean operations, we shall extend the study to powers of a max-convex mean fuzzy matrix in this paper. We show that its powers are always convergent. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Lee2:2008:fuzz, author = "ChangSu Lee and Anthony Zaknich and Thomas Bräunl", title = "A Framework of Adaptive T-S Type Rough-Fuzzy Inference Systems (ARFIS)", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0141.pdf}, url = {}, size = {}, abstract = {The Rough-Fuzzy hybridization scheme has become of research interest in a variety of areas over the past decade. The present paper proposes a general framework for Adaptive T-S type Rough-Fuzzy Inference Systems (ARFIS) for many practical applications. Rough set theory is used to reduce the number of attributes and to obtain a minimal set of decision rules based on input-output data sets. A T-S type fuzzy inference system is constructed by the automatic generation of membership functions and rules by the fuzzy c-means clustering algorithm and the rough set approach, respectively. The generated T-S type rough-fuzzy inference system is then adjusted by the least squares fit and the conjugate gradient descent algorithm towards better performance with a validity checking for the generated minimal set of rules. The proposed framework of ARFIS is able to reduce the number of rules which increases exponentially when more input variables are involved and also to assess the validity of the minimized decision rules. The performance of the proposed framework of ARFIS is compared with other existing approaches in a variety of application areas and shown to be very competitive. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Yan:2008:fuzz, author = "Cong-hua Yan and Jin-xuan Fang", title = "Linear L-Topologies on the Space of L-Continuous Bi-Induced Linear Operators", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0142.pdf}, url = {}, size = {}, abstract = {The main purpose of this paper is to introduce linear L-topologies on the space of L-continuous bi-induced linear operators, a characterization of this space in terms of L-fuzzy pseudo-norms Is given in the case where its range is a locally convex L-topological vector space. Some interesting properties of this space are discussed. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Kuo:2008:fuzz, author = "T. C. Kuo and B. W. Hong and Y. J. Huang and C. Y. Chen", title = "Adaptive Fuzzy Controller Design for Robotic Manipulators with Sliding Mode Control", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0145.pdf}, url = {}, size = {}, abstract = {In this paper, an adaptive fuzzy sliding mode controller is proposed for a three-axis SCARA manipulator. The proposed controller possesses the advantages of adaptive control, fuzzy control, and sliding mode control. Based on the concept of sliding mode, fuzzy rules are developed to alleviate the input chattering effectively by using the developed adaptation law. The stability of the three-axis SCARA manipulator is guaranteed by using the Lyapunov method. The simulation results demonstrate that the chattering and steady state errors are eliminated and satisfactory trajectory tracking is achieved. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Li3:2008:fuzz, author = "Xin Li and Xue-Ping Zhao and Jie Chen ", title = "Active Reduction of Pressure Ripple for Electric Power Steering Systems via Fuzzy Control Approach", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0147.pdf}, url = {}, size = {}, abstract = {Pressure ripple in electric power steering (EPS) system can be caused by the phase lag between driver's steering torque and steer angle, the nonlinear frictions, and the disturbances from road and sensor noise especially during high frequency maneuvers. This paper novelly applied the robust fuzzy control method of active reduction of pressure ripple for EPS system, which achieves remarkable progress on steering maneuverability. First, an EPS dynamics are described by an eight-order nonlinear state-space model and approximated by a Takagi-Sugeno (T-S) fuzzy model with time-varying delays and external disturbances. Then, a fuzzy controller is applied based on the fuzzy model of the EPS system. The closed loop stability conditions of EPS system with the fuzzy controller are parameterized in terms of Linear Matrix Inequality (LMI) problem, which can be solved efficiently by using the convex optimization techniques. The numerical simulation of the EPS system with and without the use of the developed controller has been carried out. The simulation result shows that the proposed fuzzy control method can reduce the torque ripple to achieve a better steering feel and more stable driving. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Lin:2008:fuzz, author = "Tzu-Chao Lin and Mu-Kun Liu and Chien-Ting Yeh ", title = "Two-Pass Switching Filter Based on Modification of Dempster's Combination Rule for Fuzzy Image Restoration", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0149.pdf}, url = {}, size = {}, abstract = {A new two-pass switching rank-ordered arithmetic mean (TSRAM) filter controlled by evidence fusion is proposed for fuzzy image restoration. The new filter mechanism is composed of an efficient impulse detector based on Dempster-Shafer evidence theory and a rank-ordered arithmetic mean filter that works by estimating the noise-free ordered mean values. A modified Dempster's combination rule is proposed and applied to the impulse detector. In addition, to improve the filtering performance, a second pass filtering is performed using a simple switching median filter. Experimental results have demonstrated that the proposed filter outperforms other switching-based median filters in terms of both noise suppression and detail preservation. The proposed new filter also provides excellent robustness at various percentages of impulsive noises. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Palm:2008:fuzz, author = "Rainer Palm and Boyko Iliev", title = "Grasp Recognition by Time-Clustering, Fuzzy Modeling, and Hidden Markov Models (HMM) — A Comparative Study", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0150.pdf}, url = {}, size = {}, abstract = {This paper deals with three different methods for grasp recognition for a human hand. Grasp recognition is a major part of the approach for Programming-by-Demonstration (PbD) for five-fingered robotic hands. A human operator instructs the robot to perform different grasps wearing a data glove. For a number of human grasps, the finger joint angle trajectories are recorded and modeled by fuzzy clustering and Takagi-Sugeno modeling. This leads to grasp models using the time as input parameter and the joint angles as outputs. Given a test grasp by the human operator the robot classifies and recognizes the grasp and generates the corresponding robot grasp. Three methods for grasp recognition are presented and compared. In the first method the test grasp is compared with model grasps using the difference between the model outputs. In the second one, qualitative fuzzy models are used for recognition and classification. The third method is based on Hidden-Markov-Models (HMM) which are commonly used in robot learning. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wee:2008:fuzz, author = "Chua Teck Wee and Tan Woei Wan", title = "EFSVM-FCM: Evolutionary Fuzzy Rule-Based Support Vector Machines Classifier with FCM Clustering", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0152.pdf}, url = {}, size = {}, abstract = {This paper presents a hybrid TSK fuzzy rule-based classifier. Fuzzy c-means clustering and Genetic algorithm and are used to optimize the number of rules and antecedent parameters. By using the relationship between a SVM and a TSK FLS, an efficient method for learning the consequent parts of the TSK fuzzy system is introduced. The resulting hybrid fuzzy classifier has a compact rule base and good generalization capabilities compared to existing algorithms in the literature. In this sense, the curse of dimensionality which is often associated with fuzzy rule-based classifier can be avoided. The performance of the proposed hybrid fuzzy classifier is verified through extensive tests and comparison with other methods. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Pouzols:2008:fuzz, author = "Federico Montesino Pouzols and Amaury Lendasse and Angel Barriga", title = "Fuzzy Inference Based Autoregressors for Time Series Prediction Using Nonparametric Residual Variance Estimation", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0153.pdf}, url = {}, size = {}, abstract = {We apply fuzzy techniques for system identification and supervised learning in order to develop fuzzy inference based autoregressors for time series prediction. An automatic methodology framework that combines fuzzy techniques and statistical techniques for nonparametric residual variance estimation is proposed. Identification is performed through the learn from examples method introduced by Wang and Mendel, while the Marquard-Levenberg supervised learning algorithm is then applied for tuning. Delta test residual noise estimation is used in order to select the best subset of inputs as well as the number of linguistic labels for the inputs. Experimental results for three time series prediction benchmarks are compared against LS-SVM based autoregressors and show the advantages of the proposed methodology in terms of approximation accuracy, generalization capability and linguistic interpretability. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Pouzols2:2008:fuzz, author = "Federico Montesino Pouzols and Angel Barriga and Diego R. Lopez and Santiago Sanchez-Solano", title = "Linguistic Summarization of Network Traffic Flows", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0154.pdf}, url = {}, size = {}, abstract = {We address, by means of fuzzy linguistic summaries, two related problems: summarizing network flow statistics and making these statistics human-readable. Two complementary summarization methods are developed. First, a fixed set of protoforms of interest is defined, and the ones with a higher truth value are shown to the user as simple on-line summaries. This first method is suitable for real-time monitoring. Then, an association rules mining process is carried out in order to find hidden relations in flow records. Both approaches are implemented in a tool capable of real-time and off-line processing of network flow records. Experimental results for a number of heterogeneous NetFlow records show the usefulness of linguistic summaries to both network practitioners and users. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Zhou3:2008:fuzz, author = "Shang-Ming Zhou and Francisco Chiclana and Robert I. John and Jonathan M. Garibaldi", title = "Type-2 OWA Operators — Aggregating Type-2 Fuzzy Sets in Soft Decision Making", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0157.pdf}, url = {}, size = {}, abstract = {Yager's ordered weighted averaging (OWA) operator has been widely used in soft decision making to aggregate experts' individual opinions or preferences for achieving an overall decision. The traditional Yager's OWA operator focuses exclusively on the aggregation of crisp numbers. However, human experts usually tend to express their opinions or preferences in a very natural way via linguistic terms, like “important”, “very important”, “good” etc. Type-2 fuzzy sets provide an efficient way of knowledge representation for modelling linguistic terms. In order to aggregate linguistic opinions via the OWA mechanism, we propose a new type of OWA operator, termed type-2 OWA operator that is able to aggregate type-2 fuzzy sets, and therefore to aggregate the linguistic opinions or preferences in human decision making. The necessary equations for performing type-2 OWA operations on aggregating interval type-2 fuzzy sets are derived in this paper. Some examples are provided to illustrate the proposed technique. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Lendek:2008:fuzz, author = "Zs. Lendek and R. Babuška and B. De Schutter", title = "Stability Analysis and Observer Design for Decentralized TS Fuzzy Systems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0158.pdf}, url = {}, size = {}, abstract = {A large class of nonlinear systems can be well approximated by Takagi-Sugeno (TS) fuzzy models, with linear or affine consequents. It is well-known that the stability of these consequent models does not ensure the stability of the overall fuzzy system. Stability conditions developed for TS fuzzy systems in general rely on the feasibility of an associated system of linear matrix inequalities, whose complexity may grow exponentially with the number of rules. We study distributed systems, where the subsystems are represented as TS fuzzy models. For such systems, a centralized analysis is often unfeasible. We analyze the stability of the overall TS system based on the stability of the subsystems and the strength of the interconnection terms. For naturally distributed applications, such as multi-agent systems, when adding new subsystems “on-line”, the construction and tuning of a centralized observer is often intractable. Therefore, we also propose a decentralized approach to observer design. Applications of such systems Include distributed process control, traffic networks, and economic systems. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Stoilos:2008:fuzz, author = "Giorgos Stoilos and Giorgos Stamou and Stefanos Kollias ", title = "Reasoning with Qualified Cardinality Restrictions in Fuzzy Description Logics", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0159.pdf}, url = {}, size = {}, abstract = {Description Logics (DLs) are modern knowledge representation formalisms which are used today in many applications for reasoning with structured knowledge. Moreover, they are used in the Semantic Web (an extension of the current web) through the ontology language OWL. On the other hand fuzzy Description Logics (fuzzy-DLs) have been proposed as expressive logical formalisms capable of capturing and reasoning with vague and imprecise knowledge in the Semantic Web. In the current paper we investigate on the problem of reasoning with qualified cardinality restrictions (QCRs) in fuzzy DLs, extending previous results on simple number restrictions, thus we present a tableaux algorithm for the the fuzzy-DL fKD-ALCIQ. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Chen5:2008:fuzz, author = "Hsiao-Ching Chen and Mei-Huei Chen and Li-Nuo Wu and Keng-Yin Wang", title = "The Relationship Between Teacher's Performance and the Merit System Based on GM(1,N)", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0160.pdf}, url = {}, size = {}, abstract = {The main purpose of this paper is to apply GM(1,N) model to weigh problem on the topic of the relationship between teacher's performance and the merit system. Teachers play key roles in facilitating successful teaching and administrative operating. The merit system — set by the Ministry of Education — has been practiced for over 50 years. Therefore, how it influences teacher's quality and how to promote teacher's performance and teaching quality attracts a lot of attention. First, this paper designs a research structure and creates a questionnaire based on the experts' experiences and the CTU's merit system. This paper divided the questionnaire into 3 input blocks (15 factors) and 1 output blocks (1 factor). 50 teachers in Chienkuo Technology University had completed the questionnaire. Second, apply GM (1,N) model to help the calculation of the whole data. Also, a Matlab toolbox has developed to weigh the problem. Finally, this paper got two results. One is the factor 6; the present merit system pushes me to devote myself to getting involved in administrative duty, influences the output block most. This may imply that administrative duty is a plus to teacher's overall performance. The other is the factor 3; the present merit system pushes me to cooperate with administrative duty, influences the output block least. This is indicative of teachers' perceptions of and attitudes toward school administrators. Then, this paper adopts a new approach, and can extend it into other relative fields. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Gong:2008:fuzz, author = "Zaiwu Gong and Weijun Cui ", title = "Approach to Group Decision Making Based on the Incomplete Interval Fuzzy Number Complementary Judgment Matrices", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0161.pdf}, url = {}, size = {}, abstract = {The priority problem of the interval fuzzy number complementary judgment matrices with incomplete information is investigated. Transformation relation between the interval fuzzy number reciprocal judgment matrix and the interval fuzzy number complementary judgment matrix is set up. The concept of additive consistency for the fuzzy number complementary judgment matrix is introduced. A least-square approach to group decision making based on the incomplete interval fuzzy number complementary judgment matrices is proposed, of which the existence condition of the solution is studied. Finally, it is illustrated by a numerical example that this method is feasible and effective. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(He:2008:fuzz, author = "Hongmei He and Jonathan Lawry", title = "Linguistic Attribute Hierarchies for Downwards Propagation of Information", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0162.pdf}, url = {}, size = {}, abstract = {We investigate the propagation of label information for multi-attribute decision making problems downwards a linguistic attribute hierarchy, which represents the complex and often imprecise functional relationships between low-level attributes or measurements and high-level decision or classification variables. The downward propagation algorithm identifies the branches in linguistic decision trees for which the probability of a high-level goal exceeds a given threshold. The sensitivity of the method to this threshold is then reduced by integrating with respect to a probability distribution on high threshold values. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Hsu2:2008:fuzz, author = "Pi-Fang Hsu and Hsin-Tien Han", title = "Applying Grey Relational Analysis to Select the Optimal Public Relations Agency for the High-Tech Industry", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0163.pdf}, url = {}, size = {}, abstract = {This study develops a model for selecting public relations (PR) agencies based on high-tech industrial perceptions. The proposed model adopts “nominal group technique (NGT)” to identify suitable assessment criteria for selecting PR agencies, and then applies the “Grey Relational Analysis (GRA)” to rank alternatives and select the optimum PR firm for the high-tech industry. Furthermore, this study uses the example of a renowned high-tech communications manufacturer in Taiwan to demonstrate the effectiveness of the model in PR firm selection. The proposed model helps high-tech enterprises to effectively select PR agencies, making it highly applicable in academia and commerce. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Pizzi:2008:fuzz, author = "Nick J. Pizzi and Witold Pedrycz", title = "An Analysis of Potentially Imprecise Class Labels Using a Fuzzy Similarity Measure", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0164.pdf}, url = {}, size = {}, abstract = {Accurate classification of biomedical data is often confounded by potentially imprecise class labels assigned by an external reference test. We present a gradation method using fuzzy set theory and a dispersion-adjusted similarity measure to assign, for each pattern in a design set, a degree of belongingness to each class. After training a classifier using this adjusted design set, its performance is measured using a validation set of patterns with their original class labels. We empirically demonstrate the effectiveness of this method using three publicly available biomedical datasets. Using the same classifier, we benchmark the results against the original datasets without gradation. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Nakata:2008:fuzz, author = "Michinori Nakata and Hiroshi Sakai", title = "Rough Sets Approximations in Data Tables Containing Missing Values", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0165.pdf}, url = {}, size = {}, abstract = {Rough sets are applied to data tables containing missing values. A new method, called a method of possible equivalence classes, is proposed. Discernibility as well as indiscernibility of missing values is considered in order to improve previous results. A family of possible equivalence classes is obtained, in which each possible equivalence class has the possibility that it is an actual one. By using the family of possible equivalence classes, we derive lower and upper approximations. The lower and the upper approximations coincide with ones obtained from methods of possible worlds. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Coupland:2008:fuzz, author = "Simon Coupland and James Wheeler and Mario Gongora ", title = "A Generalised Type-2 Fuzzy Logic System Embedded Board and Integrated Development Environment", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0166.pdf}, url = {}, size = {}, abstract = {This paper describes the design and construction of the first hardware running a generalised type-2 fuzzy logic system. A rationale for the design is given and hardware representations are discussed. An integrated development environment, also developed as part of this project, for type-2 fuzzy system is described along with the software which links this IDE to the novel hardware. The performance of the novel type-2 development board is tested under two scenarios. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Bisgambiglia:2008:fuzz, author = "Paul-Antoine Bisgambiglia and Emmanuelle de Gentili and Paul Bisgambiglia and Jean-Fraois Santucci", title = "Fuzzy Simulation for Discrete Events Systems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0167.pdf}, url = {}, size = {}, abstract = {The aim of our research is to develop a new method of taking into account the imperfect parameters in a discrete events modeling and simulation formalism. This article describes our approach to modeling and compares several methods for fuzzy simulations. }, keywords = { Fuzzy Sets, Fuzzy Interval, Defuzzycation, Modeling, Simulation, DEVS.}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Han:2008:fuzz, author = "Liyan Han and Juan Zhou", title = "European Option Pricing and Hedges Under Heterogeneity with λ-Fuzzy Measures and Choquet Intergral", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0168.pdf}, url = {}, size = {}, abstract = {Classical option pricing formulas are facing many challenges among which heterogeneity of investors enjoys abroad concerns. This paper studies the option pricing in a single period in the presence of investors' heterogeneous beliefs. We aim to make use of fuzzy instruments to highlight non-identical rationality which enter into option pricing and influence hedge strategies of investors, and to deduce fuzzy price representation of the option. The price of the option is not a determinate number but an interval containing the Black-Scholes price. Further we discuss some hedge ratios that can be represented by fuzzy numbers, which are convenient for application. The basic analysis is generalized to incorporate multiple sources of risk, disagreement about non-fundamentals, and multiple investors. Other applications involving portfolio insurance and credit risk measure are discussed. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Lughofer:2008:fuzz, author = "Edwin Lughofer and Stefan Kindermann", title = "Improving the Robustness of Data-Driven Fuzzy Systems with Regularization", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0171.pdf}, url = {}, size = {}, abstract = {Regularization is an important aspect whenever a matrix inversion during the training phase is required, as this inversion may lead to an unstable (ill-posed) problem, usually simply because of a matrix with a high condition or even a singular matrix, guiding the learning algorithm to wrong solutions. In this paper we present regularization issues applied to off-line and on-line training of Takagi-Sugeno fuzzy systems for increasing the robustness of the learning procedure and the accuracies of the models. After defining the problem of ill-posedness for the learning of linear consequent parameters (when applying least squares optimization measure), we describe several methods for finding an optimal parameter setting in the Tichonov regularization. We also describe the way how to apply regularization to evolving fuzzy models. The paper is concluded with a comparison of conventional (not regularized) FLEXFIS resp. FLEXFIS-Class method with their regularized extensions and with (not-regularized) genfis2. This comparison will be based on high-dimensional real-world data sets from engine test benches and from an image classification framework and will underline the impact of the regularized methods on prediction and classification accuracy. }, keywords = {Takagi-Sugeno fuzzy models, data-driven (evolving) modelling, consequent parameters, regularization, high-dimensional real-world data sets}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Mavrinac:2008:fuzz, author = "Aaron Mavrinac and Ahmad Shawky and Xiang Chen ", title = "A Fuzzy Associative Approach for Recognition of 3D Objects in Arbitrary Pose", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0173.pdf}, url = {}, size = {}, abstract = {Once the human vision system has seen a 3D object from a few different viewpoints, depending on the nature of the object, it can generally recognize that object from new arbitrary viewpoints. This useful interpolative skill relies on the highly complex pattern matching systems in the human brain, but the general idea can be applied to a computer vision recognition system using comparatively simple machine learning techniques. An approach to the recognition of 3D objects in arbitrary pose relative the the vision equipment given only a limited training set of views is presented. This approach involves computing a disparity map using stereo cameras, extracting a set of features from the disparity map, and classifying it via a fuzzy associative map to a trained object. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wang6:2008:fuzz, author = "Qiu-Ping Wang and Xiao-Feng Wang and Hai-Qing Hu", title = "A New Method for Priorities of Fuzzy Complementary Judgment Matrix", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0174.pdf}, url = {}, size = {}, abstract = {The aim of this paper is to propose a method for priorities from fuzzy complementary judgment matrix, which can be either consistent or inconsistent. In the method, firstly, based on property of fuzzy complementary consistent judgment matrix, the deviation term is introduced and deviation function is constructed. Then, in order to minimize the inconsistency of fuzzy complementary matrix, mathematical programming model is constructed. The formula of computing priorities is deduced by applying Lagrange's method of multipliers to the mathematic programming model and an algorithm for priority of fuzzy complementary judgment matrix is given. Finally, a numerical example is given to demonstrate proposed method is feasible and practical. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Waldock:2008:fuzz, author = "A. Waldock and B. Carse", title = "Fuzzy Q-Learning with an Adaptive Representation", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0175.pdf}, url = {}, size = {}, abstract = {Reinforcement Learning (RL) is learning how to map states to actions so as to maximise a numeric reward signal. Fuzzy Q-Learning (FQL) extends the RL technique Q-Learning to large or continuous problems and has been applied to a wide range of applications from data mining to robot control. Typically, FQL uses a uniform or pre-defined internal representation provided by the human designer. A uniform representation usually provides poor generalisation for control applications, and a pre-defined representation requires the designer to have an in-depth knowledge of the desired control policy. In this paper, the approach taken is to reduce the reliance on a human designer by adapting the internal representation, to improve the generalisation over the control policy, during the learning process. A Hierarchical Fuzzy Rule Based System (HFRBS) is used to improve the generalisation of the control policy through iterative refinement of an initial coarse representation on a classical RL problem called the Mountain Car problem. The process of adapting the representation is shown to significantly reduce the time taken to learn a suitable control policy. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Popescu:2008:fuzz, author = "Mihail Popescu and James C. Bezdek and James M. Keller and Timothy C. Havens and Jacalyn M. Huband", title = "A New Cluster Validity Measure for Bioinformatics Relational Datasets", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0176.pdf}, url = {}, size = {}, abstract = {Many important applications in biology have underlying datasets that are relational, that is, only the (dis)similarity between biological objects (amino acid sequences, gene expression profiles, etc.) is known and not their feature values in some feature space. Examples of such relational datasets are the gene similarity matrices obtained from BLAST, gene expression data, or Gene Ontology (GO) similarity measures. Once a relational dataset is obtained, a common question asked is how many groups of objects are represented in the original dataset. The answer to this question is usually obtained by employing a clustering algorithm and a cluster validity measure. In this article we describe a cluster validity measure for non-Euclidean relational fuzzy c-means that is based on the correlation between a relation induced on the data by the cluster memberships and the original relational data. This validity measure can be applied to partitions made by any fuzzy relational clustering algorithm. We illustrate our measure by validating clusters in several dissimilarity matrices for a set of 194 gene products obtained using BLAST and GO similarities. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Lee3:2008:fuzz, author = "Mahnhoon Lee ", title = "Mapping of Ordinal Feature Values to Numerical Values Through Fuzzy Clustering", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0179.pdf}, url = {}, size = {}, abstract = {Objects are represented by feature values, and the feature values are in general numerical, ordinal or nominal. The feature values of an ordinal type are totally ordered labels, and the labels can be considered as fuzzy sets. The formulation of proper fuzzy sets for the labels is important for the systems to deal with the objects of mixed feature types. When a proper ordinal-numerical mapping of the ordinal feature of interest is given, fuzzy sets for the labels of the ordinal feature can easily be formulated. In this paper, we present an algorithm to obtain an ordinal-numerical mapping of an ordinal feature of interest from a given object set in which objects have the ordinal feature values, in the way that the obtained mapping reflects the information structure in the object set. The proposed algorithm starts with an initial ordinal-numerical mapping, and iteratively obtains a fuzzy partition matrix with the ordinal-numerical mapping and computes a new ordinal-numerical mapping from the fuzzy partition matrix. In this way both of them become improved gradually. The information structure, i.e., the fuzzy partition matrix, stored in the given object set is eventually reflected in the ordinal-numerical mapping. We also show the validity of the proposed algorithm through experiments with synthetic object sets. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Miyamoto:2008:fuzz, author = "Sadaaki Miyamoto and Yuichi Kawasaki ", title = "Kernel Space for Text Analysis Based on Fuzzy Neighborhoods", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0181.pdf}, url = {}, size = {}, abstract = {A natural Euclidean space is defined on a set of texts as sequences or hierarchical structures. Unlike the traditional term-document model, the present model takes local topological structure of texts. Kernel functions are defined that enable the use of Euclidean spaces and hence methods of data analysis based on kernels are applicable to the present model. Applications include agglomerative as well as c-means clustering and principal component analysis. Numerical examples are shown. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Zhao:2008:fuzz, author = "Chunhua Zhao and Xinze Zhao and Hongliang Gao and Gang Wu", title = "Knowledge Mining for Fault Diagnosis Based on Rough Sets Theory", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0184.pdf}, url = {}, size = {}, abstract = {The fault diagnosis in tribosystem was a difficult problem due to the complex structure of the tribosystem, the nonlinear character of the tribosystem and the presence of multi-excite sources. Usually, one method of fault diagnosis can only inspect one corresponding fault category. In this paper, oil monitoring and vibration monitoring methods were used together to diagnose the rolling bearing faults on a homemade bearing bench. Five tests were conducted under different conditions. Oil samples and vibration data were collected regularly and analyzed respectively. Then rough sets theory was introduced into the process of choosing parameters and the knowledge discovery in union diagnosis. Some knowledge was obtained finally. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Yukihiro:2008:fuzz, author = "Hamasuna Yukihiro and Endo Yasunori and Miyamoto Sadaaki", title = "Support Vector Machine for Data with Tolerance based on Hard-Margin and Soft-Margin", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0187.pdf}, url = {}, size = {}, abstract = {This paper presents two new types of Support Vector Machine (SVM) algorithms, one is based on Hardmargin SVM and the other is based on Soft-margin SVM. These algorithms can handle data with tolerance of which the concept includes some errors, ranges or missing values in data. First, the concept of tolerance is introduced into optimization problems of Support Vector Machine. Second, the optimization problems with the tolerance are solved by using the Karush-Kuhn-Tucker conditions. Next, new algorithms are constructed based on the unique and explicit optimal solutions of the optimization problem. Finally, the effectiveness of the proposed algorithms is verified through some numerical examples for the artificial data. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wang7:2008:fuzz, author = "Wei Wang and Shimin Wei and Qizheng Liao and Liao Yaqin Xia and Danlin Li and Junzi Li", title = "Fuzzy K-Means Clustering on Infrasound Sample", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0190.pdf}, url = {}, size = {}, abstract = {Infrasound is ideally suited to provide essential information of these earthquakes without any invasive measures. This necessitates automatic processing of the data as the captured phenomena need to be sorted before further analysis can be undertaken. Extracting the physical characters of different signals by signal processing such as Fourier Transform and Wavelets Transform are generally employed to carry out the necessary expansion. This article reviews pattern recognition as it applies to earthquake prediction and discusses the concept of fuzzy logic approach as a means of seismic infrasound classification. An example is presented in which this approach was used for classifying preprocessed infrasound signals to identify precursory strong earthquake. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Guan:2008:fuzz, author = "Donghai Guan and Weiwei Yuan and Young-Koo Lee and Sungyoung Lee", title = "Training Data Selection Based on Fuzzy C-Means", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0191.pdf}, url = {}, size = {}, abstract = {The performance of supervised learning could be improved when valuable data are selected for training. In this paper, we proposed three data selection methods based on fuzzy c-means algorithm. They are: center-based selection, border-based selection and bin-based selection. In center-based selection, the data with high degree of membership in each cluster are selected for training. In border-based selection, the data around the borders between clusters are selected. In bin-based selection, the data in each cluster are sorted based on their membership degrees. Then for each cluster, the sorted data are divided into bins. Finally, there is one data selected from each bin for training. The effects of them are empirically studied on a set of UCI data sets. Experimental results indicate that bin-based selection could effectively improve the performance of learning compared to randomly selecting training samples. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(He2:2008:fuzz, author = "Yong He and Kejun Zhu and Siwei Gao and Ting Liu and Haixiang Guo", title = "The Optimal Cluster Number of FCM in Complex Economic Systems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0192.pdf}, url = {}, size = {}, abstract = {When modeling the complex economic systems, a target system often need to be categorized into a certain clusters by FCM. The optimal cluster number often is dependent of the selected cluster validity function, but there are so many validity functions proposed, it is difficult to get the optimal cluster number in real target system. A method to get the optimal cluster number of FCM in real systems is proposed: Presets some reasonable cluster numbers, and then chooses a cluster number as the optimal cluster number by some representative validity functions. Testing on the X30 and Bensaid data sets demonstrates the effectiveness and reliability of the proposed method, and finally gives an experiment on china's 31 regions according to the level of Science and Technology (S&T) progress. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Sassi:2008:fuzz, author = "M. Sassi and A. Grissa Touzi and H. Ounelli", title = "A Fuzzy Linguistic Approach to Database Summarization", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0193.pdf}, url = {}, size = {}, abstract = {In this paper, a new approach for linguistic database summarization is proposed. It is naturally designed to provide to the user synthetic views of groups of tuples over the database. Summaries are represented as concepts which organized into a hierarchy, defining different levels of granularity to intelligently parse the database and refine user queries. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Simon:2008:fuzz, author = "Levente L. Simon and Konrad Hungerbuehler", title = "Real Time Takagi-Sugeno Fuzzy Model Based Pattern Recognition in the Batch Chemical Industry", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0196.pdf}, url = {}, size = {}, abstract = {This contribution describes the real time pressure check pattern recognition of an industrial batch dryer. The goal is to identify the start of the drying process and to calculate the time elapsed between two consequent batch starts (batch time) right after the batch has completed. The presented pattern recognition method implements a supervised learning approach based on Takagi-Sugeno fuzzy (TS) models. The decision maker design is based on plant data compressed by the PI algorithm (OSI Software, Inc). It is concluded that the developed classifier is able to perform real time classification and the compressed PI data can be used in order to design data analysis tools which are useful for chemical batch plant operation investigations. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Tao:2008:fuzz, author = "Ted Tao and Chao-Peng Wei and Liang-Yu Chen ", title = "The Auxiliary CMAC Applied to Online Tuning Robust Fuzzy Controllers", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0197.pdf}, url = {}, size = {}, abstract = {A novel auxiliary CMAC robust fuzzy control schemes is proposed in this paper. There are two structures in the proposed schemes: one is the robust fuzzy controller and the other is the auxiliary CMAC learning algorithm. The robust fuzzy controller can achieve a certain goal without concern for instability of the controlled system in the presence of significant plant uncertainties, if the nominal parameter is roughly estimated. In order to improve the performance of robust fuzzy controller, the nominal parameter should be adjusted. Thus, an auxiliary CMAC learning algorithm under the robust fuzzy control structure is employed to online tuning the nominal parameter. Finally, simulation results demonstrate the excellent capability of the proposed structure for improving the output performance. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Chen6:2008:fuzz, author = "Ting-Yu Chen and Li-Hsuan Yen and Che-Wei Tsui", title = "A Causal Model of Consumer Involvement: A New Approach with Intuitionistic Fuzzy Automata", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0198.pdf}, url = {}, size = {}, abstract = {Involvement has become an important variable in consumer behavior and marketing research for a long time. The purpose of this study is to develop a model that can explain the complicated relationship between antecedents, consequences, and different types of involvement at the same time to understand the internal state of consumers better. Specifically, this model has to be capable of handling the vague interactions between different types of involvement. We develop this integrated model by the using of the intuitionistic fuzzy automata. We choose the cell phone as the stimulus product in this research. There are 19 antecedents, 5 types of involvement, and 10 consequences in the integrated involvement model. According to the results, we find that the intuitionistic fuzzy automata can deal with the interactions between different types of involvement. Second, the intuitionistic fuzzy automata can observe the relations between antecedents, consequences, and 5 types of involvement at the same time. Besides, we can observe the internal state inside consumers through the intuitionistic fuzzy automata, especially the degree of hesitation when they make decisions. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Chen7:2008:fuzz, author = "Ting-Yu Chen and Hsiao-Pin Wang and Che-Wei Tsui", title = "Validating the Integrated Paradigm for Advertising Involvement with the Intuitionistic Fuzzy Set Theory", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0199.pdf}, url = {}, size = {}, abstract = {As far as marketing researchers are concerned, advertising involvement is an important segmentation variable; the advertisers view advertising involvement as a vital factor resulting in advertising effects. Advertising involvement has been discussed in several decades while little literature proposed a complete integrated model. Hence, we collect the antecedents and consequences for advertising involvement. Because the model that we attempt to develop includes too many variables, it is difficult to judge the functional relations among these variables and not appropriate to use a traditional statistical method. We take advantage of the automata to develop an integrated model of advertising involvement. In the social science, a great number of questions are abstract and hard to possess a certain answer. The intuitionistic fuzzy sets, which are generated from the fuzzy sets, more completely express the degree of uncertainty for people. We use the automata in the intuitionistic fuzzy sets, which are named intuitionistic fuzzy automata, to develop an integrated model of advertising involvement. The model is successfully generated. In the future, as long as obtaining consumers' degree of antecedents, we can predict their degree of advertising involvement and consequences in terms of this model. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Chen8:2008:fuzz, author = "Ting-Yu Chen and Cing-Chan Chou and Che-Wei Tsui", title = "Conceptualizing Product Involvement Using Fuzzy Automata and Intuitionistic Fuzzy Sets", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0200.pdf}, url = {}, size = {}, abstract = {Product involvement has been developed for a long period of time, and has been mature gradually. Intuitionistic fuzzy sets contain the concept of interval that could be used to solve the uncertain problems when respondents have uncertainty in answering questions. The intuitionistic fuzzy automata are mathematical machine for a finite state with a dynamic system operating in discrete time. The operation of the intuitionistic fuzzy automata is similar to the operation of individuals' product involvement. Both of them do have the internal state to transform output state. This research tried to use the intuitionistic fuzzy automata to develop an integrated model of product involvement, and search for which one is the most suitable product involvement scale when we discuss product involvement in the intuitionistic fuzzy automata. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Ohtake:2008:fuzz, author = "Hiroshi Ohtake and Kazuo Tanaka and Hua O. Wang", title = "Switching Fuzzy Model Based Model Following Control for Discrete-Time Systems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0201.pdf}, url = {}, size = {}, abstract = {This paper presents nonlinear model following control for discrete-time nonlinear systems using the switching fuzzy model-based control approach. We propose the construction method of augmented switching fuzzy model following control system for discrete-time nonlinear systems. Then we introduce the switching fuzzy controller which can make outputs of the nonlinear system converge to outputs of the reference nonlinear system, and derive the controller design conditions in terms of LMIs. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Huang:2008:fuzz, author = "Chun-Che Huang and Yu-Neng Fan and Tzu-Liang (Bill) Tseng and Chia-Hsun Lee and Horng-Fu Chuang", title = "A Hybrid Data Mining Approach to Quality Assurance of Manufacturing Process", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0203.pdf}, url = {}, size = {}, abstract = {Quality assurance (QA) is a process employed to ensure a certain level of quality in a product or service. One of the techniques in QA is to predict the product quality based on the product features. However, traditional QA techniques have faced some drawbacks such as heavily depending on the collection and analysis of data and frequently dealing with uncertainty processing. In order to improve the effectiveness during a QA process, a hybrid approach incorporated with data mining techniques such as rough set theory (RST), fuzzy logic (FL) and genetic algorithm (GA) is proposed in this paper. Based on an empirical case study, the proposed solution approach provides great promise in QA. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Yang:2008:fuzz, author = "Yingjie Yang and Sifeng Liu", title = "Kernels of Grey Numbers and Their Operations", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0206.pdf}, url = {}, size = {}, abstract = {Grey numbers can be represented as their kernels and associated degrees of greyness. Therefore, the operation between kernels of grey numbers has significance in the application of grey numbers. This paper investigates the operations of grey numbers using their kernels and degrees of greyness and provides conditions for the application of real number operations to their kernels. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Yuan:2008:fuzz, author = "Jie Yuan and Hai-bo Shi and Chang Liu and Wen-li Shang ", title = "Backward Concurrent Reasoning Based on Fuzzy Petri Nets", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0213.pdf}, url = {}, size = {}, abstract = {This paper presents a backward reasoning approach based on fuzzy Petri nets (FPNs). This approach takes full advantage of the structural and behavioral properties of a FPN. It can identify middle places by a vector-computational manner rather than the conventional search way, improving inference efficiency. To reduce the complexity and scale of a FPN, man-machine interaction is introduced to it. It is suggested that high efficiency and low costs which an inference method brings in practice, play a more important role than the operational efficiency of the method itself. An instance illustrates that the reasoning approach is feasible and effective. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Borgelt:2008:fuzz, author = "Christian Borgelt ", title = "Feature Weighting and Feature Selection in Fuzzy Clustering", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0214.pdf}, url = {}, size = {}, abstract = {This paper studies the problem of weighting and selecting attributes and principal axes in fuzzy clustering. Its main contribution is a selection method that is not based on simply applying a threshold to computed feature weights, but directly assigns zero weights to features that are not informative enough. This has the important advantage that the clustering result that can be obtained on the selected subspace coincides with the projection (to the selected subspace) of the clustering result that is obtained on the full data space. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Al-Hiary:2008:fuzz, author = "Heba Al-Hiary and Malik Braik and Alaa Sheta and Aladdin Ayesh", title = "Identification of a Chemical Process Reactor Using Soft Computing Techniques", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0215.pdf}, url = {}, size = {}, abstract = {This paper discusses the application of Artificial Neural Networks (ANNs) in the area of identification and control of nonlinear dynamical systems. Since chemical processes are getting more complex and complicated, the need of schemes that can improve process operations is highly demanded. ANNs are capable of learning from examples, perform non-linear mappings, and have a special capacity to approximate the dynamics of nonlinear systems in many applications. This paper will describe the application of neural network for modeling reactor level, reactor pressure, reactor cooling water temperature, and reactor temperature problems in the Tennessee Eastman (TE) Chemical Process Reactor. The potential of neural network technology in the process industries is great. Its ability to model process dynamics makes it powerful tool for modeling and control processes. A comparison between the applications of ANNs to model the TE Plant is compared with other soft computing techniques like Fuzzy Logic (FL) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS). }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Torres:2008:fuzz, author = "Patricio Torres and Doris Saez", title = "Type-2 Fuzzy Logic Identification Applied to the Modeling of a Robot Hand", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0216.pdf}, url = {}, size = {}, abstract = {In this work, a new application for robot hand identification using type-2 fuzzy intervals is presented. Type-2 identification method by using only input-output data determines the parameters of upper and lower membership functions of interval sets. The identification method was successfully applied to the modeling of a robot hand. Moreover, we show how a type-2 fuzzy model improves the description of the robot hand in comparison with type-1 fuzzy model, if uncertain inputs are considered. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Beliakov:2008:fuzz, author = "Gleb Beliakov ", title = "Fitting Fuzzy Measures by Linear Programming Programming Library fmtools", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0217.pdf}, url = {}, size = {}, abstract = {We discuss the problem of learning fuzzy measures from empirical data. Values of the discrete Choquet integral are fitted to the data in the least absolute deviation sense. This problem is solved by linear programming techniques. We consider the cases when the data are given on the numerical and interval scales. An open source programming library which facilitates calculations involving fuzzy measures and their learning from data is presented. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Xu4:2008:fuzz, author = "X. Xu and L. M. Wan and X. L. Wang and L. K. Wang and Y. C. Liang", title = "A Time Delay Neural Network for Dynamical System Control", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0219.pdf}, url = {}, size = {}, abstract = {A novel time delay neural network is proposed for dynamical system control. In this work, A continuous recurrent neural network with time delay neurons in hidden layer is constructed, and the novel training algorithm and control law independent of delay are developed based on Lyapunov's stability approach. Using the proposed method, the control error converges to a range near the zero point and remains within the domain throughout the course of the execution. The usefulness and validity of the presented algorithm are examined by numerical experiments. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Pei:2008:fuzz, author = "Zheng Pei ", title = "Extracting Association Rules Based on Intuitionistic Fuzzy Special Sets", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0220.pdf}, url = {}, size = {}, abstract = {Intuitionistic fuzzy special sets is a special case of intuitionistic fuzzy sets. In this paper, under the framework of information systems, based on the intuitionistic fuzzy special sets representation of rough sets, Hamming distance of association rule between condition and conclusion is discussed. By Hamming distance and the confidence of association rule, optimization model for extracting association rule are provided. The advantage of the optimization model is that it could be used to dynamically extract association rule from information systems and distinguish association rules with the same confidence by Hamming distance. Example shows that this paper's method is an alternative method for extracting association rule. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Yu2:2008:fuzz, author = "Wen-Shyong Yu ", title = "T-S Fuzzy Controller Design for Nonlinear Large-Scale Systems with Time-Varying Uncertainties", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0221.pdf}, url = {}, size = {}, abstract = {In this paper, we present Takagi-Sugeno (T-S) fuzzy logic control scheme for a class of large-scale time-varying uncertain systems. The proposed control scheme is comprised of a fuzzy system for the given system with uncertainties and a state feedback fuzzy controller. The parameters of the fuzzy system are updated by learning rules. The state feedback fuzzy controller is used to restrain the external disturbances. Based on the Lyapunov stability theorem, we will prove that the closedloop control system is uniformly ultimately bounded. The main point of the proposed control scheme is that we can use the P matrix in Riccati-like LMI equation for each rule to synthesize the state feedback control gain K other than that to find a common P of the traditional approach. Finally, a numerical example and a TMD system is given to validate the proposed scheme. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Lin2:2008:fuzz, author = "Jin-Cherng Lin and Kuo-Chiang Wu", title = "Evaluation of Software Understandability Based on Fuzzy Matrix", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0223.pdf}, url = {}, size = {}, abstract = {As programmers try to reuse codes written by other programmers a chain of fatal faults shall be made due to misunderstanding of the original intention, this is because of poor understandability to the relevant documents in software engineering, we also call it as an adverse software understandability, however, software understandability is the mental activity of programmers, so, it is not easy to be quantized or measured. Since internal mental activity is hardly visible to us, we are obliged to determine software understandability by the external visible artifact. We reviewed factors suggested by literatures to affect software understandability. Furthermore, this paper brings forward an integrated view to determine good or bad of software understandability at that time. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Castro:2008:fuzz, author = "Juan R. Castro and Oscar Castillo and Patricia Melin and Antonio Rodríguez-Díaz", title = "Intelligent Control Using an Interval Type-2 Fuzzy Neural Network with a Hybrid Learning Algorithm", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0224.pdf}, url = {}, size = {}, abstract = {In this paper, a class of Interval Type-2 Fuzzy Neural Networks (IT2FNN) is proposed, which is functionally equivalent to interval type-2 fuzzy inference systems. The computational process envisioned for fuzzy neural systems is as follows: it starts with the development of an ``Interval Type-2 Fuzzy Neuron'', which is based on biological neural morphologies, followed by the learning mechanisms. We describe how to decompose the parameter set such that the hybrid learning rule of adaptive networks can be applied to the IT2FNN architecture for the Takagi-Sugeno-Kang (TSK) type of reasoning. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Martínez:2008:fuzz, author = "Ricardo Martínez and Oscar Castillo and Luis T. Aguilar", title = "Optimization with Genetic Algorithms of Interval Type-2 Fuzzy Logic Controllers for an Autonomous Wheeled Mobile Robot: A Comparison Under Different Kinds of Perturbations", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0225.pdf}, url = {}, size = {}, abstract = {We describe a tracking controller for the dynamic model of a unicycle mobile robot by integrating a kinematic and a torque controller based on Type-2 Fuzzy Logic Theory and Genetic Algorithms. Computer simulations are presented confirming the performance of the tracking controller and its application to different navigation problems. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Yoshida:2008:fuzz, author = "Yuji Yoshida ", title = "A Risk-Minimizing Portfolio Model with Fuzziness", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0226.pdf}, url = {}, size = {}, abstract = {A variance-minimizing portfolio model is discussed under randomness and fuzziness. The randomness and fuzziness are evaluated respectively by the probailistic expectation and mean values with evaluation weights and λ-mean functions. The means and variances for fuzzy numbers/fuzzy random variables are applied in the possibility case and the necessity case. By quadratic programming approach, we derive a solution of the risk-minimizing portfolio problem and we show the solution is a tangency portfolio. A numerical example is given to illustrate our idea. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Angelov:2008:fuzz, author = "Plamen Angelov and Xiaowei Zhou ", title = "On Line Learning Fuzzy Rule-Based System Structure from Data Streams", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0228.pdf}, url = {}, size = {}, abstract = {A new approach to fuzzy rule-based systems structure identification in on-line (possibly real-time) mode is described in this paper. It expands the so called evolving Takagi-Sugeno (eTS) approach by introducing self-learning aspects not only to the number of fuzzy rules and system parameters but also to the number of antecedent part variables (inputs). The approach can be seen as on-line sensitivity analysis or on-line feature extraction (if in a classification application, e.g. in eClass which is the classification version of eTS). This adds to the flexibility and self-learning capabilities of the proposed system. In this paper the mechanism of formation of new fuzzy sets as well as of new fuzzy rules is analyzed from the point of view of on-line (recursive) data density estimation. Fuzzy system structure simplification is also analyzed in on-line context. Utility- and age-based mechanisms to address this problem are proposed. The rule-base structure evolves based on a gradual update driven by; (i) information coming from the new data samples; (ii) on-line monitoring and analysis of the existing rules in terms of their utility, age, and variables that form them. The theoretical theses are supported by experimental results from a range of real industrial data from chemical, petro-chemical and car industries. The proposed methodology is applicable to a wide range of fault detection, prediction, and control problems when the input or feature channels are too many. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Bobillo:2008:fuzz, author = "Fernando Bobillo and Umberto Straccia", title = "fuzzy DL: An Expressive Fuzzy Description Logic Reasoner", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0229.pdf}, url = {}, size = {}, abstract = {In this paper we present fuzzy DL, an expressive fuzzy Description Logic reasoner.We present its salient features, including some novel concept constructs and queries, and examples of use cases: matchmaking and fuzzy control. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Lam2:2008:fuzz, author = "H. K. Lam and M. Narimani and J. C. Y. Lai and F. H. F. Leung", title = "Stability Analysis of T-S Fuzzy-Model-Based Control Systems Using Fuzzy Lyapunov Function", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0231.pdf}, url = {}, size = {}, abstract = {This paper investigates the system stability of T-S fuzzy-model-based control systems based on an improved fuzzy Lyapunov function. Various non-PDC fuzzy controllers are proposed to close the feedback loop. The characteristic of T-S fuzzy model is considered to facilitate the stability analysis. Under a particular case, the time-derivative information of the membership functions vanishes, which simplifies the stability analysis and leads to relaxed stability analysis results. A general case is then considered. An improved non-PDC fuzzy controller is proposed based on the properties of the T-S fuzzy model. The improved non-PDC fuzzy controller exhibits a favourable property to relax the stability conditions. Based on the fuzzy Lyapunov function, stability conditions in terms of linear matrix inequalities are derived to guarantee the system stability. Simulation examples are given to illustrate the effectiveness of the proposed non-PDC fuzzy control schemes. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Akhlaghinia:2008:fuzz, author = "M. Javad Akhlaghinia and Ahmad Lotfi and Caroline Langensiepen and Nasser Sherkat", title = "A Fuzzy Predictor Model for the Occupancy Prediction of an Intelligent Inhabited Environment", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0232.pdf}, url = {}, size = {}, abstract = {In this paper, the prediction of the occupancy of different areas in a single-occupant intelligent inhabited environment is addressed. It is aimed to deliver a wellbeing monitoring and assistive environment to support elderly to live independently. A wireless sensor network of motion detection sensors is constructed to collect the required occupancy data. Individual sensory data are combined to form an occupancy time series. Then, a Fuzzy Predictor model is proposed to model the occupancy time series and the results of this technique are compared with other techniques in time series prediction including Auto Regressive Moving Average, Adaptive Network-based Fuzzy Inference System, as well as Transductive Neuro-Fuzzy Inference model with Weighted data normalization. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Begian:2008:fuzz, author = "Mohammad Biglar Begian and William W. Melek and Jerry M. Mendel", title = "Stability Analysis of Type-2 Fuzzy Systems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0233.pdf}, url = {}, size = {}, abstract = {Type-2 fuzzy systems have successfully been applied in control applications. Due to the complicated structure of type-2 systems, they lack systematic control design and hence the stability of the system is not guaranteed. This paper presents stability analysis of dynamic type-2 Takagi-Sugeno-Kang (TSK) fuzzy systems. Novel inference mechanisms for type-2 TSK systems for the case when antecedents are type-2 and consequents are crisp numbers (A2-C0) are developed and used in fuzzy model generation. Owing to the simple nature of the proposed methods, they are easy to implement in real-time applications. One of the proposed inference mechanisms is used and the sufficient stability conditions for these systems are derived. It is shown that the criteria obtained herein must satisfy some linear matrix inequalities (LMI) and an algorithm is also presented to solve the obtained LMI. Two numerical examples are provided that detail the design method. The methodology presented proves to be an efficient approach to systematically design stable dynamic type-2 TSK fuzzy systems. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Bernal:2008:fuzz, author = "Hector Bernal and Karina Duran and Miguel Melgarejo", title = "A Comparative Study Between Two Algorithms for Computing the Generalized Centroid of an Interval Type-2 Fuzzy Set", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0234.pdf}, url = {}, size = {}, abstract = {This paper presents a comparative study between two iterative algorithms for computing the generalized centroid of an interval type-2 fuzzy set. The first procedure is the so called Enhanced Karnik-Mendel (EKM) algorithm. The latter, introduced here as a Recursive Algorithm with Unique Loop (RAUL), is a modification of a previously reported procedure. The study compares the computing time of both algorithms for three prototype Footprints of Uncertainty and several discretizations of the universe of discourse. Results point out that RAUL is faster than the EKM algorithm when less than 100 discretization points are used to describe the footprint of uncertainty. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Li4:2008:fuzz, author = "Ming-Bin Li and Meng Joo Er", title = "Channel Equalization Using Self-constructing Fuzzy Neural Networks with Extended Kalman Filter (EKF)", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0235.pdf}, url = {}, size = {}, abstract = {In this paper, a self-constructing fuzzy neural networks with extended Kalman filter (SFNNEKF) is proposed. The whole network generalization capability is considered in the hidden neuron growing criterion, which makes the growing process more smoothly. The extended Kalman filter method is used to adjust the free parameters of the fuzzy neural networks. The proposed SFNNEKF learning algorithm is evaluated in channel equalization problems for communication systems. simulation results show that the SFNNEKF equalizer is superior to other equalizers such as recurrent neural network (RNN), minimal resource allocation network (MRAN), the radial basis function neural network (RBFNN) and the growing and pruning RBF (GAP-RBF) network in terms of bit error rate (BER). }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Yang2:2008:fuzz, author = "Xun Yang and Wie-Xin Xie and Jian-Jun Huang", title = "A C-Means Clustering Approach Based on Cloud Model", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0236.pdf}, url = {}, size = {}, abstract = {A cloud model based c-means clustering approach (CMCM) is presented in this paper. Each cluster in CMCM is modeled by a cloud model, which characterizes the fuzziness and randomness of cluster and makes the clustering process more applicable than FCM. In the meantime, the new approach can avoid a trivial solution of the object function in FCM by removing the normalization from FCM. Experiments for the comparison between CMCM and other clustering algorithms including FCM show the effectiveness and efficiency of the proposed method. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Cpałka:2008:fuzz, author = "Krzysztof Cpałka and Leszek Rutkowski", title = "Evolutionary Learning of Flexible Neuro-Fuzzy Systems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0240.pdf}, url = {}, size = {}, abstract = {In the paper the evolutionary strategy (μ,λ) is applied for learning flexible neuro-fuzzy systems [13]-[15]. In the process of evolution we determine: (i) fuzzy inference (Mamdani type or logical type - described by an S-implication), (ii) concrete fuzzy implication, if the logical type system is found in the process of evolution or concrete t-norm connecting antecedents and consequences, if the Mamdani type system is found in the process of evolution, (iii) concrete t-norm for aggregation of antecedents in each rule, (iv) concrete triangular norm describing aggregation operator, (v) shapes and parameters of fuzzy membership functions, (vi) weights describing importance of antecedents of rules and weights describing importance of rules, (vii) parameters of adjustable triangular norms. It should be noted that the crossover and mutation operators are chosen in a self-adaptive way. The method is tested using well known benchmarks. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Shang:2008:fuzz, author = "Changjing Shang and Qiang Shen", title = "Aiding Neural Network Based Image Classification with Fuzzy-Rough Feature Selection", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0241.pdf}, url = {}, size = {}, abstract = {This paper presents a methodological approach for developing image classifiers that work by exploiting the technical potential of both fuzzy-rough feature selection and neural network-based classification. The use of fuzzy-rough feature selection allows the induction of low-dimensionality feature sets from sample descriptions of real-valued feature patterns of a (typically much) higher dimensionality. The employment of a neural network trained using the induced subset of features ensures the runtime classification performance. The reduction of feature sets reduces the sensitivity of such a neural network-based classifier to its structural complexity. It also minimises the impact of feature measurement noise to the classification accuracy. This work is evaluated by applying the approach to classifying real medical cell images, supported with comparative studies. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Yao:2008:fuzz, author = "JingTao Yao and Yiyu Yao and Vladik Kreinovich and Paulo Pinheiro da Silva", title = "Towards More Adequate Representation of Uncertainty: From Intervals to Set Intervals, with the Possible Addition of Probabilities and Certainty Degrees", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0242.pdf}, url = {}, size = {}, abstract = {In the ideal case of complete knowledge, for each property Pi (such as "high fever", "headache", etc.), we know the exact set Si of all the objects that satisfy this property. In practice, we usually only have partial knowledge. In this case, we only know the set Si of all the objects about which we know that Pi holds and the set Si about which we know that Pi may hold (i.e., equivalently, that we have not yet excluded the possibility of Pi). This pair of sets is called a set interval.}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Rakus-Andersson:2008:fuzz, author = "Elisabeth Rakus-Andersson ", title = "Rough Sets Based on Reducts of Conditional Attributes in Medical Classification of the Diagnosis Status", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0243.pdf}, url = {}, size = {}, abstract = {Rough sets constitute helpful mathematical tools of the classification of objects belonging to a certain universe when dividing the universe in two collections filled with sure and possible members. In this work we adopt the rough technique to verify diagnostic decisions concerning a sample of patients whose symptoms are typical of a considered diagnosis. The objective is to extract the patients who surely suffer from the diagnosis, to indicate the patients who are free from it, and even to make decisions in undefined diagnostic cases. We also consider a decisive power of reducts being minimal collections of symptoms, which preserve the previous classification results. We use them in order to minimize a number of numerical calculations in the classification process. Finally, we test influence of symptom intensity levels on the diagnosis indisputable appearance to select these levels that are expected to be found in patients suffering from the considered diagnosis. The presence or the absence of these symptom levels in the patients allow us to add complementary remarks to earlier classification effects making them even more readable. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(:2008:fuzz, author = " and Yueqing Yu and Qixiao Xia and Liying Su ", title = "A New Fuzzy Method for the Motion Control of Underactuated Robots Based on Genetic Algorithm", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0244.pdf}, url = {}, size = {}, abstract = {A new fuzzy method for the motion control of underactuated robots is proposed in this paper. The control objective is to move the end-effector from a given position to a target point. A new fuzzy controller for the motion control of underactuated robots is present. The best fuzzy control rules and optimal membership functions are automatically generated off-line by the global optimization of genetic algorithm. Because the proposed method does not make any hypothesis about the degree of freedom and unconsidering the rigorous linearizations to the original nonlinear system, it can be used without modification for arms with a large number of degree of freedom. At last, numerical simulations which are carried on the planar 2R underactuated robots show the effectiveness of the proposed method. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Liu2:2008:fuzz, author = "Chang Liu and Hai-bo Shi and Jie Yuan", title = "Quality Prediction of Complex Manufacturing Processes Based on Fuzzy Petri Nets", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0245.pdf}, url = {}, size = {}, abstract = {Quality prediction capability plays a crucial role in ensuring high quality of products as well as reliability of systems during manufacturing processes. In this study, the prediction reasoning mechanism based on fuzzy Petri nets (FPNs) is presented. The feasibility and validity of the reasoning mechanism are proved. A quality prediction model and reasoning process is presented for submerged arc welding process. This application example illustrates the proposed method based on FPNs predict the potential quality problems and quality accidents effectively. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Liu3:2008:fuzz, author = "Huixue Liu and Cailian Chen and Xinping Guan and Xiaojing Wu ", title = "H∞ Piecewise Filtering for Continuous T-S Fuzzy Systems with Time Delays", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0246.pdf}, url = {}, size = {}, abstract = {Based on a piecewise Lyapunov-Krasovskii functional (PLKF), this paper presents an H∞ piecewise filtering method for T-S fuzzy systems with time delays. It is shown that the H∞ filtering performance can be satisfied if there exists a PLKF. The PLKF and the corresponding filter can be obtained by solving a set of linear matrix inequalities (LMIs). An example is presented to demonstrate the efficiency of the proposed method. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Ramos:2008:fuzz, author = "Fausto de Oliveira Ramos and Ernesto Araujo", title = "Fuzzy-Scored Genetically-Designed Controller for the VLS-1 Launcher", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0247.pdf}, url = {}, size = {}, abstract = {A fuzzy system intertwined with an optimization technique based on genetic algorithm for designing a VLS-1 Launcher Attitude Control System is proposed in this paper. A linear-quadratic optimization yields gain-scheduled controllers, where Q and R weighting matrices are scored by means of a fuzzy system, instead of directly being chosen by a human designer. As a consequence, not only the optimization technique is extended to the entire launcher simulated trajectory, but also the specifications can be dealt individually. The linguistic variables of the fuzzy system are defined according to performance (rise time and overshoot) and robustness (gain and phase margins) issues, regarding gain vector smoothness, as well. The control system obtained with Fuzzy Design presents many favorable results according a linear analysis and nonlinear digital simulations. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Urenda:2008:fuzz, author = "Julio C. Urenda and Olga Kosheleva", title = "How to Reconcile Physical Theories with the Idea of Free Will: From Analysis of a Simple Model to Interval and Fuzzy Approaches", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0248.pdf}, url = {}, size = {}, abstract = {Most modern physical theories are formulated in terms of differential equations. As a result, if we know exactly the current state of the world, then this state uniquely determines all the future events - including our own future behavior. This determination seems to contradict the intuitive notion of a free will, according to which we are free to make decisions - decisions which cannot be determined based on the past locations and velocities of the elementary particles. In quantum physics, the situation is somewhat better in the sense that we cannot determine the exact behavior, but we can still determine the quantum state, and thus, we can determine the probabilities of different behaviors - which is still inconsistent with our intuition. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Villaverde:2008:fuzz, author = "Karen Villaverde and Gang Xiang", title = "Estimating Variance Under Interval and Fuzzy Uncertainty: Parallel Algorithms", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0249.pdf}, url = {}, size = {}, abstract = {Traditional data processing in science and engineering starts with computing the basic statistical characteristics such as the population mean E and population variance V. In computing these characteristics, it is usually assumed that the corresponding data values x1, ..., xn are known exactly. In many practical situations, we only know intervals [xi, `xi] that contain the actual (unknown) values of xi or, more generally, a fuzzy number that describes xi. In this case, different possible values of xi lead, in general, to different values of E and V. In such situations, we are interested in producing the intervals of possible values of E and V - or fuzzy numbers describing E and V. There exist algorithms for producing such interval and fuzzy estimates. However, these algorithms are more complex than the typical data processing formulas and thus, require a larger amount of computation time. If we have several processors, then, it is desirable to perform these algorithms in parallel on several processors, and thus, to speed up computations. In this paper, we show how the algorithms for estimating variance under interval and fuzzy uncertainty can be parallelized. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Lim:2008:fuzz, author = "Edward H. Y. Lim and Raymond S. T. Lee and James N. K. Liu", title = "KnowledgeSeeker – An Ontological Agent-Based System for Retrieving and Analyzing Chinese Web Articles", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0250.pdf}, url = {}, size = {}, abstract = {In this paper, we present the KnowledgeSeeker, an ontological agent-based system that is designed to help users find, retrieve, and analyze news article from the Internet and then present the content in a semantic web. We present the benefits of using ontologies to analyze the semantics of Chinese text, and also the advantages of using a semantic web to organize information semantically. KnowledgeSeeker also demonstrates the advantages of using ontologies to identify topics. We use a Chinese document corpus to evaluate KnowledgeSeeker and the testing result was compared to other approaches. KnowledgeSeeker is able to identify the topics of Chinese web articles with an accuracy of nearly 87percent and has a processing speed of less than one second per article. It is also able to organize content flexibly and understands knowledge more accurately than methods that use ontology definition. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Torra:2008:fuzz, author = "Vicenç Torra and Sadaaki Miyamoto and Yasunori Endo and Josep Domingo-Ferrer", title = "On Intuitionistic Fuzzy Clustering for its Application to Privacy", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0251.pdf}, url = {}, size = {}, abstract = {Motivated by our research on specific information loss measures (in privacy preserving data mining) and our need to compare fuzzy clusters, we proposed in a recent paper a definition for intuitionistic fuzzy partitions. We showed how to define them in the framework of fuzzy clustering. That is, we introduced a method to define intuitionistic fuzzy partitions from the results of fuzzy clustering. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Fazendeiro:2008:fuzz, author = "Paulo Fazendeiro and Jose Valente de Oliveira", title = "A Fuzzy Clustering Algorithm with a Variable Focal Point", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0254.pdf}, url = {}, size = {}, abstract = {In our everyday life the number of groups of similar objects that we visually perceive is deeply constrained by how far we are from the objects and also by the direction we are approaching them. Based on this metaphor, in this work we present a generalization of partitional clustering aiming at the inclusion into the clustering process of both distance and direction of the point of observation towards the dataset. This is done by incorporating a new term in the objective function, accounting for the distance between the clusters' prototypes and the point of observation. It is a well known fact that the chosen number of partitions has a major effect on the objective function based partitional clustering algorithms, conditioning both the level of granularity of the data grouping and the capability of the algorithm to accurately reflect the underlying structure of the data. Thus the correct choice of the number of clusters is essential for any successful application of such algorithms. The experimental part of this work shows how the proposed algorithm can be used to produce a set of valid alternatives for the appropriate number of partitions. The proposed method can be used in order to assist the data analyst when looking for a partition that correctly reflects a particular view of the data. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Boongoen:2008:fuzz, author = "Tossapon Boongoen and Qiang Shen", title = "Clus-DOWA: A New Dependent OWA Operator", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0257.pdf}, url = {}, size = {}, abstract = {Aggregation operators are crucial to integrating diverse decision makers' opinion. While minimum and maximum can represent optimistic and pessimistic extremes, an Ordered Weighted Aggregation (OWA) operator is able to reflect varied human attitudes lying between the two using distinct weight vectors. Several weight determination techniques ignore characteristics of data being aggregated. In contrary, data-oriented operators like centered OWA and dependent OWA use the centralized data structure to generate reliable weights. Values near the center of a group receive higher weights than those further away. Despite its general applicability, this perspective entirely neglects any local data structures representing strong agreements or consensus. This paper presents a new dependent OWA operator (Clus-DOWA) that applies distributed structure of data or data clusters to determine its weight vector. The reliability of weights created by DOWA and Clus-DOWA operators are experimentally compared in the tasks of classification and unsupervised feature selection. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Ling2:2008:fuzz, author = "Bingo Wing-Kuen Ling and Charlotte Yuk-Fan Ho and Hak-Keung Lam and Thomas Pak-Lin Wong", title = "Fuzzy Rule Based Multiwavelet ECG Signal Denoising", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0258.pdf}, url = {}, size = {}, abstract = {Since different multiwavelets, pre- and post-filters have different impulse responses and frequency responses, different multiwavelets, pre- and post-filters should be selected and applied at different noise levels for signal denoising if signals are corrupted by additive white Gaussian noises. In this paper, some fuzzy rules are formulated for integrating different multiwavelets, pre- and post-filters together so that expert knowledge on employing different multiwavelets, preand post-filters at different noise levels on denoising performances is exploited. When an ECG signal is received, the noise level is first estimated. Then, based on the estimated noise level and our proposed fuzzy rules, different multiwavelets, pre- and post-filters are integrated together. A hard thresholding is applied on the multiwavelet coefficients. According to extensive numerical computer simulations, our proposed fuzzy rule based multiwavelet denoising algorithm outperforms traditional multiwavelet denoising algorithms by 30percent. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Gamez:2008:fuzz, author = "J. Esteban Gamez and François Modave and Olga Kosheleva", title = "Selecting the Most Representative Sample is NP-Hard: Need for Expert (Fuzzy) Knowledge", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0259.pdf}, url = {}, size = {}, abstract = {One of the main applications of fuzzy techniques is to formalize the notions of "typical", "representative", etc. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Dujmovi\'c:2008:fuzz, author = "Jozo J. Dujmovi\'c ", title = "Characteristic Forms of Generalized Conjunction/Disjunction", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0260.pdf}, url = {}, size = {}, abstract = {In this paper we investigate seven special cases and nine characteristic forms of generalized conjunction/disjunction. They include the concepts of hard and soft partial conjunction and hard and soft partial disjunction, and their combinations. We analyze the threshold values of andness and orness that define the border between the hard and soft partial conjunction and partial disjunction. In addition, we propose new forms of aggregators that have adjustable values of andness and orness thresholds. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Kanzawa:2008:fuzz, author = "Yuchi Kanzawa and Yasunori Endo and Sadaaki Miyamoto", title = "Fzzy Classification Function of Fuzzy c-Means Algorithms for Data with Tolerance", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0261.pdf}, url = {}, size = {}, abstract = {In this paper, two fuzzy classification functions of fuzzy c-means for data with tolerance are proposed. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Yeh:2008:fuzz, author = "Ming-Feng Yeh and Chia-Ting Chang", title = "Admissible or Bounded Zones of Parameters in GM(1,1) Model", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0266.pdf}, url = {}, size = {}, abstract = {This paper attempts to study the admissible or bounded zones of some important parameters in GM(1,1) model. The newly admissible zone of the developing coefficient is derived as (-1, +1) while the newly bounded zone of the class ratio is (e-1, e+1). The bounded zones of other parameters are also discussed in the paper. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Pathinathan:2008:fuzz, author = "T. Pathinathan and Joseph M. Arul", title = "Discrimination of Female Children in School Education – Induced Fuzzy Associative Memories (IFAM) Analysis on the Causes and Consequences", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0268.pdf}, url = {}, size = {}, abstract = {Modern developments and scientific technology advancements have engulfed every sphere of life yet the abolition of gender discrimination and equal rights with respect to education for the girl child has not become a reality. In this paper using Fuzzy Associative Memories (FAM) first and then the newly introduced Induced Fuzzy Associative Memories (IFAM), we analyze the causes for school drop outs among female children and suggest ways and means to reduce the rate of dropouts. }, keywords = { school dropouts, FAM, fixed point, limit cycle, Induced FAM, hidden pattern, merged graph of induced patterns.}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Bartl:2008:fuzz, author = "Eduard Bartl and Radim Belohlavek and Vilem Vychodil", title = "Compositions of Fuzzy Relations with Hedges", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0269.pdf}, url = {}, size = {}, abstract = {We present extensions of ordinary compositions of fuzzy relations. The extensions consist in parameterizing the ordinary compositions by means of particular unary functions on the scales of truth degrees. The approach is inspired by our previous work on formal concept analysis of data with fuzzy attributes where such parameterization of one particular type of fuzzy relational composition was used to control the number of clusters extracted from data. We present definitions and basic properties of the parameterized compositions, examples, and implications for several domains of application of fuzzy relational compositions. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Yang3:2008:fuzz, author = "Yingjie Yang and Robert John ", title = "Global Roughness of Approximation and Boundary Rough Sets", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0270.pdf}, url = {}, size = {}, abstract = {This paper defines a new parameter for describing the uncertainty of rough sets. Different from the roughness of a rough set, a global roughness measures the uncertainty of rough sets with respect to the entire information system. This is essential especially for a special rough set - boundary rough sets.We give the definition of global roughness of approximation and boundary rough sets, and analyse their properties. Some examples are also provided to show the complementary features of global roughness and roughness of rough sets. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Hsueh:2008:fuzz, author = "Yao-Chu Hsueh and Shun-Feng Su", title = "Compensate Controller Design for Solving the Parameter Drift Problem of Learning Fuzzy Control Systems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0271.pdf}, url = {}, size = {}, abstract = {In order to solve the parameter drift problem of learning fuzzy control systems, a compensate controller is proposed based on the H-infinite control theory. The proposed compensate controller has the H-infinite tracking capability and thus is named as the H-infinite tracking compensator (HTC). The characteristic of the HTC design is that the concept of Lyapunov energy convergence is employed to solve the H-infinite controller design problem. Besides, for a leaning fuzzy control system, the HTC ensures the tracking error of the learning fuzzy control system is bounded. Therefore, the dead-zone modification can be used to stabilize the final learning stage of the adaptive fuzzy control system. In other words, the parameter drift problem of a learning fuzzy control system can be resolved by the use of dead-zone modification with HTC so that the stability problem does not appear. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Seiffert:2008:fuzz, author = "Udo Seiffert and Felix Bollenbeck", title = "Fuzzy Image Segmentation by Potential Fields", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0273.pdf}, url = {}, size = {}, abstract = {Many natural phenomena, and human knowledge about these respectively, can only be described by gradually varying or diffuse entities. This generally motivates imprecise or fuzzy information processing. However, since the transition from exact to fuzzy descriptions offers not just more degrees of freedom but often completely different structures to specify particular knowledge, it requires extended methods or tools enabling the potential user to appropriately transfer his knowledge into a machine-readable form. In terms of image processing, fuzzy techniques have become widely spread. Nevertheless, just this knowledge transfer from a human expert to a potentially available computer system is still an open issue in many cases. The present paper addresses this by means of fuzzy image segmentation against the background of biomedical image processing, where, for example the borderline between adjacent tissues often can not be specified sharply and unequivocally. Despite its particular application in the described context of plant biology, the presented approach is much more versatile and can be applied to a large variety of similar problems. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Zhang3:2008:fuzz, author = "Xixiang Zhang and Jianxun Liu and Jing Lei and Bao'an Yang", title = "The Weak Consistency of an Interval-valued Intuitionistic Fuzzy Matrix", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0274.pdf}, url = {}, size = {}, abstract = {During the process of decision making, due to time limitation, knowledge structure and environmental factors, decision makers can not provide accurate numbers to express their preferences and have some hesitation to make decisions. In this condition, it will be better to use interval-valued intuitionistic fuzzy numbers to express decision makers' preferences. Therefore, the weak consistency of an interval-valued intuitionistic fuzzy matrix was defined, and some properties were proposed to judge the weak consistency of an interval-valued intuitionistic fuzzy matrix. Then the acceptability of a given interval-valued intuitionisitic fuzzy matrix can be measured and advice can be given to a decision maker to adapt his/her preference if the given matrix is not weakly consistent. Meanwhile, illustrated examples were given. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Chen9:2008:fuzz, author = "Youjun Chen and Hongying He and Yong Wei", title = "By Using Grey Area Relational Grade Combined with NLP Method to Optimize GM(1,1) Model", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0276.pdf}, url = {}, size = {}, abstract = {In this paper, we suggest a new optimization method by using grey area relational grade combined with nonlinear programming (abbreviated to NLP) method for GM(1,1) model's parameters, we introduce the grey area relational grade to establish a NLP optimization parameters model for a GM(1,1) model that has been established, by using mathematical software LINGO 10.0 for its optimal solution. By a lot of data's analysis, we conclude the new method is effective and feasible for the GM(1,1) models that have been established by using any other methods. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Han2:2008:fuzz, author = "Lixin Han and Hong Yan", title = "Fuzzy Biclustering for DNA Microarray Data Analysis", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0277.pdf}, url = {}, size = {}, abstract = {Fuzzy biclustering analysis is a useful tool for identifying relevant subsets of microarray data. This paper proposes a fuzzy biclustering clustering method for microarray data analysis. The method employs a combination of the Nelder-Mead and min-max algorithm to construct hierarchically structured biclustering. The method can automatically identify the groups of genes that show similar expression patterns under a specific subset of the samples. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Bernal2:2008:fuzz, author = "Miguel Bernal and Thierry-Marie Guerra and Alexandre Kruszewski", title = "A Membership-Function-Dependent H∞ Controller Design for Takagi-Sugeno Models", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0278.pdf}, url = {}, size = {}, abstract = {This paper presents a new approach for stability analysis and H∞ controller design of Takagi-Sugeno (TS) models. The analysis considers information derived from existing or induced order relations among the membership functions. Partitioning of the state-space and the use of piecewise conditions arise naturally as a consequence of induced order relations. Conditions under the novel approach can be expressed as linear matrix inequalities (LMIs). Examples are provided that show the advantages over the classical quadratic approach. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Kacprzyk:2008:fuzz, author = "Janusz Kacprzyk and Anna Wilbik", title = "Linguistic Summarization of Time Series Using Linguistic Quantifiers: Augmenting the Analysis by a Degree of Fuzziness", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0279.pdf}, url = {}, size = {}, abstract = {Taking as a point of departure our works on linguistic summarization of time series (cf. Kacprzyk, Wilbik and Zadrożny [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11]) in which an approach based on a calculus of linguistically quantified propositions was proposed, and the essence of the problem was equated with a linguistic quantifier driven aggregation of partial scores (trends), we present here some reformulation and extension mainly towards a more complex evaluation of results resulting linguistic summaries obtained. We use the classic Zadeh's calculus of linguistically quantified propositions but, in addition to the basic criterion of a degree of truth (validity), we also use as the second criterion a degree of fuzziness to make it possible to account for a frequent case that though the degree of truth of a very general (not precise) summary is high, its usefulness may be low due to its high fuzziness. We show an application to the absolute performance type analysis of daily quotations of an investment fund. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Araujo:2008:fuzz, author = "Ernesto Araujo", title = "Improved Takagi-Sugeno Fuzzy Approach", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0280.pdf}, url = {}, size = {}, abstract = {In this paper Takagi-Sugeno fuzzy approach in analyzed under the fuzzy mapping perspective. Although similar to classical Takagi-Sugeno fuzzy approach in structure, this rule based system differs when employing a subnormal fuzzy mapping instead of using a normalized one. This approach might be considered to be a generalization of traditional Takagi-Sugeno approach to treat uncertainty in mapping procedure. It carries the ability of yielding a completely different inputoutput mapping by only introducing a new degree of freedom in parameters of fuzzy system design. Additionally, it might be considered to be a generalization of traditional Takagi-Sugeno approach to treat uncertainty, imprecision, vagueness and partial truth in mapping procedure. The proposed approach may be applied for finding out fuzzy models, designing fuzzy controllers, and decision-making process, as well. A zero-order Takagi-Sugeno model is employed to exemplify this nonzero mapping perspective. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Tovar:2008:fuzz, author = "Julio Cesar Tovar and Wen Yu ", title = "Automated Fuzzy Neural Networks for Nonlinear System Identification", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0281.pdf}, url = {}, size = {}, abstract = {This paper discusses the identification of nonlinear dynamic system using fuzzy neural networks. It focuses on both the structure uncertainty and the parameter uncertainty which have been widely explored in the literature of nonlinear system identification. The main contribution is that an integrated analytic framework is proposed for automated fuzzy neural network structure selection, parameter identification and hysteresis network switching with guaranteed neural identification performance. Firstly, an automated support vector machine is proposed within a fixed time interval for a given network construction criterion. Then the network parameter updating algorithm is proposed with guaranteed bounded identification error. To cope structure uncertainty, a hysteresis strategy is proposed to enable fuzzy neural identifier switching with guaranteed network performance along the switching process. Both theoretic analysis and simulation example show the efficacy of the proposed method. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Kar:2008:fuzz, author = "Indrani Kar and Premkumar P and Laxmidhar Behera", title = "Visual Motor Control of a 6 DOF Robot Manipulator Using a Fuzzy Learning Paradigm", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0282.pdf}, url = {}, size = {}, abstract = {This paper is concerned with the inverse kinematic control of a 6DOF robot manipulator using visual feedback. Two different frameworks have been proposed to learn the inverse kinematics of the manipulator. In the first framework, the robot work-space has been discretized using a priori fixed number of fuzzy regions. Within each fuzzy region, the inverse kinematic relationship from image plane as observed by two fixed cameras to joint space of the manipulator is expressed as a linear map using first order approximation. This proposed framework allows the inverse kinematics to be represented by a Takagi-Sugeno (T-S) fuzzy model whose parameters are learned on-line using gradient descent algorithm. In the second framework, the robot workspace in image plane is discretized into a number of clusters whose centers are determined using Fuzzy C Mean (FCM) clustering algorithm. The FCM algorithm allows each data vector to belong to every cluster with a fuzzy truth value between 0 and 1. The inverse kinematics problem is solved without using any knowledge about orientation of the manipulator. This leads to redundant solutions in the joint angle space for a given target position. This redundancy in the joint angle space is achieved using the concept of sub clustering in the joint space. Inclusion of sub-clustering also improves the position tracking accuracy. The proposed algorithms have been successfully implemented on a 6 DOF PowerCube manipulator from Amtec robotics with a reasonable position tracking accuracy. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Tang:2008:fuzz, author = "Zhe Tang and Meng Joo Er and C.-J. Chien", title = "Analysis of Human Gait Using an Inverted Pendulum Model", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0284.pdf}, url = {}, size = {}, abstract = {IPM(Inverted Pendulum Model) has been widely used for modeling of human motion gaits. There is a common condition in most of these models, the reaction force between the floor and the humanoid must go through the CoG (Center of Gravity) of the a humanoid or human being. However, the recent bio-mechanical studies show that there are angular moments around the CoG of a human being during human motion. In other words, the reaction force does not necessarily pass through the CoG. In this paper, the motion of IPM is analyzed by taking into consideration two kinds of rotational moments, namely around the pivot and around the CoG. The human motion has been decomposed into the sagittal plane and front plane in the double support phase and single support phase. The motions of the IPM in these four different phases are derived by solving four differential equations with boundary conditions. Simulation results show that a stable human gait is synthesized by using our proposed IPM. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Kiguchi:2008:fuzz, author = "Kazuo Kiguchi and Qilong Quan", title = "Muscle-Model-Oriented EMG-Based Control of an Upper-Limb Power-Assist Exoskeleton with a Neuro-Fuzzy Modifier", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0285.pdf}, url = {}, size = {}, abstract = {Many studies on power-assist exoskeleton robots have been carried out in order to assist daily activities and/or rehabilitation of physically weak persons. EMG-based control is one of the most effective control methods to realize the power-assist with the exoskeleton based on user's motion intention. In this paper, a muscle-model, which is adjusted by a neuro-fuzzy modifier according to the user's upper-limb posture, is introduced to realize an effective EMG-based controller of the power-assist exoskeleton. Force/torque generated between the user's wrist part and the tip of the exoskeleton is used to train the neuro-fuzzy modifier. The effectiveness of the proposed control method was evaluated by performing experiment. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Kuwajima:2008:fuzz, author = "Isao Kuwajima and Hisao Ishibuchi and Yusuke Nojima", title = "Effectiveness of Designing Fuzzy Rule-Based Classifiers from Pareto-Optimal Rules", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0286.pdf}, url = {}, size = {}, abstract = {In the field of data mining, two rule evaluation criteria called confidence and support are often used to evaluate a rule. Pareto-optimality of rules can be defined using these two criteria. The rules that are Pareto-optimal in the maximization of confidence and support are called Pareto-optimal rules. In this paper, we examine the effectiveness of designing fuzzy rule-based classifiers from Pareto-optimal rules and near Pareto-optimal rules. To show the effectiveness, we compare the Pareto-optimal (and near Pareto-optimal) rules with rules extracted by various rule evaluation criteria. In the design of classifiers, we use evolutionary multiobjective rule selection to obtain simple and accurate classifiers. Through computational experiments, we show that the best fuzzy rule with respect to each rule evaluation criterion is one of Pareto-optimal rules. We also show that fuzzy rule-based classifiers designed from Pareto-optimal rules have higher accuracy. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Celikyilmaz:2008:fuzz, author = "Asli Celikyilmaz and I. Burhan Turksen", title = "Uncertainty Bounds of Fuzzy C-Regression Method", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0287.pdf}, url = {}, size = {}, abstract = {The Fuzzy C-Regression Method (FCRM) based on Fuzzy C-Means (FCM) clustering algorithm was proposed by Hathaway and Bezdek to solve the switching regression problems, and it was applied to fuzzy models by many to build more powerful fuzzy inference systems. The FCRM methods require initialization parameters which are in need for proper identification, since uncertain information can create imperfect expressions, which may hamper the predictive power of these models. This paper investigates the behavior of the FCRM models under uncertain parameters. The upper and lower bounds of the membership values can be identified based on the limits of level of fuzziness parameter around the certain information points such as local functions and ensemble point values. This is a further step to identify the footprint-of-uncertainty of membership values when FCRM is used. It is shown that the uncertainty of membership values induced by the level of fuzziness parameter can be identified based on first order approximations of the membership value calculation function. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Yu3:2008:fuzz, author = "Xiangyang Yu and Junwei Tian and Yongxuan Huang and Haipeng Nan", title = "Adaptive Double Immune Sliding Mode Control for a Class of Uncertain Nonlinear Systems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0288.pdf}, url = {}, size = {}, abstract = {An adaptive double immune sliding mode controller(ADISMC) for a class of nonlinear uncertain systems is investigated in this paper. The T-S fuzzy system is applied to approximate the unknown nonlinear function of plant, and then the estimation value of nonlinear function is used to design the control law, the double immune feedback system is used as the compensator for the fuzzy sliding control. The proposed approach does not need a known bound, but requires the existence of such a bound, and has not any assumption for initial condition. The approach is applied to an inverted pendulum system with disturbances. Analysis of simulations shows that proposed method is robust in the presence of uncertainties and bounded external disturbances. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Papageorgiou:2008:fuzz, author = "Elpiniki I. Papageorgiou and Nikolaos I. Papandrianos and Dimitrios J. Apostolopoulos and Pavlos J. Vassilakos", title = "Fuzzy Cognitive Map Based Decision Support System for Thyroid Diagnosis Management", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0289.pdf}, url = {}, size = {}, abstract = {Knowledge-based systems are the most common type of artificial intelligence in medicine systems in routine clinical use. They contain medical knowledge, usually about a very specifically defined task, and are able to reason with data from individual patients to come up with reasoned conclusions. Although there are many variations, the knowledge within an expert system is typically represented in the form of a set of rules. Fuzzy Cognitive Map (FCM) is a knowledge based modeling methodology based on exploiting knowledge and experience from experts. It can handle uncertainty and can be constructed basely by experts' knowledge.

The thyroid gland is one of the most important organs in the body as thyroid hormones are responsible for controlling metabolism. As a result, thyroid function impacts on every essential organ in the body. Thyroid disease diagnosis via proper interpretation of the thyroid data is an important classification problem. This study aims at characterizing thyroid diseases with an alternative medical decision support system comprising of knowledge extraction methods and FCMs producing the FCM based decision support system for thyroid disease management (FCM-TDM). The results of FCM-TDM are acceptable and encourage our research towards this type of decision support systems in medicine. Furthermore this system could benefit the students in medicine for their training in clinical routine. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Guelton:2008:fuzz, author = "K. Guelton and T. Bouarar and N. Manamanni ", title = "Fuzzy Lyapunov LMI Based Output Feedback Stabilization of Takagi-Sugeno Systems using Descriptor Redundancy", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0290.pdf}, url = {}, size = {}, abstract = {This paper deals with Takagi-Sugeno systems stabilization based on a dynamic output feedback compensators. The closed-loop system stability conditions, based on a fuzzy Lyapunov candidate function and the descriptor redundancy properties, are provided in terms of LMI. Finally, an academic example illustrates the efficiency of the proposed approach. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wu3:2008:fuzz, author = "Dongrui Wu and Jerry M. Mendel", title = "Perceptual Reasoning Using Interval Type-2 Fuzzy Sets: Properties", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0291.pdf}, url = {}, size = {}, abstract = {Perceptual Reasoning (PR) is an Approximate Reasoning mechanism that can be used as a Computing with Words (CWW) Engine, i.e., given input words, PR can infer the output from a rulebase. When the input words and the words in the rulebase are modeled by interval type-2 fuzzy sets (IT2 FSs), the output of PR, YPR, is also an IT2 FS, and it will be mapped to a word in a codebook. For accurate mapping, we need to ensure that YPR resembles the IT2 FSs in the codebook. The concept of PR using IT2 FSs was originally proposed in [10]. In this paper, the procedures to compute PR are introduced, and the properties of PR are studied in more detail. More specifically, we show under what conditions YPR can be a shoulder or interior footprint of uncertainty. }, keywords = { Computing with words, perceptual reasoning, interval type-2 fuzzy sets, linguistic weighted average}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Walker:2008:fuzz, author = "Carol L. Walker and Elbert A. Walker and Ronald R. Yager", title = "Some Comments on Level Sets of Fuzzy Sets ", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0292.pdf}, url = {}, size = {}, abstract = {This paper discusses level sets of functions from a set U into a set C with an order structure, for example a poset or a lattice. Such sets are prevalent in fuzzy set theory. We provide some general facts and then consider the particular case when C is the complete lattice of closed intervals in the unit interval [0, 1]; that is, when the function is an interval-valued fuzzy set. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Chen10:2008:fuzz, author = "Chun-Hao Chen and Tzung-Pei Hong and Vincent S. Tseng", title = "A Divide-and-Conquer Genetic-Fuzzy Mining Approach for Items with Multiple Minimum Supports", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0293.pdf}, url = {}, size = {}, abstract = {Since items may have their own characteristics, different minimum support values and membership functions may be specified for different items. In this paper, an enhanced approach is proposed, which processes the items in a divide-and-conquer strategy. The approach is designed for finding minimum support values, membership functions, and fuzzy association rules. Possible solutions are evaluated by their requirement satisfaction divided by their suitability of derived membership functions. The proposed GA framework maintains multiple populations, each for one item's minimum support value and membership functions. The final best minimum support values and membership functions in all the populations are then gathered together to be used for mining fuzzy association rules. Experimental results also show the effectiveness of the proposed approach. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Yoneyama:2008:fuzz, author = "Jun Yoneyama ", title = "Robust Stabilization of Uncertain Fuzzy Systems under Sampled-Data Control", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0294.pdf}, url = {}, size = {}, abstract = {This paper is concerned with robust sampled-data stabilization for uncertain fuzzy systems. A new approach to robust sampled-data control is introduced. A practical system is usually modelled as a continuous-time fuzzy system, while the control input has a piecewise-continuous delay. Sufficient robust stability conditions for the closed-loop system with a sampled-data state feedback controller are given in terms of linear matrix inequalities(LMIs). We derive such robust stability conditions via descriptor approach to fuzzy time-delay systems under the assumption that sampling interval may vary but is not greater than some prescribed bounded number. As such a prescribed number goes to zero, our robust stability conditions become sufficient robust stability conditions for continuous-time state feedback stabilization for fuzzy systems. We also propose a design method of robust sampled-data state feedback controller for uncertain fuzzy systems. A numerical example is given to illustrate our sampled-data state feedback control. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Chan:2008:fuzz, author = "Chee Seng Chan and Honghai Liu and David Brown and Naoyuki Kubota", title = "A Fuzzy Qualitative Approach to Human Motion Recognition", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0295.pdf}, url = {}, size = {}, abstract = {The understanding of human motions captured in image sequences pose two main difficulties which are often regarded as computationally ill-defined: 1) modelling the uncertainty in the training data, and 2) constructing a generic activity representation that can describe simple actions as well as complicated tasks that are performed by different humans. In this paper, these problems are addressed from a direction which uses the concept of fuzzy qualitative robot kinematics [9]. First of all, the training data representing a typical activity is acquired by tracking the human anatomical landmarks in an image sequences. Then, the uncertainty arise when the limitations of the tracking algorithm are handled by transforming the continuous training data into a set of discrete symbolic representations - qualitative states in a quantisation process. Finally, in order to construct a template that is regarded as a combination ordered sequence of all body segments movements, robot kinematics, a well-defined solution to describe the resulting motion of rigid bodies that form the robot, has been employed. We defined these activity templates as qualitative normalised templates, a manifold trajectory of unique state transition patterns in the quantity space. Experimental results and a comparison with the hidden Markov models have demonstrated that the proposed method is very encouraging and shown a better successful recognition rate on the two available motion databases. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Riečan:2008:fuzz, author = "Beloslav Riečan ", title = "Probability Theory and the Operations with IF-Sets", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0296.pdf}, url = {}, size = {}, abstract = {Probability on IF-events is considered as a continuous and additive mapping. Of course, the notion of additivity depends on operations with IF-events. In this paper four types of probability theory are considered with respect to four types of operations with IF-events. In all the types the existence of the joint observable can be proved what is a key for a construction of a good probability theory. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Liu4:2008:fuzz, author = "Sifeng Liu and Zhigeng Fang and Wei zhou and Ruan Aiqing ", title = "A Gray Input-Output model based on the Standard Interval Grey Number", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0297.pdf}, url = {}, size = {}, abstract = {A gray input-output model based on the standard interval grey number, the condition of convergence of M steps grey matrix and the solution method were given, then produced the corresponding mathematics proof in this paper. The model and solution method obtained a good result in the solution process of the gray input-output model. Finally, the validity and feasibility of this model was confirmed when used in a input-output example that just has two-departments. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Yuanyuan:2008:fuzz, author = "Zhang Yuanyuan and Fang Zhigeng and Liu Sifen and Wu Xin and Shi Hongxing", title = "Study on the Model of Grey Matrix Game Based on Grey Mixed Strategy — Solution of Grey Linear Program Model on Grey Matrix Game", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0298.pdf}, url = {}, size = {}, abstract = {The key to grey matrix game lies in the solution of grey linear program model. On this paper, we have proved the necessary and sufficient condition of the existence of grey basic feasible solution in grey linear program problem. We have also certified that grey basic feasible solution corresponds to grey apex of grey feasible field. Then, having proved that any point of grey convex set can be linearly expressed by the grey apex of the grey convex set, we proved that grey optimal value of the objective function corresponds to the grey apex of the grey convex in the grey linear program problem of the grey matrix game. Therefore, grey optimal value of it certainly corresponds to some unity grey numbers of grey element in the grey game matrix . }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Shi:2008:fuzz, author = "Hongxing Shi and Sifeng Liu and Zhigeng Fang and Yong Zhao", title = "Study on the Model of Grey Matrix Game Based on Grey Mixed Strategy — Properties of Grey Mixed Strategy and Model of Linear Program", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0299.pdf}, url = {}, size = {}, abstract = {Model of grey linear program is one of good methods on the process of finding the solution of grey matrix game. Studying interchange abilities of grey mixed optimal strategy, we certify necessary and sufficient condition that there is grey mixed optimal strategy in the grey matrix game, which the grey game value between player 1 and 2 is equality in the grey saddle point. Based on these, we prove that grey mixed optimal strategy of grey matrix game can be found by the way of the grey linear program model. And, we build up the solution framework system of grey linear program on the program of grey matrix game by using of the grey system and operation research theories. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Chiclana:2008:fuzz, author = "Francisco Chiclana and Sergio Alonso and Enrique Herrera-Viedma and Francisco Herrera", title = "Construction of Consistent Fuzzy Preference Relations using Uninorms", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0301.pdf}, url = {}, size = {}, abstract = {Under a set of conditions consistency of fuzzy preference relations can be characterised by almost continuous self-dual uninorms. Consequently, the concept of U-consistent fuzzy preference relation is introduced. Amongst the many consistency properties proposed for reciprocal fuzzy preference relations, Tanino's multiplicative transitivity property, being an example of such type of uninorms, seems to be an appropriate U-consistency property for reciprocal fuzzy preference relations. We present results towards the characterization of the Uconsistency property of a fuzzy preference relation based on a restricted set of n-1 preference values, which can be used in practical cases to construct perfect U-consistent preference relations. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Li5:2008:fuzz, author = "Tieshan Li and Gang Feng and Zaojian Zou", title = "DSC-Backstepping Based Robust Adaptive Fuzzy Control for a Class of Strict-Feedback Nonlinear Systems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0302.pdf}, url = {}, size = {}, abstract = {A robust adaptive tracking control problem is discussed for a class of strict-feedback uncertain nonlinear systems. Takagi-Sugeno type fuzzy logic systems are used to approximate the uncertainties. A unified and systematic procedure is developed to derive a novel robust adaptive tracking controller by use of the input-to-state stability (ISS) and by combining the dynamic surface control(DSC)-based backstepping technique and generalized small gain approach. The key features of the algorithm are that, firstly, the problem of "explosion of complexity" inherent in the conventional backstepping method is circumvented, secondly, the number of parameters updated on line for each subsystem is reduced dramatically to 2. These features result in a much simpler algorithm, which is convenient to realize in application. In addition, it is shown that all closed-loop signals are semi-global uniformly ultimately bounded(SGUUB). Finally, simulation results via an application example of a pendulum system with motor is used to demonstrate the effectiveness and performance of the proposed scheme. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Parthalain:2008:fuzz, author = "Neil Mac Parthalain and Richard Jensen and Qiang Shen", title = "Finding Fuzzy-Rough Reducts with Fuzzy Entropy", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0305.pdf}, url = {}, size = {}, abstract = {Dataset dimensionality is undoubtedly the single most significant obstacle which exasperates any attempt to apply effective computational intelligence techniques to problem domains. In order to address this problem a technique which reduces dimensionality is employed prior to the application of any classification learning. Such feature selection (FS) techniques attempt to select a subset of the original features of a dataset which are rich in the most useful information. The benefits can include improved data visualisation and transparency, a reduction in training and run times and potentially, improved prediction performance. Methods based on fuzzy-rough set theory have demonstrated this with much success. Such methods have employed the dependency function which is based on the information contained in the lower approximation as an evaluation step in the FS process. This paper presents three novel feature selection techniques employing fuzzy entropy to locate fuzzy-rough reducts. This approach is compared with two other fuzzy-rough feature selection approaches which use other measures for the selection of subsets. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Acampora:2008:fuzz, author = "Giovanni Acampora and Maurizio Di Meglio and Vincenzo Loia", title = "An Integrated Development Environment for Transparent Fuzzy Agents Design: An Application to Automotive Electronic Stability Program", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0306.pdf}, url = {}, size = {}, abstract = {In the last years several computing frameworks based on the ubiquitous and embedding properties have been designed and realized. These systems, characterized by the interconnection of several devices embedded into a microenvironment and interacting among them in order to achieve a common goals, offer new fascinating challenges to face as, for instance, the interoperability and safety problems. The automotive environments are a typical sample of ubiquitous and embedded systems, in fact, modern cars can be considered as mobile computer network composed of intelligent devices capable of controlling the mechanical and hydraulic car components. This study presents an advanced Integrated Development Environment modeling FML-based fuzzy controllers useful to design an efficient Electronic Stability Program (ESP) to be reprogrammed on different hardware without additional effort. In this scenario, multi-agent paradigm and transparent fuzzy control methodology represent the natural technologies exploited to achieve the proposed aims. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Parry:2008:fuzz, author = "David Parry ", title = ""Tell Me The Important Stuff" - Fuzzy Ontologies And Personal Assessments for Interaction with The Semantic Web", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0308.pdf}, url = {}, size = {}, abstract = {The semantic web attempts to make the web an universal medium of data exchange. To achieve this it needs to both make data sources accessible to machine-based systems, such as software agents and allow humans to easily create, understand and search for these data sources. Crisp ontologies are extremely useful for improving data extraction from structured data. However many ontological approaches require an unnatural level of precision and rigidity when dealing with real user queries or data sources. Previous work has suggested that fuzzification of ontologies may increase their utility. A major area of activity in the search and ubiquitous computing space is the development of location aware services. This paper suggests that in the mobile and context-aware semantic web environment, fuzzification needs to extend to the representation of the importance of particular. Additionally, mobile devices tend to require simple interfaces and work with low bandwidth, this implies that obtaining small numbers of relevant results is extremely important. The work previously done in representing uncertainty in geographical information systems may assist in this development. This paper suggest that the combination of the use of fuzzy ontologies and fuzzy spatial relations may be effective in increasing the usefulness of the mobile semantic web}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Houshmand:2008:fuzz, author = "Kaveh Houshmand and Hamid Reza Tizhoosh", title = "Filtering and Fusion of THz Images for Defect Detection in Composite Materials", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0309.pdf}, url = {}, size = {}, abstract = {Until recent years, terahertz (THz) waves were an undiscovered, or most importantly, an unexploited area of electromagnetic spectrum. This was due to difficulties in generation and detection of THz waves. Recent advances in hardware technology have started to open up the field to new applications such as THz imaging. THz waves can penetrate through diverse materials such that internal structures, invisible to other imaging modalities, can be visulaized. However, automated processing of THz images can be quite challenging. Low contrast and the presence of a widely unknown type of noise make the analysis of these images difficult. In this paper we attempt to detect defects in composite material using a Terahertz imaging system. According to our knowledge this is the first time that this type of materials are being tested under Terahertz cameras using filtering and information fusion. In this preliminary report, we employ stick filter and a simple fuzzy approach to detect defects by fusing information from both amplitude and phase images. The results show that using fuzzy techniques can easily incorporate domain knowledge and assist the defect detection. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Ji:2008:fuzz, author = "Zhen Ji and Taikang Yang and Lai Jiang and Wenhuan Xu", title = "A Novel Fuzzy Reinforced Learning Strategy in Vector Quantisation", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0310.pdf}, url = {}, size = {}, abstract = {This paper presents a new approach toward the design of optimised codebooks by vector quantisation (VQ). A strategy of fuzzy k-means reinforced learning (FRL) is proposed which exploits the advantages offered by fuzzy clustering algorithms, competitive learning and knowledge of training vector and codevector configurations. Reinforced learning, which is consisted of attractive factor and repulsive factor, is used as a pre-process before using the conventional VQ algorithm, i.e. fuzzy k-means (FKM) algorithm. At each iteration of RL, codevectors move intelligently and intentionally toward an improved optimum codebook design. This is distinct from the standard FKM in which a random variation is introduced in the movement of the codevectors to escape from local minima. Experiments demonstrate that this results in a more effective representation of the training vectors by the codevectors and that the final codebook is nearer to the optimal solution in applications such as image compression. It has been found that the standard FKM yields improved quality of codebook design in this application when RL is used as a pre-process. The investigations have also indicated that new fuzzy k-means reinforced learning vector quantisation (FRLVQ) strategy is insensitive to the selection of both the initial codebook and a learning rate control parameter, which is the only additional parameter introduced by FRL from the standard FKM. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Beliakov2:2008:fuzz, author = "Gleb Beliakov and Simon James", title = "Using Choquet Integrals for kNN Approximation and Classification", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0311.pdf}, url = {}, size = {}, abstract = {k-nearest neighbors (kNN) is a popular method for function approximation and classification. One drawback of this method is that the nearest neighbors can be all located on one side of the point in question x. An alternative natural neighbors method is expensive for more than three variables. In this paper we propose the use of the discrete Choquet integral for combining the values of the nearest neighbors so that redundant information is canceled out. We design a fuzzy measure based on location of the nearest neighbors, which favors neighbors located all around x. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wang8:2008:fuzz, author = "Wei-Yen Wang and I-Hsum Li and Shu-Chang Li and Men-Shen Tsai and Shun-Feng Su", title = "A Dynamic Hierarchical Fuzzy Neural Network for a General Continuous Function", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0314.pdf}, url = {}, size = {}, abstract = {A serious problem limiting the applicability of the fuzzy neural networks is the ``gcurse of dimensionality'', especially for general continuous functions. A way to deal with this problem is to construct a dynamic hierarchical fuzzy neural network. In this paper, we propose a two-stage genetic algorithm to intelligently construct the dynamic hierarchical fuzzy neural network (HFNN) based on the merged-FNN for general continuous functions. First, we use a genetic algorithm which is popular for flowshop scheduling problems (GA_FSP) to construct the HFNN. Then, a reduced-form genetic algorithm (RGA) optimizes the HFNN constructed by GA_FSP. For a real-world application, the presented method is used to approximate the Taiwanese stock market. Key words: hierarchical structures, genetic algorithms,Fuzzy neural networks}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wu4:2008:fuzz, author = "Bing-Fei Wu and Li-Shan Ma and Jau-Woei Perng and Hung-I Chin", title = "Absolute Stability Analysis in Uncertain Static Fuzzy Control Systems with the Parametric Robust Popov Criterion", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0315.pdf}, url = {}, size = {}, abstract = {This study analyzes the absolute stability in static fuzzy logic control systems with certain and uncertain parameters. For certain static fuzzy control systems, the absolute stability can be analyzed with Popov criterion. The uncertain parameters for absolute stability analysis include the reference input, actuator gain and interval linear plant. The parametric robust Popov criterion based on Lur'e systems is applied to stability analysis respect to uncertain parameters. In our work, the parametric robust Popov criterion is applied to analyze absolute stability in static fuzzy logic control systems first time. This study can provide a valuable reference in designing fuzzy control systems. Finally, numerical simulations are provided to verify the analytical results. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Chytas:2008:fuzz, author = "Panagiotis Chytas and Michael Glykas and George Valiris", title = "A Proactive Fuzzy Cognitive Balanced Scorecard", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0318.pdf}, url = {}, size = {}, abstract = {This paper describes a methodology for the development of a Proactive Balanced Scorecard (PBSCM). The Balanced Scorecard is one of the most popular approaches developed in the field of performance measurement. However, in spite of its reputation, there are issues that require further research. The present research addresses the problems of the Balanced Scorecard by utilizing the soft computing characteristics of Fuzzy Cognitive Maps (FCMs). By using FCMs, the proposed methodology generates a dynamic network of interconnected Key Performance Indicators (KPI), simulates each KPI with imprecise relationships and quantifies the impact of each KPI to other KPIs in order to adjust targets of performance. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Schuh:2008:fuzz, author = "Christian J. Schuh ", title = "Usage of Fuzzy Systems in Critical Care Environments", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0320.pdf}, url = {}, size = {}, abstract = {In critical care, patients are surrounded by a multitude of devices for monitoring, diagnostics, and therapy. These devices include patient monitors to measure and monitor vital signs, therapeutic devices to support or replace impaired or failing organs and also to administer medications and fluids for the patients. The concept of fuzzy set theory, which was developed by Zadeh (1965), makes it possible to define inexact medical entities as fuzzy sets. It provides an excellent approach for approximating medical text. Furthermore, fuzzy logic provides reasoning methods for approximate inference. Fuzzy relations and fuzzy control were two initiated research areas from the origin fuzzy set theory in the 1970s. Therefore, this approach may be suitable for intensive care medicine, where experience and intuition play an important role in decisionmaking. This paper surveys the use of the fuzzy set theory of two research areas, i.e. fuzzy relations and fuzzy control in medical sciences in general, as well as on the basis of three concrete medical fuzzy applications. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Ma:2008:fuzz, author = "Z. M. Ma and Jingwei Cheng and Hailong Wang and Li Yan ", title = "A Vague Description Logic SI", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0323.pdf}, url = {}, size = {}, abstract = {In the real world, human knowledge and natural language have a big deal of imprecision and uncertainty. Imprecision and uncertainty play in the Semantic web context, as well as to many applications that use description logics (DLs) to capture, represent and perform reasoning with domain knowledge. In this paper, a fuzzy extension of description logic language SI is presented, which combines vague sets with SI. Its syntax, semantics and inference problems are investigated in the paper. Also the tableau algorithm is developed for reasoning in the vague SI. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Ding:2008:fuzz, author = "Liya Ding ", title = "Inference in Hybrid KBS with Interval-Valued Confidence", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0325.pdf}, url = {}, size = {}, abstract = {This article discusses confidence handling for inference in hybrid knowledge-based system (KBS) with hierarchical knowledge representation. The knowledge content (precise or imprecise) represented in multiple units of knowledge hierarchy and the confidence obtained during inference process are treated as two levels separately but simultaneously based on the concept of truth value flow inference. Three-parametric triangular truth values as special subsets defined on the interval [0, 1] are adopted to describe the confidence of inference, and operations on such interval-valued confidence are defined to meet commonsense interpretation of decision making. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Zhang4:2008:fuzz, author = "Fu Zhang and Z. M. Ma and Li Yan ", title = "Representation and Reasoning of Fuzzy ER Model with Description Logic", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0326.pdf}, url = {}, size = {}, abstract = {Information imprecision and uncertainty exist in many real-world applications and hence fuzzy data modeling has been extensively investigated in various data models. This paper focuses on the representation and reasoning of fuzzy ER data model with description logic. Firstly, we give the formal definition and semantics of fuzzy ER model. Then based on the description logic DLR, a kind of new fuzzy description logic, i.e., fuzzy description logic FDLR (fuzzy DLR), is presented thoroughly. The definitions of syntax, semantics, and knowledge base form are given for FDLR. The fuzzy ER model with fuzzy description logic FDLR is investigated to translate fuzzy ER model into FDLR knowledge bases. With an example, the fact that the fuzzy ER model can be well represented by FDLR can be explained. The reasoning problem of satisfiability, subsumption relation, and redundancy of fuzzy ER model may reason automatically through reasoning mechanism of fuzzy description logic FDLR. The correctness of translation and reasoning problems are proved. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Dey:2008:fuzz, author = "Lipika Dey and Muhammad Abulaish", title = "Fuzzy Ontologies for handling Uncertainties and Inconsistencies in Domain Knowledge Description", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0327.pdf}, url = {}, size = {}, abstract = {Ontologies represent a method of formally expressing a shared understanding of information, and have paved the way for sharing concepts across applications in an unambiguous way. However, these ontologies are assumed to be hand-crafted, pre-defined structures with crisp concept descriptions and inter-concept relations. Crisp definitions however are not sufficient for realworld applications like ontology-based information extraction from unstructured text. In this paper we propose an enhancement of the ontology structure to a fuzzy ontology, which provides a mechanism to store imprecise concept definitions. The proposed fuzzy ontology framework can also help in ascertaining similarities and dissimilarities of concept definitions across distributed ontologies representing the same domain. Our design of fuzzy ontology is motivated by fuzzy set theoretic representation and reasoning, and is entirely different from other fuzzy ontology designs, which use only co-occurrence of concepts to determine their closeness. We have cited examples from various domains to show the necessity and capability of the structure in representing real-world knowledge. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Nagy:2008:fuzz, author = "Szabolcs Nagy and Zoltan Petres and Peter Baranyi", title = "TP Model Transformation Based Controller Design for the Parallel-type Double Inverted Pendulum", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0328.pdf}, url = {}, size = {}, abstract = {The main objective of the paper is to present a non-heuristic, mathematically well defined control design to the parallel-type double inverted pendulum. First we apply the recently proposed TP model transformation to generate a finite element convex polytopic representation, namely a Tensor Product model representation (that is equivalent with a kind of TS fuzzy model) of the derived quasi linear parameter varying model of the pendulum system. Then we apply linear matrix inequalities under the parallel distributed compensation control design framework to derive decay rate control with constrain on the control value. Since both steps are executable numerically and automatically one after other, we perform a uniform, tractable and straightforward control design to the pendulum system. In order to validate the resulting controller the paper also presents numerical simulations. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Fu2:2008:fuzz, author = "Xin Fu and Qiang Shen", title = "Fuzzy Model Fragment Retrieval", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0329.pdf}, url = {}, size = {}, abstract = {Given a set of collected evidence and a knowledge base, Fuzzy Compositional Modelling (FCM) begins by retrieving model fragments which are the most likely to be relevant to the available data. Since FCM often involves imprecise and uncertain information, a match between the available data and the knowledge base cannot in general be done precisely, partial matching may suffice. This paper proposes a more flexible fuzzy model fragment retrieval mechanism to match data items with broader, including possibly subjective information in the knowledge base. It is capable of retrieving those model fragments that can approximately match the collected evidence, when no exact match occurs. The retrieval process and its capability is illustrated by means of an application example. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Rajesh:2008:fuzz, author = "R. Rajesh and M. R. Kaimal", title = "GAVLC: GA with Variable Length Chromosome for the Simultaneous Design and Stability Analysis of T-S Fuzzy Controllers", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0330.pdf}, url = {}, size = {}, abstract = {Most of the design techniques of T-S fuzzy controllers assumes that there exists an approximate T-S model of the system with fixed antecedent parts & rules and uses techniques like GA, LMI, etc for the optimal design of the gain values. This paper presents a novel integrated approach for the design and stability analysis of T-S fuzzy controllers using GA with Variable Length Chromosomes (VLCs) and LMI. This approach helps to find out the optimal parameters of the antecedent parts of the rules along with rule optimization and also to optimize the consequent parts. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Rogova:2008:fuzz, author = "Ermir Rogova and Panagiotis Chountas and Krassimir Atanassov", title = "The Notion of H-IFS in Data Modelling", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0331.pdf}, url = {}, size = {}, abstract = {In this paper we revise the context of ``value imprecision'', as part of an knowledge-based environment. We present our approach for including value imprecision as part of a non-rigid hierarchical structures of organization. This led us to introduce the concept of closure of an Intuitionistic fuzzy set over a universe that has a hierarchical structure. Intuitively, in the closure of this Intuitionistic fuzzy set, the ``kind of'' relation is taken into account by propagating the degree associated with an element to its sub-elements in the hierarchy. We introduce the automatic analysis according to concepts defined as part of a knowledge hierarchy in order to guide the query answering as part of an integrated database environment with the aid of hierarchical intuitionistic fuzzy sets. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Anderson:2008:fuzz, author = "Derek Anderson and Robert H. Luke and James M. Keller and Marjorie Skubic", title = "Extension of a Soft-Computing Framework for Activity Analysis from Linguistic Summarizations of Video", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0334.pdf}, url = {}, size = {}, abstract = {Video cameras are a relatively low-cost, rich source of information that can be used for ``well-being'' assessment and abnormal event detection for the goal of allowing elders to live longer and healthier independent lives. We previously reported a soft-computing fall detection system, based on two levels from a hierarchy of fuzzy inference using linguistic summarizations of activity acquired temporally from a three dimensional voxel representation of the human found by back projecting silhouettes acquired from multiple cameras. This framework is extremely flexible and rules can be modified, added, or removed, allowing for per-resident customization based on knowledge about their cognitive and physical ability. In this paper, we show the flexibility of our activity analysis framework by extending it to additional common elderly activities and contextual awareness is added for reasoning based on location or static objects in the apartment. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Hwang3:2008:fuzz, author = "Yuan-Chun (Peter) Hwang and Qun Song and Nikola Kasabov", title = "MUFIS: A Neuro-Fuzzy Inference System Using Multiple Types of Fuzzy Rules", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0335.pdf}, url = {}, size = {}, abstract = {This paper introduces a novel neuro-fuzzy inference system denoted as ``MUFIS: A Neuro-Fuzzy Inference System Using Multiple Types of Fuzzy Rules'', for allowing multiple types of fuzzy rules to be used together to achieve a better performance. At each data point, the output of MUFIS is calculated through a fuzzy inference system based on m-most activated fuzzy rules which are dynamically chosen from multi-type fuzzy rules. It is demonstrated that MUFIS can effectively implement prediction and function approximation. We evaluate its performance on two case studies - a benchmark time-series prediction problem - Mackey Glass, and a real life medical prediction problem - glomerular filtration rate prediction. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Yao2:2008:fuzz, author = "Tian-Xiang Yao and Sifeng Liu and Yao-Guo Dang and Zhi-Geng Fang and Chuanmin Mi", title = "The Improvement of Discrete GM(1,1) Prediction Model and its Solution Arithmetic", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0337.pdf}, url = {}, size = {}, abstract = {The GM(1,1) model assume the sequence is analogous to exponential law. Great error appears when it is used to simulate many non-lineal sequences. The paper proves that the growth rates of the simulated value of the GM(1,1) model and the discrete GM(1,1) model are both fixed value. If the growth rates of the primary sequence are equate, the fitted value deriving from the discrete GM(1,1) model the same as the primary sequence. The paper improves the discrete GM(1,1) model. Using the optimization method, the paper studies the initial value. The paper puts forward the solution arithmetic to the optimization and proves the efficiency of the arithmetic by means of a example. The research indicates the discrete grey extension model can greatly improve the simulated intensity and it can solve the simulated of the non-lineal non-negative sequence. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Kung2:2008:fuzz, author = "Chung-Chun Kung and Ti-Hung Chen and Ho-Yu Cheng", title = "Discrete Sliding Mode Controller Design with Fast Output Sampling Technique for Discrete-Time T-S Fuzzy System", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0338.pdf}, url = {}, size = {}, abstract = {In this paper, the discrete sliding mode control (DSMC) design with fast output sampling (FOS) technique for discrete-time T-S fuzzy system is presented. To guarantee the stability of the overall system, the control law of DSMC with the system state is adopted. Instead of applying the state observer to estimate the system state, we apply the FOS technique and identify the relation between the system state and the system output for the multi-rate system. Thus, the control law of DSMC would be redesigned as the function of the system output. This method is more practical and simple to implement the applying the state observer. Simulation results would demonstrate the effectiveness of the proposed control strategy. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Nie:2008:fuzz, author = "Maowen Nie and Woei Wan Tan ", title = "Towards an Efficient Type-Reduction Method for Interval Type-2 Fuzzy Logic Systems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0339.pdf}, url = {}, size = {}, abstract = {This paper introduces an alternative type-reduction method for interval type-2 (IT2) fuzzy logic systems (FLSs), with either continuous or discrete secondary membership function. Unlike the Karnik-Mendel type reducer which is based on the wavy-slice representation of a type-2 fuzzy set, the proposed type reduction algorithm is developed using the vertical-slice representation. One advantage of the approach is the output of the type reducer can be expressed in closed form, thereby providing a tool for the theoretical analysis of IT2 FLSs. The computational complexity of the proposed method is also lower than the uncertainty bounds method and the enhanced Karnik- Mendel method. To assess the feasibility of the proposed typereducer, it is used to calculate the output of an IT2 fuzzy logic controller (FLCs). Results from a simulated coupled tank experiment demonstrated that IT2 FLCs that employ the proposed type reduction algorithm share similar robustness properties as FLCs based on the Karnik-Mendel type reducer. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Kung3:2008:fuzz, author = "Chung-Chun Kung and Shuo-Chieh Chang", title = "Design of Model Reference Adaptive Fuzzy Sliding Mode Controller for Uncertain Time-Delay Systems with Input Containing Sector Nonlinearities and Dead-Zone", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0340.pdf}, url = {}, size = {}, abstract = {This paper addresses the problem of model reference adaptive fuzzy sliding mode controller (MRAFSMC) for uncertain time-delay systems with input containing sector nonlinearities and dead-zone. The model reference tracking control design method and the adaptive fuzzy sliding mode control technique are combined in the design of the MRAFSMC. By the Lyapunov stability theorem, it is shown that the proposed controller not only possesses the advantages of sliding mode control (SMC) and adaptive fuzzy control technique, but also eliminates the disadvantage of traditional SMC. An illustrative example is provided to demonstrate the usefulness of the proposed controller. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Lv:2008:fuzz, author = "Yanhui Lv and Z. M. Ma and Li Yan ", title = "Fuzzy RDF: A Data Model to Represent Fuzzy Metadata", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0341.pdf}, url = {}, size = {}, abstract = {Resource Description Framework (RDF) together with RDF Schema (RDFS) is important Semantic Web standards proposed from W3C. RDF is a knowledge representation language to deal with hard semantics in the description and manipulation of crisp metadata. However, information is often vague or ambiguous. Many metadata are fuzzy by nature, and the Semantic Web should be capable to represent fuzzy data. In this paper, a fuzzy RDF data model is presented including fuzzy RDF syntax and fuzzy RDF semantics. A distinctive feature of fuzzy RDF data model, which is based on fuzzy logic theory, is able to deal with special data whose values are no more crisp values, but may be a fuzzy set. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Pham2:2008:fuzz, author = "Tuan D. Pham and Jonathan Golledge", title = "Geostatistically Constrained Fuzzy Segmentation of Abdominal Aortic Aneurysm CT Images", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0343.pdf}, url = {}, size = {}, abstract = {Abdominal aortic aneurysm (AAA) is a common disease affecting elderly people and increasing in incidence. The most feared complication of AAA is the rupture of which most will result in death. The AAA involves the excessive dilation of the abdominal aorta in diameter. As a result, open surgery or endoluminal repair is indicated in AAA greater than 55mm. Currently screening and assessment of AAA can be achieved by either ultrasound or computed tomography (CT) angiography, where the latter imaging technology is the current gold standard. Each AAA is different having varying percentage of thrombus, total volume, luminal volume and calcification all of which are thought to play a critical role for assessing the rupture risk and determining management. Currently measurement of these parameters is based on manual or semiautomatic CT image segmentation - it is time-consuming, inaccurate and becomes unrealistic in clinical practice. The development of an automated method for the segmentation of AAA CT images is therefore demanding. We introduce in this paper a geostatistically constrained fuzzy c-means based algorithm as an automatic and effective segmentation of such images. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Gao:2008:fuzz, author = "Ya Gao and Guangquan Zhang and Jie Lu", title = "A Particle Swarm Optimization based Algorithm for Fuzzy Bilevel Decision Making", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0344.pdf}, url = {}, size = {}, abstract = {Bilevel decision techniques are developed for decentralized planning problems with decision makers located in a two-level system. This study develops a particle swarm optimization based algorithm to solve fuzzy linear bilevel (FLBL) decision problems. A main advantage of this algorithm is that the optimization technique is adopted directly on FLBL problems by fully considering the original information carried by the fuzzy parameters, thus minimizing information loss. Experiments reveal that this algorithm can effectively solve the fuzzy linear bilevel decision problems. keywords: Bilevel decision making; Particle swarm optimization;Artificial intelligence, Optimization; Fuzzy sets.}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Kubota:2008:fuzz, author = "Naoyuki Kubota and Naohide Aizawa", title = "Intelligent Control of Multi-Agent System based on Multi-Objective Behavior Coordination", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0345.pdf}, url = {}, size = {}, abstract = {Recently multi-agent systems have been discussed to realize a large size of distributed autonomous system. This paper proposes an intelligent control of multiple partner robots as one of multi-agent systems. First of all, we discuss the current state of researches on the multi-agent systems. Next, to realize a formation behavior, we propose a multi-objective behavior coordination to realize formation behavior based on the integration of the intelligent control from the local viewpoint of individual intelligence and the spring model from the global viewpoint of collective intelligence. Finally, we discuss the effectiveness of the proposed method through several computer simulation results. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Mendis:2008:fuzz, author = "B. S. U. Mendis and T. D. Gedeon", title = "A Comparison: Fuzzy Signatures and Choquet Integral", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0346.pdf}, url = {}, size = {}, abstract = {Fuzzy Signatures are hierarchical multi aggregative descriptors of objects. They have reduced computational complexity compared to formal fuzzy rule based systems. Weighted Relevance Aggregation enhances the performance of hierarchical Fuzzy Signatures. Thus, they are very robust and flexible under perturbed input data. On the other hand the Choquet Integral, which is based on fuzzy measures, is a powerful aggregation tool in multi-criteria decision making. We compared Fuzzy Signatures and the Choquet Integral as practical applications for hierarchical and non-hierarchical data aggregation/organization methods. }, keywords = {Fuzzy Signatures, Aggregation Operators, generalised Weighted Relevance Aggregation Operator, Fuzzy Integrals, Choquet Integral, Fuzzy Measure, Multi-criteria Decision Making.}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Beliakov3:2008:fuzz, author = "Gleb Beliakov and Simon James and Luigi Troiano", title = "Texture Recognition by using GLCM and Various Aggregation Functions", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0347.pdf}, url = {}, size = {}, abstract = {We discuss the problem of texture recognition based on the grey level co-occurrence matrix (GLCM). We performed a number of numerical experiments to establish whether the accuracy of classification is optimal when GLCM entries are aggregated into standard metrics like contrast, dissimilarity, homogeneity, entropy, etc., and compared these metrics to several alternative aggregation methods. We conclude that k nearest neighbors classification based on raw GLCM entries typically works better than classification based on the standard metrics for noiseless data, that metrics based on principal component analysis inprove classification, and that a simple change from the arithmetic to quadratic mean in calculating the standard metrics also improves classification. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Kubota2:2008:fuzz, author = "Naoyuki Kubota and Yu Tomioka and Toru Yamaguchi", title = "Gesture Recognition for a Partner Robot Based on Computational Intelligence", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0350.pdf}, url = {}, size = {}, abstract = {Recently, various types of human-friendly robot have been developed. Such robots should perform voice recognition, gesture recognition, and others. This paper discusses the learning capability of a human gesture recognition method based on computational intelligence. The proposed method is composed of image processing for human face and hand detection based on a steady-state genetic algorithm, an extraction method for human hand motion based on a fuzzy spiking neural network, and an unsupervised classification method for human hand motion based on a self-organizing map. We show several experimental results and discuss their effectiveness. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Weng:2008:fuzz, author = "Chien-Chih Weng and Wen-Shyong Yu", title = "Adaptive Fuzzy Sliding Mode Control for Linear Time-Varying Uncertain Systems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0351.pdf}, url = {}, size = {}, abstract = {In this paper, we propose an adaptive fuzzy sliding mode control scheme (AFSCS) for continuous-time multiple-input-output (MIMO) linear time-varying uncertain systems. The AFSCS consists of a fuzzy controller with adaptive mechanism to re construct the system states using the tracking error and to make the state error reach the equilibrium point in a finite time period quickly. The sliding surface is first employed to represent the state error to reach the equilibrium point in a finite time period. Then, an adaptive fuzzy controller using sliding mode is developed to achieve the control performance and the state tracking errors performance quickly. The reaching mode of the uncertain system using the proposed adaptive fuzzy sliding mode controller is guaranteed. Moreover, the chattering around the sliding mode control can be reduced. A 2-dof parallel robot system will be used to verify the effectiveness of the proposed method. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Roopaei:2008:fuzz, author = "Mehdi Roopaei and Mansoor Zolghadri and Abbas Emadi", title = "Economical Forecasting by Exogenous Variables", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0352.pdf}, url = {}, size = {}, abstract = {Political and social issues play a big role in economical systems. Macroeconomic variables which are affected by the above mentioned factors can be used in economical forecasting. Time series are used as a very powerful tool in economical systems for short time predicting. As time series predict the future output according to the past behaviors of the system, therefore they can not sense sudden changes in the behavior of the economical system. In this paper, macroeconomic variables are used as exogenous variables in forecasting model. Traditional methods using transformation and differentiation suffer from a decrease in accuracy forecasting. To get rid of the problems in the above mentioned methods, a Neuro-Fuzzy (NF) structure is used as a strong nonlinear mapping tool even on nonstationary time series. Combination of statistical methods on time series and other dynamical models with NF structure, provide a better model in forecasting. Using ``NF-ARMAX'', ``NN-ARMAX'' models and implementing them on real-life data of ``Tehran Stock Market'' show a good accuracy in our new designed predictive model. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Lee4:2008:fuzz, author = "Ching-Hung Lee and Tzu-Wei Hu and Chung-Ta Lee and Yu-Chia Lee ", title = "A Recurrent Interval Type-2 Fuzzy Neural Network with Asymmetric Membership Functions for Nonlinear System Identification", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0354.pdf}, url = {}, size = {}, abstract = {This paper proposes a recurrent interval type-2 fuzzy neural network with asymmetric membership functions (RT2FNN-A). The RT2FNN-A uses the interval asymmetric type-2 fuzzy sets and it implements the FLS in a five layer neural network structure which contains four layer forward network and a feedback layer. Each asymmetric fuzzy member function (AFMF) is constructed by parts of four Gaussian functions. The corresponding learning algorithm is derived by gradient descent method. Finally, the RT2FNN-A is applied in identification of nonlinear dynamic system. Simulation results are shown to illustrate the effectiveness of the RT2FNN-A systems. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Carvalho:2008:fuzz, author = "Joao Paulo Carvalho and Laura Wise and Alberto Murta and Marta Mesquita", title = "Issues on Dynamic Cognitive Map Modelling of Purse-Seine Fishing Skippers Behavior", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0356.pdf}, url = {}, size = {}, abstract = {This paper focus on obtaining a qualitative dynamic model based on real world data taken from a real world qualitative system: the day to day behavior of purse seine fishing fleet skippers. The model is based on a Dynamic Cognitive Mapping approach (Rule Based Fuzzy Cognitive Maps - RB-FCM) where several developments had to be made in order to obtain a workable system. Most changes were due to timing issues, which are essential in the study of System Dynamics but have traditionally been avoided in most Dynamic Cognitive Maps modelling approaches. }, keywords = { Rule Based Fuzzy Cognitive Maps, Purse-Seine Fishing, Modelling of dynamic qualitative systems.}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Jasinevicius:2008:fuzz, author = "Raimundas Jasinevicius and Vytautas Petrauskas ", title = "Fuzzy Expert Maps: The New Approach", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0357.pdf}, url = {}, size = {}, abstract = {The paper presents a new approach to fuzzy knowledge management and provides fuzzy expert maps (FEM) as a tool. FEM is a systematic extension for a well-known paradigm of fuzzy cognitive maps combined with open fuzzy control systems. Transparent examples from international politics are presented to illustrate the entire extension chain. Recommendations for further research are given as well. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Takahashi:2008:fuzz, author = "Yasutake Takahashi and Yoshihiro Tamura and Minoru Asada", title = "Behavior Development through Interaction between Acquisition and Recognition of Observed Behaviors", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0358.pdf}, url = {}, size = {}, abstract = {Life-time development of behavior learning seems based on not only self-learning architecture but also explicit/implicit teaching from other agents that is expected to accelerates the learning. This paper presents a method for a robot to understand unfamiliar behaviors shown by others through the collaboration between behavior acquisition and recognition of observed behaviors, where the state value has an important role not simply for behavior acquisition (reinforcement learning) but also for behavior recognition (observation). That is, the state value updates can be accelerated by observation without real trials and errors while the learned values enrich the recognition system since it is based on estimation of the state value of the observed behavior. The validity of the proposed method is shown by applying it to a dynamic environment where two robots play soccer. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Delmotte:2008:fuzz, author = "F. Delmotte and J. Lauber and T. M. Guerra ", title = "A Multi-Model Controller", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0359.pdf}, url = {}, size = {}, abstract = {This paper aims to present a new architecture of control law called multi-model controller. Indeed, it uses constant linear models as with a classical TS approach, but the interpolation between the models is adaptive based on an observer structure. A comparison with an output TS feedback controller for uncertain models shows the interest of the proposed method. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Mi:2008:fuzz, author = "Chuanmin Mi and Hanchong Qian and Sifeng Liu and Zhansheng Chang", title = "Study on Case Retrieving in Case-Based Reasoning Based on Grey Incidence Theory and Its Application in Bank Regulation", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0360.pdf}, url = {}, size = {}, abstract = {A critical issue in case-based reasoning (CBR) is to retrieve a usefully similar case to the problem. There are three approaches to case retrieving: nearest-neighbor, inductive, and knowledge-guide. This article uses a hybrid approach using grey incidence theory to case-based retrieval process in an attempt to increase the overall classification accuracy. We propose a new approach based on the absolute degree of grey incidence to calculate the degree of nearest-neighbor matching, use grey incidence order to priority analysis. The case which has the highest degree of grey incidence is the nearest neighbor case to the input case. When there is no case whose degree of grey incidence is higher than others on all attributes, we propose using analytic hierarchy process (AHP) to calculate the weight of every index, calculating the integrated degree of grey incidence (weighted sum) to find the nearest-neighbor case. Thus a new frame-work of CBR based on grey incidence analysis is built. It is an effective method for case indexing and retrieving because it is very easy to apply and its effect is good. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wang9:2008:fuzz, author = "Hailong Wang and Z. M. Ma and Li Yan and Jingwei Cheng ", title = "A Fuzzy Description Logic with Fuzzy Data Type Group", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0361.pdf}, url = {}, size = {}, abstract = {The Semantic Web is expected to process concept knowledge and data information in an intelligent and automatic way. Recent research has shown that OWL has a serious limitation on data types; i.e., it does not support customized data types and customized data type predicates. Furthermore, it can't process imprecise and uncertain information which widely exists in the Semantic Web and Ontology. These issues are being addressed by W3C Semantic Web Best Practices and Development Working Group. In the current paper, we make the following two contributions to solve the above limitations: (i) present a new kind of fuzzy description logic F-SHOIQ(G) which can not only support the representation and reasoning of fuzzy concept knowledge, but also support fuzzy data information with customized fuzzy data types and customized fuzzy data type predicates; (ii) give the tableau algorithm for F-SHOIQ(G) and prompt a flexible reasoning architecture for fuzzy data type reasoning, also the design for fuzzy data type reasoner is discussed here. The example in paper witnesses the representation and reasoning capabilities of F-SHOIQ(G) go clearly beyond the other DLs. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Petres:2008:fuzz, author = "Zoltan Petres and Szabolcs Nagy and Peter Gaspar and Peter Baranyi", title = " H∞ Gain-Scheduling Based Control of the Heavy Vehicle Model, a TP Model Transformation Based Control", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0363.pdf}, url = {}, size = {}, abstract = {This paper is focusing on rollover prevention to provide a heavy vehicle with the ability to resist overturning moments generated during cornering. A combined yaw-roll model including the roll dynamics of unsprung masses is studied. This model is nonlinear with respect to the velocity of the vehicle. In our model the velocity is handled as an LPV scheduling parameter. The Linear Parameter-Varying model of the heavy vehicle is transformed into a proper polytopic form by Tensor Product model transformation. The H∞ gain-scheduling based control is immediately applied to this form for the stabilization. The effectiveness of the designed controller is demonstrated by numerical simulation. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Schockaert:2008:fuzz, author = "Steven Schockaert and Martine De Cock and Etienne E. Kerre", title = "Modelling Nearness and Cardinal Directions Between Fuzzy Regions", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0364.pdf}, url = {}, size = {}, abstract = {A significant part of real-world spatial information is affected by vagueness. For example, boundaries of non-administrative geographical regions tend to be ill-defined, while information about the nearness and relative orientation of two places is typically expressed through vague linguistic descriptions. In this paper, we propose a general framework to represent such information, using the concept of relatedness measures for fuzzy sets. Regions are represented as fuzzy sets in a two-dimensional Euclidean space, and nearness and relative orientation are expressed as fuzzy relations. To support fuzzy spatial reasoning, we derive transitivity rules and provide efficient techniques to deal with the complex interactions between nearness and cardinal directions. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Liu5:2008:fuzz, author = "Honghai Liu ", title = "A Fuzzy Qualitative Framework for Robot Intelligent Connection", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0366.pdf}, url = {}, size = {}, abstract = {This paper proposes a novel framework in fuzzy qualitative terms for an attempt of attacking the robot intelligent connection problem. Robot intelligent connection is in the context of closing the gap between symbolic or qualitative functions and numerical sensing and control tasks through a generalized robot kinematics. First, fuzzy qualitative robot kinematics is revisited which provides theoretical preliminaries for the proposed robot motion representation. Secondly, a motion representation based on Gaussian mixture models is presented where Gaussian functions are combined to model a multimodal density of fuzzy qualitative kinematics parameters of a robotic endeffector using clustering. Finally, simulation results in a PUMA 600 robot demonstrated that the proposed method effectively provides a two-way connection for robot representations used for numerical and symbolic robot tasks. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wang10:2008:fuzz, author = "Ying-Chung Wang and Chiang-Ju Chien and Der-Tsai Lee", title = "An Output Recurrent Fuzzy Neural Network Based Iterative Learning Control for Nonlinear Systems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0367.pdf}, url = {}, size = {}, abstract = {In this paper, we present a design method for a discrete-time iterative learning control system by using output recurrent fuzzy neural network (ORFNN). Two ORFNNs are employed to design the control structure. One is used as an identifier called output recurrent fuzzy neural identifier (ORFNI) and the other used as a controller called output recurrent fuzzy neural controller (ORFNC). The ORFNI for identification of the unknown plant is introduced to provide the plant sensitivity which is then applied to the design of ORFNC. All the weights of ORFNI and ORFNC will be tuned during the control iteration and identification process respectively in order to achieve a desired learning performance. The adaptive laws for the weights of ORFNI and ORFNC and the analysis of learning performances are determined via a Lyapunov like analysis. It is shown that the identification error will asymptotically converge to zero and output tracking error will asymptotically converge to a residual set which depends on the initial resetting error. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Tu:2008:fuzz, author = "Hui-Wen Tu and Kuang-Yow Lian", title = "LMI-Based Adaptive Tracking Control for a Class of Nonlinear Stochastic Systems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0369.pdf}, url = {}, size = {}, abstract = {In this paper, we investigate the adaptive output tracking control for fuzzy stochastic parametric strict-feedback systems. To deal with the output tracking problem, we convert it into a stabilization one via the concept of virtual desired variables. Then all the analysis and synthesis are carried out using LMI (linear matrix inequality) technique. Here we emphasize that, due to the specific feature of the strict-feedback systems, the virtual desired variables can be well-defined such that our stochastic adaptive tracking control can be well developed. From the numerical simulations, it is found that the proposed schemes are feasible and the performance is satisfactory. }, keywords = { Adaptive control, output tracking, stochastic T-S fuzzy control, LMI.}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Aatashpaz-Gargari:2008:fuzz, author = "Esmaeil Aatashpaz-Gargari and Babak N. Araabi and Caro Lucas", title = "Optimal Fuzzy Passing Strategy for Robot Soccer Players", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0370.pdf}, url = {}, size = {}, abstract = {This paper presents the implementation of an intelligent pass strategy for soccer robots using fuzzy logic. The proposed strategy calculates pass confidence and detects the best destination robot and best position for passing. The most important factors of a proper pass are detected and are deployed to construct the inputs of fuzzy system. The rule base of the fuzzy system is extracted by the expert knowledge. The performance of fuzzy inference system is improved by optimally determining the fuzzy membership functions using an evolutionary algorithm. In order to evaluate the designed system a visual simulation environment is prepared which enables the user to arrange players and to implement the designed strategy to detect the best destination player and send the ball. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Valle:2008:fuzz, author = "Marcos Eduardo Valle and Peter Sussner", title = "Fuzzy Morphological Associative Memories Based on Uninorms", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0371.pdf}, url = {}, size = {}, abstract = {Fuzzy morphological associative memories (FMAMs) extend morphological associative memories (MAMs) to the fuzzy domain. FMAMs were derived using concepts of fuzzy set theory and mathematical morphology. In general, FMAMs either perform a maximum of conjunctions or a minimum of disjunctions at every node. We recently introduced a general class of learning strategies for FMAMs called fuzzy leaning by adjunction (FLA).The focus of this paper is on FMAMs that are based on (conjunctive or disjunctive) uninorms. We show that uninormbased FMAMs generalize implicative fuzzy associative memories (IFAMs) and their dual versions. Moreover, we provide an isomorphism between FMAMs based on representable uninorms and MAMs. Finally, we point out that auto-associative uninorm-based FMAMs exhibit optimal absolute storage capacity and one-step convergence. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Drayer:2008:fuzz, author = "Gregorio Drayer and Miguel Strefezza", title = "Integral Control with Error Modulation in a FAM-Based Agent for a Furuta Inverted Pendulum", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0372.pdf}, url = {}, size = {}, abstract = {The task-oriented approach in the FAM-based agent architecture creates a problem for control laws with integral action. This article proposes a solution to the integral control problem of this architecture. It consists in the modulation of the error signals in the control laws to limit the integral action when the associated condition is not operative. The error modulation makes use of the membership value of the associated fuzzy condition in the agent. Three simulations prepared with the dynamic model of a Furuta inverted pendulum illustrate the integral control problem and the solution proposed. The agent is evaluated in terms of its temporal response. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Cornelis:2008:fuzz, author = "Chris Cornelis and Richard Jensen", title = "A Noise-Tolerant Approach to Fuzzy-Rough Feature Selection", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0373.pdf}, url = {}, size = {}, abstract = {In rough set based feature selection, the goal is to omit attributes (features) from decision systems such that objects in different decision classes can still be discerned. A popular way to evaluate attribute subsets with respect to this criterion is based on the notion of dependency degree. In the standard approach, attributes are expected to be qualitative; in the presence of quantitative attributes, the methodology can be generalized using fuzzy rough sets, to handle gradual (in)discernibility between attribute values more naturally. However, both the extended approach, as well as its crisp counterpart, exhibit a strong sensitivity to noise: a change in a single object may significantly influence the outcome of the reduction procedure. Therefore, in this paper, we consider a more flexible methodology based on the recently introduced Vaguely Quantified Rough Set (VQRS) model. The method can handle both crisp (discrete-valued) and fuzzy (real-valued) data, and encapsulates the existing noise-tolerant data reduction approach using Variable Precision Rough Sets (VPRS), as well as the traditional rough set model, as special cases. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Gurský:2008:fuzz, author = "Peter Gurský and Veronika Vanekova and Jana Pribolova", title = "Fuzzy User Preference Model for Top-k Search", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0377.pdf}, url = {}, size = {}, abstract = {The task of modeling complex user preferences face the problem of understandability to common users and the problem of querying the dataset for preferred objects. We propose natural and complex model of user preferences decomposed with respect to particular attribute values. Partial preferences are combined by monotone aggregation function. The user model is stored in ontology structure. We also present an extension of top-k objects search algorithm to provide a query evaluation of the proposed model. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Bustince:2008:fuzz, author = "Humberto Bustince and Javier Montero and Edurne Barrenechea and Miguel Pagola", title = "Laws for Conjunctions and Disjunctions in Interval Type 2 Fuzzy Sets", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0380.pdf}, url = {}, size = {}, abstract = {In this paper we study in depth certain properties of interval type 2 fuzzy sets. In particular we recall a method to construct different interval type 2 fuzzy connectives starting from an operator. We further study the law of contradiction and the law of excluded middle for these sets. Furthermore we analyze the properties: idempotency, absorption, and distributiveness. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Mendonça:2008:fuzz, author = "L. F. Mendonça and J. M. C. Sousa", title = "Fault Accommodation of an Experimental Three Tank System Using Fuzzy Predictive Control", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0383.pdf}, url = {}, size = {}, abstract = {This paper proposes the application of faulttolerant control (FTC) using weighted fuzzy predictive control. The FTC approach is based on two steps, fault detection and isolation (FDI) and fault accommodation. Fault detection is performed by a model-based approach using fuzzy modeling. Fault isolation uses a fuzzy decision making approach. The model of the isolated fault is used in fault accommodation with a model predictive control (MPC) scheme. This paper uses a weighted fuzzy predictive control scheme, where fuzzy goals and fuzzy constraints are described in a fuzzy objective function. Each criterion (goal or constraint) has an associated weight factor, which is chosen by the decision-maker. Two faults were considered in an experimental three tanks process and the respective fuzzy models were identified. The fuzzy FTC scheme was able to accommodate the faults of the experimental process. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Zhou4:2008:fuzz, author = "Shang-Ming Zhou and Francisco Chiclana", title = "Inducing Linguistic Weights for Type-1 OWA Operators in Soft Decision Making", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0385.pdf}, url = {}, size = {}, abstract = {Type-1 OWA operators provide an efficient way of aggregating linguistic opinions in the form of type-1 fuzzy sets for decision-makers. Like in Yager's OWA operations, the identification of an appropriate type-1 OWA operator, i.e., to determine the linguistic weights in the form of type-1 fuzzy sets, is crucial in type-1 OWA based aggregation. Indeed, these weights reflect the decision-makers' desired agenda for aggregating the preferences or criteria. In this paper, for the sake of identifying linguistic weights used in the type-1 OWA operators, type-2 linguistic quantifiers are proposed, in which the higher level of uncertainty of linguistic quantifiers is modelled by type-2 fuzzy sets. Some examples are provided to illustrate the proposed concepts. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Szmidt:2008:fuzz, author = "Eulalia Szmidt and Janusz Kacprzyk", title = "Dealing with Typical Values by Using Atanassov's Intuitionistic Fuzzy Sets", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0386.pdf}, url = {}, size = {}, abstract = {In this paper we consider the problem of determining typical values. We heavily refer to some results of linguistic experiments in psychology and cognitive science. Since analyzes employed in those areas, which make use of some considerations on how an object is typical to a category and its related so called contrast category, we propose the use of Atanassov's intuitionistic fuzzy sets (to be called A-IFSs, for short)1. We follow the line of reasoning known from psychology and cognitive sciences, in particular those resulting from linguistic experiments, and verify how those results work in the case of classification, an omnipresent problem in computer science, decision sciences, etc. Our considerations concentrate around a typical example discussed in cognitive sciences. We start from nominal data, i.e. names are assigned to objects as labels, and we investigate to which extent a linguistic representation in a psychological space succeeds in predicting categories via the intuitionistic fuzzy sets. We only consider here a model of categories with a geometrical centroid model in which the similarity is defined in terms of a distance to the centroids. Next, we verify if the extreme ideals, which are important in cognitive processes when categories are learnt in the presence of the alternative (contrast) category, give comparative results. We show that Atanassov's intuitionistic fuzzy sets make it possible to reflect a positive and negative information via the concept of membership and non-membership. Though the paper presents an ongoing research, the first results obtained are promising and point out the usefulness and strength of Atanassov's intuitionistic fuzzy sets as a tool to account for more aspects of vague data and information. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Genoe:2008:fuzz, author = "Ray Genoe and Tahar Kechadi", title = "On the Recognition of Online Handwritten Mathematics Using Feature-Based Fuzzy Rules and Relationship Precedence", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0390.pdf}, url = {}, size = {}, abstract = {This paper describes an online recognition system based on a pen-based interface that can be used to analyse the structure of mathematical expressions. Some of the topics include a feature-based spatial analysis technique and a procedure for constructing expressions based on relationship precedence. The algorithm discussed combines symbol recognition, spatial analysis and parsing techniques, to generate an expression tree of the given mathematical expression. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Prados-Suarez:2008:fuzz, author = "B. Prados-Suarez and D. Sanchez and J. Chamorro-Martínez", title = "A Similarity Measure Between Fuzzy Regions to Obtain a Hierarchy of Fuzzy Image Segmentations", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0391.pdf}, url = {}, size = {}, abstract = {In image segmentation it is well known that a given image can be analyzed with different detail levels, this is why some hierarchical approaches have been proposed to give a different segmentation for each detail level. Most of these proposals are specially designed for precise and well defined regions. However regions usually have blurred contours, soft colour shades, and brightness that give rise to the problem of the imprecision in the regions. In this paper we face both problems considering the imprecision of the regions at the definition of the criteria to obtain a hierarchy detail levels. Concretely, we propose to calculate a similarity relation between fuzzy regions, based on two measures that take into account the imprecision in the transition between the regions, as well as the likeness of their characteristics. Then we use this fuzzy similarity relation to obtain a nested hierarchy of fuzzy segmentations by means of its α-cuts. In this way we obtain a tool to easily change the detail level and obtain a new fuzzy segmentation of the image, just changing the value of α }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Sapkota:2008:fuzz, author = "Achyut Sapkota and Kazuo Ohmi", title = "Detection of PIV Outliers Using Rule-Based Fuzzy Logic", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0392.pdf}, url = {}, size = {}, abstract = {Particle Image Velocimetry (PIV) is a widely used tool for the measurement of the different kinematic properties of the fluid flow. In this measurement technique, a pulsed laser light sheet is used to illuminate a flow field seeded with tracer particles and at each instance of illumination, the positions of the particles are recorded on digital CCD cameras. The resulting two camera frames can then be processed by various techniques to obtain the velocity vectors. However, such velocity information is always prone to outliers. The outliers degrade the quantitative information of the velocity field and gives misleading information of velocity based quantities like vorticity, streamlines, divergence etc. In this paper, a novel technique based on rule-based fuzzy logic has been proposed for the detection of such outliers. This technique overcomes the limitation of most of the detection techniques which are based on simple type of nearest neighborhood similarity constraint. The methodology is demonstrated to different PTV results. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wang11:2008:fuzz, author = "Di Wang and Xiao-Jun Zeng and John A. Keane", title = "An Incremental Construction Learning Algorithm for Identification of T-S Fuzzy Systems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0393.pdf}, url = {}, size = {}, abstract = {This paper proposes an incremental construction learning algorithm for identification of T-S Fuzzy Systems. The mechanism of the algorithm is that it is an error-reducing driven learning method. Beginning with a simplest T-S fuzzy system, the algorithm develops the system structure by adding more fuzzy terms and rules to reduce the model errors in a `greedy' way. The main features of the proposed algorithm are that, firstly, it can automatically determines and controls the number and location of fuzzy terms needed by following the error-reducing driven evolving process to achieve the desired accuracy; secondly, it adds new fuzzy terms and rules by evenly distributing error to each sub-region aiming at an efficient set of fuzzy rules, thirdly, it uses triangular membership functions and the regular partitions in constructing T-S fuzzy systems and leads to identified T-S fuzzy system models with good transparency and interpretability and suitable for advanced stability analysis and design approaches such as piecewise Lyapounov methods. Two dynamical system identification examples are given to illustrate the advantages of the proposed algorithm. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Tung:2008:fuzz, author = "W. L. Tung and C. Quek", title = "An Adaptive Fuzzy Semantic Memory Model Based on the Computational Principles of the Human Hippocampus", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0395.pdf}, url = {}, size = {}, abstract = {Fuzzy systems have been successfully applied to solve many engineering problems. However, traditional fuzzy systems are often manually crafted, and their structures (knowledge rule-bases) are static and cannot be trained or tuned to improve the system performance. This subsequently leads to an intense research on the autonomous construction and tuning of a fuzzy system directly from the observed training data to address the knowledge acquisition bottleneck. However, the complex and dynamic nature of real-world problems demanded that fuzzy systems be able to adapt their structures, parameters and ultimately evolve their intelligence to continuously address the non-stationary characteristics of their operating environments. This paper presents the evolving fuzzy semantic memory (eFSM) model, a neuro-fuzzy architecture with a continuously adaptive structure (rule-base). The computational principles responsible for the online identification of the proposed eFSM model and its evolving capability are based on the functional mechanisms of the human hippocampus, a brain construct that plays a significant role in the acquisition of the long-term human declarative memories. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Chitcharoen:2008:fuzz, author = "Doungrat Chitcharoen and Puntip Pattaraintakorn", title = "Towards Theories of Fuzzy Set and Rough Set to Flow Graphs", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0396.pdf}, url = {}, size = {}, abstract = {Mathematical rough set theory and fuzzy set theory have attracted both practical and theoretical researchers from their efficiently and effectively to analyze real-world data. A novel and significant extension is called flow graphs. In this paper, we introduced how to calculate certainty, coverage and strength coefficients of decision rules from fuzzy attributes in a flow graph. Furthermore, we relax concept of mutual exclusion and introduced four new propositions of certainty and coverage coefficients for decision rules extracted from flow graph. An example calculation of these coefficients is provided. We also demonstrate real-world experiment on POSN data set. Several case studies illustrate a desirable outcome. }, keywords = {Fuzzy set theory, rough set theory, flow graphs.}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Lovassy:2008:fuzz, author = "Rita Lovassy and Laszló T. Kóczy and Laszló Gal", title = "Multilayer Perceptron Implemented by Fuzzy Flip-Flops", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0397.pdf}, url = {}, size = {}, abstract = {The paper introduces a novel method for constructing Multilayer Perceptron (MLP) Neural Networks (NN) with the aid of fuzzy systems, particularly by deploying fuzzy J-K flip-flops as neurons. The next state Q(t+1) of the J-K fuzzy flip-flops (F3) in terms of input J can be characterized by a more or less S-shaped function, for each F3 derived from the Yager, Dombi, and Fodor norms and co-norms. In this approach, J represents the neuron input. The other input K is wired to the complemental output (K= 1-Q), thus an elementary fuzzy sequential unit with a single input and a single output is received. The algebraic F3 having linear J-Q(t+1) characteristics is added to the above three. The paper proposes the investigation of the possibility of constructing multilayer perceptrons from such real fuzzy hardware units. Each of the four candidates for F3-based neurons is examined for its training capability by evaluating and comparing the approximation capabilities for two different transcendental functions. Simulation results are presented. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Akbarzadeh:2008:fuzz, author = "Vahab Akbarzadeh and Alireza Sadeghian and Marcus V. {dos Santos}", title = "Derivation of Relational Fuzzy Classification Rules Using Evolutionary Computation", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0398.pdf}, url = {}, size = {}, abstract = {An evolutionary system for derivation of fuzzy classification rules is presented. This system uses two populations: one of fuzzy classification rules, and one of membership function definitions. A constrained-syntax genetic programming evolves the first population and a mutation-based evolutionary algorithm evolves the second population. These two populations co-evolve to better classify the underlying dataset. Unlike other approaches that use fuzzification of continuous attributes of the dataset for discovering fuzzy classification rules, the system presented here fuzzifies the relational operators ``greater than'' and ``less than'' using evolutionary methods. For testing our system, the system is applied to the Iris dataset. Our experimental results show that our system outperforms previous evolutionary and non-evolutionary systems on accuracy of classification and derivation of interrelation between the attributes of the Iris dataset. The resulting fuzzy rules of the system can be directly used in knowledge-based systems. }, keywords = {genetic algorithms, genetic programming}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Lee5:2008:fuzz, author = "Ching-Yi Lee and Li-Chun Liao ", title = "Recognition of Facial Expression by Using Neural-Network System with Fuzzified Characteristic Distances Weights", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0401.pdf}, url = {}, size = {}, abstract = {A neural-network with fuzzified characteristic distances weights (NNFCDW) is proposed in this paper to recognize the facial expressions effectively. During the recognition process, the characteristic distances that represent the relationship between the facial expressions and the muscle movement are used to be the major basis for recognition. The different expressions will somehow dominate the characteristic distances defined from different feature-area (mouth, eye or eyebrow). Therefore, the weights of the characteristic distances will be an important factor to determine the recognition rate. In this paper, a reasonable method of tuning the weights without trial-and-error is proposed. A fuzzy system based on the recognition results is developed to generate the weights rationally. The characteristic distances are multiplied with the fuzzified weights and sent to a neural-network system for recognition of the facial expressions. The proposed neural-network system is composed of the self-organizing map (SOM) neural network and back-propagation neural network (BPNN). When BPNN used the pre-classified data as its training data, the training cycles can be obviously reduced. The experimental results demonstrate that the recognition rate of using the proposed NNFCDW obviously increased about 10percent ~ 13percent as comparing with the results obtained by using pure BPNN. The computational time of using the proposed NNFCDW is also effectively decreased about 60percent as comparing with the results obtained by using pure BPNN. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Lu:2008:fuzz, author = "Jie Lu and Xiaoguang Deng and Philippe Vroman and Jun Ma and Guangquan Zhang", title = "A Fuzzy Multi-Criteria Group Decision Support System for Nonwoven Based Cosmetic Product Development Evaluation", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0402.pdf}, url = {}, size = {}, abstract = {Product prototype evaluation is an important phase in new product development (NPD). Such evaluation often requires multiple criteria that are within a hierarchy and a group of evaluators. The evaluation process and these evaluation criteria often involve uncertain and fuzzy data in the weights of these criteria and the judgments of these evaluators. To evaluate nonwoven cosmetic product prototypes, this study first develops a NPD evaluation model, which has evaluation criteria within three levels, based on the features of nonwoven products. It then proposes a fuzzy (multi-level) multi-criteria group decision-making (FMCGDM) method for supporting the evaluation task. A fuzzy multi-criteria group decision support system (FMCGDSS) is developed to implement the proposed method and applied in nonwoven cosmetic product development evaluation. }, keywords = { Decision support systems, multi-criteria decision making, group decision making, fuzzy sets, new product development}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Lee6:2008:fuzz, author = "Y. H. Lee and C. W. Han and B. K Kim", title = "Depth Range Component Based 3D Face Recognition Using Fuzzy Methods", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0403.pdf}, url = {}, size = {}, abstract = {The face shape using depth information in the face represents personal features in detail. In particular, the surface curvatures extracted from the face contain the most important personal facial information. In this paper, we develop a method for recognizing range face images by combining the multiple-face-regions (region component based), using fuzzy integral. For the proposed approach, the first step uses face curvatures that helps extract facial features for range face images, after normalization using the SVD. As a result of this process, we obtain curvature feature for each region range face. The second step of approach concerns the application of PCA and Fisherface method to each component range face. The reason for adapted PCA and Fisherface method is these can maintain the surface attribute for face curvature, even though these can generate the reduced image dimension. In the last step, the aggregation of the individual classifiers using the fuzzy integral and the fuzzy neural network (CAFNN) are explained for each region component based. The experimental results obtained that the approach presented in this paper have outstanding classification in comparison to the results obtained by other methods. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Narukawa:2008:fuzz, author = "Yasuo Narukawa and Vicenç Torra", title = "Domain Extension for Multidimensional Generalized Fuzzy Integrals", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0406.pdf}, url = {}, size = {}, abstract = {We have recently studied multidimensional fuzzy integrals, partly motivated by the definition of citation indices. In this paper, we further study these integrals and consider the problem of domain extension. This problem arises when we consider the definition of a measure on the product space from two measures.We also discuss how these results are applied to the definition of citation indices. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wang12:2008:fuzz, author = "Tsaipei Wang ", title = "Possibilistic Clustering of Generic Shapes Derived from Templates", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0408.pdf}, url = {}, size = {}, abstract = {We present in this paper a new type of alternating-optimization based possibilistic c-shell algorithm for clustering template-based shapes. A cluster prototype consists of a copy of the template after translation, scaling, rotation, and/or affine transformations. We use a number of two-dimensional data sets, both synthetic and from real-world images, to illustrate the capability of our algorithm in detecting generic template-based shapes in images. We also describe a progressive clustering procedure aimed to relax the requirements of known number of clusters and good initialization. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Baf:2008:fuzz, author = "Fida El Baf and Thierry Bouwmans and Bertrand Vachon ", title = "Fuzzy Integral for Moving Object Detection", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0409.pdf}, url = {}, size = {}, abstract = {Detection of moving objects is the first step in many applications using video sequences like video-surveillance, optical motion capture and multimedia application. The process mainly used is the background subtraction which one key step is the foreground detection. The goal is to classify pixels of the current image as foreground or background. Some critical situations as shadows, illumination variations can occur in the scene and generate a false classification of image pixels. To deal with the uncertainty in the classification issue, we propose to use the Choquet integral as aggregation operator. Experiments on different data sets in video surveillance have shown a robustness of the proposed method against some critical situations when fusing colour and texture features. Different colour spaces have been tested to improve the insensitivity of the detection to the illumination changes. Then, the algorithm has been compared with another fuzzy approach based on the Sugeno integral and has proved its robustness. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Sato-Ilic:2008:fuzz, author = "Mika Sato-Ilic ", title = "Clustering Objects with Degree of Classification", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0410.pdf}, url = {}, size = {}, abstract = {This paper proposes a fuzzy clustering method under the intrinsically classified structure of data through dissimilarity of objects at each variable. In order to extract the classification structure, the variable-based fuzzy clustering method is exploited and the degree of classification for each object with respect to each variable is defined. This degree shows individually classified power of an object with respect to a variable. By applying this degree to the data, a stable classification solution which is not sensitive to the outlier is obtained.Several numerical examples show the improved performance and the applicability of our proposed method. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Korytkowski:2008:fuzz, author = "Marcin Korytkowski and Robert Nowicki and Rafa Scherer and Leszek Rutkowski", title = "Ensemble of Rough-Neuro-Fuzzy Systems for Classification with Missing Features", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0413.pdf}, url = {}, size = {}, abstract = {Most methods constituting the soft computing concept can not handle data with missing or unknown features. Neural networks are able to perfectly fit to data and fuzzy logic systems use interpretable knowledge. To achieve better accuracy learning systems can be combined into larger ensembles. In this paper we combine logical neuro-fuzzy systems into the AdaBoost ensemble and extract fuzzy rules from the ensemble. The rules are used in rough-neuro-fuzzy classifier which can operate on data with missing values. The rough systems perform very well on these rules which was illustrated on a well known benchmark. The features were being removed to check the performance on incomplete data sets. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Hinde:2008:fuzz, author = "C. J. Hinde and R. S. Patching and S. A. McCoy", title = "Managing Contradictory Evidence", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0414.pdf}, url = {}, size = {}, abstract = {The paper draws on the theory of mass assignment to refine the underlying semantics of intuitionistic fuzzy sets. Inconsistency can arise from several sources and it is dealt with in different ways. All the representations of inconsistency and contradiction in this paper arise from considering restricting and positive evidence lattices. In particular this paper formally addresses the operators, intersection and conjunction in detail. Because union and disjunction are required to compute the values for intersection and conjunction these are also covered as part of the analysis. }, keywords = { Contradiction, Inconsistency, Intuitionistic Fuzzy Sets, Mass Assignment}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Fang:2008:fuzz, author = "Zhigeng Fang and Sifeng Liu and Chaoqing Yuan and Chuanmin Mi", title = "Forecast of Electricity Consumption of Jiangsu Province by Grey Methods", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0415.pdf}, url = {}, size = {}, abstract = {As Jiangsu Province ranks in rapid economic development area in China, its electricity consumption gets an increasingly grow in recent years. It is meaningful and significant to forecast its electricity consumption tendency. GM(1,1) and GM(0,N) are applied to look insight into the tendency in the paper. The models have good accuracy. Through the models, we find that the electricity consumption of Jiangsu will proceed to increase rapidly. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Vieira:2008:fuzz, author = "S. M. Vieira and J. M. C. Sousa and T. A. Runkler", title = "Fuzzy Classification in Ant Feature Selection", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0417.pdf}, url = {}, size = {}, abstract = {One of the most important techniques in data preprocessing for data mining is feature selection. Real-world data analysis, data mining, classification and modeling problems usually involve a large number of candidate inputs or features. Less relevant or highly correlated features decrease, in general, the classification accuracy, and enlarge the complexity of the classifier. The goal is to find a reduced set of features that reveals the best classification accuracy for a fuzzy classifier. This paper proposes an ant colony optimization (ACO) algorithm for feature selection, which minimizes two objectives: the number of features and the error classification. Two pheromone matrices and two different heuristics are used for each objective. The performance of the method is compared to other features selection methods, revealing higher performance. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wichard:2008:fuzz, author = "Jorg D. Wichard and Ronald Kühne and Antonius ter Laak", title = "Binding Site Detection via Mutual Information", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0418.pdf}, url = {}, size = {}, abstract = {About 40percent of all marketed drugs have the so-called G-protein coupled receptors (GPCRs) as their target protein. There exist more than 800 different GPCRs in humans, of which at least 300 GPCRs are believed to be druggable. Yet, for only two GPCRs there are three-dimensional (3D) protein crystal structures available and consequently little is known about the molecular interactions between pharmacologically active substances and the receptors in this important drug target protein family. A chemogenomics approach as an alternative to the lack of 3D structural information appears attractive for the rational design of GPCR drugs, as an enormous amount of biological activity data for various GPCRs exist and can be used to deduce GPCR structure-function relationships. In this work, we suggest a new approach for the detection of interdependance of features in the GPCR protein sequences and properties of the related small molecule ligands based on mutual information between ligand and sequence space. }, keywords = {Drug Discovery, GPCR, Mutual Information, Chemogenomics}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Graña:2008:fuzz, author = "Manuel Graña ", title = "A Brief Review of Lattice Computing", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0419.pdf}, url = {}, size = {}, abstract = {Defining lattice computing as the lass of algorithms that either apply lattice operators inf and sup or use lattice theory to produce generalizations or fusions of previous approaches, we find that a host of algorithms for data processing, classification, signal filtering, have been produced over the last decades. We give a fast and brief review, which by no means could be exhaustive; with the aim of showing that this area has been growing during the past decades and to highlight the ones that we think are broad avenues for future research. Although our emphasis is on Artificial Neural Networks and Fuzzy Systems in this review we include Mathematical Morphology as a notorious instance of Lattice Computing.1 }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Singh:2008:fuzz, author = "Lotika Singh and Apurva Narayan and Satish Kumar", title = "Dynamic Fuzzy Load Balancing on LAM/MPI Clusters with Applications in Parallel Master-Slave Implementations of an Evolutionary Neuro-Fuzzy Learning System", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0422.pdf}, url = {}, size = {}, abstract = {In the context of parallel master-slave implementations of evolutionary learning in fuzzy-neural network models, a major issue that arises during runtime is how to balance the load - the number of strings assigned to a slave for evaluation during a generation - in order to achieve maximum speed up. Slave evaluation times can fluctuate drastically depending upon the local computational load on the slave (given fixed node specifications). Communication delays compound the problem of proper load assignment. In this paper we propose the design of a novel dynamic fuzzy load estimator for application to load balancing on heterogeneous LAM/MPI clusters. Using average evaluation time and communication delay feedback estimates from slaves, string assignments for evaluation to slaves are dynamically changed during runtime. Extensive tests on heterogenous clusters shows that considerable speedups can be achieved using the proposed fuzzy controller. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Yakhchali:2008:fuzz, author = "S. H. Yakhchali and S. H. Ghodsypour", title = "A Hybrid Genetic Algorithm for Computing the Float of an Activity in Networks with Imprecise Durations", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0423.pdf}, url = {}, size = {}, abstract = {This paper deals with two relevant problems: calculating bounds on the float and determining the type of criticality, in the network with imprecise durations which are represented by means of intervals or fuzzy intervals. There exist different types of critical activity in the network with interval durations; an activity can be either necessarily noncritical, or necessarily critical, or possibly critical at the time. Lemmas, provided in this paper, elaborate on the connections between the notion of critical paths and critical activities.The minimal float problem is NP-Hard while the maximal float problem is polynomial. Due to difficulty of obtaining the lower bound on the floats in medium and large-scaled networks, a hybrid genetic algorithm (HGA) is developed. The proposed HGA incorporates a neighbourhood search (NS) into a basic genetic algorithm that enables the algorithm to perform genetic search over the subspace of local optimum. Then the results are extended to network with fuzzy durations. }, keywords = { Fuzzy PERT/CPM, Critical path analysis, Fuzzy interval, Genetic algorithms.}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Singh2:2008:fuzz, author = "Lotika Singh and Satish Kumar and Sandeep Paul", title = "Automatic Simultaneous Architecture and Parameter Search in Fuzzy Neural Network Learning Using Novel Variable Length Crossover Differential Evolution", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0424.pdf}, url = {}, size = {}, abstract = {The automatic simultaneous search of structure and parameters in fuzzy-neural networks is a pressing research problem. This paper introduces a novel and powerful variablelength- crossover differential evolution algorithm, vlX-DE, which is applied to ASuPFuNIS fuzzy-neural model learning, and permits simultaneous evolution of mixed-length populations of strings representing ASuPFuNIS network instances in different rules spaces. As hybrid populations of strings evolve using vlX-DE, the population gradually converges to a single rule space after which parameter search within that space proceeds till the end of the algorithm run. Search can be directed to stress either rule node economy or minimize the sumsquare- error, or trade-off between these two. Tests on three benchmark problems - Iris classification, CHEM classification, and Narazaki-function approximation - clearly highlight the effectiveness of the algorithm in being able to perform this simultaneous search. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Lee7:2008:fuzz, author = "Chang-Shing Lee and Mei-Hui Wang and Huan-Chung Li and Wen-Hui Chen", title = "Intelligent Ontological Agent for Diabetic Food Recommendation", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0425.pdf}, url = {}, size = {}, abstract = {Diabetes is a chronic illness that food intake affects the body's needs and insulin's ability to lower blood sugar. This paper proposes an intelligent agent, called the personal food recommendation agent, based on the ontology model for diabetic food recommendation. The agent can create a meal plan according to a person's lifestyle and particular health needs. The required knowledge is stored in the ontology model predefined by domain experts. It contains the Taiwanese food ontology and a set of personal food ontology. The personal food recommendation agent includes the ontology creating mechanism, the personal ontology filter, the food fuzzy number creating mechanism, the fuzzy inference mechanism, and the real-time recommendation mechanism. It retrieves the personal ontology and meal records to recommend a personal meal plan based on the fuzzy inference mechanism. An experimental platform has been constructed to test the performance of the agent. The results indicate that the proposed method can work effectively and alleviate the effort of a registered dietician. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Lai:2008:fuzz, author = "K. Robert Lai and Yi-Yuan Chiang", title = "A Constraint-Based Framework for Incorporating {\it A Priori} Knowledge into Fuzzy Modelling", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0426.pdf}, url = {}, size = {}, abstract = {Incorporation of various sources of a priori knowledge into data-driven fuzzy modelling is an important task. But a major problem with current approaches is that they are mostly problem-specific and lacking an effective framework to bring different sources of knowledge into the task of modelling. In this paper, we propose a constraint-based framework for the incorporation of a priori knowledge into data-driven-based fuzzy modelling. We first investigate a logical taxonomy of background knowledge in learning a fuzzy model. Then, based on this taxonomy, we can develop a framework for incorporating prior knowledge into a constraint-based fuzzy modelling. Finally, two simulation examples, a nonlinear function fitting problem and a dynamic time series prediction problem, are provided for the embodiment of the proposed idea. }, keywords = {A Priori Knowledge, Constraint-based Problem Solving, Fuzzy Constraints, Fuzzy Modelling.}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Runkler:2008:fuzz, author = "Thomas A. Runkler and Hans Georg Seedig", title = "Fuzzy c-Auto Regression Models", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0429.pdf}, url = {}, size = {}, abstract = {Fuzzy c-auto regression models (FCARM) combine clustering with time series prediction. Given a set of time series, FCARM finds clusters of time series with similar dynamics. More specifically, FCARM finds a partition matrix that quantifies to which degree each time series is associated with each prediction model, and the parameters of the (linear) auto regression models for each cluster. FCARM can thus be used for two different purposes: (i) the automatic identification of clusters of time series with similar dynamics and (ii) the forecast of a large number of time series using only a small number of generic forecast models, leading to higher data efficiency and lower model validation and maintenance effort. We illustrate the application of FCARM to sales forecasts for products that can be clustered into groups with similar sales dynamics. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Li6:2008:fuzz, author = "Qiaoxing Li ", title = "The Grey Basic Element", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0430.pdf}, url = {}, size = {}, abstract = {The grey system theory is new methodology useful for the study of unascertained situations whose information is missing and extenics is a another one to solve the contradictory problems. Under the information-missing situation, the values of some characteristics are grey. We propose the grey basicelement to treat this situation. The decision-makers can use it to solve the contradictory problems under uncertain situation. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Zhong:2008:fuzz, author = "Haoming Zhong and Chunyan Miao and Zhiqi Shen and Yuhong Feng", title = "Temporal Fuzzy Cognitive Maps", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0431.pdf}, url = {}, size = {}, abstract = {This paper is concerned with the design, implementation, and evaluation of a novel extension of FCMs, temporal fuzzy cognitive maps (tFCMs). FCMs have advantages such as simplicity, supporting of inconsistent knowledge, and circle causalities for knowledge modeling and inference. However, the lack of the time dimension limits the usage of FCMs from the long term inference and the time related knowledge modeling. In order to narrow down the gap, a temporalized FCM is proposed to define a complete discrete temporal extension of the FCM. To reduce the complexities brought by the temporalization, a design approach is also introduced to construct the map, using simplified patterns and fuzzy logic based effect functions to capture fuzzy knowledge from domain experts. The paper also discusses how the errors in the fuzzy knowledge affect the result. Two different causality models are studied in theory and experiments to compare the error effects during the inferring. The result shows a significant difference in error accumulation in different causality models and gives a guideline to help users balance the error accumulation and sensitivities of their maps. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wang13:2008:fuzz, author = "Ziliang Wang and Zhen Wang and Ting Wei ", title = "Grey Trend Models", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0433.pdf}, url = {}, size = {}, abstract = {First the modeling procedure of time-varying grey dynamic model is introduced on the basis of grey dynamic model theory. Then the relation between the ordinary Gompertz model and time-varying grey dynamic model is discussed, thus one kind of trend model, grey Gompertz model is derived using time-varying grey dynamic model with logarithmic transformation. Similarly, another kind of trend model, grey Logistic model is derived with reciprocal transformation. The validity of the modeling methods are verified via two instances comparing with other modeling methods. The new models and methods will enrich the modeling techniques of grey system theory and of trend models. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Mazinan:2008:fuzz, author = "A. H. Mazinan and N. Sadati", title = "Fuzzy Multiple Models Predictive Control of Tubular Heat Exchanger", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0434.pdf}, url = {}, size = {}, abstract = {In this paper, a new strategy for control of tubular heat exchanger system has been presented. The proposed approach is realized using generalized predictive control (GPC) scheme and multiple models method. By using the multiple models approach, different operating environments of the system are first modeled. Then in each instant of time, the best model of the system is identified by a fuzzy decision mechanism. Finally, the best control input is chosen appropriately. For demonstrating the effectiveness of the proposed approach, simulations are done and the results are compared with those obtained using the single model predictive controller approach. The results can verify the validity of the proposed control scheme. }, keywords = {Generalized predictive control, multiple models, fuzzy decision mechanism, tubular heat exchanger system.}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Yu4:2008:fuzz, author = "Miao Yu and L. X. Zhu and X. M. Dong and C. R. Liao ", title = "Adaptive Fuzzy Logical Control for Impact Absorbing", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0435.pdf}, url = {}, size = {}, abstract = {A new adaptive fuzzy logical control (FLC) strategy using a hybrid Taguchi genetic algorithm (HTGA) is proposed to absorb the impact of car body caused by road bump. The controller consists of two control loops. The inner open loop controls a nonlinear magnet-orheological (MR) damper to achieve tracking of a desired force. The outer loop implements a fuzzy logic controller using HTGA. The HTGA is used to tune the membership functions and control rules of FLC with initial skyhook control rules. To verify the control performance, simulation and road test of the adaptive FLC are carried out. The simulation and experiment results show that adaptive FLC can achieve smaller acceleration peak and shorter adjusting time than sky-hook and passive system under bump input. }, keywords = {fuzzy logical control, magneto-rheological damper, genetic algorithm, impact control}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Fiot:2008:fuzz, author = "Celine Fiot and Florent Masseglia and Anne Laurent and Maguelonne Teisseire", title = "TED and EVA: Expressing Temporal Tendencies among Quantitative Variables Using Fuzzy Sequential Pattern", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0436.pdf}, url = {}, size = {}, abstract = {Temporal data can be handled in many ways for discovering specific knowledge. Sequential pattern mining is one of these relevant approaches when dealing with temporally annotated data. It allows discovering frequent sequences embedded in the records. In the access data of a commercial Web site, one may, for instance, discover that ``5percent of the users request the page register.php 3 times and then request the page help.html''. However, symbolic or fuzzy sequential patterns, in their current form, do not allow extracting temporal tendencies that are typical of sequential data. By means of temporal tendency mining, one may discover in the same access data that ``an increasing number of accesses to the register form preceeds an increasing number of accesses to the help page a few seconds later''. It would be easy to conclude that the users either quickly succeed in registering or make several attempts before they look at the help page within a few seconds. In this paper, we propose the definition of evolution patterns that allow discovering such knowledge. We show how to extract evolution patterns thanks to fuzzy sequential pattern mining techniques. We introduce our algorithms TED and EVA, designed for evolution pattern mining. Our proposal is validated by experiments and a sample of extracted knowledge is discussed. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Tafti:2008:fuzz, author = "Abdolreza Dehghani Tafti and Nasser Sadati", title = "A Hybrid Fuzzy Adaptive Tracking Algorithm for Maneuvering Targets", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0437.pdf}, url = {}, size = {}, abstract = {A new hybrid fuzzy adaptive algorithm for tracking maneuvering targets is proposed in this paper. The algorithm is implemented with fuzzy inference system (FIS) and current statistical model and adaptive filtering (CSMAF). The CSMAF algorithm is one of most effective methods for tracking the maneuvering targets. It has a higher precision in tracking the maneuvering targets with larger accelerations while it has a lower precision in tracking the maneuvering targets with smaller acceleration. In the proposed algorithm, to overcome the disadvantage of the CSMAF algorithm, the covariance of process noise CSMAF is adjusted adaptively by the output of a FIS. The input of the FIS is discrepancy of the covariance process noise from its actual value, where the actual value can be obtained based on filter innovation properties. Simulation results show that in spite of having simple structure for the proposed algorithm, it can also improve the precision of the CSMAF algorithm in a wide range of target maneuvers significantly. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Lian:2008:fuzz, author = "Kuang-Yow Lian and Ya-Lun Ouyang and Wei-Lun Wu", title = "Realization of Maximum Power Tracking Approach for Photovoltaic Array Systems Based on T-S Fuzzy Method", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0438.pdf}, url = {}, size = {}, abstract = {A micro-grid power system consisting of a photovoltaic (PV) array panel, DC/DC converter, and a battery is considered in this research. This thesis proposes a T-S fuzzy method to deal with the power tracking problem of the power generating systems. Then, the stability analysis is carried out using Lyapunov direct method whereas the control problem is formulated into the feasibility of solving a set of linear matrix inequality (LMIs). To verify the performance, we focus on the experiment in hardware realization. To this end, we establish an experiment environment for the solar power system, which includes an SP75 solar module, a DC/DC buck converter, an A/D DS1103 card, and a real-time interface, dSPACE. The results show satisfactory performances for the proposed scheme. }, keywords = { Output tracking, PV array, T-S fuzzy system, virtual desired variables.}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Paul:2008:fuzz, author = "Sandeep Paul and Satish Kumar and Lotika Singh", title = "A Novel Evolutionary TSK-Subsethood Model and Its Parallel Implementation", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0439.pdf}, url = {}, size = {}, abstract = {A novel evolutionary TSK-subsethood fuzzyneural network model along with its parallel implementation on a LAM/MPI cluster is presented in this paper. The proposed four-layered network is inspired by the subsethood class of models, which have ability to seamlessly compose numeric and linguistic data simultaneously. The proposed model embeds TSK rules into the network architecture, while transmitting information using subsethood products and a linear weighted sum of fuzzy sets. L-R arithmetic is used in the internal operation of the network. Differential evolution learning is employed to evolve tunable parameters of the network. A parallel implementation using a master-slave approach efficiently distributes the computational load of string evaluations on a LAM/MPI cluster.The proposed model is tested on two benchmark problems: Iris Classification, Mackey Glass time series prediction. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Aguzzoli:2008:fuzz, author = "Stefano Aguzzoli and Brunella Gerla and Vincenzo Marra", title = "Defuzzifying Formulas in Godel Logic Through Finitely Additive Measures", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0440.pdf}, url = {}, size = {}, abstract = {Godel logic is the fuzzy logic of the minimum triangular norm and its residuum. Using the functional representation of the Lindenbaum algebra of Godel logic, we analyze the interaction between the integral operator and the logical connectives. On these grounds, we put forth a notion of finitely additive probability measure for Godel logic. Our first main result shows that such measures precisely correspond to integrating the truth value functions induced by Godel formulas with respect to a Borel probability measure on the real unit cube [0, 1]n. Our second main result shows that they also coincide with convex combinations of finitely many [0, 1]-valued assignments. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Yao3:2008:fuzz, author = "Leehter Yao and Hau-Ren Lu ", title = "Optimization of Two-Way Direct Load Control Based on Fuzzy Linear Programming", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0441.pdf}, url = {}, size = {}, abstract = {A novel real-time optimization approach for two-way direct load control (TWDLC) of central air conditioning chillers are proposed. For TWDLC, the main computer in the control center constantly monitors the average controllable load of every customer. For real time calculations, a computationally fast and effective approach based on fuzzy linear programming is proposed to determine the shedding ratio of every customer. The proposed optimization approach will minimize the difference between the load required to shed and the load actually shed at each sampling interval. Every customer's contribution to the load shedding is also leveled by the proposed approach. A gateway is installed at every customer's site to coordinate shedding control with the main computer through the Internet. As soon as the gateway receives the expected shedding ratio, it calculates the load required to shed and coordinates all chiller units in operation to conduct load shedding, thereby achieving the load reduction amount. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Zhon:2008:fuzz, author = "Hong-Jian Zhon and Wei-Min Hsieh and Yih-Guang Leu and Chin-Ming Hong", title = "Adaptive Learning Approach of Integrating Evolution Fuzzy-Neural Networks and Q-Learning for Mobile Robots", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0442.pdf}, url = {}, size = {}, abstract = {In the paper, an adaptive learning approach of integrating evolution fuzzy-neural networks and Q-learning is developed so that a mobile robot can adapt itself to a real and complex environment. Specifically, based on Q-value and an evolution method that adjusts their parameter values of the fuzzy-neural networks, the mobile robot evolves better strategies to adapt to the environment. However, in most studies of evolution learning, the learning of mobile robots often requires a simulator and an enormous amount of evolution time so as to perform a task. Therefore, we are to integrate Q-learning into the evolution fuzzy-neural networks to avoid the requirement of the simulator. Experiment results of a mobile robot illustrate the performance of the proposed approach. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Ho:2008:fuzz, author = "Duc Thang Ho and Jonathan M. Garibaldi ", title = "A Novel Fuzzy Inferencing Methodology for Simulated Car Racing", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0443.pdf}, url = {}, size = {}, abstract = {This paper describes and further extends the fuzzy inferencing system which won the simulated car racing competition that was arranged as part of FuzzIEEE 2007 conference. The details of the winning non-stationary fuzzy controller and its results are presented. A novel approach to further improve the performance of the winning controller is described and formalised. We term the new fuzzy inferencing method a `context-dependent fuzzy inference system'. The concept of a `context-dependent fuzzy set' that is used by the fuzzy system is introduced. Finally, a comparison between context-dependent fuzzy inference system and various existing techniques are carried out on the simulated car racing application. The results show a better performance for context-dependent fuzzy inference systems in stochastic circumstances. }, keywords = {Context-dependent Fuzzy Inference System (CDFIS), Context-dependent Fuzzy Sets (CDFS), Nonstationary Fuzzy Inference System (NSFIS), Non stationary Fuzzy Sets (NSFS), Fuzzy Inference System (FIS).}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Molina-Lozano:2008:fuzz, author = "Herón Molina-Lozano and Edgar E. Vallejo-Clemente and Juan E. Morett-Sanchez", title = "DNA Sequence Analysis Using Fuzzy Grammars", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0444.pdf}, url = {}, size = {}, abstract = {We propose to use fuzzy context-free grammars for the analysis of DNA sequences by using the Cocke-Younger- Kasami algorithm to estimate membership grades of a DNA sequence against the language of a fuzzy grammar. As a first example of the application of the proposed method we prove that is possible to determine a fuzzy grammar of a prototype DNA sequence and then found the membership grade of any arbitrary sequence against the specific pattern. As a second example, we formulate a fuzzy grammar from an alignment of promoters by a logo sequence of Escherichia Coli K12 then show how the proposed method can be used for the discovery of regulatory motifs. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Sato-Shimokawara:2008:fuzz, author = "Eri Sato-Shimokawara and Yusuke Fukusato and Jun Nakazato and ToruYamaguchi", title = "Context-Dependent Human-Robot Interaction Using Indicating Motion via Virtual-City Interface", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0445.pdf}, url = {}, size = {}, abstract = {This paper presents interactive system using indicating motion which is used in human communication. Gesture motion has different means according to circumstances. Human recognize other person's intention or attending points from gesture, face direction, situation and so on. Authors has researched gesture recognition considering situation for natural interaction between human and robot. Moreover, human find a object which was wanted other person from the interaction; To realize interactive system, authors construct Virtual-City interface.In this paper, authors describe the context-based gesture interaction using Virtual-City, and show the experiment that car-navigation using the system. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Tomaru:2008:fuzz, author = "Masahiro Tomaru and Motohide Umano and Yuji Matsumoto and Kazuhisa Seta", title = "Learning by Switching Generation and Reasoning Methods - Acquisition of Meta-Knowledge for Switching with Reinforcement Learning", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0446.pdf}, url = {}, size = {}, abstract = {When we generate knowledge, we initially have no knowledge and acquire it by observing data one by one. We memorize the raw data when the number of observed data is small and generate general knowledge when it becomes large. To simulate this learning process, we proposed a learning model with switching several knowledge representation and reasoning methods. In this model, the time when to switch is decided with the fixed rules. These rules are considered to be metaknowledge because they control the learning process. In this paper, we propose a method acquiring the meta-knowledge for deciding the time of switching knowledge representation or reasoning method. For learning of the meta-knowledge, the correct answers can not to be given but just the evaluation of the learning process. We use Q-learning, therefore, a method of reinforcement learning. In the simulation, we apply the method to the iris plant data to acquire the meta-knowledge. The system with the acquired meta-knowledge has smaller number of rules than the old method for the similar rate correctly classified. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(McCulloch:2008:fuzz, author = "Daniel R. McCulloch and Jonathan Lawry and I. D. Cluckie", title = "Real-Time Flood Forecasting Using Updateable Linguistic Decision Trees", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0447.pdf}, url = {}, size = {}, abstract = {This paper focuses on the application of LID3 (Linguistic Decision Tree Induction Algorithm) to Real-Time Flood Forecasting. Specifically the prediction of the river level at locations along the River Severn, Britain's largest river. Modelling river dynamics implies modelling a system that changes over time. It is therefore inappropriate to use a static model to model river levels, that are driven by an underlying dynamic system. Hence, an updateable version of LID3 is proposed. There are two main features of ULID3 (Updateable LID3). The first being Error-Based Updating, which weights new instances depending on the tree's current ability to describe each new example. The ability to update probability distributions at each node enables the tree to adapt and capture the new dynamic concept more effectively. The second feature is the ability to extend both the input and output domains, given new examples. This is necessary when the data available for updating, exceeds the current domain set by the training data. An algorithm is presented to update the new probability distributions throughout the tree, without the need for storing the complete set of examples at each node. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Lee8:2008:fuzz, author = "Keum-Chang Lee and Ludmil Mikhailov", title = "Fuzzy Reasoning Method Based on Voting Techniques for Building Fuzzy Systems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0448.pdf}, url = {}, size = {}, abstract = {In this paper a novel fuzzy reasoning method based on the weighted maximum rule is proposed, which was initially inspired from the weighted majority vote algorithm and a fuzzy inference method using the maximum rule. This fuzzy reasoning method could be used for fuzzy if-then rules systems performing classification, control, and function approximation. The applications of the proposed fuzzy reasoning method bring two main effects: heuristic weights tuning of already generated fuzzy rules and establishments of new paradigms for training and testing fuzzy if-then rules systems. The heuristic weights tuning of already generated fuzzy rules is enabled through learning results of the testing, therefore the proposed fuzzy reasoning method can be regarded as a fuzzy rules tuning method. Based on the testing performance of individual rules, each fuzzy rule obtains a measure of accuracy, as the rule with the higher accuracy gets the higher weight. Each weight is then appended to the corresponding fuzzy rule for the future decision makings. The future decisions are made based on the weights as well as the other conventional information of fuzzy decision making. The weight tuning capability of the proposed method also allows the establishments of the new paradigms for training and testing the fuzzy rules systems. This construction of fuzzy rules systems employs comparatively more diversified processes in order to reduce numbers of decision making errors. The proposed fuzzy reasoning method and its new paradigms of building fuzzy rules systems are explained as well as examined with the classification problems. Experiments were carried out to prove the applicability of the method by using the numerical `Iris Data' set. The obtained results show the good properties of the proposed method. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Mazhar:2008:fuzz, author = "Raazia Mazhar and Paul D. Gader and Joseph N. Wilson ", title = "A Matching Pursuit Based Similarity Measure for Fuzzy Clustering and Classification of Signals", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0449.pdf}, url = {}, size = {}, abstract = {Matching pursuits is a well known technique for signal representation and has also been used as a feature extractor for some classification systems. However, applications that use matching pursuits (MP) algorithm in their feature extraction stage are quite problem domain specific, making their adaptation for other types of problems quite hard. In this paper we propose a matching pursuits based similarity measure that uses only the dictionary, coefficients and residual information provided by theMP algorithm while comparing two signals. Hence it is easily applicable to a variety of problems. We show that using the MP based similarity measure for competitive agglomerative fuzzy clustering leads to an interesting and novel update equation that combines the standard fuzzy prototype updating equation with a term involving the error between approximated signals and approximated prototypes. The potential value of the similarity measure is investigated using the fuzzy K-nearest prototype algorithm of Frigui for a two-class, signal classification problem. It is shown that the new similarity measure significantly outperforms the Euclidean distance. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Alvim:2008:fuzz, author = "Leandro G. M. Alvim and Adriano Joaquim de Oliveira Cruz", title = "A Fuzzy State Machine Applied to an Emotion Model for Electronic Game Characters", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0450.pdf}, url = {}, size = {}, abstract = {In this paper, we investigate an emotion model for characters of electronic games. Aiming at simulating the emotion model, a Fuzzy State Machine(FuSM) with a new function for transferring membership degrees was proposed. The FuSM was applied to a virtual character of a chess game, that was able to react, in the form of facial expressions, according to moves of the game. Simulation results show the great potential of expression of FuSM, due to the fact that it can act on several states at the same time. Promising results were obtained with the proposed new FuSM transfer function, which was able to efficiently smoothen transitions among emotion states, creating good quality facial expressions. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Almeida:2008:fuzz, author = "R. J. Almeida and U. Kaymak and J. M. C. Sousa", title = "Fuzzy Rule Extraction from Typicality and Membership Partitions", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0451.pdf}, url = {}, size = {}, abstract = {This paper proposes extracting fuzzy rules from data using Fuzzy Possibilistic C-Means and Possibilistic Fuzzy C-Means algorithms, which provide more than one partition information: the typicality matrix and the membership matrix. Usually to extract fuzzy rules from data only one of the partition matrix is used, resulting in one rule per cluster. In our work we extract rules from both the membership partition matrix and the typicality matrix, resulting in deriving multiple rules for each cluster. These methods are applied to fuzzy modeling of four different classification problems: Iris, Wine, Wisconsin Breast Cancer and Altman data sets. The performance of the obtained models is compared and we consider the added value of the proposed approach in fuzzy modeling. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Nayagam:2008:fuzz, author = "V. Lakshmana Gomathi Nayagam and G. Venkateshwari and Geetha Sivaraman", title = "Ranking of Intuitionistic Fuzzy Numbers", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0452.pdf}, url = {}, size = {}, abstract = {The notion of fuzzy subsets was introduced by L. A. Zadeh (1965) and it was generalised to intuitionistic fuzzy subsets by K. Atanassov [1]. After the invention of intuitionistic fuzzy subsets, many real life problems are studied accurately [7, 13, 14]. The measure of fuzziness was studied in [12, 16]. The ranking of intuitionistic fuzzy numbers plays a main role in modelling many real life problems involving intuitionistic fuzzy decision making, intuitionistic fuzzy clustering. H. B. Mitchell introduced a method of ranking intuitionistic fuzzy numbers in [10]. In this paper, a new method of intuitionistic fuzzy scoring to intuitionistic fuzzy numbers that generalizes Chen and Hwang's scoring method for ranking of intuitionistic fuzzy numbers has been introduced and studied. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Stach:2008:fuzz, author = "Wojciech Stach and Lukasz Kurgan and Witold Pedrycz", title = "Data-Driven Nonlinear Hebbian Learning Method for Fuzzy Cognitive Maps", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0453.pdf}, url = {}, size = {}, abstract = {Fuzzy Cognitive Maps (FCMs) are a convenient tool for modeling of dynamic systems by means of concepts connected by cause-effect relationships. The FCM models can be developed either manually (by the experts) or using an automated learning method (from data). Some of the methods from the latter group, including recently proposed Nonlinear Hebbian Learning (NHL) algorithm, use Hebbian law and a set of conditions imposed on output concepts. In this paper, we propose a novel approach named data-driven NHL (DD-NHL) that extends NHL method by using historical data of the input concepts to provide improved quality of the learned FCMs. DD-NHL is tested on both synthetic and real-life data, and the experiments show that if historical data are available, then the proposed method produces better FCM models when compared with those formed by the generic NHL method. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Bede:2008:fuzz, author = "Barnabas Bede and Hajime Nobuhara and Imre J. Rudas and Janos Fodor", title = "Discrete Cosine Transform Based on Uninorms and Absorbing Norms", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0455.pdf}, url = {}, size = {}, abstract = {Recently it has been shown that in Image Processing, the usual sum and product of the reals are not the only operations that can be used. Several other operations provided by fuzzy logic perform well in this application. We continue this line of research and we propose the use of a pair consisting of a uninorm and an absorbing norm determined by a continuous, strictly increasing generator instead of the classical sum and multiplication. In the present paper the Discrete Cosine Transform (DCT)-based image compression method is generalized by using a pair consisting of a uninorm and an absorbing norm. We show that the results of the proposed method outperform in several cases the classical DCT image compression algorithm. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Dias:2008:fuzz, author = "C. G. Dias and I. E. Chabu", title = "A Fuzzy Logic Approach for the Detection of Broken Rotor Bars in Squirrel Cage Induction Motors", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0457.pdf}, url = {}, size = {}, abstract = {This paper presents a fuzzy logic approach in order to aid in detection of broken rotor bars. A mathematical model based on magnetic flux density monitoring is used to identify rotor faults. A Fuzzy approach has been developed as a tool in order to diagnosis broken bars and to estimate the failure severity. Simulation results are presented from the model implemented using the MATLAB Toolbox software. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Frigui:2008:fuzz, author = "Hichem Frigui and Joshua Caudill and Ahmed Chamseddine Ben Abdallah", title = "Fusion of Multi-Modal Features for Efficient Content-Based Image Retrieval", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0459.pdf}, url = {}, size = {}, abstract = {The optimal combination of the outputs of multimodal features in content-based image retrieval (CBIR) is an important task that can have a significant impact on the overall performance of the CBIR system. This problem has not received much attention from the CBIR research community, and only simple methods have been used. In this paper, we treat the problem as an information fusion problem and propose an approach that is generic and can be adapted to various features and distance measures using a small set of training images. Our approach is based on associating a fuzzy membership function with the distribution of the features' distances, and assigning a degree of worthiness to each feature based on its average performance. The memberships and the feature weights are then aggregated to produce a confidence that could be used to rank the retrieved images. We describe and experiment with two distinct aggregation methods. The first one is linear and is based on a simple weighted combination. The second one is non-linear and is based on the discrete Choquet integral. The proposed fusion methods were trained and tested using a large collection of images with several low-level visual and high-level textual features. The results were compared to other methods used in typical CBIR systems. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Bolton:2008:fuzz, author = "Jeremy Bolton and Paul Gader", title = "Random Set Model for Context-Based Classification", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0460.pdf}, url = {}, size = {}, abstract = {In many scientific fields, data classification may be hindered by population correlated factors or hidden contexts. These factors greatly affect samples' values making it difficult for standard classification models to perform well on a consistent basis. A general random set model is presented for context-based classification. An implementation is provided based on Possibility Theory. The result is a robust classifier that can intrinsically identify hidden contexts and classify data accordingly. The random set model is compared to standard kNN and set-based kNN. Results from synthetic data illustrate the random set model's ability to consistently improve classification through context estimation. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Mendez-Vazquez:2008:fuzz, author = "Andres Mendez-Vazquez and Paul Gader", title = "Maximum a Posteriori EM MCE Logistic LASSO for Learning Fuzzy Measures", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0461.pdf}, url = {}, size = {}, abstract = {A novel algorithm is introduced for learning fuzzy measures for Choquet integral-based information fusion. The new algorithm goes beyond previously published MCE-based approaches. It has the advantage that it is applicable to general measures, as opposed to only the Sugeno class of measures. In addition, the monotonicity constraints are handled easily with minimal time or storage requirements. Learning the fuzzy measure is framed as a Maximum A Posteriori (MAP) parameter learning problem. In order to maintain the constraints, this MAP problem is solved with a Gibbs sampler using an Expectation Maximization (EM) framework. For these reasons, the new algorithm is referred to as the MAP-EM MCE Logistic LASSO algorithm. Results are given on synthetic and real data sets, the latter obtained from a landmine detection problem. Average reductions in false alarms of about 25percent are achieved on the landmine detection problem and probabilities of detection in the interesting and meaningful range of 85percent-95percent. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Cross:2008:fuzz, author = "Valerie V. Cross and Wenting Yi ", title = "Formal Concept Analysis for Ontologies and their Annotation Files", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0463.pdf}, url = {}, size = {}, abstract = {A generic formal concept analysis tool Multi- FCA takes as input a set of objects and a set of attributes describing those objects with the restriction that the attributes exist as concepts in an ontological terminology. It produces a variety of concept lattices, incorporates the characteristics of the structure of the ontological terminology in the creation of the concept lattices, provides options for tailoring the creation of the concept lattices based on user needs, and permits 3D visualization of the results. The initial domain for evaluating Multi-FCA is genomic research; specifically a formal context consists of objects that are genes or gene products and attributes that are annotating terms from an ontological terminology such as the Gene Ontology. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Li7:2008:fuzz, author = "Qiang Li and Yoichiro Maeda", title = "Distributed Adaptive Search Method for Genetic Algorithm Controlled by Fuzzy Reasoning", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0464.pdf}, url = {}, size = {}, abstract = {In this paper, we proposed FASPGA based on diversity measure (DM-FASPGA) and FASPGA based on evolution history (EH-FASPGA) as the improvement method of Fuzzy Adaptive Search method for Parallel Genetic Algorithm (FASPGA). In DM-FASPGA, genetic parameters is tuning by fuzzy rule based on diversity of sub-population. Many kinds of diversity measure parameters are imported into the fuzzy rule. And in EH-FASPGA, we imported the evolution history information for improving the accuracy to estimate the evolution degree. Simulation results are also further presented to show the effectiveness and performance of method we proposed in this paper. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Cao2:2008:fuzz, author = "Tru H. Cao and Hai T. Do and Dung T. Hong and Tho T. Quan ", title = "Fuzzy Named Entity-Based Document Clustering", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0466.pdf}, url = {}, size = {}, abstract = {Traditional keyword-based document clustering techniques have limitations due to simple treatment of words and hard separation of clusters. In this paper, we introduce named entities as objectives into fuzzy document clustering, which are the key elements defining document semantics and in many cases are of user concerns. First, the traditional keywordbased vector space model is adapted with vectors defined over spaces of entity names, types, name-type pairs, and identifiers, instead of keywords. Then, hierarchical fuzzy document clustering can be performed using a similarity measure of the vectors representing documents. For evaluating fuzzy clustering quality, we propose a fuzzy information variation measure to compare two fuzzy partitions. Experimental results are presented and discussed. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Nishi:2008:fuzz, author = "Hitoshi Nishi and Hidekazu Suzuki", title = "Animal Gait Generation Based on Human Feeling for Quadrupedal Robot", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0467.pdf}, url = {}, size = {}, abstract = {In the field of the pet robot and robot assisted therapy(RAT), the creatural motion is important for the robots imitated the form of various animals. This paper presents the generation method of animal gait for quadrupedal robot. Here, we have employed AIBO as experimental quadrupedal robot and created the gait of AIBO in imitation of animal gait. At first,We have optimized the orbit of mono-leg, which can output propulsive force efficiently, by imitating dog gait and Genetic Algorithm. Moreover, We have generated the quadrupedal gait of AIBO using both the optimum orbit of mono-leg and animal's gait, which is classified the gait of walking dog based on the zoology. And furthermore, we have performed questionnaires to include the human feeling, and chosen the best gait for AIBO's animal walking from among the above-mentioned various dog's gait. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Zuqiang:2008:fuzz, author = "Meng Zuqiang and Shi Zhongzhi and Gong Tao", title = "On Modeling Cognitive Process with Granular Computing", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0469.pdf}, url = {}, size = {}, abstract = {Exploring mechanism of cognitive process is a valuable means of investigating artificial intelligence. But human being's perception of cognitive process is very limited so far, and related researches are required to be further deepened. This paper focuses on modeling cognitive process based on theory of granular computing(GrC). First, fundamental principle of GrC is used to analyze cognitive process and establish its model. Secondly, a tolerance relation on vector space(``rv_space'' for short) is defined by using a distance function dis, and a tolerance granular space model is established, in which the problem of approximate representation of unknown concept(abstraction of perceptive information) is discussed and analyzed. The analyses show that a given concept, sometimes, is difficult to be learned(i.e., difficult to be accurately represented), so appropriate granular world is required. The solution to this problem involves relationship and transformation between granular worlds. At last, such relationship and transformation are investigated, and many related properties and theorems are deduced, solving the facing problem to some extent. The theory and approach of representation of a concept in a granular world and transformation between different granular worlds are made to support cognitive process'f granular model. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Nyuwa:2008:fuzz, author = "Taro Nyuwa and Daisuke Katagami and Katsumi Nitta", title = "Robotic Social Imitation Depends on Self-Embodiment and Self-Evaluation by Direct Teaching Under Multiple Instructors", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0471.pdf}, url = {}, size = {}, abstract = {We have been worked about robotic social imitation in order to learn self-behavior depending on selfembodiment through interaction with multiple human. In this paper, we propose a learning method which allows to select behavioral patterns depending on self-embodiment and selfevaluation from multiple instructors by using the robot simulator Webots. We confirmed that results demonstrated that our proposal allows to improve the representative teaching data by the clustering includes two evaluation values (the distance moved forward and the impact shock for the body). }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Yu5:2008:fuzz, author = "Ting-Jung Yu and K. Robert Lai and Baw-Jhiune Liu", title = "Beliefs Learning in Fuzzy Constraint-Directed Agent Negotiation", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0472.pdf}, url = {}, size = {}, abstract = {This paper presents a belief learning model for fuzzy constraint-directed agent negotiation. The main features of the proposed model include: (1) fuzzy probability constraints for increasing the efficiency on the convergence of behavior patterns, and eliminating the noisy hypotheses or beliefs, (2) fuzzy instance matching method for reusing the prior opponent knowledge to speed up the problem-solving, and inferring the proximate regularities to acquire a desirable result on forecasting opponent behavior, and (3) adaptive interaction for making a dynamic concession to fulfill a desirable objective. Experimental results suggest that the proposed framework can improve both negotiation qualities. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Sanchez:2008:fuzz, author = "D. Sanchez and M. Delgado and M. A. Vila", title = "RL-Numbers: An Alternative to Fuzzy Numbers for the Representation of Imprecise Quantities", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0473.pdf}, url = {}, size = {}, abstract = {In this paper we define imprecise quantities on the basis of a new representation of imprecision introduced by the authors called RL-representation (for restriction-level representation). We call the corresponding RL-representation of imprecise quantities RL-numbers. We first define RL-natural numbers on the basis of the notion of cardinality. The usual arithmetic operations of addition, product and division are extended and RL-integers, RL-rationals and RL-real numbers are defined so that solution is provided to any kind of equation involving those operations, as with precise numbers. We show that the algebraic properties of precise numbers with respect to the ordinary arithmetic operators are preserved. In addition, and remarkably, we show that the imprecision of the quantities being operated can be increased, preserved or diminished. Ranking of RL-numbers is introduced by means of the notion of RL-ranking as an extensive RL-representation defined on the set { < , = , > }. In our view, fuzzy numbers correspond to the definition of imprecise intervals corresponding to linguistic concepts like approximately x. We discuss about the relationship between RL-numbers and fuzzy numbers, and how they complement each other. Specifically, we propose to use RL-numbers in order to represent imprecise quantities obtained by measuring properties, and fuzzy numbers (equivalently, RL-intervals) to define concepts and to provide a linguistic approximation of RL-numbers. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Maeda:2008:fuzz, author = "Yoichiro Maeda and Satoshi Hanaka", title = "Differential Reinforcement-Type Shaping Q-Learning Method Based on Animal Training for Autonomous Mobile Robot", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0474.pdf}, url = {}, size = {}, abstract = {Recently, the general idea of ``shaping'' used by ethology, behavior analysis or animal training is a remarkable method. ``Shaping'' is a general idea that the learner is given a reinforcement signal step by step gradually and inductively forward the behavior from easy tasks to complicated tasks. In this paper, we propose a shaping reinforcement learning method took in a general idea of Shaping to the reinforcement learning that can acquire a desired behavior by the repeated search autonomously. Three different shaping reinforcement learning methods used Q-Learning, Profit Sharing, and Actor- Critic to check the efficiency of the Shaping were proposed at first. Furthermore, we proposed the Differential Reinforcementtype Shaping Q-Learning (DR-SQL) applied a general idea of ``differential reinforcement'' to reinforce a special behavior step by step such as real animal training, and confirmed the effectiveness of these methods by the simulation experiment of grid search problem. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Irfan:2008:fuzz, author = "Danish Irfan and Xu Xiaofei and Deng Shengchun and Ye Yunming", title = "Feature-Based Unsupervised Clustering for Supplier Categorization", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0475.pdf}, url = {}, size = {}, abstract = {This paper outlines the feature-based unsupervised clustering for supplier categorization. Traditionally, when categorizing suppliers, companies have considered factors such as price, quality, flexibility etc. An enterprise is considered for design and manufacture with the objective of acquiring & developing a sophisticated technological base for systems and enlarging & expanding production of components. In this scenario, our intuition lies in supplier categorization based upon the selected features of suppliers. Lastly, we present results of segmentation of supplier data. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Nuovo:2008:fuzz, author = "Alessandro G. Di Nuovo and Vincenzo Catania", title = "An Evolutionary Fuzzy c-Means Approach for Clustering of Bio-Informatics Databases", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0476.pdf}, url = {}, size = {}, abstract = {Recently, the scientific community has started to show increasing interest in finding clusters in high-dimensional data sets such as gene product (protein or RNA) data sets in bio-informatics. In this paper we consider the problem of finding fuzzy clusters in such very high dimensional data. In fact, even if fuzzy clustering has been successfully applied to numerous data sets, for such high-dimensional databases it often produces trivial solutions where all cluster centers coincide and all memberships are equal. To solve this problem, we present an evolutionary approach that integrates fuzzy c-means clustering and feature selection. Reducing the dimensionality of the space, feature selection improves the quality of the partitions generated, and, at the same time, can help to build both faster and more cost-effective predictors, as well as a better understanding of the underlying generation process. We exhibit the good quality of the clustering results by applying our approach to two real-world data sets from bio-informatics. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Baek:2008:fuzz, author = "Gyeongdong Baek and Yountae Kim and Sungshin Kim", title = "Fault Diagnosis of Identical Brushless DC Motors Under Patterns of State Change", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0477.pdf}, url = {}, size = {}, abstract = {In this paper we proposed a model of a fault diagnosis expert system with high reliability to compare identical well-functioning motors. The purpose of the survey was to determine if any differences exit among these identical motors and to identify exactly what these differences were, if in fact they were found. Using measured data for many identical brushless dc motors, this study attempted to find out whether normal and fault can be classified by each other. Measured data was analyzed using the Change of State Model (CSM). Based on a proposed CSM method, the effect of a different normal state is minimized and the detection of fault is improved in identical motor system. Experimental results are presented to prove that CSM method could be a useful tool for diagnosing the condition of identical BLDC motors. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Bisserier:2008:fuzz, author = "A. Bisserier and S. Galichet and R. Boukezzoula ", title = "Fuzzy Piecewise Linear Regression", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0479.pdf}, url = {}, size = {}, abstract = {Fuzzy regression using possibilistic concepts allows the identification of models from uncertain data sets. However, some limitations still exist about the possible evolution of the output spread with respect to inputs. We present here a modified form of fuzzy linear model whose output can have any kind of output spread tendency. The formulation of the linear program used to identify the model introduces a modified criterion that assesses the model fuzziness independently of the collected data. These concepts are used in a global identification process in charge of building a piecewise model able to represent every kind of output evolution. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Hinde2:2008:fuzz, author = "C. J. Hinde and R. S. Patching and S. A. McCoy", title = "Semantic Transfer and Contradictory Evidence in Intuitionistic Fuzzy Sets", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0480.pdf}, url = {}, size = {}, abstract = {The relationship between object level intuitionistic fuzzy sets and predicate based intuitionistic fuzzy sets is explored. Mass assignment uses a process called semantic unification to evaluate the degree to which one set supports another, the inverse function is semantic separation. Intuitionistic fuzzy sets are mapped onto a mass assignment framework and the semantic unification operator is generalised to support both mass assignment and intuitionistic fuzzy sets, as is semantic separation. Transfer of inconsistent and contradictory evidence are also dealt with. }, keywords = { Inconsistency, Contradiction, Intuitionistic Fuzzy Sets, Mass Assignment, Semantic Unification, Conditionalisation, Semantic Separation, Abduction.}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Ho2:2008:fuzz, author = "K. C. Ho and Joe N. Wilson and Paul D. Gader", title = "On the Use of Aggregation Operator for Humanitarian Demining Using Hand-Held GPR", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0481.pdf}, url = {}, size = {}, abstract = {This paper applies the OWA aggregation operator to hand-held GPR data to improve the detection of landmines. Data from a number of sweeps are collected when the handheld detector is operating in discrimination mode. The energy density spectra of the GPR signal return from individual sweeps are estimated and two OWA aggregation operations are performed to select the good quality sweeps that will be used for landmine detection. Experimental results using the real data collected from two different test sites show that the OWA operations provide significant performance improvement for landmine detection at high probability of detection. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Xu5:2008:fuzz, author = "Du Xu and Uzay Kaymak", title = "Value-at-Risk Estimation by Using Probabilistic Fuzzy Systems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0482.pdf}, url = {}, size = {}, abstract = {Value at Risk (VaR) measures the worst expected loss of a portfolio over a given horizon at a given confidence level. It summarises the financial risk a company faces into one single number. Recent methods of VaR estimation use parametric conditional models of portfolio volatility to adapt risk estimation to changing market conditions. However, more flexible methods that adapt to the underlying data distribution would be better suited for VaR estimation. In this paper, we consider VaR estimation by using probabilistic fuzzy systems, a semi-parametric method, which combines a linguistic description of the system behaviour with statistical properties of data. The performance of the proposed model is compared to the performance of a GARCH model for VaR estimation. It is found that statistical back testing always accepts PFS models after tuning, while GARCH models may be rejected. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Yang4:2008:fuzz, author = "M. D. Yang and J. Y. Lin and T. C. Su and Y. F. Yang", title = "Fuzzy Estimation of Trophic State Using Satellite Data", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0484.pdf}, url = {}, size = {}, abstract = {Eutrophication is one of the major water quality problems in developing and developed countries. Generally, trophic state of water sources is under a strict watch and is determined by in situ water sampling for some important water resources. However, a limited number of water samples provide insufficient statistical confidence with an overall eutrophic status by using point-basis water sampling data which is usually executed under a physical and financial limitation. Also, traditional trophic state indices, such as Carlson index and OECD index, employ a crisp determination that often causes debated argument. This research applies fuzzy set theory on satellite data to describe the trophic state for reservoir water. A multi-spectral SPOT satellite image was converted into water quality variables, such as phosphorus, Secchi depth, and chlorophyll that are the major affecting factors of eutrophication. Fuzzy synthetic evaluation for the trophic state was developed by using satellite-derived two-dimensional water variables. Feitsui Reservoir, which is the most important water supply for over five million people in the great Taipei area, Taiwan, was the study site for demonstrating the trophic state determination through the fuzzy evaluation method. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Stylios:2008:fuzz, author = "Chrysostomos D. Stylios and Voula C. Georgopoulos", title = "Genetic Algorithm Enhanced Fuzzy Cognitive Maps for Medical Diagnosis", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0486.pdf}, url = {}, size = {}, abstract = {This paper presents a new hybrid modeling methodology for the Complex Decision Making processes. It extends previous work on Competitive Fuzzy Cognitive Maps for Medical Decision Support Systems by complementing them with Genetic Algorithms Methods. The synergy of these methodologies is accomplished by a new proposed algorithm that leads to more dependable Advanced Medical Decision Support Systems that are suitable to handle situations where the decisions are not clearly distinct. The methodology developed here is applied successfully to model and test a differential diagnosis problem from the speech pathology area for the diagnosis of language impairments. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Araujo2:2008:fuzz, author = "Ernesto Araujo", title = "Social Relationship Explained by Fuzzy Logic", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0487.pdf}, url = {}, size = {}, abstract = {Social relationship is explained under the fuzzy set and fuzzy logic theory. A model for describing the individual nature of mind in the fuzzy environment is presented first, then extended for groups, couples, and social relationship dealing with both perfect and approximate reasoning. The fuzzy social relationship as proposed here also devise that goals of one individual may be seen as constraints upon others, and viceversa. In this sense, the proposed fuzzy social relationship description is a special case of fuzzy decision-making in fuzzy environment. Moreover, the social relationship is, actually, a simple case of logic mapping. According to the fuzzy social model, the common sense and the advantage of being flexible is emphasized mainly one is interested in a social relationship and in avoiding conflicts. The proposed approach addresses not only human beings, yet it can be extended to describe a sociological perspective of relationship, as well as to overall relationship between societies, nations by including trading and diplomacy. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Hassanien:2008:fuzz, author = "Aboul Ella Hassanien and Ajith Abraham and James F. Peters and Gerald Schaefer", title = "An Overview of Rough-Hybrid Approaches in Image Processing", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0488.pdf}, url = {}, size = {}, abstract = {Rough set theory offers a novel approach to manage uncertainty that has been used for the discovery of data dependencies, importance of features, patterns in sample data, feature space dimensionality reduction, and the classification of objects. Consequently, rough sets have been successfully employed for various image processing tasks including image segmentation, enhancement and classification. Nevertheless, while rough sets on their own provide a powerful technique, it is often the combination with other computational intelligence techniques that results in a truly effective approach. In this paper we show how rough sets have been combined with various other methodologies such as neural networks, wavelets, mathematical morphology, fuzzy sets, genetic algorithms, bayesian approaches, swarm optimization, and support vector machines in the image processing domain. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wang14:2008:fuzz, author = "Zhenyuan Wang and Rong Yang and Kin-Hong Lee and Kwong-Sak Leung", title = "The Choquet Integral with Respect to Fuzzy-Valued Signed Efficiency Measures", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0489.pdf}, url = {}, size = {}, abstract = {As an aggregation tool in information fusion and data mining, the Choquet integral is generalized to allow the involved set function being fuzzy-valued. A calculation formula of such a Choquet integral is developed when the universal set is finite, such as the set of attributes in a database. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Chamorro-Martínez:2008:fuzz, author = "J. Chamorro-Martínez and D. Sanchez and J. M. Soto-Hidalgo", title = "A Novel Histogram Definition for Fuzzy Colour Spaces", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0491.pdf}, url = {}, size = {}, abstract = {In this paper we introduce fuzzy naturals-based histograms on fuzzy colour spaces. In our approach, histograms are fuzzy probability distributions on a fuzzy colour space, where the fuzzy probability is calculated as the quotient between a fuzzy natural number and the number of pixels in the image, the former being a fuzzy (non-scalar) cardinality of a fuzzy set. This approach to histograms avoids the well-known disadvantages of the ordinary sigma-count as an estimation of the probability.We illustrate the potential application of the proposal by applying it to the problem of dominant colour selection. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Nayagam2:2008:fuzz, author = "V. Lakshmana Gomathi Nayagam and David Gauld and Geetha Sivaraman and G. Venkateshwari", title = "Intuitionistic Fuzzy Translation Invariant Spaces", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0492.pdf}, url = {}, size = {}, abstract = {The notion of fuzzy sets was introduced by L. A. Zadeh and was extended to intuitionistic fuzzy subsets by K. Atanassov. The notions of fuzzy and intuitionistic fuzzy topological spaces were introduced and studied by C. L. Chang, D. Coker, K. Hur et al. The notion of induced topology on fuzzy singletons has been introduced and it has been extended to the induced topology on intuitionistic fuzzy singletons in [15]. The notion of fuzzy subgroups was introduced by A. Rosenfeld. The notion of intuitionistic fuzzy subgroups was studied by K.Hur and et al. The notion of translation invariant topology in fuzzy topological spaces was studied by A. K. Katsaras. In image processing, translation invariantness plays a vital role. In this paper, a new notion of intuitionistic fuzzy translation invariantness is introduced and the relation between the existing notion of translation invariantness is studied. The properties of intuitionistic fuzzy translation invariant topological spaces have also been studied. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Tian:2008:fuzz, author = "Min Tian and Sifeng Liu and Zhikun Bu", title = "Review of the Algorithm Models of Degrees of Grey Incidence", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0493.pdf}, url = {}, size = {}, abstract = {This article reviews the existing algorithm models of the Grey incidence and finds that no method has the properties of normality and isotonicity synchronously, and then analyzes the corresponding reasons. Finally, it gives several study results. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Ozkaya:2008:fuzz, author = "N. Ozkaya and S. Sagiroglu", title = "Intelligent Face Border Generation System from Fingerprints", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0494.pdf}, url = {}, size = {}, abstract = {Although biometrics is a deeply studied field, relationships among biometric features have not been studied in the field so far. In this study, we have analysed the existence of any relationship among biometric features. Also we have tried to generate the face border of a person using only fingerprint biometric feature of the same person without any information about his or her face. Consequently, for generating face borders from only fingerprints, we have designed and introduced a novel intelligent system based on artificial neural networks having the absolute percent errors between 1.49 and 9.86. Experimental results have shown that fingerprints and face borders have relations among each other closely. In addition, it is demonstrated that generating face borders from fingerprints without knowing any information about faces is possible. Although this study has been the first step of the research, the results are very encouraging and promising for new challenges. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Varkonyi-Kóczy:2008:fuzz, author = "A. R. Varkonyi-Kóczy and A. Rovid ", title = "Fuzzy Logic Supported Primary Edge Extraction in Image Understanding", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0495.pdf}, url = {}, size = {}, abstract = {Recently, the importance of information enhancement methods in digital image processing has increased significantly. A large amount of research has been focused on information retrieval and image understanding. Typical examples are searching for similar objects/images in large databases and understanding the objects in images. The main point of these tasks is to extract the most characteristic features of the objects in the images, like edges, corners, characteristic textures, etc. Another very important aspect can be the separation of the ``significant'' and ``unimportant'' parts of these features, i.e. the enhancement of those features which carry primary information and to filter out the part which represents information of minor importance. By this, the complexity of the searching and/or interpreting algorithms can be decreased while the performance increased. This paper describes a new edge processing method which is able to extract the ``primary'', i.e. those edges which can advantageously be used in sketch based image retrieval algorithms. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Handa:2008:fuzz, author = "Hisashi Handa and Maiko Isozaki", title = "Evolutionary Fuzzy Systems for Generating Better Ms.PacMan Players", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0496.pdf}, url = {}, size = {}, abstract = {PacMan is one of the most popular video games played in all over the world. Ms. PacMan, a mutant of PacMan, employs probabilistic transitions of ghosts. Therefore, there is no deterministic (surefire) routes to solve for each stage in Ms.PacMan. That is, real time control mechanisms are needed to realize auto-play for Ms.PacMan by computers. This paper proposes evolutionary fuzzy systems for playing Ms.PacMan. The proposed method employs fuzzy logic and the evolution of their fuzzy rule parameters. That is, basic structure of fuzzy rule is given in advance. Then, the rule parameter is evolved by using (1+1) ES. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Tsipouras:2008:fuzz, author = "Markos G. Tsipouras and Themis P. Exarchos and Dimitrios I. Fotiadis", title = "Automated Fuzzy Model Generation Through Weight and Fuzzification Parameters' Optimization", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0498.pdf}, url = {}, size = {}, abstract = {In this paper we explore the use of weights in the generation of fuzzy models. We automatically generate a fuzzy model, using a three-stage methodology: (i) generation of a crisp model from a decision tree, induced from the data, (ii) transformation of the crisp model into a fuzzy one, and (iii) optimization of the fuzzy model's parameters. Based on this methodology, the generated fuzzy model includes a set of parameters, which are all the parameters included in the sigmoid functions. In addition, local, global and class weights are included, thus the fuzzy model is optimized with respect to both sigmoid function parameters and weights. The class weight introduction, which is a novel approach, grants to the fuzzy model the ability to identify the individual importance of each class and thus more accurately reflect the underlying properties of the classes under examination, in the domain of application. The above described methodology is applied to five known classification problems, obtained from the UCI machine learning repository, and the obtained classification accuracy is high. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(awomirZadro\.zny:2008:fuzz, author = "S awomirZadro\.zny and Janusz Kacprzyk and Grzegorz Sobota", title = "Avoiding Duplicate Records in a Database Using a Linguistic Quantifier Based Aggregation - A Practical Approach", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0500.pdf}, url = {}, size = {}, abstract = {We show how Zadeh's calculus of linguistically quantified propositions can be applied to avoid duplicate names in a publication database of a research institute. This problem, in its most general form, is studied in the literature by various communities. Here we focus on a specific scenario in which a need for its solution arises. Moreover, we make an attempt to apply fuzzy logic based concepts to solve it. The approach proposed yields promising results for the data for which it has been initially conceived and seems to be applicable also in a more general context. Its primary advantage is the ease and intuitiveness of customization. The main parameters take the form of linguistic quantifiers whose meaning is arguably familiar for an average user and which are fairly easy to be tuned to the changing requirements. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Georgiou:2008:fuzz, author = "Dimitrios A. Georgiou and Sotirios D. Botsios", title = "Learning Style Recognition: A Three Layers Fuzzy Cognitive Map Schema", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0501.pdf}, url = {}, size = {}, abstract = {In Adaptive Educational Hypermedia Systems, among other parameters, the user's Learning Style plays a crucial role in effective on-line asynchronous learning. Cognitive psychology provides tools, such as questionnaires, that can monitor user's learning style. In this paper we introduce an adjustable tool for Learning Style recognition. It is built upon a well known and generally accepted Learning Style Inventory and applies a three Layer Fuzzy Cognitive Map Schema, which allows experts of cognitive psychology or experienced educators to tune up the system's parameters to adjust the accuracy of the learning style recognition. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Baruch:2008:fuzz, author = "Ieroham Baruch and Rosalba Galvan-Guerra and Carlos-Roman Mariaca-Gaspar and Oscar Castillo", title = "Fuzzy-Neural Control of a Distributed Parameter Bioprocess Plant", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0502.pdf}, url = {}, size = {}, abstract = {In the paper it is proposed a new recurrent Fuzzy- Neural Multi-Model (FNMM) identifier applied for decentralized identification of a distributed parameter anaerobic wastewater treatment digestion bioprocess, carried out in a fixed bed and a recirculation tank. The distributed parameter analytical model of the digestion bioprocess is reduced to a lumped system using the orthogonal collocation method, applied in three collocation points (plus the recirculation tank), which are used as centers of the membership functions of the fuzzyfied space variable of the plant. The states of the proposed FNMM identifier are implemented by a direct feedback-feedforward hierarchical fuzzy-neural controller. The comparative graphical simulation results of the digestion wastewater treatment system identification and control, obtained via learning, exhibited a good convergence, and precise reference tracking outperforming the optimal control. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Mateou:2008:fuzz, author = "Nicos Mateou and Andreas S. Andreou and Constantinos Stylianou", title = "A New Traversing and Execution Algorithm for Multilayered Fuzzy Cognitive Maps", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0504.pdf}, url = {}, size = {}, abstract = {This paper introduces a new algorithm for traversing and executing Multilayered Fuzzy Cognitive Maps (ML-FCMs) that aim to enhance this methodology, which is designed for handling complicated large scale problems. The methodology is based on the decomposition of the parameters of the problem under investigation into smaller quantities, organised in a hierarchical structure forming a multilayered FCM model. The present work aspires to eliminate the weaknesses of the existing ML-FCM algorithm, which reside in the way activation levels are calculated for those concepts decomposed into a set of parameters at lower layers in the map. The current algorithm calculates these levels by completing a full iteration cycle at the lower level thus losing the information produced between the iterative steps. We attempt to solve this problem by introducing the Enhanced ML-FCM algorithm, (EML-FCM) which allows calculations in-between iterations and takes into consideration the change of activation levels in a more detailed form. The strong features of the proposed EMLFCM algorithm are presented and discussed, in addition to the provision of a comparison between the two algorithms. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Martin:2008:fuzz, author = "Trevor Martin and Yun Shen and Ben Azvine Trevor", title = "Automated Semantic Tagging Using Fuzzy Grammar Fragments", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0506.pdf}, url = {}, size = {}, abstract = {One of the bottlenecks preventing wider adoption of the semantic web is the overhead in annotating existing web content. In cases where we have unstructured text, it is useful to extract fragments of structured data which can then be used as the basis for automatic tagging. A common approach is to use pattern matching (e.g. regular expressions) or more general grammar-based techniques, but these are not robust against small deviations. Fuzzy grammars allow partial matches, and we outline an efficient parsing technique to determine the degree to which a string is parsed by a grammar fragment. A simple application shows the method's validity. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Shirani:2008:fuzz, author = "Farshad Shirani and Babak Nadjar Araabi and Mohammad Javad Yazdanpanah", title = "Fuzzy Modelling of Nonlinear Systems for Stability Analysis Based on Piecewise Quadratic Lyapunov Functions", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0507.pdf}, url = {}, size = {}, abstract = {This paper presents a constructive Takagi-Sugeno fuzzy modeling method for a general class of nonlinear systems. This method is particularly suitable for stability analysis based on piecewise quadratic Lyapunov functions. The modeling error is appropriately inserted into the model and an algorithm is proposed to automatically determine the model parameters to keep the modeling error smaller than a desired upper bound. Based on the constructed fuzzy model, exponential stability analysis is performed and the stability constraints are transformed into linear matrix inequalities. Modeling error is also included in the stability analysis to validate the results for the original nonlinear system. The way to use the modeling method and stability analysis to systematically find a Lyapunov function for a nonlinear system is demonstrated via an example and the potential capability of the method in estimating the domain of attraction is discussed. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(CailianChen:2008:fuzz, author = "CailianChen and Zhengtao Ding and Gang Feng and Xinping Guan", title = "On Output Regulation of Discrete-Time T-S Fuzzy Systems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0508.pdf}, url = {}, size = {}, abstract = {The output regulation problem is discussed for a class of discrete-time T-S fuzzy systems under periodic disturbances generated form the so-called exosystems. With the assumption that the subsystem in each rule is of the controllable canonical form, the regulation equations are solvable if and only if the poles of the exosystem are different from those of the fuzzy system. By exploiting the structural information encoded in the fuzzy rules, a piecewise state feedback control law can then be constructed to achieve asymptotic rejecting and/or tracking of the unwanted disturbances or the desired trajectory. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Hotta:2008:fuzz, author = "Hajime Hotta and Ayumi Takano and Masafumi Hagiwara", title = "Mining KANSEI Fuzzy Rules from Photos on the Internet", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0509.pdf}, url = {}, size = {}, abstract = {In this paper, we propose a method of mining KANSEI fuzzy rules from photos on the Internet. KANSEI is a Japanese word which means human impressions and KANSEI engineering is a method for translating KANSEI into product parameters through the analysis of data. In KANSEI engineering, some quantitative data are required for analyzing KANSEI and conventional approach uses questionnaire to collect quantitative data. However, questionnaire tends to become strained for subjects in order to collect enough data.The proposed method is an improved version of the method which authors have proposed. With the conventional system, fuzzy rules of KANSEI can be extracted from some questionnaire data and the system also requires heavy questionnaire surveys. The main purpose of the proposed method is to enable the method to work without questionnaire data by using photo data and tags on the Internet. By preparing learning data from the Internet and improving algorithms, the proposed method can extract fuzzy rules of correspondence of colours to impressions. The experimental results show that the proposed method worked efficiently. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Li8:2008:fuzz, author = "Wei Li and Jianwei Zhang", title = "Moth-Inspired Chemical Plume Tracing by Integration of Fuzzy Following-Obstacle Behavior", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0511.pdf}, url = {}, size = {}, abstract = {This paper presents a strategy for chemical plume tracing (CPT) with soft obstacle avoidance, such as kelp forests or seaweed in near-shore, oceanic environments. The basic idea is to integrate a low-level fuzzy following obstacle behavior into a subsumption architecture for moth-inspired plume tracing. By on-line generation of dynamic reference targets (DRTs) on a course of the most recent CPT activity, an autonomous underwater vehicle (AUV) can quickly and correctly resume CPT maneuvering after passing obstacles. Simulation studies of CPT with obstacle avoidance are performed using a simulated plume with significant meander and filament intermittency in an environment with length scales of 100 m. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Li9:2008:fuzz, author = "Wei Li and Yunyi Li and Jianwei Zhang", title = "Fuzzy Colour Extractor Based Algorithm for Segmenting an Odor Source in Near Shore Ocean Conditions", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0512.pdf}, url = {}, size = {}, abstract = {A mission of chemical plume tracing (CPT) in near-shore and ocean environments is to find out an odor source via an autonomous underwater vehicle (AUV). It is necessary to confirm the detected odor source using a visual system interactively or automatically, when a chemical sensor identifies the odor source. However, colour images taken in nearshore ocean environments are very vague due to dim illumination conditions and fluid advection effects.This paper presents a fuzzy algorithm for recognizing the chemical plume and its source in near-shore and ocean environments. This algorithm iteratively generates colour patterns based on a defined reference colour and extracts colour components of the chemical plume and its source from fuzzy images using a fuzzy colour extractor (FCE). The proposed approach to colour image segmentation might be of general interest to robot vision. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Weser:2008:fuzz, author = "Martin Weser and Sascha Jockel and Jianwei Zhang", title = "Fuzzy Multisensor Fusion for Autonomous Proactive Robot Perception", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0513.pdf}, url = {}, size = {}, abstract = {Robot perception still lacks reliability in complex natural environments. A commonly used method to improve perception is to incorporate more sensors with different modalities. This leads to increased computational requirements due to the parallel processing of huge amounts of sensor data. Appropriate sensor fusion methods are needed if contradictory information is provided by different sensors. We propose a feature-based technique to fuse multimodal sensor data using fuzzy rules. Probabilistic methods are avoided by applying fuzzyfication at the feature level. We propose a higher information gain of the available sensors by using robot actions to focus sensors on objects of interest. Therefore sensor readings, algorithms and robot actions are combined into feature detectors. A goaldirected activation of these feature detectors renders parallel processing of all sensor data unnecessary. }, keywords = {Fuzzy behavior selection, autonomous robot, multimodal perception, active perception}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Frigui2:2008:fuzz, author = "Hichem Frigui and Jason Meredith", title = "Image Database Categorization Under Spatial Constraints Using Adaptive Constrained Clustering", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0514.pdf}, url = {}, size = {}, abstract = {We propose an adaptive constrained clustering (ACC) algorithm that performs clustering and feature weighting simultaneously and that can incorporate partial supervision information. This information consists of a set of constraints on which instances should or should not reside in the same cluster. The algorithm is dynamic in the sense that the optimal number of clusters can expand or shrink depending on the distribution of the data and the set of constraints. The ACC algorithm is used as the main tool to organize and navigate through a collection of geo-referenced images. In this application, the constraints are generated automatically based on the spatial distribution of the coordinates of the images and the dynamic range of the current view. As a result, images would be assigned to the same cluster if they are similar in content and close spatially. Icons of the clusters' representatives are superimposed on a map to provide the user with a global overview of the content and location of the photo collection. If the user selects a region to zoomin, images within this region will be re-clustered with stricter spatial constraints. The proposed clustering and visualization application were applied to a collection of 2,023 photos taken with a digital camera that tags each photo with the location of where the photo was taken using a GPS receiver. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Ralescu:2008:fuzz, author = "Anca Ralescu and Takeshi Ishino and Michihiko Minoh", title = "Fuzzy Sets in Concept Representation Using Conceptual Spaces", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0515.pdf}, url = {}, size = {}, abstract = {We consider concept representation in the framework of Conceptual Spaces. We propose the introduction of fuzzy sets to express the entries of the concept (co-occurrence) matrix as fuzzy probabilities. Initial experimental results indicate that the proposed approach can support a much more sophisticated and useful concept representation. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Perfilieva:2008:fuzz, author = "Irina Perfilieva and Hans De Meyer and Bernard De Baets and Dagmar Plskova", title = "Cauchy Problem with Fuzzy Initial Condition and Its Approximate Solution with the Help of Fuzzy Transform", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0516.pdf}, url = {}, size = {}, abstract = {We investigate the Cauchy problem for ordinary differential equation (ODE) with fuzzy initial condition. We define a solution to this problem and propose a new method of how an approximate solution can be constructed. The proposed method is based on the technique of fuzzy transforms and ends up with a solution of a system of fuzzy relation equations. The approximate solution is expressed symbolically and the quality of approximation is estimated. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Balaji:2008:fuzz, author = "P. G. Balaji and D.Srinivasan and Chen-Khong Tham", title = "Coordination in Distributed Multi-Agent System Using Type-2 Fuzzy Decision Systems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0519.pdf}, url = {}, size = {}, abstract = {Coordination is one of the key components in distributed multi-agent systems. Establishing a coordination scheme with minimum communication requirements and robustness to communication failure is a difficult task. A new multi-agent architecture based on type-2 fuzzy decision making is proposed here for achieving coordination with minimum communication. The decision making module has been designed to achieve the coordination between agents by calculating the weight of the input to be used for deciding on the action plans in a dynamic manner. The effectiveness of the coordination scheme proposed was tested by applying it to a complex, non-linear and stochastic application of the traffic signal control. The size of the network chosen also serves to show the scalability of the agent architecture. The results obtained were compared with adaptive systems, fixed coordination schemes and no coordination schemes. Considerable improvement in the time delay was achieved while using the dynamic coordination scheme proposed. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Fujii:2008:fuzz, author = "Seiya Fujii and Tomoharu Nakashima and Hisao Ishibuchi", title = "A Study on Constructing Fuzzy Systems for High-Level Decision Making in a Car Racing Game", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0522.pdf}, url = {}, size = {}, abstract = {In this paper, we examine the performance of fuzzy rule-based systems in a car racing domain. Fuzzy rulebased systems are used for high-level decision making of a car agent. We examine two methods that generate a set of training patterns for constructing fuzzy rule-based systems. We also examine the effect of sensory information on the high-level decision making.The performance of four types of fuzzy rule-based systems is compared in a series of computational experiments. The analysis of using different types of sensory information and different methods for generating training patterns is also performed. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Pasila:2008:fuzz, author = "Felix Pasila ", title = "Multivariate Inputs for Electrical Load Forecasting on Hybrid Neuro-Fuzzy and Fuzzy C-Means Forecaster", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0523.pdf}, url = {}, size = {}, abstract = {Multivariate inputs play important role in system with many dependent variables. By using some different inputs as input in neuro-fuzzy networks, complex nonlinear model can be modeled and also be forecasted with better results. This paper describes a neuro-fuzzy approach with additional fuzzy C-Means clustering method before the input entering the networks. Afterwards, the network can be used to efficiently forecast electrical load competition data using the Takagi-Sugeno (TS) type multi-input single-output (MISO) neuro-fuzzy network. The training algorithm is efficient in the sense that it can bring the performance index of the network, such as the sum squared error (SSE), down to the desired error goal much faster than the simple Levenberg-Marquardt algorithm (LMA). }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Garibaldi:2008:fuzz, author = "Jonathan M. Garibaldi and Marcin Jaroszewski ", title = "Generalisations of the Concept of a Non-Stationary Fuzzy Set — A Starting Point to a Formal Discussion", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0525.pdf}, url = {}, size = {}, abstract = {In this paper we propose the concept of an instantiative fuzzy set which, in our opinion, constitutes a meaningful addition to the notion of a non-stationary fuzzy set. We begin with a formal definition of an instantiative fuzzy set, and follow specifying basic classes of instantiative fuzzy set operators: the union, the intersection and the complement. Furthermore, we provide formal definitions of selected notions relevant to instantiative fuzzy sets. At the end, we present a subclasses of instantiative fuzzy sets that might be useful for dealing with randomness and vagueness simultaneously. The work presented in this paper is at a very preliminary stage; it is meant as a starting point to a formal discussion on the capture of different facets of uncertainty. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Cai:2008:fuzz, author = "Yundong Cai and Chunyan Miao and Ah-Hwee Tan and Zhiqi Shen", title = "Context Modeling with Evolutionary Fuzzy Cognitive Map in Interactive Storytelling", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0527.pdf}, url = {}, size = {}, abstract = {To generate a believable and dynamic virtual world is a great challenge in interactive storytelling. In this paper, we propose a model, namely Evolutionary Fuzzy Cognitive Map (E-FCM), to model the dynamic causal relationships among different context variables. As an extension to conventional FCM, E-FCM models not only the fuzzy causal relationships among the variables, but also the probabilistic property of causal relationships, and asynchronous activity update of the concepts. With this model, the context variables evolve in a dynamic and uncertain manner with the according evolving time. As a result, the virtual world is presented more realistically and dynamically. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Vachkov:2008:fuzz, author = "Gancho Vachkov ", title = "Classification of Images Based on Information Compression and Fuzzy Rule Based Similarity Analysis", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0528.pdf}, url = {}, size = {}, abstract = {This paper proposes a computational scheme for fuzzy similarity analysis and classification of images by comparison of the new (unknown) images with a predetermined number of known (core) images, contained in an Image Base. As a first step, an unsupervised competitive learning algorithm is used to create the so called Compressed Information Model (CIM) which replaces the original ``raw data'' (the RGB pixels) of the image with much smaller number of neurons. Then two specially introduced parameters of the CIM are computed, namely the center-of-gravity of the model and the generalized model size. These parameters are used as inputs of a special fuzzy inference procedure that computes numerically the similarity between a given pair if images as a difference degree between them. Finally, a sorting procedure with a predefined threshold is used to obtain the results from the classification. The flexibility and applicability of the whole proposed unsupervised classification scheme is illustrated on the example of classification of 18 different images by use of three different Image Bases containing, 3, 5 and 7 ``core'' images respectively. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Megri:2008:fuzz, author = "F. Megri and R. Boukezzoula ", title = "MIN and MAX Operators for Trapezoidal Fuzzy Intervals PART I: Formalization and Application", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0529.pdf}, url = {}, size = {}, abstract = {This two part paper proposes a methodology for determining an extension of the MIN and MAX general analytical expression, initially developed for triangular fuzzy intervals [1] to trapezoidal ones when Zadeh's extension principle is considered. In order to determine the MIN and MAX analytical expressions, the first part exhibits the conventional interval relations and their extension in fuzzy case where trapezoidal fuzzy intervals are assumed. The formalization and justification of the so-built analytical expressions are detailed in part II of this paper. The potential use of these operators in the framework of uncertain aggregation operators and ranking fuzzy intervals is illustrated with an illustrative example. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Megri2:2008:fuzz, author = "F. Megri and R. Boukezzoula ", title = "MIN and MAX Operators for Trapezoidal Fuzzy Intervals PART II: Analytical Expressions Proof", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0530.pdf}, url = {}, size = {}, abstract = {This part paper aims at the proof and the justification of the general MIN and MAX analytical expressions, determined according to the Zadeh extension principle and used in the Part I of this paper. The so-proposed MIN and MAX operators are different from the standard fuzzy intersection and union. Indeed, according to fuzzy extension principle, MIN and MAX are the lattice operations to be used for ordering fuzzy intervals. These analytical expressions can be used in aggregation operators and ranking fuzzy intervals (see the Part I of this paper). }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Feng:2008:fuzz, author = "Du Feng and Qian Qingquan ", title = "The Research of Heterogeneous Networked Control Systems Based on Modify Smith Predictor and Fuzzy Adaptive Control", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0533.pdf}, url = {}, size = {}, abstract = {Fuzzy control is simple in design, and has strong robustness. It can obtain ideal control effect in the case of not firmly knowing the system model, and can be applied to many nonlinear systems. In order to effectively restrain the impact of network delays for networked control systems (NCS), a novel approach is proposed that modified Smith predictor combined with fuzzy adaptive PID control for the heterogeneous networked control systems (HNCS). The HNCS adopt the cascade control system structure, use P control in the inner loop and fuzzy adaptive control in the outer loop. The data communications of loops adopt heterogeneous wired networks. Based on modified Smith predictor, achieve complete compensations for delays of networks and controlled plants. Because modified Smith predictor does not include network delay models, it is no need for measuring, identifying or estimating network delays on line. Therefore it is applicable to occasions that network delays are larger than one, even tens of sampling periods. Based on CSMA/AMP (CAN bus) in the inner loop and CSMA/CD (Ethernet) in the outer loop, and there are data packets loss, the results of simulation show validity of the control scheme, and can improve dynamic performance, enhance robustness, self-adaptability and anti-jamming abilities. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Chen11:2008:fuzz, author = "ZhiHang Chen and M. Abul Masrur and Yi L. Murphey", title = "Intelligent Vehicle Power Management Using Machine Learning and Fuzzy Logic", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0534.pdf}, url = {}, size = {}, abstract = {We present our research in optimal power management for a generic vehicle power system that has multiple power sources using machine learning and fuzzy logic. A machine learning algorithm, LOPPS, has been developed to learn about optimal power source combinations with respect to minimum power loss for all possible load requests and various system power states. The results generated by the LOPPS are used to build a fuzzy power controller (FPC). FPC is integrated into a simulation program implemented by using a generic simulation software as indicated in reference [22] and is used to dynamically allocate optimal power sources during online drive. The simulation results generated by FPC show that the proposed machine learning algorithm combined with fuzzy logic is a promising technology for vehicle power management. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Xiong:2008:fuzz, author = "Ning Xiong ", title = "Generating Fuzzy Rules to Identify Relevant Cases in Case-Based Reasoning", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0541.pdf}, url = {}, size = {}, abstract = {This paper proposes a new fuzzy case-based reasoning system in which fuzzy rule-based reasoning is used as a mechanism for matching between cases. The motivation is that fuzzy if-then rules present a more powerful and flexible means to represent the knowledge about case relevance than traditional distance based similarity measurements. With such fuzzy rules available, every case in the case base can be examined via fuzzy reasoning to predict whether it is relevant to a target problem in query. Those cases that are predicted as relevant are then retrieved and delivered to the next stage of decision fusion. Further, we claim that the set of fuzzy rules for case relevance prediction can be learned from the case base. The key to this is doing pair-wise comparisons of cases with known solutions in the case base such that sufficient samples of case relevance can be derived for fuzzy rule learning. The evaluations conducted on a benchmark data set have shown that the fuzzy rules in demand can be learned from a rather small case base without the risk of over-fitting and that the proposed system yields high information recall rate by capturing more cases that are relevant while not undermining the precision for the set of retrieved cases. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Jirousek:2008:fuzz, author = "Radim Jirousek ", title = "Conditional Independence and Factorization of Multidimensional Models", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0542.pdf}, url = {}, size = {}, abstract = {In this paper, three different frameworks for uncertainty description are considered: probability and possibility theories, and Dempster-Shafer theory of belief functions. For all of them special operators of composition are introduced, which enable, among others, defining the concept of factorization (used here as an alternative notion for conditional independence) meeting all the semigraphoid axioms. It is showed that whilst for probability and possibility theories factorization and conditional independence coincide, they differ from each other for belief functions. Since the introduced factorization manifests most of the properties required for the concept of conditional independence, the question arises whether it would be useful to substitute the often used concept of the conditional independence with the factorization introduced in this paper. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(V.:2008:fuzz, author = "Jose L. Gonzalez V. and Oscar Castillo and Luis T. Aguilar", title = "Performance Analysis of Cognitive Map-Fuzzy Logic Controller Model for Adaptive Control Application", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0544.pdf}, url = {}, size = {}, abstract = {A Cognitive Map and Fuzzy Logic Controller hybrid model is presented in this paper. Sample control applications are included to demonstrate incorporation of analytical and empirical knowledge on the fuzzy cognitive map construction with the purpose of generating Fuzzy Logic Controller (FLC) design on-line. Cognitive map state vector includes all FLC-defining concepts. Controller performance concepts are also included in order to automate FLC parameters adjustments. This model targets control applications when only incomplete plant model is available, or for cases where plant parameters values are not known with certainty, or for cases when plant parameters are expected to change while in operation. On these cases, the CM portion designs the FLC, and fine-tunes controller parameters whenever controller performance is unsatisfactory until userdefined control objectives are achieved; this model emulates expert empirical FLC design and fine-tuning within cognitive map knowledge domain. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Fatima:2008:fuzz, author = "S. Shifana Fatima and C. Natarajan and A. Rajaraman", title = "Fuzzy Information Processing for Damage Assessment & Rehabilitation", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0545.pdf}, url = {}, size = {}, abstract = {Disaster mitigation and rehabilitation are needed in a fast and efficient manner in a country like India where most of the calamities occur in places which are rural or not well connected by transportation or communication. Recent spread of cellular phones and internet -both wired and wireless-have brought in new possibilities and open new dimensions in terms of alleviation of public duress. This study presents results of visual and oral communication formats used by different strata of people-politicians to engineers- visiting the site and the implication in terms of assessment of damage and possible rehabilitation, based on engineering modeling and analysis. Based on this, it may be seen that rehabilitation measures could vary significantly, depending on the vagueness and interpretation of visual data Fuzzy representation and the associated logic are found to be most effective in treating uncertainties and a suitable damage model is able to assess the damage and later suggest rehabilitation measures in literal terms, so that on-site adoption will be easier. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Cao3:2008:fuzz, author = "Hua Cao and Nathan Brener and Hilary Thompson and S. S. Iyengar and Zhengmao Ye", title = "Automated Control Point Detection, Registration, and Fusion of Fuzzy Retinal Vasculature Images", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0547.pdf}, url = {}, size = {}, abstract = {Multi-modality biomedical images' feature detection, registration, and fusion are usually scene dependent which requires intensive computational effort. A novel automated approach of the multimodality retinal image control point detection, registration, and fusion is proposed in this paper. The new algorithm is reliable and time efficient, which implements automatic adaptation from frame to frame with a few tunable thresholds. The reference and input images are from two different modalities, i.e., the angiogram grayscale and fundus true colour images. Retinal image's properties determine the fuzzy vessel boundaries and bifurcations. The retinal vasculature is extracted using Canny Edge Detector and the control points are detected at the fuzzy vasculature bifurcations using the Adaptive Exploratory Algorithm. Shape similarity criteria are employed to match the control point pairs. The proposed heuristic optimization algorithm adjusts the control points at the sub-pixel level in order to maximize the objective function Mutual-Pixel-Count (MPC). The iteration stops either when fMPC reaches the maximal, or when the maximum allowable loop count is reached. The comparative analysis with other existing approaches has shown the advantages of the new algorithm in terms of novelty, efficiency, and accuracy. }, keywords = {Control Point Detection, Biomedical Imaging, Biomedical Image Registration, Biomedical Image Fusion, Fuzzy Vasculature Boundaries, Adaptive Exploratory Algorithm, Mutual-Pixel-Count, Heuristic Optimization. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Miyahira:2008:fuzz, author = "Susana Abe Miyahira and Ernesto Araujo", title = "Fuzzy Obesity Index for Obesity Treatment and Surgical Indication", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0548.pdf}, url = {}, size = {}, abstract = {A Fuzzy Obesity Index for being used as an alternative in obesity treatment and bariatric surgery indication (BSI) is presented in this paper. Obesity is nowadays understood as universal epidemy and became an important source of death and co-morbidities. The search for a more accurate method to evaluate obesity and to indicate a better treatment is important in the world health context. In this paper the Body Mass Index (BMI) is first modified and treated as fuzzy sets. BMI is characterized by its capacity of weight excess and considered the main criteria for obesity treatment and BSI. Nevertheless, the fat excess related to the Body Fat (BF) is actually the principal harmful factor in obesity disease, that is usually neglected. Due to that this paper also presents a new fuzzy mechanism for evaluating obesity by associating BMI with Body Fat (BF) that yields a fuzzy obesity index for obesity evaluation and treatment and allows to build up a Fuzzy Decision Support System (FDSS) for BSI. Different values of BMI and BF (in terms of percentBF) used for validating the proposed method classify individuals in distinct categories with degrees of compatibility more realistic than those accomplished by Boolean classification, as usually occur. The proposed method may assume an important whole in medicine as an index for obesity evaluation and surgery treatment by using the advantages of BMI and BF in synergy. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Yang5:2008:fuzz, author = "Chao-Tung Yang and Wen-Chung Shih and Shian-Shyong Tseng", title = "A Heuristic Data Distribution Scheme for Data Mining Applications on Grid Environments", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0549.pdf}, url = {}, size = {}, abstract = {Effective data distribution techniques can significantly reduce the total execution time of a program on grid computing environments, especially for data mining applications. In this paper, we describe a linear programming formulation for the data distribution problem on grids. Furthermore, a heuristic method, named HDDS (Heuristic Data Distribution Scheme), is proposed to solve this problem. We implement the parallel association rule mining method and conduct the experimentations on our grid testbed. Experimental results showed that data mining programs using our HDDS to distribute data could execute more efficiently than traditional schemes could. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Lin3:2008:fuzz, author = "TsauYoung T. Y. Lin ", title = "Granular Computing: Common Practices and Mathematical Models", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0550.pdf}, url = {}, size = {}, abstract = {A very general formal model is proposed, it formalize almost all the known examples. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Hirano:2008:fuzz, author = "Shoji Hirano and Shusaku Tsumoto", title = "Partial Statistical Independence in Contingency Matrix", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0552.pdf}, url = {}, size = {}, abstract = {This paper focuses on how statistical independence can be observed in a contingency table when the table is viewed as a matrix. Statistical independence in a contingency table is represented as a special form of linear dependence, where all the rows or columns are described by one row or column, respectively. This also means that the rank of the matrix is equal to 1.0. When the rank is equal to 1, we also have some interesting properties corresponding to collinearity in project geometry. Then, we consider the cases where the rank of a given matrix is not full. In these cases, partial statistical independence is observed, where at least one row (column) can be represented by linear combinations of other rows (columns). }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Hirano2:2008:fuzz, author = "Shoji Hirano and Shusaku Tsumoto", title = "Trajectory Mining Using Multiscale Matching and Clustering", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0553.pdf}, url = {}, size = {}, abstract = {This paper focuses on clustering of trajectories of temporal sequences of two laboratory examinations. First, we map a set of time series containing different types of laboratory tests into directed trajectories representing temporal change in patients' status. Then the trajectories for individual patients are compared in multiscale and grouped into similar cases by using clustering methods. Experimental results on the chronic hepatitis data demonstrated that the method could find the groups of trajectories which reflects temporal covariance of platelet, albumin and choline esterase. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Lin4:2008:fuzz, author = "Tsau Young (T. Y.) Lin ", title = "Deductive Data Analysis and Mining Granular Computing on Relational Data", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0554.pdf}, url = {}, size = {}, abstract = {This article is bout the data analysis and mining aspects of Granular Computing (GrC) that includes Rough Set Theory (RST). This subject involves four concepts, deductive data analysis and mining (DDAM), relational databases (RDB), rough set theory (RST) and granular computing (GrC). The results are quite far reaching. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Chan2:2008:fuzz, author = "Chien-Chung Chan", title = "Approximate Dominance-Based Rough Sets Using Equivalence Granules", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1819-0", file = {FS0555.pdf}, url = {}, size = {}, abstract = {The rough set theory introduced by Pawlak has provided a solid foundation for developing many useful learning algorithms and tools for data analysis. Dominance-based rough set introduced by Greco et al. is an extension of classical rough sets for dealing with multiple criteria decision analysis problems. In this paper, we look into the relationship between the two theories and introduce a procedure for approximating dominance-based rough sets by a family of equivalence relations. We use the concept of indexed blocks to represent dominance-based approximation space, and it is assumed that the family of indexed blocks forms a partition on the universe of objects. Objects in lower approximations are used to approximate the dominance-based approximation space. An example is given to illustrate the feasibility of our approach. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Carpentieri:2008:cec, author = "Marco Carpentieri ", title = "On the Relationships Between Genetic Algorithms and Neural Systems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0006.pdf}, url = {}, size = {}, abstract = {We consider a marginal distribution genetic model based on crossover of sequences of genes and provide relations between the associated infinite population genetic system and the neural networks. A lower bound on population size is exhibited stating that the behaviour of the finite population system, incase of sufficiently large sizes, can be approximated by the behaviour of the corresponding infinite population system. The attractors (with binary components) of the infinite population genetic system are characterized as equilibrium points of a discrete(neural network) system that can be considered as a variant of a Hopfield's network; it is shown that the fitness is a Lyapunov function for the variant of the discrete Hopfield's net. Our main result can be summarized by stating that the relation between marginal distribution genetic systems and neural nets is much more general than that already shown elsewhere for other simpler models. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Candela:2008:cec, author = "R. Candela and E. Riva Sanseverino ", title = "Partial Discharges Analysis and Parameters Identification by Continuous Ant Colony Optimization", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0007.pdf}, url = {}, size = {}, abstract = {The technique of Ant Colony Optimization has been employed in this paper to efficiently deal with the problem of parameters identification in partial discharge, PD, analysis. The latter is a continuous optimization problem. From the technical point of view the identification of these parameters allows the modeling of the phenomenon of Partial Discharges in dielectrics. In this way it is possible the early diagnosis of defects in Medium Voltage cable lines and components and thus it is possible to prevent possible outages and service interruptions. Analytically, the problem consists of finding the Weibull parameters of the Pulse Amplitude Distribution (PAD) distributions allowing the identification and classification of the defects in dielectrics. The accuracy in this identification is crucial for correct classification of defects. The proposed algorithm, called DACS, Dynamic Ant Colony Search, allows the easy investigation of complex problems both in discrete and continuous search spaces. It dynamically redefines the search tree through which the ants (agents) move using an adaptive parameter in order to increase exploration or exploitation. In order to check the efficiency of the proposed algorithm in solving continuous optimization problems, many Partial Discharges, PD, experimental tests at various temperatures have been performed on some lumped capacity specimens. In this way, the experimental cumulative probability of amplitude histograms has been compared with those attained using the Weibull analysis. All the applications show that the error is quite limited and that the calculation times are considerably low compared to other techniques employed for the same purpose. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Huang:2008:cec, author = "Fu-Zhuo Huang and Ling Wang and Qie He", title = "A Hybrid Differential Evolution with Double Populations for Constrained Optimization", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0008.pdf}, url = {}, size = {}, abstract = {How to balance the objective and constraints is always the key point of solving constrained optimization problems. This paper proposes a hybrid differential evolution with double populations (HDEDP) to handle it. HDEDP uses a two-population mechanism to decouple constraints from objective function: one population evolves by Differential Evolution only according to either objective function or constraint, while the other stores feasible solutions which are used to repair some infeasible solutions in the former population. Thus, this technique allows objective function and constraints to be treated separately with little costs involved in the maintenance of the double population. In addition, to enhance the exploitation ability, simplex method (SM) is applied as a local search method to the best feasible solution of the first population. Simulation results based on three well-known engineering design problems as well as comparisons with some existed methods demonstrate the effectiveness, efficiency and robustness of the proposed method. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Nickabadi:2008:cec, author = "Ahmad Nickabadi and Mohammad Mehdi Ebadzadeh and Reza Safabakhsh ", title = "DNPSO: A Dynamic Niching Particle Swarm Optimizer for Multi-Modal Optimization", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0009.pdf}, url = {}, size = {}, abstract = {In this paper, a new variant of the PSO algorithm called Dynamic Niching Particle Swarm Optimizer (DNPSO) is proposed. Similar to basic PSO, DNPSO is a global optimization algorithm in which the main population of the particles is divided into some sub-swarms and a group of free particles. A new sub-swarm forming algorithm is proposed. This new form of sub-swarm creation, combined with free particles which implement a cognition-only model of PSO, brings about a great balance between exploration and exploitation characteristics of the standard PSO. DNPSO is tested with some well-known and widely used benchmark functions and the results are compared with several PSO-based multi-modal optimization methods. The results show that in all cases, DNPSO provides the best solutions. }, keywords = { PSO, multi-modal function optimization, niching, dynamic, DNPSO.}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Zhang:2008:cec, author = "Rui Zhang and Cheng Wu ", title = "Decomposition and Immune Genetic Algorithm for Scheduling Large Job Shops", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0016.pdf}, url = {}, size = {}, abstract = {A decomposition and optimization algorithm is presented for large-scale job shop scheduling problems in which the total weighted tardiness must be minimized. In each iteration, a new subproblem is first defined by a heuristic approach and then solved using a genetic algorithm. We construct a fuzzy controller to calculate the characteristic values which describe the the bottleneck jobs in different optimization stages. Then, these characteristic values are used to guide the process of subproblem-solving in an immune mechanism. Numerical computational results show that the proposed algorithm is effective for solving large-scale scheduling problems. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Cheng:2008:cec, author = "Ba-Yi Cheng and Hua-Ping Chen and Hao Shao and Rui Xu and George Q. Huang ", title = "A Chaotic Ant Colony Optimization Method for Scheduling a Single Batch-processing Machine with Non-Identical Job Sizes", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0017.pdf}, url = {}, size = {}, abstract = {The problem of minimizing makespan on a single batch-processing machine with non-identical job sizes is strongly NP-hard. This paper proposes an Ant Colony Optimization (ACO) algorithm with chaotic control to solve the problem. The Metropolis criterion is adopted to select the paths of ants to escape immature convergence. In order to improve the solutions of ACO, a chaotic optimizer is designed and integrated into ACO to reinforce the capacity of global optimization. Batch First Fit is introduced to decode the paths into feasible solutions of the problem. In the experiment, the instances of 24 levels are simulated and the results show that the proposed CACO outperforms Genetic Algorithm and Simulated Annealing on all the instances. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Shen:2008:cec, author = "Shuhan Shen and Haolong Deng and Yuncai Liu", title = "Probability Evolutionary Algorithm Based Human Motion Tracking Using Voxel Data", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0022.pdf}, url = {}, size = {}, abstract = {A novel evolutionary algorithm called Probability Evolutionary Algorithm (PEA), and a method based on PEA for visual tracking of human body using voxel data are presented. PEA is inspired by the Quantum computation and the Quantum-inspired Evolutionary Algorithm, and it has a good balance between exploration and exploitation with very fast computation speed. The individual in PEA is encoded by the probabilistic compound bit, defined as the smallest unit of information, for the probabilistic representation. The observation step is used in PEA to obtain the observed states of the individual, and the update operator is used to evolve the individual. In the PEA based human tracking framework, tracking is considered to be a function optimization problem, so the aim is to optimize the matching function between the model and the image observation. Since the matching function is a very complex function in high-dimensional space, PEA is used to optimize it. Experiments on 3D human motion tracking using voxel data demonstrate the effectiveness, significance and computation efficiency of the proposed human tracking method. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Han:2008:cec, author = "Xue Han and Ma Hong-Xu", title = "Maximum Lifetime Data Aggregation in Distributed Intelligent Robot Network Based on ACO", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0025.pdf}, url = {}, size = {}, abstract = {Providing multimedia traffic support in distributed intelligent robot network (DIRN) as a kind of wireless sensor and actor network (WSAN) is addressed. Since multimedia traffic has stringent bounds on end-to-end delay resource reservation for transmitting such traffic has to be done. The existing methods used for multimedia traffic provide inefficient use of network resources and affect call acceptance and drop ratio of multimedia traffic severely. Hence a data aggregation scheme based on ant optimization algorithm using bionic swarm intelligence for supporting multimedia traffic is proposed to overcome those limitations and to help reduce the traffic to the sink node in turn reducing the power consumption of intermediate node. Lifetime maximization can balance the traffic across the network so as to avoid overwhelming the bottleneck nodes. Key issues and configurations are discussed and studied, such as influence of location of aggregation point, impact of network shape and balance based energy-efficient methods. Extensive simulations are done to assess the performance of the scheme under varying network condition for carrying multimedia traffic. A practical implementation with real DIRN has been carried out to validate the enhanced efficiency, stability and accuracy of the proposed algorithm, which is proved to lead to longer network lifetime in comparison to other traditional data aggregation schemes such as Minimum Energy Gathering Algorithm (MEGA) for supporting multimedia traffic in real time. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Yuan:2008:cec, author = "Xiao-Lei Yuan and Yan Bai and Ling Dong", title = "Identification of Linear Time-invariant, Nonlinear and Time Varying Dynamic Systems Using Genetic Programming", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0029.pdf}, url = {}, size = {}, abstract = {An improved genetic programming (GP) algorithm was developed in order to use a unified way to identify both linear and nonlinear, both time-invariant and time-varying discrete dynamic systems. 'D' operators and discrete time 'n' terminals were used to construct and evolve difference equations. Crossover operations of the improved GP algorithm were different from the conventional GP algorithm. Two levels of crossover operations were defined. A linear time-invariant system, a nonlinear time-invariant system and a time-varying system were identified by the improved GP algorithm, good models of object systems were achieved with accurate and simultaneous identification of both structures and parameters. GP generated obvious mathematical descriptions (difference equations) of object systems after expression editing, showing correct input-output relationships. It can be seen that GP is good at handling different kinds of dynamic system identification problems and is better than other artificial intelligence (AI) algorithms like neural network or fuzzy logic which only model systems as complete black boxes. }, keywords = {genetic algorithms, genetic programming}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Khor:2008:cec, author = "Susan Khor ", title = "Where Genetic Drift, Crossover and Mutation Play Nice in a Freemixing Single-Population Genetic Algorithm", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0030.pdf}, url = {}, size = {}, abstract = {A variant of the HIFF problem called HIFF-M is compared with HIFF-D- the discrete version of the original HIFF problem. By the SWO statistic, HIFF-M is less epistatic than HIFF-D. Using operator specific FDC measurements, we find that HIFF-M is less crossover-easy and less mutation-hard than HIFF-D. Nevertheless, from our experiments, HIFF-M is still difficult for an unspecialized hill climber and for a mutation-only multi-individual stochastic search algorithm to solve efficiently and reliably. HIFF-M also has a more symmetrical fitness distribution than HIFF-D thus increasing the possibility of useful neutral spaces at higher levels of fitness. Notably, explicit mechanisms to reduce diversity loss made it more difficult for crossover-only GAs to solve HIFF-M than HIFF-D. Over all configurations that we experimented with, the best search performance for HIFF-M was obtained with upGA- a single-population, steady-state GA which uses random parent selection, 1-2 point crossover and no explicit diversity preservation mechanism. This result suggests that HIFF-M has the kind of epistasis to create fitness landscapes where genetic drift, crossover and mutation work well together to balance the exploitative and explorative facets of a GA. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Tang:2008:cec, author = "H. Tang and W. Zhang and C. Fan and S. Xue", title = "Parameter Estimation Using a CLPSO Strategy", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0031.pdf}, url = {}, size = {}, abstract = {As a novel evolutionary computation technique, particle swarm optimisation (PSO) has attracted much attention and wide applications for solving complex optimisation problems in different fields mainly for various continuous optimisation problems. However, it may easily get trapped in a local optimum when solving complex multimodal problems. This paper uses an improved PSO by incorporating a comprehensive learning strategy into original PSO to discourage premature convergence, namely CLPSO strategy to estimate parameters of structural systems, which could be formulated as a multi-modal optimisation problem with high dimension. Simulation results for identifying the parameters of a structural system under conditions including limited output data and no prior knowledge of mass, damping, or stiffness are presented to demonstrate the effectiveness of the proposed method. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Pullan:2008:cec, author = "Wayne Pullan ", title = "A Population Based Hybrid Metaheuristic for the $p$-median Problem", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0032.pdf}, url = {}, size = {}, abstract = {The p-median problem is one of choosing p facilities from a set of candidates to satisfy the demands of n clients such that the overall cost is minimised. In this paper, PBS, a population based hybrid search algorithm for the p-median problem is introduced. The PBS algorithm uses a genetic algorithm based meta-heuristic, primarily based on cut and paste crossover operators, to generate new starting points for a hybrid local search. For larger p-median instances, PBS is able to effectively use a number of computer processors. It is shown empirically that PBS is able to effectively solve p-median problems for a large range of the commonly used p-median benchmark instances. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(He:2008:cec, author = "Qie He and Ling Wang and Fu-Zhuo Huang", title = "Nonlinear Constrained Optimization by Enhanced Co-evolutionary PSO", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0035.pdf}, url = {}, size = {}, abstract = {Penalty function methods have been the most popular methods for nonlinear constrained optimisation due to their simplicity and easy implementation. However, it is often not easy to set suitable penalty factors or to design adaptive mechanisms. By employing the notion of co-evolution to adapt penalty factors, we present a co-evolutionary particle swarm optimisation approach (CPSO) for nonlinear constrained optimisation problems, where PSO is applied with two kinds of swarms for evolutionary exploration and exploitation in spaces of both solutions and penalty factors. To enhance the performance of our proposed algorithm, three improvement strategies are proposed. The proposed algorithm is population-based and easy to implement in parallel, in which the penalty factors to evolve in a self-tuning way. Simulation results based on three famous engineering constrained optimisation problems demonstrate the effectiveness, efficiency and robustness of the proposed enhanced CPSO (EC PSO). }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Chen:2008:cec, author = "Mingquan Chen ", title = "Second Generation Particle Swarm Optimization", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0040.pdf}, url = {}, size = {}, abstract = {Second Generation Particle Swarm Optimization (SGPSO) is a new swarm intelligence optimization algorithm. SGPSO is based on the PSO. But the SGPSO will sufficiently use the information of the optimum swarm. The optimum swarm consists of the local optimum solution of every particle. In the SGPSO, every particle in the swarm not only moves to the local optimum solution and the global optimum solution, but also moves to the geometric center of optimum swarm. SGPSO, PSO and PSO with Time-Varying Acceleration Coefficients(PSO_TVAC) are compared on some benchmark functions. And experiment results show that SGPSO performs better in the accuracy and in getting red of the premature than PSO and PSO_TVAC. And according to the different swarm centers which every particle moves to, I will show some kinds of the variation of SGPSO. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wai:2008:cec, author = "Rong-Jong Wai and Kun-Lun Chuang and Jeng-Dao Lee", title = "Supervisory Particle-Swarm-Optimization Control Design for Maglev Transportation System", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0041.pdf}, url = {}, size = {}, abstract = {This study focuses on the design of an on-line levitation and propulsion control for a magnetic-levitation (maglev) transportation system. First, the dynamic model of a maglev transportation system including levitated electromagnets and a propulsive linear induction motor (LIM) based on the concepts of mechanical geometry and motion dynamics is developed. Then, a total sliding-mode (TS) control strategy is introduced, and the concept of TS control is incorporated into particle swarm optimization (PSO) to form an on-line TSPSO control framework with varied inertial weights for preserving the robust control characteristics and reducing the chattering control phenomena of TS control. In this TSPSO control scheme, a PSO control system is used to be the major controller, and the stability can be indirectly ensured by the concept of TS control without strict constraint and detailed system knowledge. In order to further directly stabilize the system states around a predefined bound region and effectively accelerate the searching speed of the PSO control, a supervisory mechanism is embedded into the TSPSO control to constitute a supervisory TSPSO (STSPSO) control strategy. The effectiveness of the proposed control schemes for the maglev transportation system is verified by numerical simulations, and the superiority of the STSPSO control scheme is indicated in comparison with the TS and TSPSO control strategies. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Gao:2008:cec, author = "Jiaquan Gao and Guixia He and Yushun Wang and Feng Liu", title = "Multi-Objective Scheduling Problems Subjected to Special Process Constraint", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0042.pdf}, url = {}, size = {}, abstract = {The problem of parallel machine multi-objective scheduling subjected to special process constraint in the textile industries, as one of the most important combinational optimization problems, is different from other parallel machine scheduling problems in the following characteristics. On one hand, processing machines are non-identical; on the other hand, the sort of job processed on every machine can be restricted. Considering one of the multi-objective problems, either minimizing the maximum completion time among all the machines(makespan) or minimizing the total earliness/tardiness penalty of all the jobs has been cornerstone of most studies done so far. However, under special process constraint, taking them into account as a multi-objective problem has not been well studied. Therefore, in this paper, a multi-objective model based on them is presented and a new parallel genetic algorithm based on a vector group coding method is also proposed in order to effectively solve this model. The algorithm shows the following advantages: the coding method is simple and can effectively reflect the virtual scheduling policy, which can vividly reflect the numbers and sequences of these processed jobs on every machine, and then enables the individuals generated by crossover and mutation to satisfy process constraint. Numerical experiments show that it is efficient, and is better than the common genetic algorithm, and has the better parallel efficiency. A much better prospect of application can be optimistically expected. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Beadle:2008:cec, author = "Lawrence Beadle and Colin G. Johnson", title = "Semantically Driven Crossover in Genetic Programming", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0044.pdf}, url = {}, size = {}, abstract = {Crossover forms one of the core operations in genetic programming and has been the subject of many different investigations. We present a novel technique, based on semantic analysis of programs, which forces each crossover to make candidate programs take a new step in the behavioural search space. We demonstrate how this technique results in better performance and smaller solutions in two separate genetic programming experiments. }, keywords = {genetic algorithms, genetic programming}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Tanimoto:2008:cec, author = "Jun Tanimoto ", title = "Co-Evolution Model of Networks and Strategy in a 2×2 Game Emerges Cooperation", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0048.pdf}, url = {}, size = {}, abstract = {A 2×2 game model implemented by co-evolution of both networks and strategies is established. Several numerical experiments considering various 2×2 game classes, including Prisoner's Dilemma (PD), Chicken, Leader, and Hero, reveal that the proposed co-evolution mechanism can solve dilemmas in the PD game class. The result of solving a dilemma is the development of mutual-cooperation reciprocity (R reciprocity), which arises through the emergence of several cooperative hub agents, which have many links in a heterogeneous and assortative social network. However, the co-evolution mechanism seems counterproductive in case of the Leader and Hero game classes, where alternating reciprocity (ST reciprocity) is more demanding. It is also suggested that the assortative and cluster coefficients of a network affect the emergence of cooperation for R reciprocity. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Dudley:2008:cec, author = "James Dudley and Luigi Barone and Lyndon While ", title = "Multi-Objective Spam Filtering Using an Evolutionary Algorithm", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0051.pdf}, url = {}, size = {}, abstract = {SpamAssassin is a widely-used open source heuristic-based spam filter that applies a large number of weighted tests to a message, sums the results of the tests, and labels the message as spam if the sum exceeds a user-defined threshold. Due to the large number of tests and the interactions between them, defining good weights for SpamAssassin is difficult: moreover, users with different needs may desire different sets of weights to be used. We have built a multiobjective evolutionary algorithm MOSF that evolves weights for the tests in SpamAssassin according to two independent objectives: minimising the number of false positives (legitimate messages mislabeled as spam), and minimising the number of false negatives (spam messages mislabeled as legitimate). We show that MOSF returns a set of solutions offering a range of setups for SpamAssassin satisfying different users' needs, and also that MOSF can derive solutions which beat the existing SpamAssassin weights in both objectives simultaneously. Applying these ideas could substantially increase the usefulness of SpamAssassin and similar systems. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Fang-Ying:2008:cec, author = "Xiao Fang-Ying and Chen Han-Wu and Liu Wen-Jie and Li Zhi-Qiang", title = "Fault Detection for Single and Multiple Missing-Gate Faults in Reversible Circuits", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0054.pdf}, url = {}, size = {}, abstract = {To ensure the validity and reliability of reversible circuits, fault detection is necessarily. Two methods to get complete test set with respect to missing-gate fault (MGF) in reversible circuits were introduced. They are the method that divided the circuit into subcircuit to get the complete test set which is not minimal and the set covering method to get the minimal complete test set. Comparing to DFT detection method, the methods introduced in this paper do not need additional gates; they do not change the structure of the circuits and do not depend on implement technologies. So, it can be widely applied. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Cheng2:2008:cec, author = "Wei Cheng ", title = "Different Systems, Same Matrix Representation, Similar Properties", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0056.pdf}, url = {}, size = {}, abstract = {In recent paper [W. Cheng, Phys. Lett. A 364: 517-521 (2007)] we take the original 4×4 bound entangled states in [S.-M. Fei, et al., Phys. Lett. A 352: 321-325 (2006)] as 2×8 states and show that it is still bound entangled. Whether other bound entangled states, especially 4×4 bound entangled states, have the same property or not? In this paper, we consider another 4×4 bound entangled state in [F. Benatti, et al., Phys. Lett. A 326: 187-198 (2004)]. The result again affirms our observation that different systems, having the same matrix representation, have similar properties. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Yu:2008:cec, author = "Jianping Yu and Yaping Lin and Jinhua Zheng", title = "Ant-Based Query Processing for Replicated Events in Wireless Sensor Networks", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0057.pdf}, url = {}, size = {}, abstract = {Wireless sensor networks are often deployed in diverse application specific contexts and one unifying view is to treat them essentially as distributed databases. The simplest mechanism to obtain information from this kind of database is to flood queries for named data within the network and obtain the relevant responses from sources. However, if the queries are issued for replicated data, the simple approach can be highly inefficient. As sensor networks are uniquely characterized by limited energy availability and low memory, alternative strategies need to be examined for this kind of queries. A novel query processing approach using distributed Multiple Ant Colonies algorithm with positive interaction is presented in this paper, in which ants adjust individual behavior via cooperation to make colony behavior intelligent, demanding merely local information to find named data efficiently and determine the number and allocation of event replicas adaptively. Theore- tically and experimentally, the results clearly show that the proposed protocol is more flexible and energy-efficient than existing algorithms. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Liu:2008:cec, author = "Yu-Hsin Liu ", title = "A Memetic Algorithm for the Probabilistic Traveling Salesman Problem", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0059.pdf}, url = {}, size = {}, abstract = {The probabilistic traveling salesman problem (PTSP) is an important theoretical and practical topic in the study of stochastic network problems. It provides researchers with a modeling framework for exploring the stochastic effects in routing problems. This paper focuses on developing a memetic algorithm (MA) by incorporating the nearest neighbor algorithm to generate initial solutions, 1-shift and/or 2-opt exchanges for local search, and edge recombination (ER) crossover to efficiently and effectively solve the PTSP. In addition, a mixed local search strategy by randomly selecting two different local search methods (i.e., 1-shift and 2-opt exchanges) is introduced to further enhance the effectiveness of the proposed MA for solving the PTSP. A set of numerical experiments based on both heterogeneous and homogeneous PTSP instances were conducted to test the validity of the proposed MA. The numerical results showed that the newly proposed MA enhanced the performance in terms of objective function value and/or computation time in most of the tested cases as compared to existing methods tested in previous studies. Moreover, the results indicated that incorporating mixed local search strategy into the MA can significantly increase the solution quality. These findings show the potential of the proposed MA in effectively and efficiently solving the large-scale PTSP. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Rada:2008:cec, author = "Juan Rada and Ruben Parma and Wilmer Pereira", title = "Path Optimization for Multiple Objectives in Directed Graphs Using Genetic Algorithms", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0060.pdf}, url = {}, size = {}, abstract = {This paper presents a genetic algorithmic approach for finding efficient paths in directed graphs when optimizing multiple objectives. Its aim is to provide solutions for the game of Animat where an agent must evolve paths to achieve the greatest amount of bombs in the fewest moves as possible. The nature of this problem suggests agents with memory abilities to choose different edges from a vertex v such that each time v is reached, the agent can avoid cycles and be encouraged to keep searching for bombs all over the directed graph. This approach was tested on several random scenarios and also on specially designed ones with very encouraging results. The multi-objective genetic algorithm chosen to evolve paths was SPEA2 using one-point crossover and low mutation to allow genetic diversity of the population and an enhanced convergence rate. Results are compared with an implementation for the same game using Ant Colony Optimization. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Liu2:2008:cec, author = "Qingbo Liu and Yueqing Yu and Liying Su and Qixiao Xia ", title = "A Fast Collision-Free Motion Planning Method for Underactuated Robots Based On Genetic Algorithm", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0061.pdf}, url = {}, size = {}, abstract = {A new approach of fast collision-free motion planning for underactuated robots based on genetic algorithm is proposed. The collision avoidance problem is formulated and solved as a position-based force control problem. Virtual generalized force representing the intrusion of the arm into the obstacle dangerous zone is computed in real time using a virtual spring-damper model. The partly stable controllers are adopted and the energy based fitness function is built, then the best switching sequence of partly stable controllers is obtained by genetic algorithm. Because the proposed method does not make any hypothesis about the degree of freedom, it can be used without modification for arms with a large number of degree of freedom. At last, numerical simulations which are carried on the planar 3R underactuated robots show the effectiveness of the proposed method. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Arcuri:2008:cec, author = "Andrea Arcuri and Xin Yao", title = "A Novel Co-Evolutionary Approach to Automatic Software Bug Fixing", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0063.pdf}, url = {}, size = {}, abstract = {Many tasks in Software Engineering are very expensive, and that has led the investigation to how to automate them. In particular, Software Testing can take up to half of the resources of the development of new software. Although there has been a lot of work on automating the testing phase, fixing a bug after its presence has been discovered is still a duty of the programmers. In this paper we propose an evolutionary approach to automate the task of fixing bugs. This novel evolutionary approach is based on Co-evolution, in which programs and test cases co-evolve, influencing each other with the aim of fixing the bugs of the programs. This competitive co-evolution is similar to what happens in nature for predators and prey. The user needs only to provide a buggy program and a formal specification of it. No other information is required. Hence, the approach may work for any implementable software. We show some preliminary experiments in which bugs in an implementation of a sorting algorithm are automatically fixed. }, keywords = {genetic algorithms, genetic programming}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Li:2008:cec, author = "Kangshun Li and Weifeng Pan and Wensheng Zhang and Zhangxin Chen", title = "A Sequence Cipher Producing Method Based on Two-layer Ranking Multi-Objective Evolutionary Algorithm", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0064.pdf}, url = {}, size = {}, abstract = {Aiming at designing a high safe and high efficiency cryptosystem, the period of the sequence cipher can not be too long, and the cipher sequence produced should approach random numbers. But the key sequence produced by traditional methods sometimes does not have randomness, which makes insecurity the system using this key sequence. Considering this, in this paper, we take two criteria usually used to evaluate the randomness of a key sequence as two objectives of Multi-Objective Evolutionary Algorithm (MOEA), and a new sequence cipher producing method based on two-layer MOEA is proposed (called TLEASCP). Because of TLEASCP is based on the randomness of crossover operator and mutation operator of the high efficient MOEA, the key sequences produced by TLEASCP have the merits of high randomness, chaos and long period. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Lopez:2008:cec, author = "Oscar Javier {Romero Lopez} and Angelica {de Antonio}", title = "Hybrid Behaviour Orchestration in a Multilayered Cognitive Architecture Using an Evolutionary Approach", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0065.pdf}, url = {}, size = {}, abstract = {Managing and arbitrating behaviours, processes and components in multilayered cognitive architectures when a huge amount of environmental variables are changing continuously with increasing complexity, ensue in a very comprehensive task. The presented framework proposes an hybrid cognitive architecture that relies on subsumption theory and includes some important extensions. These extensions can be condensed in inclusion of learning capabilities through bioinspired reinforcement machine learning systems, an evolutionary mechanism based on gene expression programming to self-configure the behaviour arbitration between layers, a co-evolutionary mechanism to evolve behaviour repertories in a parallel fashion and finally, an aggregation mechanism to combine the learning algorithms outputs to improve the learning quality and increase the robustness and fault tolerance ability of the cognitive agent. The proposed architecture was proved in an animat environment using a multi-agent platform where several learning capabilities and emergent properties for selfconfiguring internal agent's architecture arise.}, keywords = {genetic algorithms, genetic programming, gene expression programming}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Gong:2008:cec, author = "Wenyin Gong and Zhihua Cai", title = "A Multiobjective Differential Evolution Algorithm for Constrained Optimization", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0067.pdf}, url = {}, size = {}, abstract = {Recently, using multiobjective optimization concepts to solve the constrained optimization problems (COPs) has attracted much attention. In this paper, a novel multiobjective differential evolution algorithm, which combines several features of previous evolutionary algorithms (EAs) in a unique manner, is proposed to COPs. Our approach uses the orthogonal design method to generate the initial population; also the crossover operator based on the orthogonal design method is employed to enhance the local search ability. In order to handle the constraints, a novel constraint-handling method based on Pareto dominance concept is proposed. An archive is adopted to store the nondominated solutions and a relaxed form of Pareto dominance, called e-dominance, is used to update the archive. Moreover, to use the archive solution to guide the search, a hybrid selection mechanism is proposed. Experiments have been conducted on 13 benchmark COPs. And the results prove the efficiency of our approach. Compared with five state-ofthe- art EAs, our approach provides very good results, which are highly competitive with those generated by the compared EAs in constrained evolutionary optimization. Furthermore, the computational cost of our approach is relatively low. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Bi:2008:cec, author = "Chengpeng Bi ", title = "Evolutionary Metropolis Sampling in Sequence Alignment Space", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0068.pdf}, url = {}, size = {}, abstract = {Metropolis sampling is the earliest Markov chain Monte Carlo (MCMC) method and MCMC has been widely used in motif-finding via sequence local alignment. A key issue in the design of MCMC algorithms is to improve the proposal mechanism and the mixing behavior. To overcome these difficulties, it is common either to run a population of chains or incorporate the evolutionary computing techniques into the MCMC framework. This paper combines a simple evolutionary (genetic) algorithm (GA) with the Metropolis sampler and proposes the new motif algorithm GAMS to carry out motif heuristic search throughout the multiple alignment space. GAMS first initializes a population of multiple local alignments (initial MCMC chains) each of which is encoded on a chromosome that represents a potential solution. GAMS then conducts a genetic algorithm-based search in the sequence alignment space using a motif scoring function as the fitness measure. The genetic algorithm gradually moves this population towards the best alignment from which the motif model is derived. Experimental results show that the new algorithm compares favorably to the simple multiple-run MCMC in some difficult cases, and it also exhibits higher precision than some popular motif-finding algorithms while testing on the annotated prokaryotic and eukaryotic binding sites data sets. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Cheng3:2008:cec, author = "Wei Cheng ", title = "Trace Norm Related to Concurrence", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0069.pdf}, url = {}, size = {}, abstract = {By investigating the property of the trace norm which appeared in the analytical lower bound for the concurrence of arbitrary dimensional bipartite quantum states [Phys. Rev. Lett. 95, 040504 (2005)], we demonstrate the limitation of the bound in that paper and provide several concrete examples. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Jian:2008:cec, author = "Li Jian and Wang Cheng ", title = "Resource Planning and Scheduling of Payload for Satellite with Genetic Particles Swarm Optimization", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0070.pdf}, url = {}, size = {}, abstract = {The resource planning and scheduling technology of payload is a key technology to realize an automated control for earth observing satellite with limited resources on satellite, which is implemented to arrange the works states of various payloads to carry out missions by optimizing the scheme of the resources. The scheduling task is a difficult constraint optimization problem with various and mutative requests and constraints. Based on the analysis of the satellite's functions and the payload's resource constraints, a proactive planning and scheduling strategy based on the availability of consumable and replenishable resources in time-order is introduced along with dividing the planning and scheduling period to several pieces, where then the planning and scheduling is modeled as a combinatorial optimization. The genetic particle swarm optimization algorithm (GPSO) is proposed to address the problem, which was derived from the original continuous particle swarm optimization (PSO) and incorporated with the genetic reproduction mechanisms, namely crossover and mutation. The simulation results have shown that GPSO significantly improved the search efficacy of PSO for the combinatorial optimizations. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wang:2008:cec, author = "Yu-Xuan Wang and Qiao-Liang Xiang", title = "Exploring New Learning Strategies in Differential Evolution Algorithm", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0071.pdf}, url = {}, size = {}, abstract = {In the field of evolutionary algorithm, Differential Evolution (DE) has gained a great focus due to its strong global optimization capability and simple implementation. In DE, mutant vector, which plays the role of leading individuals to explore the search space, is generated by combining a base vector and a difference vector. However, these two vectors are selected either randomly or greedily according to the conventional strategies. In this paper, we propose three different learning strategies for conventional DE, one is for selecting the base vector and the other two are for constructing the difference vector. Experimental results on six benchmark functions validate the effectiveness of the proposed strategies. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Ma:2008:cec, author = "Patrick C. H. Ma and Keith C. C. Chan and Xin Yao", title = "An Effective Evolutionary Algorithm for Discrete-valued Data Clustering", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0072.pdf}, url = {}, size = {}, abstract = {Clustering is concerned with the discovery of interesting groupings of records in a database. Of the many algorithms have been developed to tackle clustering problems in a variety of application domains, a lot of effort has been put into the development of effective algorithms for handling spatial data. These algorithms were originally developed to handle continuous-valued attributes, and the distance functions such as the Euclidean distance measure are often used to measure the pair-wise similarity/distance between records so as to determine the cluster memberships of records. Since such distance functions cannot be validly defined in non-Euclidean space, these algorithms therefore cannot be used to handle databases that contain discrete-valued data. Owing to the fact that data in the real-life databases are always described by a set of descriptive attributes, many of which are not numerical or inherently ordered in any way, it is important that a clustering algorithm should be developed to handle data mining tasks involving them. In this paper, we propose an effective evolutionary clustering algorithm for this problem. For performance evaluation, we have tested the proposed algorithm using several real data sets. Experimental results show that it outperforms the existing algorithms commonly used for discrete-valued data clustering, and also, when dealing with mixed continuous- and discrete-valued data, its performance is also promising. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Chan:2008:cec, author = "K. Y. Chan and H. L. Zhu and C. C. Lau and S. H. Ling", title = "Gene Signature Selection for Cancer Prediction Using an Integrated Approach of Genetic Algorithm and Support Vector Machine", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0073.pdf}, url = {}, size = {}, abstract = {Classification of tumor types based on genomic information is essential for improving future cancer diagnosis and drug development. Since DNA microarray studies produce a large amount of data, effective analytical methods have to be developed to sort out whether specific cancer samples have distinctive features of gene expression over normal samples or other types of cancer samples. In this paper, an integrated approach of support vector machine (SVM) and genetic algorithm (GA) is proposed for this purpose. The proposed approach can simultaneously optimize the feature subset and the classifier through a common solution coding mechanism. As an illustration, the proposed approach is applied in searching the combinational gene signatures for predicting histologic response to chemotherapy of osteosarcoma patients, which is the most common malignant bone tumor in children. Cross-validation results show that the proposed approach outperforms other existing methods in terms of classification accuracy. Further validation using an independent dataset shows misclassification of only one of fourteen patient samples suggesting that the selected gene signatures can reflect the chemoresistance in osteosarcoma. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Li2:2008:cec, author = "Bi Li and Tu-Sheng Lin and Liang Liao and Ce Fan", title = "Genetic Algorithm Based on Multipopulation Competitive Coevolution", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0075.pdf}, url = {}, size = {}, abstract = {Coevolutionary algorithms assess individuals by their performance in relation to others. The assessing offers the possibility of subduing premature convergence which is a long-standing problem of standard genetic algorithms (SGA's). This paper presents a novel genetic algorithm based on multipopulation competitive coevolution (GAMCC) with inter-population assessment. GAMCC comprises three simultaneously coevolving populations: the learner population, the evaluator population and the fame hall. Learners are assessed by their competitive performance relative to evaluators. Learners and evaluators take turns learning and evaluating, reciprocally driving one another to increase levels of performance. The fame hall saves the elites selected from the learner population. The competitive exclusion principle in ecological theory is applied in the fame hall to maintain the chromosome diversity. Different mutation probabilities are employed to balance the tradeoff between exploration and exploitation. Experimental results show that GAMCC is more likely to avoid the occurrence of premature convergence, and maintains the chromosome diversity more effectively, outperforming the competing genetic algorithms. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Plessis:2008:cec, author = "Mathys C. du Plessis and Andries P. Engelbrecht", title = "Improved Differential Evolution for Dynamic Optimization Problems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0076.pdf}, url = {}, size = {}, abstract = {This article reports improvements on DynDE, a approach to using Differential Evolution to solve dynamic optimization problems. Three improvements are suggested, namely favored populations, migrating individuals and a combination of these approaches. The effects of varying the change frequency, peak widths and the number of dimensions of the dynamic environment are investigated. Experimental results are presented that indicate that the suggested approaches constitute considerable improvements on previous research. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Sung:2008:cec, author = "Chi Wan Sung and Shiu Yin Yuen", title = "On the Analysis of the (1+1) Evolutionary Algorithm with Short-Term Memory", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0077.pdf}, url = {}, size = {}, abstract = {Given any randomized search algorithm, we can avoid re-evaluating the fitness of previously visited points by storing the information in memory. This idea is applied to the (1+1) Evolutionary Algorithm with standard mutation and the Randomized Local Search (RLS) algorithm. Our analysis shows that a large reduction in running time can be obtained if we store recently visited points and execute those algorithms on some pseudo-boolean functions. Besides, the stored information can also be used to affect the generation of new search points. We illustrate this idea by designing an algorithm called Progressive Randomized Local Search. In contrary to RLS, it is capable of escaping from local maxima. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Gras:2008:cec, author = "Robin Gras ", title = "How Efficient are Genetic Algorithms to Solve High Epistasis Deceptive Problems?", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0078.pdf}, url = {}, size = {}, abstract = {We present an overview of the properties that are involved in the complexity of global combinatorial optimization problems with a focus on epistasis and deceptiveness. As the complexity of a problem is linked to the exploration operators and algorithm used, we propose at first a bibliography of genetic algorithms. We discuss their efficiency to solve global combinatorial optimization problems following the canonical and the statistical approaches. We propose two strategies to handle such problems. In order to evaluate the capabilities and limitations of each of them, we undertake a comparison on a set of problems with varying levels of epistasis and deceptiveness. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Mouhoub:2008:cec, author = "Malek Mouhoub and Zhijie Wang ", title = "Improving the Ant Colony Optimization Algorithm for the Quadratic Assignment Problem", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0079.pdf}, url = {}, size = {}, abstract = {The Quadratic Assignment Problem (QAP) is a well known important combinatorial problem. Indeed, many real world applications such as backboard wiring, typewriter keyboard design and scheduling can be formulated as QAPs. Recently, tackling this problem has been addressed by Ant Colony Optimization (ACO) Algorithms. To do so, ACOs, and more precisely Min-Max Ant System (MMAS) Algorithms, are usually combined with two kinds of Stochastic Local Search (SLS) methods: the 2-opt local search and the tabu local search. We talk then respectively about MMAS2opt and MMAStabu. In this paper, we propose an improvement of these two methods according to the properties of ACO and QAP. In the case of MMAS2opt, a new random walk strategy is used to avoid a quick stagnation into local optima. Moreover, a forwardlooking strategy is proposed to explore the neighborhood more thoroughly. In the case of MMAStabu, a random walk strategy is also employed to avoid getting stuck at local optima. In order to show the merits of our proposed techniques we have conducted experimental tests comparing respectively MMAS2opt and MMAStabu with and without the improvements. The results demonstrate that the improved local method, have better performance in terms of the quality of the solution returned than the original ones. Moreover, we also noticed that the improved methods outperform each other for different classes of problems. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Ding:2008:cec, author = "Nan Ding and Shude Zhou and Ji Xu and Zengqi Sun", title = "A Bayesian View on the Polynomial Distribution Model in Estimation of Distribution Algorithms", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0083.pdf}, url = {}, size = {}, abstract = {Estimation of distribution algorithms (EDA) are a class of recently-developed evolutionary algorithms in which the probabilistic model are used to explicitly characterize the distribution of the population and to generate new individuals. The polynomial distribution is applied by discrete EDAs and continuous EDAs based on discretization of the domain such as histogram-based EDA. We can unify those kinds of EDA from their distribution and call them PolyEDA. In this paper, we theoretically analyze PolyEDA from a Bayesian analysis view. Our analysis is based on the assumption that the prior distribution of the parameters satisfies a Dirichlet Distribution, because under this assumption the formulation can be analytically solved. Furthermore, we notice that the prior distribution is always overlooked by previous algorithms, so we follow this way and propose some strategies to improve the PolyEDA. The experimental results show that these new strategies can help the polynomial model based estimation of distribution algorithms achieve better convergence and diversity. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Hong:2008:cec, author = "Yi Hong and Sam Kwong and Hanli Wang and Zhihui Xie and Qingsheng Ren", title = "SVPCGA: Selection on Virtual Population Based Compact Genetic Algorithm", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0084.pdf}, url = {}, size = {}, abstract = {This paper describes a novel virtual population based truncation selection operator that extends our previously proposed virtual population based tournament selection operator [1]. Moreover, two extensions of compact genetic algorithm (CGA) that make use of virtual population based selection operators are presented in this paper: one is the tournament selection on virtual population based compact genetic algorithm (SVPCGA-TO); the other is the truncation selection on virtual population based compact genetic algorithm (SVPCGA-TR). Both SVPCGA-TO and SVPCGA-TR are tested on several benchmark problems and their results are compared with those obtained by CGA [2] and ne-CGA [3]. Some superiorities of SVPCGA in search reliability can be achieved. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Zhang2:2008:cec, author = "Qing Zhang and Sanyou Zeng and Rui Wang and Hui Shi and Guang Chen and Lixin Ding and Lishan Kang ", title = "Constrained Optimization by the Evolutionary Algorithm with Lower Dimensional Crossover and Gradient-Based Mutation", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0086.pdf}, url = {}, size = {}, abstract = {This paper proposes a new evolutionary algorithm with lower dimensional crossover and gradient-based mutation for real-valued optimization problems with constraints. The crossover operator of the new algorithm searches a lower dimensional neighbor of the parent points where the neighbor center is the barycenter of the parents, and therefore the new algorithm converges fast. The gradient-based mutation is used to converge fast for the problems with equality constraints and active inequality constraints. And the new algorithm is simple and easy to be implemented. We have used 24 constrained benchmark problems to test the new algorithm. The experimental results show it works better than or competitive to a known effective algorithm. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Greenfield:2008:cec, author = "Gary Greenfield ", title = "Evolutionary Computation for Aesthetic Purposes Involving an Interacting Particle Simulation", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0087.pdf}, url = {}, size = {}, abstract = {We present the results of our efforts to evolve ``fountain paintings'' — interacting streams consisting of several hundred encapsulated virtual paint particles that move under the influence of artificial gravity, are subject to collision detection and resolution, and burst open when they impact a virtual canvas. Using algorithmic art examples to motivate the orchestration of such fountains, we explore the formulation of computational criteria for aesthetic fitness evaluation which then lead indirectly to evolved compositions in a new and unexpected style. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Huang2:2008:cec, author = "Min Huang and Guihua Bo and Wei Tong and W. H. Ip and Xingwei Wang", title = "A Hybrid Immune Algorithm for Solving Fourth-Party Logistics Routing Optimizing Problem", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0088.pdf}, url = {}, size = {}, abstract = {Recently, Fourth-Party Logistics (4PL) is receiving considerable attention in the manufacturing and retail industries. However, due to the complexity, the research of routing problem in 4PL is in an initial stage. The existing study does not consider the complicated problem with node-edge property. This paper studies the node-to-node routing problem in 4PL. A mathematical model is set up based on nonlinear integer programming and multigraph. With respect to the problem's characteristics a hybrid immune algorithm is designed. The simulation shows that the hybrid immune algorithm is effective for solving the problem and provides an efficient method for making decision on routing in 4PL. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wei:2008:cec, author = "Wei Wei and Huiyu Zhou and Kaoru Shimada and Shingo Mabu and Kotaro Hirasawa", title = "Comparative Association Rules Mining Using Genetic Network Programming (GNP) with Attributes Accumulation Mechanism and its Application to Traffic Systems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0090.pdf}, url = {}, size = {}, abstract = {In this paper, we present a method of comparative association rules mining using Genetic Network Programming (GNP) with attributes accumulation mechanism in order to uncover association rules between different datasets. GNP is an evolutionary approach which can evolve itself and find the optimal solutions. The motivation of the comparative association rules mining method is to use the data mining approach to check two or more databases instead of one, so as to find the hidden relations among them. The proposed method measures the importance of association rules by using the absolute difference of confidences among different databases and can get a number of interesting rules. Association rules obtained by comparison can help us to find and analyse the explicit and implicit patterns among a large amount of data. For the large attributes case, the calculation is very time-consuming, when the conventional GNP based data mining is used. So, we have proposed an attribute accumulation mechanism to improve the performance. Then, the comparative association rules mining using GNP has been applied to a complicated traffic system. By mining and analysing the rules under different traffic situations, it was found that we can get interesting information of the traffic system.}, keywords = {genetic algorithms, genetic programming}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Tagawa:2008:cec, author = "Kiyoharu Tagawa ", title = "Evolutionary Computation Techniques for the Optimum Design of Balanced Surface Acoustic Wave Filters", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0091.pdf}, url = {}, size = {}, abstract = {Balanced Surface Acoustic Wave (SAW) filters play a key role in the modern Radio Frequency (RF) circuits of cellular phones. The frequency response characteristics of balanced SAW filters depend on their geometrical structures. Therefore, in order to find desirable balanced SAW filters' structures, the design of them is formulated as an optimization problem. Then two types of Evolutionary Algorithms (EAs), namely Differential Evolution (DE) and Genetic Algorithm (GA), are applied to the optimization problem respectively. Experimental results indicate that DE is superior to famous GA in the quality of solution obtained with the same cost. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Zhou:2008:cec, author = "Huiyu Zhou and Wei Wei and Kaoru Shimada and Shingo Mabu and Kotaro Hirasawa", title = "Time Related Association Rules Mining with Attributes Accumulation Mechanism and its Application to Traffic Prediction", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0092.pdf}, url = {}, size = {}, abstract = {We propose a method of association rule mining using Genetic Network Programming (GNP) with time series processing mechanism and attribute accumulation mechanism in order to find time related sequence rules efficiently in association rule extraction systems. We suppose that, the database consists of a large number of attributes based on time series. In order to deal with databases which have a large number of attributes, GNP individual accumulates better attributes in it gradually round by round, and the rules of each round are stored in the Small Rule Pool using hash method, and the new rules will be finally stored in the Big Rule Pool. The aim of this paper is to better handle association rule extraction of the database in many time-related applications especially in the traffic prediction problem. In this paper, the algorithm capable of finding the important time related association rules is described and experimental results considering a traffic prediction problem are presented. }, keywords = {genetic algorithms, genetic programming}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wang2:2008:cec, author = "Zhenzhen Wang and Hancheng Xing", title = "Dynamic-Probabilistic Particle Swarm Synergetic Model: A New Framework for a More In-depth Understanding of Particle Swarm Algorithms", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0093.pdf}, url = {}, size = {}, abstract = {There always exists a phenomenon in human society that elitists lead certain progressive force and under their leadship, the whole multitude will go to some structure. So this paper presents a novel dynamic-probabilistic particle swarm algorithm by using mind on Synergetics developed by H. Haken. In this model we discuss how to produce the particles having the global optimal or the local optimal, how to propagate these particles' influences and how the whole particle swarm constructs its structure. This model is a relatively complicated PSO variant that seems to be important for us to better understand the emergence and the creative process. Indepth theoretical analysis of this model is provided. Besides the probability evolution of the swarm structure is studied with the use of the stochastic difference equations. Especially, it provides a novel framework for extending the idea of particle swarm algorithms to social realm. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Hong2:2008:cec, author = "Yi Hong and Sam Kwong and Hanli Wang and Qingsheng Ren and Yuchou Chang", title = "Probabilistic and Graphical Model Based Genetic Algorithm Driven Clustering with Instance-level Constraints", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0094.pdf}, url = {}, size = {}, abstract = {Clustering is traditionally viewed as an unsupervised method for data analysis. However, several recent studies have shown that some limited prior instance-level knowledge can significantly improve the performance of clustering algorithm. This paper proposes a semi-supervised clustering algorithm termed as the Probabilistic and Graphical Model based Genetic Algorithm Driven Clustering with Instance-level Constraints (Cop-CGA). In Cop-CGA, all prior knowledge about pairs of instances that should or should not be classified into the same groups is denoted as a graph and all candidate clustering solutions are sampled from this graph with different orders to assign instances into a certain number of groups. We illustrate how to design the Cop-CGA to guarantee that all candidate solutions satisfy the given constraints and demonstrate the usefulness of background knowledge for genetic algorithm driven clustering algorithm through experiments on several real data sets with artificial hard constraints. One advantage of Cop- CGA is both positive and negative instance-level constraints can be easily incorporated. Moreover, the performance of Cop-CGA is not sensitive to the order of assignment of instances to groups. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Yang:2008:cec, author = "Qingyun Yang ", title = "A Comparative Study of Discrete Differential Evolution on Binary Constraint Satisfaction Problems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0095.pdf}, url = {}, size = {}, abstract = {There are some variants and applications of the discretization of differential evolution. Performances of discrete differential evolution algorithms on random binary constraint satisfaction problem are studied in this paper, and a novel discrete differential evolution algorithm based on exchanging elements is proposed. We compare the proposed discrete differential evolution, evolutionary algorithms and discrete particle swarm optimization on random binary constraint satisfaction problems. Experimental results indicate though the proposed algorithm is simpler, it is competitive with other evolutionary algorithms solving constraint satisfaction problems. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Chu:2008:cec, author = "Dominique Chu and Jonathan E. Rowe", title = "Crossover Operators to Control Size Growth in Linear GP and Variable Length GAs", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0096.pdf}, url = {}, size = {}, abstract = {In various nuances of evolutionary algorithms it has been observed that variable sized genomes exhibit large degrees of redundancy and corresponding undue growth. This phenomenon is commonly referred to as ``bloat.'' The present contribution investigates the role of crossover operators as the cause for length changes in variable length genetic algorithms and linear GP. Three crossover operators are defined; each is tested with three different fitness functions. The aim of this article is to indicate suitable designs of crossover operators that allow efficient exploration of designs of solutions of a wide variety of sizes, while at the same time avoiding bloat. }, keywords = {genetic algorithms, genetic programming}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Zeng:2008:cec, author = "Sanyou Zeng and Guang Chen and Rui Wang and Hui Li and Hui Shi and Lixin Ding and Lishan Kang", title = "A New Technique for Assessing the Diversity of Close-Pareto-Optimal Front", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0099.pdf}, url = {}, size = {}, abstract = {The quality of an approximation set usually includes two aspects — approaching distance and spreading diversity. This paper introduces a new technique for assessing the diversity of an approximation to an exact Pareto-optimal front. This diversity is assessed by using an ''exposure degree'' of the exact Pareto-optimal front against the approximation set. This new technique has three advantages: Firstly, The ''exposure degree'' combines the uniformity and the width of the spread into a direct physical sense. Secondly, it makes the approaching distance independent from the spreading diversity at the most. Thirdly, the new technique works well for problems with any number of objectives, while the widely used diversity metric proposed by Deb would work poor in problems with 3 objectives or over. Experimental computational results show that the new technique assesses the diversity well. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(On:2008:cec, author = "Chin Kim On and Jason Teo and Azali Saudi", title = "Multi-Objective Artificial Evolution of RF-Localization Behavior and Neural Structures in Mobile Robots", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0102.pdf}, url = {}, size = {}, abstract = {This paper investigates the use of a multiobjective approach for evolving artificial neural networks (ANNs) that act as a controller for radio frequency (RF)-localization behavior of a virtual Khepera robot simulated in a 3D, physics-based environment. The non-elitist and elitist Pareto-frontier Differential Evolution (PDE) algorithm are used to generate the Pareto optimal sets of ANNs that optimize the conflicting objectives of maximizing the virtual Khepera robot's behavior for homing towards a RF signal source and minimizing the number of hidden neurons used in its feedforward ANNs controller. A new fitness function which involved maximizing average wheels speed and detection of the RF signal source is also proposed. The experimentation results showed that the virtual Khepera robot was able to move towards to the target with using only a small number of hidden neurons. Furthermore, the testing results also showed that the success rate for the robot to achieve the signal source was higher when the elitist PDE-EMO algorithm was used. The path analysis of the Pareto controllers elucidated many different behaviors in terms of providing a successful homing behavior for the robot to attain the RF signal source. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Azcarraga:2008:cec, author = "Arnulfo P. Azcarraga and Ming-Huei Hsieh and Rudy Setiono", title = "Market Research Applications of Artificial Neural Networks", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0107.pdf}, url = {}, size = {}, abstract = {Even in an increasingly globalized market, the knowledge about individual local markets could still be invaluable. In this cross-national study of brand image perception of cars, survey data from buyers in the top 20 automobile markets have been collected, where each respondent has been asked to associate a car brand with individual brand images and corporate brand images. These data can be used as tool for decision making at the enterprise level. We describe an algorithm for constructing auto-associative neural networks which can be used as a tool for knowledge discovery from this data. It automatically determines the network topology by adding hidden units as they are needed to improve accuracy and by removing irrelevant input attributes. Two market research applications are presented, the first is for classification, and the second is for measuring similarities in the perceptions of the respondents from the different markets. In the first application, the constructed networks are shown to be more accurate than a decision tree. In the second application, the constructed networks are able to reproduce the training data very accurately and could be used to identify country-level (i.e. local) markets which share similar perceptions about the car brands being studied. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Li3:2008:cec, author = "Hang Li and Minqiang Li and Jiezhi Wang", title = "The Performance of Genetic Algorithms in Dynamic Optimization Problems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0108.pdf}, url = {}, size = {}, abstract = {In dynamic optimization problems, both the falls and the attraction basins of local optima are time-varying. By the infinite population model and the further experiments, the influence of the dynamic environment on the performance of genetic algorithms is analyzed. The results show that genetic algorithms should keep dynamic balance between the exploitation capacity and the exploration capacity so as to keep excellent performance in the dynamic optimization problems. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Chen2:2008:cec, author = "Yan Chen and Shingo Mabu and Kaoru Shimada and Kotaro Hirasawa", title = "Real Time Updating Genetic Network Programming for Adapting to the Change of Stock Prices", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0109.pdf}, url = {}, size = {}, abstract = {The key in stock trading model is to take the right actions for trading at the right time, primarily based on accurate forecast of future stock trends. Since an effective trading with given information of stock prices needs an intelligent strategy for the decision making, we applied Genetic Network Programming (GNP) to create a stock trading model. In this paper, we present a new method called Real Time Updating Genetic Network Programming (RTU-GNP) for adapting to the change of stock prices. There are two important points in this paper: First, the RTU-GNP method makes a stock trading decision considering both the recommendable information of technical indices and the change of stock prices according to the real time updating. Second, we combine RTU-GNP with a reinforcement learning algorithm to create the programs efficiently. The experimental results on the Japanese stock market show that the trading model with the proposed RTU-GNP method outperforms other models without time updating method. It yielded significantly higher profits than the traditional trading model without time updating. We also compare the experimental results using the proposed method with Buy&Hold method to confirm its effectiveness, and it is clarified that the proposed trading model can obtain much higher profits than Buy&Hold method. }, keywords = {genetic algorithms, genetic programming}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wilson:2008:cec, author = "Garnett Wilson and Wolfgang Banzhaf", title = "Linear Genetic Programming GPGPU on Microsoft's Xbox 360", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0110.pdf}, url = {}, size = {}, abstract = {We describe how to harness the graphics processing abilities of a consumer video game console (Xbox 360) for general programming on graphics processing unit (GPGPU) purposes. In particular, we implement a linear GP (LGP) system to solve classification and regression problems. We conduct inter- and intra-platform benchmarking of the Xbox 360 and PC, using GPU and CPU implementations on both architectures. Platform benchmarking confirms highly integrated CPU and GPU programming flexibility of the Xbox 360, having the potential to alleviate typical GPGPU decisions of allocating particular functionalities to CPU or GPU. }, keywords = {genetic algorithms, genetic programming}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Jin:2008:cec, author = "Yaochu Jin and Bernhard Sendhoff ", title = "Evolving in silico Bistable and Oscillatory Dynamics for Gene Regulatory Network Motifs", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0111.pdf}, url = {}, size = {}, abstract = {Autoregulation, toggle switch and relaxation oscillators are important regulatory motifs found in biological gene regulatory networks and interesting results have been reported on theoretical analyses of these regulatory units. However, it is so far unclear how evolution has shaped these motifs based on elementary biochemical reactions. This paper presents a method of designing important dynamics such as bistability and oscillation with these network motifs using an artificial evolutionary algorithm. The evolved dynamics of the network motifs are then verified when the initial states and the parameters of the network motifs are perturbed. It has been found that while it is straightforward to evolve the switching behaviour, it is difficult to evolve stable oscillatory dynamics. We show that a higher Hill coefficient will facilitate the generation of undamped oscillation, however, an evolutionary path that can lead to a high Hill coefficient remains an open question for future research. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wang3:2008:cec, author = "Hui Wang and Yong Liu and Zhijian Wu and Hui Sun and Sanyou Zeng and Lishan Kang", title = "An Improved Particle Swarm Optimization with Adaptive Jumps", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0113.pdf}, url = {}, size = {}, abstract = {Particle Swarm Optimisation (PSO) has shown its fast search speed in many complicated optimisation and search problems. However, PSO could often easily fall into local optima. This paper presents an improved PSO with adaptive jump. The proposed method combines a novel jump strategy and an adaptive Cauchy mutation operator to help escape from local optima. The new algorithm was tested on a suite of well-known benchmark functions with many local optima. Experimental results were compared with some similar PSO algorithms based on Gaussian distribution and Cauchy distribution, and showed better performance on those test functions. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Meng:2008:cec, author = "Yan Meng and Jing Gan", title = "Self-Adaptive Distributed Multi-Task Allocation in a Multi-Robot System", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0114.pdf}, url = {}, size = {}, abstract = {Some common issues exist in the bio-inspired algorithms for a multi-robot system include considerable randomness of the robot movement during coordination and unevenly distributed robots in a multi-task environment. To address these issues, a self-adaptive distributed multi-task allocation method in a multi-robot system is proposed in this paper. In this method, each robot only communicates with its neighbours through a virtual stigmergy mechanism and makes its local movement decision based on a balance between the exploration and exploitation inspired from particle swarm optimisation (PSO) method. To further reduce the random movement, a new task utility function is developed, where not only the current available task weight and the travel cost are considered, but also the potential number of robot redundancy around the task, as well as the task/robot distribution ratio. The proposed algorithm has been implemented on the embodied robot simulator, Player/Stage, in a searching task. The simulation results demonstrate the efficiency and robustness in a power-efficient manner with the real-world constraints. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Zheng:2008:cec, author = "Yuhua Zheng and Yan Meng", title = "Swarm Intelligence Based Dynamic Object Tracking", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0115.pdf}, url = {}, size = {}, abstract = {This paper presents a new object tracking algorithm by using the particle swarm optimisation (PSO), which is a bio-inspired population-based searching algorithm. Firstly the potential solutions of the problem are projected into a state space called solution space where every point in the space presents a potential solution. Then a group of particles are initialised and start searching in this solution space. The swarm particles search for the best solution within this solution space using the Particle Swarm Optimisation (PSO) algorithm. An accumulative histogram of the object appearance is applied to build up the fitness function for the interested object pattern. Eventually the swarming particles driven by the fitness function converge to the optimal solution. Experimental results demonstrate that the proposed PSO method is efficient and robust in visual object tracking under dynamic environments. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Ye:2008:cec, author = "Fengming Ye and Shigo Mabu and Kaoru Shimada and Kotaro Hirasawa", title = "Genetic Network Programming with Rules", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0116.pdf}, url = {}, size = {}, abstract = {Genetic Network Programming (GNP) is an evolutionary approach which can evolve itself and find the optimal solutions. As many papers have demonstrated that GNP which has a directed graph structure can deal with dynamic environments very efficiently and effectively. It can be used in many areas such as data mining, forecasting stock markets, elevator system problems, etc. In order to improve GNP's performance further, this paper proposes a method called GNP with Rules. The aim of the proposal method is to balance exploitation and exploration, that is, to strengthen exploitation ability by using the exploited information extensively during the evolution process of GNP. The proposal method consists of 4 steps: rule extraction, rule selection, individual reconstruction and individual replacement. Tile-world was used as a simulation environment. The simulation results show some advantages of GNP with Rules over conventional GNPs. }, keywords = {genetic algorithms, genetic programming}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wang4:2008:cec, author = "Yu-Xuan Wang and Qiao-Liang Xiang", title = "Particle Swarms with Dynamic Ring Topology", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0118.pdf}, url = {}, size = {}, abstract = {Particle Swarm Optimiser (PSO) is a recently proposed population-based evolutionary algorithm, which exhibits good performance in many fields, and now it's becoming more and more popular due to its strong global optimisation capability and simple implementation. To achieve better performance, some variants investigated the use of different topologies in PSO. However, particles are only ``conceptually'' connected in the topology, and the neighbourhoods of a certain particle never change (i.e. the neighbourhood structure is fixed). In this paper, we propose a dynamically changing ring topology, in which particles are connected unidirectionally with respect to their personal best fitness. Meanwhile, two strategies, namely the ``Learn From Far and Better Ones'' strategy and the ``Centroid of Mass'' strategy are used to enable certain particle to communicate with its neighbours. Experimental results on six benchmarks functions validate the effectiveness of the proposed algorithm. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Worasucheep:2008:cec, author = "Chukiat Worasucheep ", title = "A Particle Swarm Optimization with Stagnation Detection and Dispersion", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0119.pdf}, url = {}, size = {}, abstract = {Particles or candidate solutions in the standard Particle Swarm Optimisation (PSO) algorithms often face the problems of being trapped into local optima. To solve such a problem, this paper proposes a modified PSO algorithm with the stagnation detection and dispersion (PSO-DD) mechanism, which can detect a probable stagnation and is able to disperse particles. This mechanism will be described and its performance is evaluated using eight well-known 30-dimensional benchmark functions that are widely used in literature. The results show a promising alternative path for solving the common problem of local optima in PSO algorithms. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Worasucheep2:2008:cec, author = "Chukiat Worasucheep ", title = "Trading Index Mutual Funds with Evolutionary Forecasting", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0120.pdf}, url = {}, size = {}, abstract = {This paper proposes an intuitive strategy for trading index mutual funds via the prediction of the next-day closing index of a stock market. The prediction model is built from a set of basic technical indicators. The model is optimised with a self-adaptive differential evolution algorithm in which users require no expertise in parameter settings. The proposed strategy is evaluated using Nikkei, FTSE, S&P500, Dow Jones Industrial Average, and NASDAQ indices. The experiment demonstrates that the proposed strategy results in higher returns than those from buy-and-hold strategy, which is generally employed by index mutual funds. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Qing-Hua:2008:cec, author = "Zhang Qing-Hua and Xu Bu-Gong", title = "A New Model of Self-Adaptive Network Intrusion Detection", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0122.pdf}, url = {}, size = {}, abstract = {A new model of self-adaptive network intrusion detection based on negative selection algorithm is presented to tackle the problem of self continuously changeable in network intrusion detection. The evolvement of self is fully expounded; a new method that generates and evolves detectors is put forward, which can update automatically to keep synchronisation with self. The result shows that the model has the properties of self-adaptability & dynamics, and can identify the intrusion effectively. }, keywords = { intrusion detection, artificial immune, negative selection algorithm, Self-adaptive}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wolff:2008:cec, author = "Krister Wolff and David Sandberg and Mattias Wahde ", title = "Evolutionary Optimization of a Bipedal Gait in a Physical Robot", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0123.pdf}, url = {}, size = {}, abstract = {Evolutionary Optimization of a gait for a bipedal robot has been studied, combining structural and parametric modifications of the system responsible for generating the gait. The experiment was conducted using a small 17 DOF humanoid robot, whose actuators consist of 17 servo motors. In the approach presented here, individuals representing a gait consisted of a sequence of set angles (referred to as states) for the servo motors, as well as ramping times for the transition between states. A hand-coded gait was used as starting point for the Optimization procedure: A population of 30 individuals was formed, using the hand-coded gait as a seed. An evolutionary procedure was executed for 30 generations, evaluating individuals on the physical robot. New individuals were generated using mutation only. There were two different mutation operators, namely (1) parametric mutations modifying the actual values of a given state, and (2) structural mutations inserting a new state between two consecutive states in an individual. The best evolved individual showed an improvement in walking speed of approximately 65percent. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Majhi:2008:cec, author = "Babita Majhi and G. Panda and A. Choubey", title = "Efficient Scheme of Pole-Zero System Identification Using Particle Swarm Optimization Technique", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0124.pdf}, url = {}, size = {}, abstract = {This paper introduces the application of Particle Swarm Optimization (PSO) technique to identify the parameters of pole-zero plants or infinite impulse response (IIR) systems. The PSO is one of the evolutionary computing tools that performs a structured randomized search of an unknown parameter space by manipulating a population of parameter estimates to converge to a suitable solution with low computational complexity. This paper applies this powerful PSO tool to identify the parameters of standard IIR systems and compares the results with those obtained using the Genetic Algorithm (GA). The comparative results reveal that the PSO shows faster convergence, involves low complexity, yields minimum MSE level and exhibits superior identification performance in comparison to its GA counterpart. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Ji:2008:cec, author = "T. Y. Ji and M. S. Li and Z. Lu and Q. H. Wu", title = "Optimal Morphological Filter Design Using a Bacterial Swarming Algorithm", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0125.pdf}, url = {}, size = {}, abstract = {Noise removal is an underlying issue of image processing. This paper proposes a generic approach to design an optimal filter which combines linear and morphological filtering techniques, so that both Gaussian and non-Gaussian noise can be rejected. The optimisation process is performed by a bacterial swarming algorithm (BSA), which is derived from the bacterial foraging algorithm (BFA) and involves underlying mechanisms of bacterial chemotaxis and quorum sensing. The performance of the combined filter optimised by BSA is analysed in comparison with the filter optimised by the genetic algorithm (GA), as well as with other commonly used filters. The simulation results demonstrated in this paper have shown the merits of the proposed filtering technique and the optimisation algorithm. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Langdon:2008:cec, author = "W. B. Langdon ", title = "A Fast High Quality Pseudo Random Number Generator for Graphics Processing Units", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0126.pdf}, url = {}, size = {}, abstract = {Limited numerical precision of nVidia GeForce 8800 GTX and other GPUs requires careful implementation of PRNGs. The Park-Miller PRNG is programmed using G80's native Value4f floating point in RapidMind C++. Speed up is more than 40. Code is available via ftp cs.ucl.ac.uk genetic/gpcode/random-numbers/gpu_park-miller.tar.gz }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Yang2:2008:cec, author = "Ming Yang and Lishan Kang and Jing Guan", title = "Multi-Algorithm Co-evolution Strategy for Dynamic Multi-Objective TSP", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0128.pdf}, url = {}, size = {}, abstract = {Dynamic Multi-Objective TSP (DMOTSP), a new research filed of evolutionary computation, is an NP-hard problem which comes from the applications of mobile computing and mobile communications. Because the characters of DMOTSP change with time, the method of designing a single algorithm can not effectively solve this extremely complicated and diverse optimization problem according to NFLTs for optimization. In this paper, a new approach to designing algorithm, multi-algorithm co-evolution strategy (MACS), for DMOTSP is proposed. Through multi-algorithm co-evolution, MACS can accelerate algorithm's convergence, make Pareto set maintain diversity and make Pareto front distribute evenly with a complementary performance of these algorithms and avoiding the limitations of a single algorithm. In experiment, taking the three-dimensional benchmark problem CHN144+5 with two-objective for example, the results show that MACS can solve DMOTSP effectively with faster convergence, better diversity of Pareto set and more even distribution of Pareto front than single algorithm. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Kang:2008:cec, author = "Zhuo Kang and Lishan Kang and Changhe Li and Yuping Chen and Minzhong Liu", title = "Convergence Properties of E-Optimality Algorithms for Many Objective Optimization Problems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0129.pdf}, url = {}, size = {}, abstract = {In the paper, for many-objective optimization problems, the authors pointed out that the Pareto Optimality is unfair, unreasonable and imperfect for Many-objective Optimization Problems (MOPs) underlying the hypothesis that all objectives have equal importance and propose a new evolutionary decision theory. The key contribution is the discovery of the new definition of optimality called E-optimality for MOP that is based on a new conception, so called E-dominance, which not only considers the difference of the number of superior and inferior objectives between two feasible solutions, but also considers the values of improved objective functions underlying the hypothesis that all objectives in the problem have equal importance. Two new evolutionary algorithms for E-optimal solutions are proposed. Because the new relation γE of E-dominance is not transitive, so a new way must be found for consideration of convergence properties of algorithms. A Boolean function better used as a select strategy is defined. The convergence theorems of the new evolutionary algorithms are proved. Some numerical experiments show that the new evolutionary decision theory is better than Pareto decision theory for many-objective function optimization problems. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Carpentieri2:2008:cec, author = "Marco Carpentieri ", title = "Hybrid Genetic Models Based on Recombination of Allele Permutations Based on Shift and Rotations for DHCP", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0131.pdf}, url = {}, size = {}, abstract = {We introduce a genetic model to solve the Directed Hamiltonian Cycle Problem (DHCP) for random directed graphs (digraphs) containing a (hidden) superposed random Hamiltonian cycle. The model represents a scheme for hybrid techniques that recombine the genetic material of allele permutation chromosomes merging ideas coming from the most recent progress in the evolutionary algorithm and the traditional combinatorial optimization areas. The methods are interpreted by rephrasing DHCP as determining the compatibility of some quadratic systems over the finite field GF(2). Genetic algorithms implementing some instances of the model and in which the recombination of the alleles is based on shift and rotations of connected traits of the chromosomes are compared with the classic Angulin and Valiant technique designed to find Hamiltonian cycles in random digraphs. The comparison is interpreted taking also into account the results about the main combinatorial techniques, for which theoretical analysis has been developed, to solve DHCP for random digraphs. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Yang3:2008:cec, author = "Guangfei Yang and Kaoru Shimada and Shingo Mabu and Kotaro Hirasawa", title = "A Personalized Association Rule Ranking Method Based on Semantic Similarity and Evolutionary Computation", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0132.pdf}, url = {}, size = {}, abstract = {Many methods have been studied for mining association rules efficiently. However, because these methods usually generate a large number of rules, it is still a heavy burden for the users to find the most interesting ones. In this paper, we propose a novel method for finding what the user is interested in by assigning several keywords, like searching documents on the WWW by search engines. We build an ontology to describe the concepts and relationships in the research domain and mine association rules by Genetic Network Programming from the database where the attributes are concepts in ontology. By considering both the semantic similarity between the rules and the keywords, and the statistical information like support, confidence, chi-squared value, we could rank the rules by a new method named RuleRank, where genetic algorithm is applied to adjust the parameters and the optimal ranking model is built for the user. Experiments show that our approach is effective for the users to find what they want. }, keywords = {genetic algorithms, genetic programming}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Beyer:2008:cec, author = "Hans-Georg Beyer and Steffen Finck", title = "On the Performance of Evolution Strategies on Noisy PDQFs: Progress Rate Analysis", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0133.pdf}, url = {}, size = {}, abstract = {This paper analyses the behaviour of the (μ/μI,λ)-ES on a class of noisy positive definite quadratic forms (PDQFs). First the equations for the normalised progress rates are derived and then analysed for constant normalised noise strength and constant (non-normalised) noise strength. Since in the latter case the strategy is not able to reach the optimum, formulas for the final distances to the optimiser (steady state) are derived. The theoretical predictions are then compared with empirical results. In both noise cases the influence of the strategy parameters will be investigated. Further, the equipartition conjecture is used to provide an alternative derivation of the steady state distances in the case of vanishing mutation strength. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Garis:2008:cec, author = "Hugo de Garis and Tang Jian Yu and Huang Zhiyong and Bai Lu and Chen Cong and Guo Junfei and Tan Xianjin and Tian Hao and Tian Xiaohan and Xiong Ye and Yu Xiangqian and Huang Di", title = "A Four Year, 3 Million RMB Project to Build a 15,000 Evolved Neural Net Module Artificial Brain in China", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0136.pdf}, url = {}, size = {}, abstract = {The first author has recently received a 3 million RMB, 4 year grant to build China's first artificial brain, starting in 2008, that will consist of approximately 15,000 interconnected neural net modules, evolved one at a time in a special accelerator board [1] (which is 50 times faster than using an ordinary PC) to control the hundreds of behaviours of an autonomous robot. The approach taken in building this artificial brain is fast and cheap (e.g. 1500 for the FPGA board, 1000 for the robot, and 500 for the PC, a total of 3000), so we hope that other brain building groups around the world will copy this evolutionary engineering approach. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Tan:2008:cec, author = "Swee Chuan Tan and Kai Ming Ting and Shyh Wei Teng", title = "Issues of Grid-Cluster Retrievals in Swarm-Based Clustering", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0139.pdf}, url = {}, size = {}, abstract = {One common approach in swarm-based clustering is to use agents to create a set of clusters on a two-dimensional grid, and then use an existing clustering method to retrieve the clusters on the grid. The second step, which we call grid-cluster retrieval, is an essential step to obtain an explicit partitioning of data. In this study, we highlight the issues in grid-cluster retrievals commonly neglected by researchers, and demonstrate the nontrivial difficulties involved. To tackle the issues, we then evaluate three methods: K-means, hierarchical clustering (Weighted Single-link) and density-based clustering (DBScan). Among the three methods, DBScan is the only method which has not been previously used for grid-cluster retrievals, yet it is shown to be the most suitable method in terms of effectiveness and efficiency. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Zhang3:2008:cec, author = "Lining Zhang and Maoguo Gong and Licheng Jiao and Jie Yang ", title = "Improved Clonal Selection Algorithm based on Baldwinian Learning", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0142.pdf}, url = {}, size = {}, abstract = {In this paper, based on Baldwin effect, an improved clonal selection algorithm, Baldwin Clonal Selection Algorithm, termed as BCSA, is proposed to deal with complex multimodal optimization problems. BCSA evolves and improves antibody population by three operations: clonal proliferation operation, Baldwinian learning operation and clonal selection operation. By introducing Baldwin effect, BCSA can make the most of experience of antibodies, accelerate the convergence, and obtain the global optimization quickly. In experiments, BCSA is tested on four types of functions and compared with the clonal selection algorithm and other optimization methods. Experimental results indicate that BCSA achieves a good performance, and is also an effective and robust technique for optimization. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Zhang4:2008:cec, author = "Lining Zhang and Maoguo Gong and Licheng Jiao and Jie Yang ", title = "Optimal Approximation of Linear Systems by an Improved Clonal Selection Algorithm", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0143.pdf}, url = {}, size = {}, abstract = {Based on the theory of clonal selection in immunology, by introducing Baldwin effect, an improved clonal selection algorithm, termed as Baldwin Clonal Selection Algorithm (BCSA), is proposed to solve the optimal approximation of linear systems. For engineering computing, the novel algorithm adopts three operations to evolve and improve the population: clonal proliferation operation, Baldwinian learning operation and clonal selection operation. The experimental study on the optimal approximation of a stable linear system and an unstable one show that the approximate models searched by the new algorithm have better performance indices than those obtained by some existing algorithms including the differential evolution algorithm, multi-agent genetic algorithm and artificial immune response algorithm. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Yang4:2008:cec, author = "Jie Yang and Maoguo Gong and Licheng Jiao and Lining Zhang ", title = "Improved Clonal Selection Algorithm Based on Lamarckian Local Search Technique", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0145.pdf}, url = {}, size = {}, abstract = {In this paper, we introduce Lamarckian learning theory into the Clonal Selection Algorithm and propose a sort of Lamarckian Clonal Selection Algorithm, termed as LCSA. The major aim is to use effectively the information of each individual to reinforce the exploitation with the help of Lamarckian local search. Recombination operator and tournament selection operator are incorporated into LCSA to further enhance the ability of global exploration. We compared LCSA with the Clonal Selection Algorithm(CSA) in solving twenty benchmark problems to test the performance of LCSA. The results demonstrate that LCSA is effective and efficient in solving numerical optimization problems. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Chang:2008:cec, author = "Pei-Chann Chang and Wei-Hsiu Huang and Julie Yu-Chih Liu and Ching-Jung Ting", title = "Dynamic Diversity Control by Injecting Artificial Chromosomes for Solving TSP Problems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0147.pdf}, url = {}, size = {}, abstract = {The applications of genetic algorithms (GAs) in solving combinatorial problems are frequently faced with a problem of early convergence and the evolutionary processes are often trapped in a local but not global optimum. This premature convergence occurs when the population of a genetic algorithm reaches a suboptimal state that the genetic operators can no longer produce offspring with a better performance than their parents. In the literature, plenty of work has been investigated to introduce new methods and operators in order to overcome this essential problem of genetic algorithms. As these methods and the belonging operators are rather problem specific in general. In this research, we take a different approach by observing the progress of the evolutionary process and when the diversity of the population dropping below a threshold level then artificial chromosomes with high diversity will be introduced to increase the average diversity level thus to ensure the process can jump out the local optimum. The proposed method is implemented independently of the problem characteristics and can be applied to improve the global convergence behavior of genetic algorithms. The experimental results using TSP instances show that the proposed approach is very effective in preventing the premature convergence when compared with the earlier approaches. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Qing:2008:cec, author = "Anyong Qing ", title = "A Study on Base Vector for Differential Evolution", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0149.pdf}, url = {}, size = {}, abstract = {One of the keys leading to the success of differential evolution is its mechanism of differential mutation for generating mutant vectors. In the community of differential evolution, the mutation operator is usually marked as x/y where x indicates how the base vector is chosen and y (≥ 1) is the number of vector differences added to the base vector. It is noted that rand/1 has been the most widely used mutation operator. However, a comprehensive comparative parametric study on differential evolution shows that strategies applying random base vector are neither efficient nor robust. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Zhuang:2008:cec, author = "Tao Zhuang and Qiqiang Li and Qingqiang Guo and Xingshan Wang", title = "A Two-Stage Particle Swarm Optimizer", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0151.pdf}, url = {}, size = {}, abstract = {This paper presents a variant of particle swarm optimizers (PSOs), called the two-stage particle swarm optimizer (TSPSO). TSPSO performs a gross searching algorithm at the first stage, and switches to a fine-grained searching algorithm if it is stagnated at the first stage. A switching criterion was proposed, and a new fine-grained searching algorithm was devised to work at the second stage of TSPSO. For the first stage, Fully Informed PSO (FIPS) with U-square topology was adopted. At the second stage, the fine-grained searching algorithm has very good performance on complex multimodal functions such as Rastrigin and Schwefel functions. The switching behavior makes TSPSO adaptive to the problems to be solved. Experimental results show that TSPSO has very good performance on both unimodal and multimodal functions compared with six other variants of PSO. Especially on complex multimodal functions, TSPSO's performance is even better than the most state of art PSOs such as CLPS and CPSO-H. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Hingee:2008:cec, author = "Kassel Hingee and Marcus Hutter", title = "Equivalence of Probabilistic Tournament and Polynomial Ranking Selection", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0152.pdf}, url = {}, size = {}, abstract = {Crucial to an Evolutionary Algorithm's performance is its selection scheme. We mathematically investigate the relation between polynomial rank and probabilistic tournament methods which are (respectively) generalisations of the popular linear ranking and tournament selection schemes. We show that every probabilistic tournament is equivalent to a unique polynomial rank scheme. In fact, we derived explicit operators for translating between these two types of selection. Of particular importance is that most linear and most practical quadratic rank schemes are probabilistic tournaments. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wang5:2008:cec, author = "Zhiwen Yu Dingwen Wang and Hau-San Wong", title = "Knowledge Learning based Evolutionary Algorithm for Unconstrained Optimization Problem", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0153.pdf}, url = {}, size = {}, abstract = {In this paper, we propose a new evolutionary algorithm called nearest neighbor evolutionary algorithm (NNE) to solve the unconstrained optimization problem. Specifically, NNE consists of two major steps: coarse nearest neighbor evolutionary and fine nearest neighbor evolutionary. The coarse nearest neighbor evolutionary step pays more attention to searching the optimal solutions in the global way, while the fine nearest neighbor evolutionary step focuses on searching the best solutions in the local way. NNE repeats two major steps until the terminate condition is reached. NNE not only adopts the elitist strategy and maintains the best individuals for the next generation, but also considers the knowledge obtained in the searching process. The experiments demonstrate that (1) NNE achieves good performance in most of numerical optimization problems; (2) NNE outperforms most of state-of-art evolutionary algorithms, such as traditional genetic algorithm (GA), the jumping gene genetic algorithm (JGGA). }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Luo:2008:cec, author = "Biao Luo and Jinhua Zheng", title = "A New Methodology for Searching Robust Pareto Optimal Solutions with MOEAs", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0156.pdf}, url = {}, size = {}, abstract = {It is of great importance for a solution with high robustness in the real application, not only with good quality. Searching for robust Pareto optimal solutions for multi-objective optimization problems (MOPs) is a challenge, no exception for multi-objective evolutionary algorithms (MOEAs). Recently, as one of the popular approach to search robust Pareto optimal solutions, ''effective objective function'' based MOEA (Eff-MOEA) can only find solutions which have average robustness and quality, but cannot find solutions which have the highest robustness and best quality. In this paper, we proposed a new methodology for robust Pareto optimal solutions and presented a novel MOEA named MOEA/R, which convert a multi-objective robust optimization problem (MROP) into a bi-objective optimization problem. Each of two objectives represents a sub-MOP, one of which optimizes solutions' quality and another optimizes solutions' robustness. Through the comparison and analysis between MOEA/R, Eff-MOEA and NSGA-II, the experimental results demonstrate that MOEA/R can acquire good purposes. The most important contribution of this paper is that MOEA/R explores a novel methodology for searching robust Pareto optimal solutions. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Liu3:2008:cec, author = "Bo Liu and Xuejun Zhang and Hannan Ma", title = "Hybrid Differential Evolution for Noisy Optimization", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0157.pdf}, url = {}, size = {}, abstract = {A robust hybrid algorithm named DEOSA for function optimization problems is investigated in this paper. In recent years, differential evolution (DE) has attracted wide research and effective applications in various fields. However, to the best of our knowledge, most of the available works did not consider noisy and uncertain environments in practical optimization problems. This paper focuses on a robust DE, which can adapt to noisy environment in real applications. By combining the advantages of DE algorithm, the optimal computing budget allocation (OCBA) technique and simulated annealing (SA) algorithm, a robust hybrid DE approach DEOSA is proposed. In DEOSA, the population-based search mechanism of DE is applied for well exploration and exploitation, and the OCBA technique is used to allocate limited sampling budgets to provide reliable evaluation and identification for good individuals. Meanwhile, SA is also applied in the hybrid approach to maintain the diversity of the population, in order to alleviate the negative influences on greedy selection mechanism of DE brought by the noises. DEOSA is tested by well-known benchmark problems with noise and the effect of noise magnitude is also investigated. The comparisons to several commonly used techniques for optimization in noisy environment are also carried out. The results and comparisons demonstrate the superiority of DEOSA. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Andras:2008:cec, author = "Peter Andras ", title = "Uncertainty in Iterated Cooperation Games", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0158.pdf}, url = {}, size = {}, abstract = {The emergence and evolution of cooperation among selfish individuals is a key question of theoretical biology. Uncertainty of outcomes of interactions between individuals is an important determinant of cooperative behavior. Here we describe a model that allows the analysis of the effects of such uncertainty on the level of cooperation. We show that in iterated cooperation games the level of cooperation increases with the level of outcome uncertainty. We show that this is the case if the individuals communicate about their cooperation intentions and also if they do not communicate their intentions. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Zeng2:2008:cec, author = "Bin Zeng and Tao Hu and Jun Wei ", title = "An Approach to Constructing Evolutionary Agent Structure for Workflow Management System Based on Simulation", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0159.pdf}, url = {}, size = {}, abstract = {Multi-agent Coordinate mechanism research has attracted increasing attention in recent years. Researches on the problem mainly focus on how to organize and coordinate relations between agents. The composition of different agents is an issue that must be faced by developers. This paper introduces an automatic agent combination method oriented to workflow which considers both task's dynamic workload and agent's evolving cognitive ability. It composes agent structure through three steps: 1)Clusters the tasks according to their resources requirements by using decision tree, which helps to define the corresponding agent set. 2)Calculates the ability and cost of agent executing workflow based on information about task workload and duration with uncertainty model. 3)Search for the optimal agents' composition with the objective to maximize the speed of workflow execution while balancing the workload among agents under the constraint of agent ability, workload threshold and execution cost based on performance analysis of simulation result. Experimental results show that this method has a good performance by identifying the optimal agent configuration to execute workflow scenario. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Jiang:2008:cec, author = "Dazhi Jiang and Zhijian Wu and Jun Zou and Ming Wei and Lishan Kang", title = "Algorithm Based on Heuristic Subspace Searching Strategy for Solving Investment Portfolio Optimization Problems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0160.pdf}, url = {}, size = {}, abstract = {There exist many difficulties when investment portfolio problems based on Markowitz model are solved by using some traditional methods, such as Newton method, conjugate gradient method, etc. One of the difficulties is that Markowitz model has rigorous constraint conditions. Evolutionary Computation is a parallel global optimization algorithm with high efficiency and it has been widely used in portfolio investment field. A heuristic subspace searching algorithm is put forward in this paper for solving investment portfolio optimization problems based on Markowitz model. The experimental results indicate that this algorithm has an improved efficiency compared with traditional Evolutionary Computation. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Jiang2:2008:cec, author = "Dazhi Jiang and Zhijian Wu and Jun Zou and Jianwei Zhang and Lishan Kang", title = "Evolutionary Modeling Based on Overlap Reuse", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0161.pdf}, url = {}, size = {}, abstract = {Reuse (or reusability) plays an important role in the software engineering. The software reuse technique, considered as an effective approach to improve the productivity, can reduce the cost in software design and development. This paper introduces the concept of reuse in the software into the chromosome and presents an evolutionary modeling algorithm based on the overlapped reuse. Furthermore, a new Gene Reading & Computing Machine is constructed for calculating the fitness of chromosome which has the characteristic of reusability. As a new kind of modeling algorithm, this is a new research way for evolutionary modeling. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Li4:2008:cec, author = "Miqing Li and Jinhua Zheng and Guixia Xiao", title = "An Efficient Multi-Objective Evolutionary Algorithm Based on Minimum Spanning Tree", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0163.pdf}, url = {}, size = {}, abstract = {Fitness assignment and external population maintenance are two important parts of multi-objective evolutionary algorithms. In this paper, we propose a new MOEA which uses the information of minimum spanning tree to assign fitness and maintain the external population. Moreover, a Minimum Spanning Tree Crowding Distance (MSTCD) is defined to estimate the density of solutions. From an extensive comparative study with three other MOEAs on a number of two and three objective test problems, it is observed that the proposed algorithm has good performance in convergence and distribution. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Li5:2008:cec, author = "Miqing Li and Jinhua Zheng and Guixia Xiao", title = "Uniformity Assessment for Evolutionary Multi-Objective Optimization", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0165.pdf}, url = {}, size = {}, abstract = {Uniformity assessment of approximations of the Pareto-optimal set is an important issue in comparing the performance of multi-objective evolutionary algorithms. Although a number of performance metrics existed, many are applicable to low objective problems (2-3 objectives). In addition, most of the existed metrics are only applied to the final non-dominated set. In this paper, we suggest a running metric which evaluates the uniformity of solutions at every generation of a MOEA run. In particular, this metric can compare the uniformity of population with different size in any number of objectives. With an agglomeration of generation-wise populations, the metric reveals the change of uniformity in a MOEA run or helps provide a comparative evaluation of two or more MOEAs. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Graaff:2008:cec, author = "A. J. Graaff and A. P. Engelbrecht", title = "Towards a Self Regulating Local Network Neighbourhood Artificial Immune System for Data Clustering", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0166.pdf}, url = {}, size = {}, abstract = {The theory of idiotopic lymphocyte networks in the natural immune system inspired the modelling of network based artificial immune systems (AIS). Many of these network based AIS models establish network links between the artificial lymphocytes (ALCs) whenever the measured Euclidean distance between the ALCs are below a certain network threshold. The linked ALCs represent an artificial lymphocyte network. Graaff and Engelbrecht introduced the Local Network Neighbourhood AIS (LNNAIS) [2]. The interpretation of the network theory is the main difference between LNNAIS and existing network based AIS models. The LNNAIS uses the concept of an artificial lymphocyte neighbourhood to determine network links between ALCs [2]. The purpose of this paper is to highlight the drawbacks of the proposed LNNAIS model and to address these drawbacks with some enhancements, improving LNNAIS towards a self regulating AIS. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Li6:2008:cec, author = "Kangshun Li and Weifeng Pan and Wensheng Zhang and Zhangxin Chen", title = "Automatic Modeling of a Novel Gene Expression Programming Based on Statistical Analysis and Critical Velocity", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0167.pdf}, url = {}, size = {}, abstract = {The basic principle of GEP is briefly introduced. And considering the defects of classic GEP such as lack of variety, the problem of convergence and blind searching without learning mechanism, a novel GEP based on statistical analysis and stagnancy velocity is proposed (called AMACGEP). It mainly has the following characteristics: First, improve the initial population by statistic analysis of repeated bodies. Second, introduce the concept of stagnancy velocity to adjust the searching space, evolution velocity, the diversity of individuals and the accuracy of prediction. Third, introduce dynamic mutation operator to improve the diversity of individuals and the velocity of convergence. Compared with other methods like traditional methods, methods of neural network, classic GEP and other improved GEPs in automatic modelling of complex function, the simulation results show that the AMACGEP set up by this paper is better. }, keywords = {genetic algorithms, genetic programming, gene expression programming}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Fu:2008:cec, author = "Jian Fu and Qing Liu and Xinmin Zhou and Kui Xiang and Zhigang Zeng ", title = "An Adaptive Variable Strategy Pareto Differential Evolution Algorithm for Multi-Objective Optimization", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0168.pdf}, url = {}, size = {}, abstract = {In the paper, we propose an adaptive variable strategy Pareto differential evolution algorithm for multi-objective optimization (AVSPDE). It is different from the general adaptive DE methods which are regulated by variable parameters and applied in single-objective area. Based on the real-time information from the tournament selection set (TSS), there are two DE variants to switch dynamically during the run, in which one aims at fast convergence and the other focus on the diverse spread. The theoretical analysis and the digital simulation show the presented method can achieved better performance. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Ashlock:2008:cec, author = "Daniel Ashlock and Taika {von Konigslow}", title = "Evolution of Artificial Ring Species", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0169.pdf}, url = {}, size = {}, abstract = {Biological ring species are a population surrounding a geographic obstruction such as a large lake or a mountain range. Adjacent sub-populations are mutually fertile, but fertility drops with distance. This study attempts to create examples of artificial ring species using evolutionary algorithms. ISAc lists, a representation with self-organised and potentially complex genetics, are used to evolve controllers for the Tartarus task. The breeding population of Tartarus controllers are arranged in a ring-shaped configuration with strictly local gene flow. Fertility is defined to be the probability that a child will have fitness at least that of its least fit parent. Fertility is found to drop steadily and significantly with distance around the ring in each of twelve replicates of the experiment. Comparison of fertility at various distances within a ring-shaped population is compared with sampled intra-population fertility. Some populations are found to have significantly higher than background fertility with other populations. This phenomena suggests the presence of aggressive genetics or dominant phenotype in which a creature has an enhanced probability of simply cloning its own phenotype during crossover. In addition to creating examples of artificial ring species this study also achieved a very high level of fitness with the Tartarus task. A comparison is made with another study that uses hybridisation to achieve record breaking Tartarus fitness. }, keywords = {genetic algorithms, genetic programming}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Ashlock2:2008:cec, author = "Daniel A. Ashlock and Fatemeh Jafargholi", title = "Behavioral Regimes in the Evolution of Extremal Epidemic Graphs", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0170.pdf}, url = {}, size = {}, abstract = {Models of epidemic spread often incorporate contact networks along which the epidemic can spread. The character of the network can have a substantial impact on the course of the epidemic. In this study networks are optimized to yield longlasting epidemics. These networks represent an upper bound on one type of network behavior. The evolutionary algorithm used searches the space of networks with a specified degree sequence, with degrees representing the number of sexual partners of each member of the population. The representation used is a linear chromosome specifying a series of editing moves applied to an initial network. The initial network specifies the degree sequence of the searched networks implicitly and the editing moves preserve the degree sequence. The evolutionary algorithm uses a non-standard type of restart in which the currently best network in the population replaces the initial network. This restart operator is called a recentering operator. The recentering operator moves the evolving population to successively higher fitness portions of the network space. In this study the algorithm is applied to networks with average degree from 2.5 to 7. In low-degree networks, short epidemics result from failure of the disease to spread through the relatively sparse links of the network. In high-degree networks, short epidemics result from the rapid infection of the entire population. The evolutionary algorithm is able to optimize both high and low degree networks to significantly increase the epidemic duration. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Vergidis:2008:cec, author = "Kostas Vergidis and Ashutosh Tiwari", title = "Business Process Design and Attribute Optimization within an Evolutionary Framework", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0171.pdf}, url = {}, size = {}, abstract = {This paper discusses the problem of business process design and attribute optimization within a multiobjective evolutionary framework. Business process design and attribute optimization is considered as the problem of constructing feasible business process designs with optimum attribute values such as duration and cost. The feasibility of a process design is based on: (i)the process requirements such as the required input and the expected output resources and (ii)the connectivity of the participating tasks in the process design through their input and output resources. The proposed approach involves the application of the Evolutionary Multi- Objective Optimization Algorithm (EMOOA) Non-dominated Sorting Genetic Algorithm II (NSGA2) in an attempt to generate a series of diverse optimized business process designs for the same process requirements. The proposed optimization framework introduces a quantitative representation of business processes involving two matrices one for capturing the process design and one for calculating and evaluating the process attributes. It also introduces an algorithm that checks the feasibility of each candidate solution (i.e. process design). The results demonstrate that for a variety of experimental problems NSGA2 produces a satisfactory number of optimized design alternatives considering the problem complexity and high rate of infeasibility. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Meuth:2008:cec, author = "Ryan J. Meuth and Donald C. Wunsch II", title = "Divide and Conquer Evolutionary TSP Solution for Vehicle Path Planning", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0172.pdf}, url = {}, size = {}, abstract = {The problem of robotic area coverage is applicable to many domains, such as search, agriculture, cleaning, and machine tooling. The robotic area coverage task is concerned with moving a vehicle with an effector, or sensor, through the task space such that the sensor passes over every point in the space. For covering complex areas, back and forth paths are inadequate. This paper presents a real-time path planning architecture consisting of layers of a clustering method to divide and conquer the problem combined with a twolayered, global and local optimization method. This architecture is able to optimize the execution of a series of waypoints for a restricted mobility vehicle, a fixed wing airplane. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Lu:2008:cec, author = "Fuqiang Lu and Min Huang and Xingwei Wang", title = "PSO Based Stochastic Programming Model for Risk Management in Virtual Enterprise", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0176.pdf}, url = {}, size = {}, abstract = {Risk management in a Virtual Enterprise (VE) is an important issue due to its agility and diversity of its members and its distributed characteristics. In this paper, a stochastic programming model of risk management is proposed. More specifically, we consider about the stochastic characters of the risk in VE, and then we build a stochastic programming model to deal with the stochastic characters of the risk. In detail, this is a chance constraint programming model, One of the great advantages of this class of model is that it can exactlly describe the risk preference of the manager. In this model, the risk level of VE is obtained from a composite result of many risk factors. In order to reduce the risk level of VE, the manager has to select effective action for every risk factor. For each risk factor, there are several actions provided. Here we only select one action for a risk factor or do nothing with it. To solve this stochastic programming model, A particle swarm optimization (PSO) algorithm is designed. On the other hand, to deal with those stochastic variables, Monte Carlo simulation is combined with PSO algorithm. Finally, a numerical example is given to illustrate the effectiveness of the PSO algorithm and the result shows that the model is very useful for risk management in VE. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Cheung:2008:cec, author = "Mars Cheung and Stephen Johnson and David Hecht and Gary B. Fogel", title = "Quantitative Structure-Property Relationships for Drug Solubility Prediction Using Evolved Neural Networks", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0177.pdf}, url = {}, size = {}, abstract = {Preclinical in vivo studies of small molecule compound libraries can be enhanced using a model of specific quantitative structure-property relationships. This may include toxicological or solubility measures such as prediction of drug solubility in mixtures of polyethylene glycol and/or water. Here we examine the utility of both multiple linear regressions and evolved neural networks for the prediction of drug solubility in aqueous solution. Initial results suggest that modeling requires compound libraries with high similarity. Clustering approaches can be used to group compounds by similarity with models built for each cluster. Linear and nonlinear models can be used for modeling, however evolved neural networks can be used to simultaneously reduce the feature space as well as optimize models for solubility prediction. With these approaches it is also possible to identify ``human interpretable'' features from the best models that can be used by chemists during preclinical drug development. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Kimura:2008:cec, author = "S. Kimura and K. Matsumura ", title = "Density Estimation using Crossover Kernels and its Application to a Real-coded Genetic Algorithm", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0183.pdf}, url = {}, size = {}, abstract = {Sakuma and Kobayashi have proposed a density estimation method that uses real-coded crossover operators. However, their method was used only to estimate normal distribution functions. In order to estimate more complicated PDFs, this study proposes a new density estimation method of using crossover operators. When we try to solve function optimization problems, on the other hand, real-coded genetic algorithms (GAs) show good performances if their crossover operators have an ability to estimate the PDF of the population well. Thus, this study then applies our density estimation method into a simple real-coded GA to improve its search performance. Finally, through numerical experiments, we verify the effectiveness of the proposed density estimation method. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Zhen-Zhao:2008:cec, author = "First Liu Zhen-Zhao and Second Liu Jie-Ping and Third Liu Yang", title = "Determination of Air Boarding Strategy Based on MINPL and Monte Carlo Simulation", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0184.pdf}, url = {}, size = {}, abstract = {With the increasing business in air travel area, reducing the plane's turnaround time is becoming more and more important. In this paper, it chooses the optimum boarding strategy to reduce the turnaround time. The MINPL model is for the small-size plane, with the boarding time mainly depending on seat interference and aisle interference. The GASimplex Algorithm (Genetic Algorithms mixed with Simplex method) is used to solve it. For the middle-size plane, a Monte Carlo Simulation model is designed which is based on probabilistic aspect. Then these two models are integrated to solve the large-plane problem. Based on these models, the paper uses Matlab 6.5 to do the calculation and found that the boarding strategy combined Reverse Pyramid with Rotation outperforms other strategies. Finally, it analyzes the strengths of the model. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Liao:2008:cec, author = "Huilian Liao and Zhen Ji and Q. H. Wu", title = "A Novel Genetic Particle-Pair Optimizer for Vector Quantization in Image Coding", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0185.pdf}, url = {}, size = {}, abstract = {This paper presents a novel Genetic Particle-Pair Optimiser (GPPO) for Vector Quantisation of image coding. GPPO only applies a particle-pair that consists of two particles, which contributes to the relief of huge computation load in most existing Vector Quantisation algorithms. GPPO combines the advantage both in Genetic Algorithms and Particle Swarm Optimization, due to the use of genetic operators and particle operators at each generation. Experimental results have demonstrated that the quality of the codebook design optimised by GPPO is better than that optimised respectively by Fuzzy K-means (FKM), Fuzzy Reinforcement Learning Vector Quantisation (FRLVQ), improved FRLVQ which uses Fuzzy Vector Quantization (FVQ) as post-process, called FRLVQFVQ, and Particle-Pair Optimiser (PPO). GPPO provides a satisfactory solution to vector quantisation, and shows a steady trend of improvement in the quality of codebook design. The dependence of the final codebook on the selection of the initial codebook is also reduced. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Li7:2008:cec, author = "Kangshun Li and Yang Xie and Wensheng Zhang and Zhangxin Chen", title = "A Novel Algorithm for Evolving Encryption Sequences Based on Particle Dynamics", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0186.pdf}, url = {}, size = {}, abstract = {In this paper a novel algorithm for evolving encryption sequences based on particle dynamics is presented to design encryption systems with high safety and efficiency. Because the algorithm based on particle dynamics is constructed by using the law of entropy increasing and the principle of energy minimum of particle systems, it has features of uniformity and diversity in particle distribution of particle systems. Therefore, the sequence encryption produced has high randomicity, more chaos and long periodicity. The experiments show that the sequence encryption by using this method has high randomicity, and the encryption system by using this method has better security and safety. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Chakraborty:2008:cec, author = "Jayasree Chakraborty and Amit Konar and Uday K. Chakraborty and L. C. Jain", title = "Distributed Cooperative Multi-Robot Path Planning Using Differential Evolution", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0189.pdf}, url = {}, size = {}, abstract = {This paper provides an alternative approach to the co-operative multi-robot path planning problem using parallel differential evolution algorithms. Both centralized and distributed realizations for multi-robot path planning have been studied, and the performances of the methods have been compared with respect to a few pre-defined yardsticks. The distributed approach to this problem out-performs its centralized version for multi-robot planning. Relative performance of the distributed version of the differential evolution algorithm has been studied with varying numbers of robots and obstacles. The distributed version of the algorithm is also compared with a PSO-based realization, and the results are competitive. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Niu:2008:cec, author = "Li Niu and Jie Lu and Guangquan Zhang", title = "Improved Business Intelligence Analytics on Manager's Experience", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0190.pdf}, url = {}, size = {}, abstract = {Current business intelligence (BI) systems bring the manager with powerful data analysis functions. However the manager is either limited to predefined queries or feels lost within torrents of data. Thus, decision making is still a task with high cognitive load. BI analytics are based on queries into the data warehouse. Rather than predefining queries or less guided ad hoc analysis, we use the manager's experience, together with decision problem statement, business ontology and heuristics to automatically construct data retrieval queries. Our method is expected to provide the manager better decision support on cognitive orientation. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Gonzales:2008:cec, author = "Eloy Gonzales and Karla Taboada and Kaoru Shimada and Shingo Mabu and Kotaro Hirasawa", title = "Evaluating Class Association Rules using Genetic Relation Programming", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0191.pdf}, url = {}, size = {}, abstract = {The number of association rules generated during the data mining process is generally very large, that is, an association rule mining algorithm could generate thousands or millions of rules. However, only a small number of rules are likely to be of any interest to the domain expert analyzing the data, i.e., many of the rules are either irrelevant or obvious. Therefore, techniques for evaluating the relevance and usefulness of discovered patterns are required. The aim of this paper is to propose a new method for evaluating the relevance and usefulness of discovered association rules by reducing the number of rules extracted using an evolutionary method named Genetic Relation Programming (GRP). The algorithm evaluates the relationships between the rules at each generation using a specific measure of distance and gives the best set of rules at the final generation. The efficiency of the proposed method is compared with other conventional methods and it is clarified that the proposed method shows comparable accuracy with others. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Prime:2008:cec, author = "Ben Prime and Tim Hendtlass", title = "An Evolutionary Reincarnation Algorithm", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0192.pdf}, url = {}, size = {}, abstract = {As there is little or no experimental experience of reincarnation in the natural world, attempts to add a reincarnation metaphor to an evolutionary algorithm must of necessity proceed cautiously. In previous work the authors have established that the reintroduction of previously stored gene values into the population can have a noticeable effect on the progress of evolution, this paper now considers a range of options for deciding which gene values to store, which to return and which individuals in the current population should receive the returned gene values. Consistent experimental results on three well known functions allow a suggestion to be made of, if not the best choices, at least a good choice selection to use on initial experiments on other problems. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Dou:2008:cec, author = "Wenxiang Dou and Jinglu Hu and Kotaro Hirasawa and Gengfeng Wu", title = "Distributed Multi-Relational Data Mining Based on Genetic Algorithm", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0193.pdf}, url = {}, size = {}, abstract = {An efficient algorithm for mining important association rule from multi-relational database using distributed mining ideas. Most existing data mining approaches look for rules in a single data table. However, most databases are multi-relational. In this paper, we present a novel distributed data-mining method to mine important rules in multiple tables (relations) and combine the method with genetic algorithm to enhance the mining efficiency. Genetic algorithm is in charge of finding antecedent rules and aggregate of transaction set that produces the corresponding rule from the chief attributes. Apriori and statistic method is in charge of mining consequent rules from the rest relational attributes of other tables according to the corresponding transaction set producing the antecedent rule in a distributed way. Our method has several advantages over most exiting data mining approaches. First, it can process multi-relational database efficiently. Second, rules produced have finer pattern. Finally, we adopt a new concept of extended association rules that contain more import and underlying information. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Tian:2008:cec, author = "Jing Tian and Weiyu Yu and Shengli Xie ", title = "An Ant Colony Optimization Algorithm For Image Edge Detection", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0194.pdf}, url = {}, size = {}, abstract = {Ant colony optimization (ACO) is an optimization algorithm inspired by the natural behavior of ant species that ants deposit pheromone on the ground for foraging. In this paper, ACO is introduced to tackle the image edge detection problem. The proposed ACO-based edge detection approach is able to establish a pheromone matrix that represents the edge information presented at each pixel position of the image, according to the movements of a number of ants which are dispatched to move on the image. Furthermore, the movements of these ants are driven by the local variation of the image's intensity values. Experimental results are provided to demonstrate the superior performance of the proposed approach. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Jorgensen:2008:cec, author = "Christopher Jorgensen and Garrison Greenwood and Peyman Arefi", title = "Practical Considerations for Implementing Intrinsic Fault Recovery in Embedded Systems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0195.pdf}, url = {}, size = {}, abstract = {Evolvable hardware provides a viable fault recovery technique for embedded systems already deployed into an operational environment. Typically the fitness of each evolved configuration in such systems must be intrinsically determined because imprecise information about faults makes extrinsic methods impractical. Most work on intrinsic circuit evolution is conducted in laboratory environments where sophisticated measurement equipment is readily available and frequency domain analysis poses no real problems. In this paper we argue intrinsic fault recovery for embedded systems has to be done in the time domain. We report the results of several experiments conducted to identify potential problems with determining fitness in the time domain for embedded systems. We also discuss the limitations embedded systems impose on GAs used for evolvable hardware applications and suggest some possible solutions. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Montera:2008:cec, author = "Luciana Montera and Maria do Carmo Nicoletti and Flavio Henrique da Silva and Pablo Moscato", title = "An Effective Mutation-Based Measure for Evaluating the Suitability of Parental Sequences to Undergo DNA Shuffling Experiments", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0196.pdf}, url = {}, size = {}, abstract = {The DNA shuffling process has been successfully used in many experiments of Directed Molecular Evolution. In a shuffling experiment genes are recombined by an iterative procedure of PCR cycles aiming at obtaining new genes, hopefully with some of the original functions being improved. The optimizations of the parameters involved in the process as well as the characteristics of the parental sequences are of extreme importance to guarantee the success of a shuffling experiment. This paper proposes a new measure, based on the number of bases between existing mutations in the parental sequences, suitable for evaluating the suitability of two sequences to be submitted to a DNA shuffling experiment. In order to investigate the usefulness of the proposed mutation-based measure versus two commonly used measures, a family of 37 DNA gene sequences codifying for snake venom metallopeptidases was used for evaluation purposes using the three measures. The parental sequences identified by each of the three measures were validated by simulating the DNA shuffling process using the software eShuffle. The eShuffle results illustrate on the benefits of the mutationbased measure proposed in this paper. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Zhou2:2008:cec, author = "Jin Zhou and Lu Yu and Shingo Mabu and Kaoru Shimada and Kotaro Hirasawa and Sandor Markon", title = "Double-Deck Elevator Systems Adaptive to Traffic Flows Using Genetic Network Programming", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0198.pdf}, url = {}, size = {}, abstract = {Double-deck elevator system (DDES) has been invented firstly as a solution to improve the transportation capacity of elevator group systems in the up-peak traffic pattern. The transportation capacity could be even doubled when DDES runs in a pure up-peak traffic pattern where two connected cages stop at every two floors in an elevator round trip. However, the specific features of DDES make the elevator system intractable when it runs in some other traffic patterns. Moreover, since almost all of the traffic flows vary continuously during a day, an optimised controller of DDES is required to adapt the varying traffic flow. In this paper, we have proposed a controller adaptive to traffic flows for DDES using Genetic Network Programming (GNP) based on our past studies in this field, where the effectiveness of DDES controller using GNP has been verified in three typical traffic patterns. A traffic flow judgement part was introduced into the GNP framework of DDES controller, and the different parts of GNP were expected to be functionally localised by the evolutionary process to make the appropriate cage assignment in different traffic flow patterns. Simulation results show that the proposed method outperforms a conventional approach and two heuristic approaches in a varying traffic flow during the work time of a typical office building. }, keywords = {genetic algorithms, genetic programming}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Xu:2008:cec, author = "Weijun Xu and Yucheng Dong and Weilin Xiao and Jinhong Xu", title = "A Nonlinear Program Model to Obtain Consensus Priority Vector in the Analytic Hierarchy Process", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0200.pdf}, url = {}, size = {}, abstract = {In group decision making, because the decisionmakers usually represent different interest backgrounds, it is worth to study how to make the different decision makers coordinate and cooperate for aggregating group opinions. In this paper, based on the analytic hierarchy process, we propose a nonlinear program model to obtain consensus priority vector, and point that the model can make decision-makers reach consensus by improving compatibility of judgement matrices. Moreover, we use the genetic-simulated annealing algorithm to obtain its optimal solution. Finally, a numerical example is presented to illustrate the application of this method. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Xue:2008:cec, author = "Guixiang Xue and Zheng Zhao and Maode Ma and Tonghua Su and Shuang Liu", title = "Task Scheduling by Mean Field Annealing Algorithm in Grid Computing", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0202.pdf}, url = {}, size = {}, abstract = {Desirable goals for grid task scheduling algorithms would shorten average delay, maximize system use and fulfill user constraints. In this work, an agent-based grid management infrastructure coupled with Mean Field Annealing (MFA) scheduling algorithm has been proposed. An agent in grid uses a neural network algorithm to manage and schedule tasks. The Hopfield Neural Network is good at finding optimal solution with multi-constraints and can be fast to converge to the result. However, it is often trapped in a local minimum. Stochastic simulated annealing algorithm has an advantage in finding the optimal solution and escaping from the local minimum. Both significant characteristics of Hopfield neural network structure and stochastic simulated annealing algorithm are combined together to yield a mean field annealing scheme. A modified cooling procedure to accelerate reaching equilibrium for normalized mean field annealing has been applied to this scheme. The simulation results show that the scheduling algorithm of MFA works effectively. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Lu2:2008:cec, author = "Xingjia Lu and Yongsheng Ding and Kuangrong Hao", title = "Adaptive Design Optimization of Wireless Sensor Networks Using Artificial Immune Algorithms", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0204.pdf}, url = {}, size = {}, abstract = {The topology control is a very important issue in wireless sensor networks (WSNs). Many approaches have been proposed to carry out in this aspect, including modern heuristic approach. In this paper, the Topology Control based on Artificial Immune Algorithm (ToCAIA) is proposed to solute the energy-aware topology control for WSNs. ToCAIA is a heuristic algorithm, which is heuristic from the immune system of human. In ToCAIA, the antibody is the solution of the problem, and the antigen is the problem. ToCAIA could be used to solve the multi-objective minimum energy network connectivity (MENC) problem, and get the approximate solution. The experiment result shows that the topology control by using ToCAIA can be used for WSNs network optimization purposes. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Shi:2008:cec, author = "Guojun Shi and Qingsheng Ren", title = "Research on Compact Genetic Algorithm in Continuous Domain", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0205.pdf}, url = {}, size = {}, abstract = {Compact genetic algorithm (CGA) is a successful probability-based evolutionary algorithm which performs equivalent to the order-one behavior of the simple genetic algorithm (SGA) with uniform crossover. However, this equivalence only applies for binary encoded problems. To extend the basic concept of CGA to continuous domain, an improved CGA is proposed in this paper. We established a continuous CGA (cCGA) model by adopting two probability vectors to represent population. We study the update rules of the probability vectors and its initial value. In further we improve this cCGA by adopting elitism selection. We propose two kinds of elitism based cCGA by applying different elitism control policies. Theoretical analysis on elitism control is given and some useful results are concluded. The numerical experiment first gives a comparison between SGA and our cCGA in continuous domain and the results show the superiority and efficiency of cCGA. Comparison between elitism selection cCGA and non-elitism cCGA is also given to show the efficiency of elitism selection and the efficiency on elitism control. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wang6:2008:cec, author = "Yu Wang and Bin Li", title = "Understand Behavior and Performance of Real Coded Optimization Algorithms via NK-linkage Model", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0206.pdf}, url = {}, size = {}, abstract = {Classical NK-landcape model was designed for analyzing optimization and evolution process in binary solution space, so it can not be used to analyze Real Coded Optimization Algorithms (RCOAs) directly, which work in continuous solution space directly. In this paper, the concept of NK-landscape model is extended to the continuous space, and a new NKlandscape model with continuous space is proposed. The new model is powerful and comprehensive with simple structure and flexible formula. Therefore, it can be used to construct test functions of various types of linkages for analyzing various performances of RCOAs. The feasibility of the proposed model is testified via experiments with 3 well-known RCOAs, (i.e. covariance matrix adapting evolutionary strategy (CMA-ES), differential evolution (DE), neighborhood search differential evolution (NSDE)). The results show that the new model can reveal the merits and demerits of RCOAs effectively. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wang7:2008:cec, author = "Shaowei Wang and Xiaoyong Ji and Lishan Kang", title = "An Efficient Heuristic Method for Multiuser Detection in DS-CDMA Systems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0207.pdf}, url = {}, size = {}, abstract = {Optimum multiuser detection (OMD) in direct-sequence code-division multiple access (DS-CDMA) communication systems is a combinatorial optimization problem and has been proven NP-complete. Many heuristics have been presented to solve this problem, but few of them consider the fitness landscape of OMD carefully. In this paper, we analyze the fitness landscape of OMD, including the neighborhood structure and the distribution of local optima. Numerical results give hints on how to design efficient heuristic algorithms for the problem. A meta-heuristic algorithm considering the analysis results is proposed. With a proper local search and a well-chosen perturbation strategy, the proposed algorithm can find the (near) optimal solution rapidly with lower computational complexity. Simulation results show it outperforms other heuristic multiuser detection algorithms when the number of users is large. In the condition of small number of users, it can achieve the bit error rate (BER) bound of OMD. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Zhan:2008:cec, author = "Song Zhan and Julian F. Miller and Andy M. Tyrrell", title = "An Evolutionary System using Development and Artificial Genetic Regulatory Networks", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0209.pdf}, url = {}, size = {}, abstract = {Biology presents incomparable, but desirable, characteristics compared to engineered systems. Inspired by biological development, we have devised a multi-layered design architecture that attempts to capture many of the favorable characteristics of biological mechanisms for application to design problems. In this paper we have identified and implemented essential features of Genetic Regulatory Networks (GRNs) and cell signaling so that our system exhibits self-organization which is reminiscent of aspects of biological systems. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Xu2:2008:cec, author = "Chunlin Xu and Xiufen Zou and Rongxiang Yuan and Chuansheng Wu", title = "Optimal Coordination of Protection Relays Using New Hybrid Evolutionary Algorithm", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0210.pdf}, url = {}, size = {}, abstract = {A reliable protection system is vital to power system. As the major equipment of protection system, protection relay plays a basilica role in power system. So searching for proper settings of relays to make them operate in a better way is significant. In this paper, a new optimization problem formulation is proposed to search the optimal relay setting of over current relays in power systems. Then, a new hybrid evolutionary algorithm based on tabu search (HEATS) is presented to solve this optimization problem, and results under different algorithm parameters are obtained. Finally, comparisons among HEATS, one of particle swarm optimizations(PSO) and test evolutionary algorithm(TEA) shown in other literatures are given. Simulation results show the formulation of protection relay setting is feasible and effective, and the proposed algorithm HEATS exhibits a good performance. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Huang3:2008:cec, author = "Qinghua Huang and Minhua Lu and Hong Yan", title = "An Evolutionary Algorithm for Discovering Biclusters in Gene Expression Data of Breast Cancer", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0212.pdf}, url = {}, size = {}, abstract = {The analysis of gene expression data of breast cancer is important for discovering the signatures that can classify different subtypes of tumors and predict prognosis. Biclustering algorithms have been proven to be able to group the genes with similar expression patterns under a number of samples and offer the capability to analyze the microarray data of cancer. In this study, we propose a new biclustering algorithm which uses an evolutionary search procedure. The algorithm is applied to the conditions to search for combinations of conditions for a potential bicluster. Preliminary results using synthetic and real yeast data sets demonstrate that our algorithm outperforms several existing ones. We have also applied the method to real microarray data sets of breast cancer, and successfully found several biclusters, which can be used as signatures for differentiating tumor types. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Yu2:2008:cec, author = "Yang Yu and Zhi-Hua Zhou", title = "On the Usefulness of Infeasible Solutions in Evolutionary Search: A Theoretical Study", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0214.pdf}, url = {}, size = {}, abstract = {Evolutionary algorithms (EAs) have been widely used in optimization, where infeasible solutions are often encountered. Some EAs regard infeasible solutions as useless individuals while some use infeasible solutions based on heuristic ideas. It is not clear yet that whether infeasible solutions are helpful or not in the evolutionary search. This paper theoretically analyzes that under what conditions infeasible solutions are beneficial. A sufficient condition and a necessary condition are derived and discussed. Then, the paper theoretically shows that the use of infeasible solutions could change the hardness of the task. For example, an EA-hard problem can be transformed to EA-easy by exploiting infeasible solutions.While, the conditions derived in the paper can be used to judge whether to use infeasible solutions or not. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Pang:2008:cec, author = "Wai-Man Pang and Tien-Tsin Wong and Pheng-Ann Heng", title = "Generating Massive High-Quality Random Numbers using GPU", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0215.pdf}, url = {}, size = {}, abstract = {Pseudo-random number generators (PRNG) have been intensively used in many stochastic algorithms in artificial intelligence, computer graphics and other scientific computing. However, the current commodity GPU design does not facilitate the efficient implementation of high-quality PRNGs that require high-precision integer arithmetics and bitwise operations. In this paper, we propose a framework to generate a high-quality PRNG shader for all kinds of GPUs. We adopt the cellular automata (CA) PRNG to facilitate high speed and parallel random number generation. The configuration of the CA PRNG is completed automatically by optimizing an objective function that accounts for quality of generated random sequences. To visually evaluate the result, we apply the best PRNG shader to photon mapping. Timing statistics show that our GPU parallelized PRNG is much faster than a pure CPU implementation. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Belkhelladi:2008:cec, author = "Kamel Belkhelladi and Pierre Chauvet and Arnaud Schaal", title = "An Agent Framework with an Efficient Information Exchange Model for Distributed Genetic Algorithms", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0216.pdf}, url = {}, size = {}, abstract = {Genetic Algorithms (GAs) are powerful search techniques that are used to solve difficult problems in many disciplines. Unfortunately, they can be very demanding in terms of computation load and memory. Parallel Genetic Algorithms (PGAs) are parallel implementations of GAs which can provide considerable gains in terms of performance and scalability. PGAs can easily be implemented on networks of heterogeneous computers or on parallel mainframes. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Yang5:2008:cec, author = "Cheng-Hong Yang and Chang-Hsuan Ho and Li-Yeh Chuang", title = "Improved Tag SNP Selection Using Binary Particle Swarm Optimization", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0220.pdf}, url = {}, size = {}, abstract = {Single nucleotide polymorphisms (SNPs) hold much promise as a basis for disease-gene association. However, they are limited by the cost of genotyping the tremendous number of SNPs. It is therefore essential to select only informative subsets (tag SNPs) out of all SNPs. Several promising methods for tag SNP selection have been proposed, such as the haplotype block-based and block-free approaches. The block-free methods are preferred by some researchers because most of the block-based methods rely on strong assumptions, such as prior block-partitioning, bi-allelic SNPs, or a fixed number or locations for tagging SNPs. We employed the feature selection idea of binary particle swarm optimization (binary PSO) to find informative tag SNPs. This method is very efficient, as it does not rely on block partitioning of the genomic region. Using four public data sets, the method consistently identified tag SNPs with considerably better prediction ability than STAMPA. Moreover, this method retains its performance even when a very small number and 100percent prediction accuracy are used for the tag SNPs. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Lauri:2008:cec, author = "Fabrice Lauri and Abderrafiâa Koukam ", title = "A Two-Step Evolutionary and ACO Approach for Solving the Multi-Agent Patrolling Problem", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0223.pdf}, url = {}, size = {}, abstract = {Patrolling an environment involves a team of agents whose goal usually consists in continuously visiting its most relevant areas as frequently as possible. For such a task, agents have to coordinate their actions in order to achieve optimal performance. Current research that tackles this complex multi-agent problem usually defines the environment as a graph, so that a wide range of applications can be dealt with, from computer network management to computer games and vehicle routing. In this paper, we consider only the instances of the multi-agent patrolling problem where all the agents are located on the same starting node. These instances are often encountered in robotics applications, where e.g. drones start from the same area, disperse over it and finally patrol around distant locations. We introduce a new Ant Colony Optimization (ACO) algorithm that is combined with an Evolutionary Algorithm (EA) technique. The novel ACO algorithm uses several ant colonies that are engaged in a competition for finding out the best multi-agent patrolling strategy. The goal of the EA is to find the best set of distant nodes enabling each agent to disperse efficiently over the graph. Experimental results show that, irrespective of the number of the involved patrolling agents and for all the graphs evaluated, our two-step EA and ACO algorithm outperforms significantly and with efficiency the best techniques proposed in the literature since now. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Simons:2008:cec, author = "Christopher L. Simons and Ian C. Parmee", title = "User-Centered, Evolutionary Search in Conceptual Software Design", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0224.pdf}, url = {}, size = {}, abstract = {Although much evidence exists to suggest that conceptual software engineering design is a difficult task for software engineers to perform, current computationally intelligent tool support for software engineers is limited. While search-based approaches involving module clustering and refactoring have been proposed and show promise, such approaches are downstream in terms of the software development lifecycle - the designer must manually produce a design before search-based clustering and refactoring can take place. Interactive, user-centered search-based approaches, on the other hand, support the designer at the beginning of, and during, conceptual software design, and are investigated in this paper by means of a case study. Results show that interactive evolutionary search, supported by software agents, appears highly promising. As an open system, search is steered jointly by designer preferences and software agents. Directly traceable to the design problem domain, a mass of useful and interesting conceptual class designs are arrived at which may be visualized by the designer with quantitative measures of structural integrity such as design coupling and class cohesion. The conceptual class designs are found to be of equivalent or better coupling and cohesion when compared to a manual conceptual design of the case study, and by exploiting concurrent execution, the performance of the software agents is highly favorable. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Mukhopadhyay:2008:cec, author = "Anirban Mukhopadhyay and Sanghamitra Bandyopadhyay and Ujjwal Maulik", title = "Combining Multiobjective Fuzzy Clustering and Probabilistic ANN Classifier for Unsupervised Pattern Classification: Application to Satellite Image Segmentation", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0226.pdf}, url = {}, size = {}, abstract = {An important approach to unsupervised pixel classification in remote sensing satellite imagery is to use clustering in the spectral domain. In this article, a recently proposed multiobjective fuzzy clustering scheme has been combined with artificial neural networks (ANN) based probabilistic classifier to yield better performance. The multiobjective technique is first used to produce a set of non-dominated solutions. A part of these solutions having high confidence level are then used to train the ANN classifier. Finally the remaining solutions are classified using the trained classifier. The performance of this technique has been compared with that of some other wellknown algorithms for two artificial data sets and a IRS satellite image of the city of Calcutta. }, keywords = { Fuzzy clustering, ANN classifier, multiobjective optimization, Pareto-optimality, cluster validity measures, remote sensing satellite imagery. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Chen3:2008:cec, author = "Yang Chen and Jinglu Hu and Kotaro Hirasawa and Songnian Yu", title = "Solving Deceptive Problems Using A Genetic Algorithm with Reserve Selection", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0227.pdf}, url = {}, size = {}, abstract = {Deceptive problems are a class of challenging problems for conventional genetic algorithms (GAs), which usually mislead the search to some local optima rather than the global optimum. This paper presents an improved genetic algorithm with reserve selection to solve deceptive problems. The concept ``potential'' of individuals is introduced as a new criterion for selecting individuals for reproduction, where some individuals with low fitness are also let survive only if they have high potentials. An operator called adaptation is further employed to release the potentials for approaching the global optimum. Case studies are done in two deceptive problems, demonstrating the effectiveness of the proposed algorithm. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Knabe:2008:cec, author = "Johannes F. Knabe and Chrystopher L. Nehaniv and Maria J. Schilstra ", title = "Regulation of Gene Regulation - Smooth Binding with Dynamic Affinity Affects Evolvability", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0228.pdf}, url = {}, size = {}, abstract = {Understanding the evolvability of simple differentiating multicellular systems is a fundamental problem in the biology of genetic regulatory networks and in computational applications inspired by the metaphor of growing and developing networks of cells. We compare the evolvability of a static network model to a more realistic regulatory model with dynamic structure. In the former model, each regulatory protein-binding site is always influenced by exactly one gene product. In the latter model, binding is only more likely to occur the better the match between site and gene product is (smooth binding) and, in addition, affinity dynamically changes under the action of specificity factors during a cell's lifetime. On evolutionary timescales, this means that often the strength of influences between nodes is perturbed instead of direct changes being made to network connectivity. A main result is that for evolutionary search spaces of increasing sizes evolved performance drops much more strongly in the classical network model as compared to the smooth binding model. This effect was even greater in the case of using smooth binding together with specificity factors. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wang8:2008:cec, author = "Jiahai Wang and Yunong Zhang and Yalan Zhou and Jian Yin", title = "Discrete Quantum-Behaved Particle Swarm Optimization Based on Estimation of Distribution for Combinatorial Optimization", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0230.pdf}, url = {}, size = {}, abstract = {Particle swarm optimization (PSO) is a population-based swarm intelligence algorithm. A quantumbehaved particle swarm optimization (QPSO) is also proposed by combining the classical PSO philosophy and quantum mechanics. These algorithms have been very successful in solving the global continuous optimization, but their applications to combinatorial optimization have been rather limited. Estimation of distribution algorithm (EDA) samples new solutions from a probability model which characterizes the distribution of promising solutions. This paper proposes a novel discrete QPSO based on EDA for the combinatorial optimization problem. The proposed algorithm combines global statistical information extracted by EDA with local information obtained by discrete QPSO to create promising solutions. To demonstrate the performance of the proposed algorithm, experiments are carried out on the unconstrained binary quadratic programming problem which numerous hard combinatorial optimization problems can be formulated as. The results show that the discrete QPSO based on EDA have superior performance to other algorithms. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Li8:2008:cec, author = "Kangshun Li and Jiusheng Liang and Wensheng Zhang and Feng Wang", title = "A New Method of Evolving Digital Circuit Based on Gene Expression Programming", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0231.pdf}, url = {}, size = {}, abstract = {Evolutionary Hardware (EHW) is a new focus in recent research work. The new method of design hardware is combined evolution algorithm with programmable logic device. Optimization digital circuit is a main domain of EHW. The algebra way and Karnaugh map way are the traditionary methods, but they will meet trouble with the large scale ones to get optimisation structure of circuit. This paper proposes a new method (GEP) to optimise the complex digital circuit and designs a new function fitness. The experiments demonstrate the GEP is not only fast convergence but also optimisation large circuit. It conquers the slow convergence even not convergence of the traditionary method. The GEP algorithm is simpler and more efficient than the traditional ones.}, keywords = {genetic algorithms, genetic programming, gene expression programming}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Zhou3:2008:cec, author = "Jianguo Zhou and Tao Bai", title = "Predicting Corporate Financial Distress using KPCA and GA-Based Support Vector Machine", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0232.pdf}, url = {}, size = {}, abstract = {In the analysis of predicting financial distress based on support vector machine (SVM), irrelevant or correlated features in the samples could spoil the performance of the SVM classifier, leading to decrease of prediction accuracy. On the other hand, the improper determining of two SVM parameters will cause either over-fitting or under-fitting of a SVM model. In order to solve the problems mentioned above, this paper used kernel principal component analysis (KPCA) as a preprocessor of SVM to extract the principal features of original data and employed the genetic algorithm (GA) to optimize the parameters of SVM. Additionally, the proposed GA-SVM model that can automatically determine the optimal parameters was tested on the prediction of financial distress of listed companies in China. Then, we compared the accuracies of the proposed GA-SVM model with those of other models of multivariate statistics (Fisher and Probit) and other artificial intelligence (BPN and fix-SVM). Especially, we adopted bootstrap technology to evaluate the reliability of validation. Experimental results showed that the GA-SVM model performed the best predictive accuracy and generalization, implying that the hybrid of GA with traditional SVM model can serve as a promising alternative for financial distress prediction. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Malan:2008:cec, author = "Katherine M. Malan and Andries P. Engelbrecht", title = "Algorithm Comparisons and the Significance of Population Size", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0233.pdf}, url = {}, size = {}, abstract = {In studies that compare the performance of population-based optimization algorithms, it is sometimes assumed that the comparison is valid as long as the number of function evaluations is equal, even if the population size differs. This paper shows that such comparisons are invalid. The performance of two algorithms: Differential Evolution (DE) and Global Best Particle Swarm Optimization (gbest PSO) are tested on standard benchmark problems with different numbers of individuals/particles (20, 50 and 100). It is shown that there are significance differences in the performance of the same algorithm with the same number of function evaluations, but with different numbers of individuals/particles. Comparisons of different algorithms should therefore always use the same population size for results to be valid. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Zhang5:2008:cec, author = "Xiangyin Zhang and Haibin Duan and Jiqiang Jin", title = "DEACO: Hybrid Ant Colony Optimization with Differential Evolution", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0234.pdf}, url = {}, size = {}, abstract = {Ant Colony Optimization (ACO) algorithm is a novel meta-heuristic algorithm for the approximate solution of combinatorial optimization problems that has been inspired by the foraging behavior of real ant colonies. ACO has strong robustness and easy to combine with other methods in optimization, but it has the shortcomings of stagnation that limits the wide application to the various areas. In this paper, a hybrid ACO with Differential Evolution (DE) algorithm was proposed to overcome the above-mentioned limitations, and this algorithm was named DEACO. Considering the importance of ACO pheromone trail for ants exploring the candidate paths, DE was applied to optimize the pheromone trail in the basic ACO model. In this way, a reasonable pheromone trail between two neighboring cities can be formed, so as to lead the ants to find out the optimum tour. The proposed algorithm is tested with the Traveling Salesman Problem (TSP), and the experimental results demonstrate that the proposed DEACO is a feasible and effective ACO model in solving complex optimization problems. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Sheng:2008:cec, author = "Weiguo Sheng and Gareth Howells and Karl Harmer and Michael Fairhurst and Farzin Deravi", title = "A Genetic Algorithm for Fingerprint Matching based on an Integrated Measure", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0235.pdf}, url = {}, size = {}, abstract = {In this paper, we develop a fingerprint matching method which operates by first introducing an integrated measure, which combines two different matching criteria based on heterogeneous features. We then devise a genetically guided algorithm to optimise the integrated measure for simultaneous fingerprint alignment and verification. The proposed method is evaluated through experiments conducted on two public domain collections of fingerprint images and compared with related work, with very encouraging results. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Rakitianskaia:2008:cec, author = "Anna Rakitianskaia and Andries P. Engelbrecht", title = "Cooperative Charged Particle Swarm Optimiser", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0236.pdf}, url = {}, size = {}, abstract = {Most optimisation algorithms from the Computational Intelligence field assume that the search landscape is static. However, this assumption is not valid for many real-world problems. Therefore, there is a need for efficient optimisation algorithms that can track changing optima. A number of variants of Particle Swarm Optimisation (PSO) have been developed for dynamic environments. Recently, the cooperative PSO [1] has been shown to significantly improve performance of PSO in static environments, especially for highdimensional problems. This paper investigates the performance of a cooperative version of the charged PSO on a benchmark of dynamic optimisation problems. Empirical results show that the cooperative charged PSO is an excellent alternative to track dynamically changing optima. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Zhao:2008:cec, author = "Xinchao Zhao ", title = "Convergent Analysis on Evolutionary Algorithm with Non-uniform Mutation", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0239.pdf}, url = {}, size = {}, abstract = {Evolutionary algorithm (EA) with non-uniform mutation has the merits of even "longer jumps" than Cauchy mutation at the early stage of the algorithm and much "finertunings" than Gaussian mutation operator at the later stage. Empirical comparisons with the recently proposed EAs show its excellence solution quality and reliability. One unified algorithmic framework with non-uniform mutation operator and its convergence analysis based on this algorithmic framework are provided in this paper. Two lemmas and two theorems are presented to show the relevant convergence properties of unimodal and multimodal functions. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Kelly:2008:cec, author = "Martin Kelly ", title = "Decentralised Urban Traffic Control Using Genetic Algorithm and Cellular Automata", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0240.pdf}, url = {}, size = {}, abstract = {This paper describes a traffic control simulation based on exchange of messages between local intersections which incorporate dynamically assembled cellular automata. A genetic algorithm is employed to determine parameters governing the messaging and cellular behaviour. This paper reports both on the convergence of the genetic algorithm towards fittest solutions; and on the performance of the genetic algorithm plus cellular automata combination, under various configurations. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Su:2008:cec, author = "Tonghua Su and Tianwen Zhang and Hujie Huang and Guixiang Xue and Zheng Zhao", title = "Transformation-Based Hierarchical Decision Rules using Genetic Algorithms and its Application to Handwriting Recognition Domain", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0241.pdf}, url = {}, size = {}, abstract = {This paper describes a new approach based on Transformation-Based Learning for extracting hierarchical decision rules. Genetic algorithms are adapted to establish the context environment for transformation operation and the transformation operation can lengthen the life cycle of "good" candidate rules. The experiments are conducted on iris, wine and glass datasets with a 10-fold cross validation setup. The results show that transformation operation can improve the precision of the classifier with a smaller number of rules and generations than hierarchical decision rules. The approach also works well in touching block extraction of Chinese handwritten text. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Duan:2008:cec, author = "Haibin Duan and Yaxiang Yu and Rui Zhou", title = "UCAV Path Planning Based on Ant Colony Optimization and Satisficing Decision Algorithm", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0243.pdf}, url = {}, size = {}, abstract = {Path planning of Uninhabited Combat Air Vehicle (UCAV) is a complicated global optimum problem. Ant Colony Optimization (ACO) algorithm was originally presented under the inspiration during collective behavior study results on real ant system, and it has strong robustness and easy to combine with other methods in optimization. In this paper, we propose a hybrid ACO with satisficing decision algorithm for solving the UCAV path planning in complicated combat field environments. When ant chooses the next node from the current candidate path nodes, the acceptance function and rejection function in satisficing decision are calculated. In this way, the efficiency of global optimization can be greatly improved. The detailed realization procedure for this hybrid approach is also presented. Series experimental comparison results show the proposed hybrid method is more effective and feasible in the UCAV path planning than the basic ACO model. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Chen4:2008:cec, author = "Dingjun Chen and Keith C. C. Chan and Xindong Wu", title = "Gene Expression Analyses Using Genetic Algorithm based Hybrid Approaches", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0244.pdf}, url = {}, size = {}, abstract = {This paper presents two Genetic Algorithm (GA) based hybrid approaches for the prediction of tumor outcomes based on gene expression data. One approach is the hybrid GA and K-medoids for grouping genes based on the commonly used distance similarity. The goal of grouping genes here is to choose some top-ranked representatives from each cluster for gene dimensionality reduction. The second proposed approach is the hybrid GA and Support Vector Machines (SVM) for selecting marker genes and classifying tumor types or predicting treatment outcomes. These two hybrid approaches have been applied to public brain cancer datasets, and the experimental results are compared with those given in a 2001 paper published in the Nature. The final prediction accuracies are found to be superior both for tumor class prediction and treatment outcome prediction. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Dornberger:2008:cec, author = "Rolf Dornberger and Lukas Frey and Thomas Hanne", title = "Single and Multiobjective Optimization of the Train Staff Planning Problem Using Genetic Algorithms", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0245.pdf}, url = {}, size = {}, abstract = {We consider the problem of assigning train drivers to scheduled trains services, a combinatorial optimization problem which involves various hard and soft constraints. The problem is formulated as a single and a multiobjective optimization problem. A genetic algorithm is designed for solving it. As the problem is a real-life problem, various issues of application and use within a railway planning suite are discussed as well. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Yi:2008:cec, author = "Zhaoxiang Yi and Xiaodong Mu and Li Zhang and Peng Zhao ", title = "A Matrix Negative Selection Algorithm for Anomaly Detection", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0247.pdf}, url = {}, size = {}, abstract = {This paper presents a matrix negative selection algorithm for anomaly detection. The proposed algorithm is a twofold improvement over conventional negative selection algorithms. In matrix representation, characteristics of the self set are emerged by multiple vectors to distinctly express the boundary of self and non-self. On the other hand, based on the matrix matching coefficient, separate match rules for generating detectors and monitoring anomaly are designed to avoid the sharp distinction caused by threshold. Results have demonstrated that the matrix negative selection algorithm is effective and reliable for anomaly detection and suitable for small sample problems of complex systems. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wang9:2008:cec, author = "Jia Wang ", title = "Evolutionary Game Analysis on Product Quality Management in the Automotive Supply Chain of China", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0248.pdf}, url = {}, size = {}, abstract = {Product quality management in the supply chain is a troublesome problem for auto industry of China. Based on Evolutionary Game Theory, the model of product quality management in the automotive supply chain of China is established and its dynamic evolutionary procedure is analyzed in this paper. The results shows that the quality-profit coefficient, the additional quality management cost of the high-quality strategy, the increment of the product quality due to the high-quality strategy and the distribution rate of the additional profits are the key factors that affect the system's evolution. Accordingly, some helpful ideas are proposed for product quality management in the automotive supply chain of China. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Gorissen:2008:cec, author = "Dirk Gorissen and Luciano De Tommasi and Jeroen Croon and Tom Dhaene", title = "Automatic Model Type Selection with Heterogeneous Evolution: An Application to RF Circuit Block Modeling", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0249.pdf}, url = {}, size = {}, abstract = {Many complex, real world phenomena are difficult to study directly using controlled experiments. Instead, the use of computer simulations has become commonplace as a cost effective alternative. However, regardless of Moore's law, performing high fidelity simulations still requires a great investment of time and money. Surrogate modeling (metamodeling) has become indispensable as an alternative solution for relieving this burden. Many surrogate model types exist (Support Vector Machines, Kriging, RBF models, Neural Networks, ...) but no type is optimal in all circumstances. Nor is there any hard theory available that can help make this choice. The same is true for setting the surrogate model parameters (Bias - Variance trade-off). Traditionally, the solution to both problems has been a pragmatic one, guided by intuition, prior experience or simply available software packages. In this paper we present a more founded approach to these problems. We describe an adaptive surrogate modeling environment, driven by speciated evolution, to automatically determine the optimal model type and complexity. Its utility and performance is presented on a case study from electronics. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Friedrich:2008:cec, author = "Tobias Friedrich and Frank Neumann", title = "When to use Bit-Wise Neutrality", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0250.pdf}, url = {}, size = {}, abstract = {Representation techniques are important issues when designing successful evolutionary algorithms. Within this field the use of neutrality plays an important role. We examine the use of bit-wise neutrality introduced by Poli and López [9] from a theoretical point of view and show that this mechanism only enhances mutation-based evolutionary algorithms if not the same number of genotypic bits for each phenotypic bit is used. Using different numbers of genotypic bits for the bits in the phenome we point out by rigorous runtime analyses that it may reduce the optimization time significantly. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wang10:2008:cec, author = "Lili Wang and Alioune Ngom and Robin Gras", title = "Non-Unique Oligonucleotide Microarray Probe Selection Method Based on Genetic Algorithms", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0252.pdf}, url = {}, size = {}, abstract = {In order to accurately measure the gene expression levels in microarray experiments, it is crucial to design unique, highly specific and sensitive oligonucleotide probes for the identification of biological agents such as genes in a sample. Unique probes are hard to obtain for closely related genes such as the known strains of HIV genes. The non-unique probe selection problem is to select a probe set that is able to uniquely identify targets, in a biological sample, while containing a minimal number of probe. This is a NP-hard problem and this paper contributes the first evolutionary method for finding near minimal non-unique probe sets. When used on benchmark data sets, our approach consistently performed better than three recently published methods. We also obtained results that are at least comparable to those of the current state-of-the-art heuristic. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Prabhu:2008:cec, author = "Raghavendra D. Prabhu ", title = "SOMGPU: An Unsupervised Pattern Classifier on Graphical Processing Unit", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0253.pdf}, url = {}, size = {}, abstract = {Graphical Processing Units (GPUs) have been, lately used for general purpose tasks owing to their implicit parallel nature. One such task is that of pattern classification. Highly parallel tasks like these suffer from performance loss owing to the sequential nature of Central Processing Unit (CPU). To match the image processing power of human brain even slightly, this problem beckons the use of enormous computational power and parallel environs of GPUs. Unless there is a task which can be parallelized to the required extent the gain obtained is lost owing to the overhead involved. Thus, it is equally important to understand some limitations of GPU before venturing in this direction and deal with it appropriately to obtain satisfactory results. Artificial Neural Networks (ANN) are found to be appropriate while dealing with pattern recognition problems. Kohonen's Self Organizing Map (SOM) has been used for classification out of other approaches for its implicit parallel nature, albeit with minor modifications to make it suit the parallel environment. nVIDIA GeForce 6150 Go with Microsoft Research Accelerator as the high level library has been chosen as the platform to provide this environment. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Ciftcioglu:2008:cec, author = "Özer Ciftcioglu and Michael S. Bittermann", title = "Solution Diversity in Multi-Objective Optimization: A Study in Virtual Reality", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0254.pdf}, url = {}, size = {}, abstract = {Solution diversity in evolutionary multi-objective optimization is considered. Although the Pareto front is ubiquitously used for the multi-objective optimization, the method of formation of the Pareto front in the evolutionary process is important to ensure the diversity of the solutions so that they are desirably evenly distributed along the front. Conventionally this is an issue and in some cases this is compromised with sub-optimality or layered Pareto fronts. This issue is dealt with in this research and a novel method termed as relaxed dominance for design applications is presented. The method is implemented for a design process as a case study and the effectiveness of the method is demonstrated. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wei2:2008:cec, author = "Shuang Wei and Henry Leung", title = "An Improved Genetic Algorithm for Pump Scheduling in Water injection systems for Oilfield", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0256.pdf}, url = {}, size = {}, abstract = {The complication of multiple reciprocating plunger pumps and centrifugal multilevel pumps connected in parallel in the pumping stations has been a challenge for decision makers to meet the demand of the efficiency and costeffective production. In this paper, an efficiency mathematical model and an effective pump scheduling problem based on Genetic Algorithm (GA) are proposed. The objective is to maximize the efficiency of pumps operation to balance the total water flow rate of the system within the constant-pressure constraint and constraints of optimal operation for pumps in the system. An improved GA with competition cross method is designed and applied to solve this problem. Computative simulations show that the proposed method is effective for pump scheduling in an oilfield water injection system. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wu:2008:cec, author = "Q. H. Wu and Z. Lu and M. S. Li and T. Y. Ji ", title = "Optimal Placement of FACTS Devices by A Group Search Optimizer with Multiple Producer", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0263.pdf}, url = {}, size = {}, abstract = {This paper presents a Group Search Optimizer with Multiple Producer (GSOMP) for reactive power dispatch incorporating with Flexible AC Transmission System (FACTS) devices, which is formulated as a nonlinear constrained multiobjective optimization problem. The optimal location of multitype FACTS devices and their control parameters are optimized by GSOMP to minimize the real power loss and also to improve voltage profile. The performance of GSOMP has been evaluated on the standard IEEE 14-bus and New England 39- bus test systems respectively. Simulation results show that the performance of the power systems is improved with multi-type FACTS devices optimally placed in the reactive power planning model. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Li9:2008:cec, author = "Li Li and Li Hong-Qi and Xie Shao-Long", title = "Particle Swarm Multi_optimizer for Locating all Local Solutions", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0265.pdf}, url = {}, size = {}, abstract = {In order to overcome the disadvantage that only one solution can be found in particle swarm optimization (PSO), a novel niche particle swarm Multi_optimizer (Multi_PSOer) which combines two strategies is devised in this paper. Firstly, Guaranteed Convergence PSO (GCPSO) is adopted to guarantee the algorithm can converge on a local point. Secondly, niche technique is used to ensure the algorithm is a global search algorithm. Different hills are looked as different niches. Particles are divided into different sub_swarms according to the Same_hill function. The function can judge whether particles are in the same hill through monitoring the change of particles' tangent. If the tangent values change from negative into positive, they are in different niches, otherwise they are in the same niche. Particle flies following the best one in the same hill with itself. Therefore each peak can be found in this way. It is necessary to know neither the niche radius nor other parameters at all. Numerical experiments show that the Multi_PSOer may, efficiently and reliably, obtain all local and global optima for multimodal optimization problems. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Ru:2008:cec, author = "Nie Ru and Yue Jianhua ", title = "A GA and Particle Swarm Optimization Based Hybrid Algorithm", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0267.pdf}, url = {}, size = {}, abstract = {In this paper an improved particle swarm algorithm is presented firstly and then a hybrid method combining Genetic Algorithm(GA) and Particle Swarm Optimization(PSO) is proposed. This hybrid technique incorporates concepts from GA and PSO and creates individuals in a new generation not only by crossover and mutation operations as found in GA but also by mechanisms of PSO. It can solve the problem of local minimum of the particle swarm optimization and has higher efficiency of search. Simulation results show that the proposed method is effective for the optimization problems. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Ruan:2008:cec, author = "Xiaogang Ruan and Jinlian Wang and Hui Li and Xiaoming Li", title = "Study of Tumor Molecular Diagnosis Model Based on Artificial Neural Network with Gene Expression Profile", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0268.pdf}, url = {}, size = {}, abstract = {We introduce a method for modeling cancer diagnosis at the molecular level using a Chinese microarray gastric cancer dataset. The method combines an artificial neural network with a decision tree that is intended to precede standard techniques, such as classification, and enhance their performance and ability to detect cancer genes. First, we used the relief algorithm to select the featured genes that could unravel cancer characteristics out of high dimensional data. Then, an artificial neural network was employed to find the biomarker subsets with the best classification performance for distinguishing cancerous tissues and their counterparts. Next a decision tree expression was used to extract rules subsets from these biomarker sets. Rules induced from the best performance decision tree, in which the branches denote the level of gene expression, were interpreted as a diagnostic model by using previous biological knowledge. Finally, we obtained a gastric cancer diagnosis model for Chinese patients. The results show that using the Chinese gastric biomarker genes with the diagnostic model provides more instruction in biological experiments and clinical diagnosis reference than previous methods. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Xu3:2008:cec, author = "Xing Xu and Yuanxiang Li and Shenlin Fang and Yu Wu and Feng Wang ", title = "A Novel Differential Evolution Scheme Combined with Particle Swarm Intelligence", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0269.pdf}, url = {}, size = {}, abstract = {Differential evolution (DE) and particle swarm optimization (PSO) are the evolutionary computation paradigms, and both have shown superior performance on complex nonlinear function optimization problems. This paper detects the underlying relationship between them and then qualitatively proves that the two heuristic approaches from different theoretical background are consistent in form. Within the general perspective, the PSO can be regarded as a kind of DE. Inspired by this, a novel variant of DE mixed with particle swarm intelligence (DE-SI) is presented. Comparison experiments involving ten test functions well studied in the evolutionary optimization literature are used to highlight some performance differences between the DE-SI, two versions of DE and two PSO variants. The results from our study show that DE-SI keeps the most rapid convergence rate of all techniques and obtains the global optima for most benchmark problems. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Shi2:2008:cec, author = "Yuhui Shi and Russell C. Eberhart", title = "Population Diversity of Particle Swarms", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0270.pdf}, url = {}, size = {}, abstract = {In the field of evolutionary computation, an important attribute of a population is diversity. This paper proposes a method for measuring the diversity of a particle swarm optimization population. It involves the measurement of position and velocity attributes of the particles that comprise the population. The proposed method is computationally straightforward and is adaptable to other evolutionary algorithms. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Ono:2008:cec, author = "Satoshi Ono and Kensuke Morinaga and Shigeru Nakayama", title = "Two-Dimensional Barcode Decoration Based on Real-Coded Genetic Algorithm", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0271.pdf}, url = {}, size = {}, abstract = {This paper proposes a system for decorating 2-dimensional barcode with some illustrations inside the code without detracting machine-readability and stored information. The proposed system formulates the task of finding appropriate positions, scales, and angles of illustrations, and solves the task by using real-coded genetic algorithm. The proposed system also uses multiple barcode decoder with the aim of improving decode feasibility of the decorated barcode. Experiments have shown that the proposed system can decorate barcodes with three illustrations, and that using more than one decoder can improve decoding feasibility of the decorated barcodes. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Xiaodong:2008:cec, author = "Duan Xiaodong and Wang Cunrui and Liu Xiangdong and Lin Yanping", title = "Web Community Detection Model using Particle Swarm Optimization", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0274.pdf}, url = {}, size = {}, abstract = {Web community detection is one of the important ways to enhance retrieval quality of web search engine. How to design one highly effective algorithm to partition network community with few domain knowledge is the key to network community detection. Traditional algorithms, such as Wu-Huberman algorithm, need priori information to detect community, the Radichi algorithm relies on the triangle number in the network, the Extremal Optimization Algorithm proposed by Duch J. is extremely sensitive to the initial solution, easy to fall into the local optimum. This article proposes a new model based on particle swarm optimization to detect network community, and with different scale network chart, Zachary, Krebs and dolphins network architecture to test the algorithm, the experimental results indicate this model can effectively find web communities of network structure without any domain information. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Chang2:2008:cec, author = "Weng-Long Chang and Ting-Ting Ren and Jun Luo and Mang Feng and Minyi Guo", title = "Quantum Algorithms for Bio-molecular Solutions to the Satisfiability Problem on a Quantum Computer", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0275.pdf}, url = {}, size = {}, abstract = {We demonstrate that the logic computation performed by the DNA-based algorithm for solving general cases of the satisfiability problem can be implemented by our proposed quantum algorithm on the quantum machine proposed by Deutsch. Moreover, we also prove that the logic computation by the bio-molecular operations proposed by Adleman can be implemented by quantum gates (for example, the Hadamard gate, NOT, CNOT, and CCNOT) on the quantum machine. Furthermore, those NP-complete problems solved on a bio-molecular computer are also solvable on a quantum computer. To test our theory, we carry out a three-qubit NMR experiment for solving the simplest satisfiability problem. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Yamamoto:2008:cec, author = "Yu Yamamoto and Akira Notsu and Hidetomo Ichihashi and Katsuhiro Honda", title = "Agent-Based Social Simulation Based on Cognitive Economic Efficiency", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0276.pdf}, url = {}, size = {}, abstract = {We propose a model based on cognitive economic efficiency that can be set more than three parameters which express the relationships in each actors cognitive image by using the eigenvalue of an adjacency-matrix that represents an actor's cognitive image in social groups. Moreover, we studied how the groups can be formed when reaching a balance state under some conditions between agents involved in communications. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Bansal:2008:cec, author = "Richa Bansal and Kamal Srivastava and Shweta and Kirti Varshney and Nidhi Sharma", title = "An Evolutionary Algorithm for the 2-Page Crossing Number Problem", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0279.pdf}, url = {}, size = {}, abstract = { Many real-life scheduling, routing and location problems can be formulated as combinatorial optimization problems whose goal is to find a linear layout of an input graph in such a way that the number of edge crossings is minimized. The minimization of edge crossings in a book drawing of a graph is one of the important goals for a linear VLSI design. In this paper, we propose an evolutionary algorithm for crossing number minimization in the 2-page book drawing of graphs in which the initial population is generated by depth first search method with edge length strategy for dividing the edges into two pages. An important feature of the evolutionary process of this algorithm is that it improves the number of crossings by exploring various depth first search trees of the graph instead of applying the usual crossover operator. Minor disturbances are created by the mutation operator. The experiments done on benchmark graphs and some standard graphs show that the algorithm achieves the crossing numbers that are either optimal or known to be the best so far, in much lesser time as compared with the existing techniques. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Bailong:2008:cec, author = "Liu Bailong and Zhang Rubo and Shi Changting", title = "Response Threshold Model of Aggregation in a Swarm: A Theoretical and Simulative Comparison", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0281.pdf}, url = {}, size = {}, abstract = {Swarm Intelligence(SI) which is inspired by social animals has been paid more and more attention. It always appeals to the collective behaviors observed in social animals. Aiming at the feature and factors in self-organization of SI system, the aggregation behavior is studied. Firstly the response threshold model of the system is built according to the rules in aggregation. Then the stability of the steady-state solutions of the model is analyzed and the bifurcation of the steady-state solution is obtained. Finally, the effects of the parameter are analyzed based on the theory model. And the Monte Carlo simulations which give certain differences against theory results are also analyzed. All of the theoretical and simulative results show that the aggregation behavior is impacted by the relationship between the swarm size and the response threshold and sensitivity significantly. It is also proved that complex behavior emerges from local interaction of individuals. The work of this paper gives the mechanism in the emergent complex pattern of self-organized aggregation and the factors which affect the system evolution. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Yang6:2008:cec, author = "Zhenyu Yang and Ke Tang and Xin Yao", title = "Self-Adaptive Differential Evolution with Neighborhood Search", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0282.pdf}, url = {}, size = {}, abstract = {In this paper we investigate several self-adaptive mechanisms to improve our previous work on NSDE [1], which is a recent DE variant for numerical optimization. The selfadaptive methods originate from another DE variant, SaDE [2], but are remarkably modified and extended to fit our NSDE. And thus a Self-adaptive NSDE (SaNSDE) is proposed to improve NSDE's performance. Three self-adaptive mechanisms are used in SaNSDE: self-adaptation for two candidate mutation strategies, self-adaptations for controlling scale factor F and crossover rate CR, respectively. Experimental studies are carried out on a broad range of different benchmark functions, and the proposed SaNSDE has shown significant superiority over NSDE. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Yan:2008:cec, author = "Yunyi Yan and Baolong Guo", title = "Particle Swarm Optimization Inspired by $r$- and $K$-Selection in Ecology", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0286.pdf}, url = {}, size = {}, abstract = {An optimization technique named r/KPSO (Particle Swarm Optimization withr- and K-selection) was developed in this paper. In Ecology, two evolutionary "strategies" are termed, r-selection for those species that breed many "cheap" offspring and live in unstable environments and K-selection for those species that produce few "expensive" offspring and live in stable environments. r-selection can be characterized as: quantitative, little parent care, large growth rate and rapid development and K-selection as: qualitative, much parent care, small growth rate and slow development. r/KPSO selects r- and K-selection to produce the progenies in the iterative procedure according to the concerned particle's fitness value. K-selection is performed for those particles (K-subswarm called in this paper) in high fitness, and K-subswarm only can produce few progenies but the progenies are nurtured delicately with much parent care. On the other hand, r-selection is performed for those particles (r-subswarm called) in relatively low fitness. And with little parent care, r-subswarm can produce a large number of progenies, the progenies have to compete with the r-subswarm for survival according to fitness and only the best ones can survive. In r/KPSO, the particles performed r-selection mainly explore the search space as possible as they can to find more potential solutions in large speed, and those particles performed K-selection keep the current optimum solutions and exploit the space as they can to find more ideal solutions. Combined the advantages of r-selection and K-selection, r/KPSO can converge in higher speed and higher precision. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Chen5:2008:cec, author = "Guo Chen and Zhao Yang Dong", title = "On the Weak Ergodicity of the Markov Chain Associated with a Chaotic Simulated Annealing Algorithm", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0287.pdf}, url = {}, size = {}, abstract = {Chaotic simulated annealing (CSA) is a relatively new heuristic optimization technique and has been widely applied to optimization problems because of its simplicity and capability of finding fairly good solutions rapidly. However, currently only experimental results are used for verifying its superiority. In this paper, a new of chaotic simulated annealing method (CSA) is introduced and then a mathematic proof is given. It shows that the Markov Chain associated with the algorithm is weakly ergodic, which guarantees that the asymptotic behavior of the algorithm is independent of initial states. Furthermore, the theoretical analysis of the proposed CSA is very important to understand the essential features which make the algorithm work well. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Olorunda:2008:cec, author = "Olusegun Olorunda and Andries P. Engelbrecht", title = "Measuring Exploration/Exploitation in Particle Swarms Using Swarm Diversity", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0288.pdf}, url = {}, size = {}, abstract = {An important factor contributing to the success of particle swarm optimization (PSO) is the balance between exploration and exploitation of the swarm. Exploration is typically preferred at the initial stages of the search but is required to gradually give way to exploitation of promising solutions as the search progresses. The diversity of a particle swarm optimization algorithm can be defined, simply, as the degree of dispersion of the particles in the swarm. This dispersion could be defined around some center-point or not. It could also be defined based on the positions of the particles or on their velocities.This paper takes a look at some of the different definitions of swarm diversity with the intention of determining their usefulness in quantifying swarm exploration/exploitation. This work is intended to lay the foundations for the development of a suitable means to quantify the rate of change from exploration to exploitation of a PSO, i.e. the rate of change of diversity. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Haider:2008:cec, author = "Sajjad Haider and Alexander H. Levis ", title = "Finding Effective Courses of Action Using Particle Swarm Optimization", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0289.pdf}, url = {}, size = {}, abstract = {The paper applies particle swarm optimization (PSO) technique to identify effective courses of action (COAs) in a dynamic uncertain situation. The uncertain situation is modeled using Timed Influence Nets (TINs), an instance of Dynamic Bayesian Networks. The TIN-based framework aids a system analyst in connecting a set of actionable events and a set of desired effects through chains of cause and effect relationships. The purpose of building these TIN models is to analyze several courses of action (COAs) and identify the ones that maximize the likelihood of achieving the desired effect(s). The paper attempts to automate this identification process of the best COA. It does so by exploring the solution space, consisting of potential courses of action, using PSO. The paper also compares the performance of PSO with that of an evolutionary algorithm (EA). The results suggest there is not a significant difference between the performances of the two techniques but PSO takes less time compared to EA. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Yu3:2008:cec, author = "Xin Yu and Ke Tang and Xin Yao", title = "An Immigrants Scheme Based on Environmental Information for Genetic Algorithms in Changing Environments", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0290.pdf}, url = {}, size = {}, abstract = {Addressing dynamic optimization problems (DOPs) has been a challenging task for the genetic algorithm (GA) community. One approach is to maintain the diversity of the population via introducing immigrants. This paper intensively examines several design decisions when employing immigrants schemes, and from these observations an environmental information-based immigrants scheme is derived for GAs to deal with DOPs. In the scheme, the environmental information (e.g., the allele distribution over the population in this paper) from previous generation is used to create immigrants to replace the worst individuals in the current population. In this way, the introduced immigrants are more adapted to the changing environment. A hybrid scheme combining immigrants based on current environmental information and its complementation is also proposed in this paper to address different degrees of changes. Experimental results validate the efficacy of the proposed environmental information-based and hybrid environmental information-based immigrants schemes. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wang11:2008:cec, author = "Zai Wang and Ke Tang and Xin Yao", title = "A Multi-Objective Approach to Testing Resource Allocation in Modular Software Systems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0291.pdf}, url = {}, size = {}, abstract = {Nowadays, as the software systems become increasingly large and complex, the problem of allocating the limited testing-resource during the testing phase has become more and more difficult. In this paper, we propose to solve the testing-resource allocation problem (TRAP) using multi-objective evolutionary algorithms. Specifically, we formulate TRAP as two multi-objective problems. First, we consider the reliability of the system and the testing cost as two objectives. In the second formulation, the total testing-resource consumed is also taken into account as the third goal. Two multi-objective evolutionary algorithms, Non-dominated Sorting Genetic Algorithm II (NSGA2) and Multi-Objective Differential Evolution Algorithms (MODE), are applied to solve the TRAP in the two scenarios. This is the first time that the TRAP is explicitly formulated and solved by multi-objective evolutionary approaches. Advantages of our approaches over the state-of-the-art single-objective approaches are demonstrated on two parallel-series modular software models. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Grobler:2008:cec, author = "Jacomine Grobler and Andries P. Engelbrecht and V. S. S. Yadavalli", title = "Multi-Objective DE and PSO Strategies for Production Scheduling", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0292.pdf}, url = {}, size = {}, abstract = {This paper investigates the application of alternative multi-objective optimization (MOO) strategies to a complex scheduling problem. Two vector evaluated algorithms, namely the vector evaluated particle swarm optimization (VEPSO) algorithm as well as the vector evaluated differential evolution (VEDE) algorithm is compared to a differential evolutionbased modified goal programming approach. This paper is considered significant since no other reference to the application of vector evaluated algorithms in a scheduling environment could be found. Algorithm performance is evaluated on real customer data and meaningful conclusions are drawn with respect to the application of MOO algorithms in a multiple machine multi-objective scheduling environment. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Mahajan:2008:cec, author = "Anjali Mahajan and M. S. Ali", title = "Hybrid Evolutionary Algorithm for Graph Colouring Register Allocation", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0293.pdf}, url = {}, size = {}, abstract = {Memory or registers are used to store the results of computation of a program. As compared to memory, accessing a register is much faster, but they are scarce resources and have to be used very efficiently. If the register set is not sufficient to hold all program variables, certain values have to be stored in memory and so-called spill code has to be inserted. The optimization goal is to hold as many live variables as possible in registers in order to avoid expensive memory accesses. We present a new hybrid evolutionary algorithm (HEA) for graph colouring register allocation problem for embedded systems. We have used MachineSUIF [19] compiler research framework for implementing our algorithm. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Sun:2008:cec, author = "Tsung-Ying Sun and Chan-Cheng Liu and Tsung-Ying Tsai and Sheng-Ta Hsieh", title = "Adequate Determination of a Band of Wavelet Threshold for Noise Cancellation Using Particle Swarm Optimization", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0295.pdf}, url = {}, size = {}, abstract = {Noise reduction problem is addressed by this study. Recently, wavelet thresholding has become popular and has gotten much attention among a number of de-noisy approaches. The most of threshold determination are developed from universal method proposed by Donoho. But, some shortcomings of the determination are caused from several incorrectly estimated factors and the lack of adaptability for whole frequency. By the reason, this paper replaces a universal threshold by multi-thresholds for matching the coefficients of each wavelet segment, and then the band of threshold will be fined by particle swarm optimization (PSO). Because original signals and noise are mutually independent, an objective function of PSO is created to evaluate the second order correlation and high order correlation. In order to confirm the validity and efficiency of the proposed algorithm, several simulations which include four benchmarks with high or low noise degree are designed. Moreover, the performance of proposed algorithm will have compared with that of other existing algorithms. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Meignan:2008:cec, author = "David Meignan and Jean-Charles Creput and Abderrafiâa Koukam", title = "A Coalition-Based Metaheuristic for the Vehicle Routing Problem", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0297.pdf}, url = {}, size = {}, abstract = {This paper presents a population based Metaheuristic adopting the metaphor of social autonomous agents. In this context, agents cooperate and self-adapt in order to collectively solve a given optimization problem. From an evolutionary computation point of view, mechanisms driving the search consist of combining intensification operators and diversification operators, such as local search and mutation or recombination. The multiagent paradigm mainly focuses on the adaptive capabilities of individual agents evolving in a context of decentralized control and asynchronous communication. In the proposed metaheuristic, the agent's behavior is guided by a decision process for the operators' choice which is dynamically adapted during the search using reinforcement learning and mimetism learning between agents. The approach is called Coalition-Based Metaheuristic (CBM) to refer to the strong autonomy conferred to the agents. This approach is applied to the Vehicle Routing Problem to emphasize the performance of learning and cooperation mechanisms. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Sun2:2008:cec, author = "Tsung-Ying Sun and Chih-Li Huo and Shang-Jeng Tsai and Chan-Cheng Liu", title = "Optimal UAV Flight Path Planning Using Skeletonization and Particle Swarm Optimizer", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0298.pdf}, url = {}, size = {}, abstract = {The purpose of this paper is to search the best flight route efficiently for Unmanned Aerial Vehicle (UAV) in the 3-dimention complicated topography. The proposed method for the best flight route is mainly using evolutionary algorithm, and give the proper initial population of evolutionary algorithm through skeletonization, efficient pre-processing procedure.In order to provide a smooth flight route for UAV, this paper adopts B-spline Curve method. Several control points of B-spline Curve method must be determined to generate flight route. The best control points can be calculated by Particle Swarm Optimizer (PSO). In this paper, the initial population of PSO is provided by skeletonization. The skeletonization of pre-processing procedure mainly includes two parts: one is Skeletonization and the other is candidate path searching. The purpose of pre-processing procedure is to reduce computation time, to prevent search the best solutions aimless, and execute evolutionary process efficiently. This paper uses Matlab as the experiment environment. The results of the experiments present the proposed method can provide the best flight route for UAV efficiently. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Hu:2008:cec, author = "Zhong-Bo Hu and Qing-Hua Su and Sheng-Wu Xiong and Fu-Gao Hu", title = "Self-Adaptive Hybrid Differential Evolution with Simulated Annealing Algorithm for Numerical Optimization", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0305.pdf}, url = {}, size = {}, abstract = {A self-adaptive hybrid differential evolution with simulated annealing algorithm, termed SaDESA, is proposed. In the novel SaDESA, the choice of learning strategy and several critical control parameters are not required to be pre-specified. During evolution, the suitable learning strategy and parameters setting are gradually self-adapted according to the learning experience. The performance of the SaDESA is evaluated on the set of 25 benchmark functions provided by CEC2005 special session on real parameter optimization. Comparative study exposes the SaDESA algorithm as a competitive algorithm for a global optimization. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Chaharsooghi:2008:cec, author = "S. K. Chaharsooghi and Amir H. Meimand Kermani", title = "An Intelligent Multi-Colony Multi-Objective Ant Colony Optimization (ACO) for the 0-1 Knapsack Problem", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0309.pdf}, url = {}, size = {}, abstract = {The knapsack problem is a famous optimization problem. Even the single objective case has been proven to be NP-hard the multi-objective is harder than the single objective case. This paper presents the modified ant colony optimization (ACO) algorithm for solving knapsack multi-objective problem to achieve the best layer of non-dominated solution. We also proposed a new pheromone updating rule for multi-objective case which can increase the learning of algorithm and consequently increase effectiveness. Finally, the computational result of proposed algorithm is compared with the NSGA II which outperforms most of the multi-objective ant colony optimization algorithm which are reviewed in this paper. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Suwannik:2008:cec, author = "Worasait Suwannik and Prabhas Chongstitvatana", title = "Solving One-Billion-Bit Noisy OneMax Problem using Estimation Distribution Algorithm with Arithmetic Coding", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0310.pdf}, url = {}, size = {}, abstract = {This paper presents an algorithm which combines Estimation Distribution Algorithm with a chromosome compression scheme to solve large scale Noisy OneMax problem. The search space reduction resulted from chromosome compression enables the algorithm to solve a one-billion-bit problem. Arithmetic Coding represents a compressed binary string with two real numbers. Using this representation, a model of highly fit individuals can be constructed. This model can be used to evolve the solution in the manner of Estimation Distribution Algorithm. The experimental result shows that the algorithm can solve billion-bit Noisy OneMax problem in about 34 hours using a normal PC-class computer. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Thiruvady:2008:cec, author = "Dhananjay R. Thiruvady and Bernd Meyer and Andreas T. Ernst", title = "Strip Packing with Hybrid ACO: Placement Order is Learnable", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0312.pdf}, url = {}, size = {}, abstract = {This paper investigates the use of hybrid metaheuristics based on Ant Colony Optimization (ACO) for the strip packing problem. Here, a fixed set of rectangular items of fixed sizes have to be placed on a strip of fixed width and infinite height without overlaps and with the objective to minimize the height used. We analyze a commonly used basic placement heuristic (BLF) by itself and in a number of hybrid combinations with ACO. We compare versions that learn item order only, item rotation only, both independently, and rotations conditionally upon placement order. Our analysis shows that integrating a learning meta-heuristic provides a significant performance advantage over using the basic placement heuristic by itself. The experiments confirm that even just learning a placement order alone can provide significant performance improvements. Interestingly, learning item rotations provides at best a marginal advantage. The best hybrid algorithm presented in this paper significantly outperforms previously reported strip packing meta-heuristics. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wang12:2008:cec, author = "Dazhi Wang and Chun-Ho Wu and Andrew Ip and Dingwei Wang and Yang Yan", title = "Parallel Multi-Population Particle Swarm Optimization Algorithm for the Uncapacitated Facility Location Problem using OpenMP", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0313.pdf}, url = {}, size = {}, abstract = {Parallel multi-population Particle Swarm Optimi- zation (PSO) Algorithm using OpenMP is presented for the Uncapacitated Facility Location (UFL) problem. The parallel algorithm performed asynchronously by dividing the whole particle swarm into several sub-swarms and updated the particle velocity with a variety of local optima. Each sub-swarm changes its best position so far of to its neighbor swarm after certain generations. The parallel multi-population PSO (PMPSO) algorithm is applied to several benchmark suits collected from OR-library. And the results are presented and compared to the result of serial execution multi-population PSO. It is conducted that the parallel multi-population PSO is time saving, especially for large scale problem and generated more robust results. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Ra:2008:cec, author = "Syungkwon Ra and Galam Park and ChangHwan Kim and Bum-Jae You", title = "PCA-Based Genetic Operator for Evolving Movements of Humanoid Robot", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0317.pdf}, url = {}, size = {}, abstract = {This paper proposes a new genetic operator in order to evolve the humanoid movements, which is composed of principal component analysis (PCA) and descent-based local optimization with respect to robot dynamics. The aim of the evolution is to let humanoid robots generate human-like and energy-efficient motions in real-time. We first capture human motions and build a set of movement primitives. The set is then evolved to the optimal movement primitives for the specific robot, which contain its dynamic characteristics, by using an evolutionary algorithm with the proposed genetic operator. Finally, the humanoid robot can generate arbitrary motions in real-time through the mathematical interpolation of the movement primitives in the evolved set. The evolved set of movement primitives endows the humanoid robot with natural motions which require minimal torque. This technique gives a systematic methodology for a humanoid robot to learn natural motions from human considering dynamics of the robot. The feasibility of our genetic operator is investigated by simulation experiments in regard to catching a ball that a man throws of the humanoid robot. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Supudomchok:2008:cec, author = "S. Supudomchok and N. Chaiyaratana and C. Phalakornkule", title = "Co-Operative Co-Evolutionary Approach for Flux Balance in Bacillus Subtilis", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0318.pdf}, url = {}, size = {}, abstract = {Flux balance analysis of the metabolic network of Bacillus subtilis was employed to investigate flux distribution with maximizing ATP and ATP per sum of all flux values. The first objective function, which is to maximize ATP, is a conventional linear objective function and is performed with a hill-climbing algorithm. The second, which is to maximize ATP per sum of all flux values, is a non-linear objective function and is performed with a co-operative co-evolutionary genetic algorithm (CCGA). The effects of co-substrate supplementation; i.e. serine, cysteine, aspartate and threonine, are investigated. Employing two different objective functions predicts different effect of substrate supplementation. The optimization results according to the first objective function suggest that no improvement can be gained by substrate supplementation, while those according to the second objective function suggest that the introduction of each alternative substrate can lead to an improvement in ATP production. Exploration of alternative objective functions by CCGA is illustrated to generate more flux scenarios. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Mase:2008:cec, author = "Motohiro Mase and Seiji Yamada and Katsumi Nitta", title = "Extracting Topic Maps from Web Pages by Web Link Structure and Content", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0320.pdf}, url = {}, size = {}, abstract = {We propose a framework to extract topic maps from a set of Web pages. We use the clustering method with the Web pages and extract the topic map prototypes. We introduced the following two points to the existing clustering method: The first is merging only the linked Web pages, thus extracting the underlying relationships between the topics. The second is introducing weighting based on the similarity from the contents of the Web pages and relevance between topics of pages. The relevance is based on the types of links with directories in the Web sites structure and the distance between the directories in which the pages are located. We generate the topic map prototypes by assuming that the clusters are the topics, the edges are the associations, and the Web pages related to the topics are the occurrences from the results of the clustering. Finally, users complete the prototype by labeling the topics and associations and removing the unnecessary items. We incrementally use a user's evaluation of the topic maps to judge whether a Web page is unnecessary or necessary and then reduce the number of unnecessary pages. We use the relevance feedback along with a Support Vector machine (SVM) to judge the Web pages. For this paper, at the first step, we mounted the proposed clustering method and conducted experiments to evaluate the effectiveness of extracting topic map prototypes. We eventually discussed the effectiveness of our two additional points by evaluating the extracted topic map prototypes. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Huang4:2008:cec, author = "Kuang Yu Huang and Chuen-Jiuan Jane and Ting-Cheng Chang", title = "A RS Model for Stock Market Forecasting and Portfolio Selection Allied with Weight Clustering and Grey System Theories", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0322.pdf}, url = {}, size = {}, abstract = {In this study, the weight clustering model which consists of GM(1,N) with K-means Clustering is combined with Grey Systems theory and Rough Set (RS) theory to create an automatic stock market forecasting and portfolio selection mechanism. In our proposed approach, financial data are collected every quarter and are inputted to an GM(1,1) predicting model to forecast the future trends of the collected data over the next quarter. Next, the forecasted data of financial statement is transformed into financial ratios using a GM(1,N) model and clustered by using a K-means clustering algorithm, and then supplied to a RS classified module which selects appropriate investment stocks by adopting a set of decision-making rules. Finally, a grey relational analysis technique is applied to specify an appropriate weighting of the selected stocks to maximize the portfolio's rate of return. The validity of our proposed approach is demonstrated to use the electronic stock data extracted from the financial database maintained by the Taiwan Economic Journal (TEJ). The portfolio's results derived by using our proposed weight clustering model are compared with those portfolio's results of a conventionally clustering method. It is found that our proposed method yielded a greater average annual rate of return (23.42percent) on the selected stocks from 2004 to 2006 in Taiwan stock market. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Singh:2008:cec, author = "Chingtham Tejbanta Singh and Ujjwal Maulik", title = "A Framework for an Artificial Immunity and Speech Based Navigation for Mobile Robots", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0325.pdf}, url = {}, size = {}, abstract = {In recent years speech recognition technology and immunity based algorithms have made an impact in various areas and are deployed for a wide range of applications. This paper describes a learning process of a mobile robot which takes speech input as commands and performs some navigation task through a distinct man-machine interaction with the application of the learning based on the Artificial Immune System. For this purpose a 4-channel radio controlled Wheelbot and Microsoft's Speech SDK for speech recognition is employed. The speech recognition system is trained to recognize defined commands and the robot has been designed to navigate based on the instruction through the Speech Commands. The position of the obstacles are learnt and avoided by the help of immune algorithm. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Huang5:2008:cec, author = "Haoming Huang and Michel Pasquier and Chai Quek ", title = "Application of a Hierarchical Coevolutionary Fuzzy System for Financial Prediction and Trading", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0326.pdf}, url = {}, size = {}, abstract = {In this paper, the application of a hierarchical coevolutionary fuzzy system called HiCEFS for predicting financial time series is investigated. A novel financial trading system using the HiCEFS as a predictive model and employing a prudent trading strategy based on the price percentage oscillator (PPO) is proposed. In order to construct an accurate predictive model, a form of generic membership function named Irregular Shaped Membership Function (ISMF) is employed and a hierarchical coevolutionary genetic algorithm (HCGA) is adopted to automatically derive the ISMFs for each input variable in HiCEFS. With the accurate prediction from HiCEFS and a prudent trading strategy, the proposed financial trading system outperforms the simple buy-and-hold strategy, the trading system without prediction and the trading system with other predictive models (EFuNN, DENFIS and RSPOP) on real-world financial data. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Dong:2008:cec, author = "Weishan Dong and Xin Yao", title = "NichingEDA: Using the Diversity Inside a Population of EDAs for Continuous Optimization", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0327.pdf}, url = {}, size = {}, abstract = {Since the Estimation of Distribution Algorithms (EDAs) have been introduced, several single model based EDAs and mixture model based EDAs have been developed. Take Gaussian models as an example, EDAs based on single Gaussian distribution have good performance on solving simple unimodal functions and multimodal functions whose landscape has an obvious trend towards the global optimum. But they have difficulties in solving multimodal functions with irregular landscapes, such as wide basins, flat plateaus and deep valleys. Gaussian mixture model based EDAs have been developed to remedy this disadvantage of single Gaussian based EDAs. A general framework NichingEDA is presented in this paper from a new perspective to boost single model based EDAs' performance. Through adopting a niching method and recombination operators in a population of EDAs, NichingEDA significantly boosts the traditional single model based EDAs' performance by making use of the diversity inside the EDA population on hard problems without estimating a precise distribution. Our experimental studies have shown that NichingEDA is very effective for some hard global optimization problems, although its scalability to high dimensional functions needs improving. Analyses and discussions are presented to explain why NichingEDA performed well/poorly on certain benchmark functions. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Burgos-Artizzu:2008:cec, author = "Xavier P. Burgos-Artizzu and Angela Ribeiro and Alberto Tellaeche and Gonzalo Pajares", title = "Optimisation of Natural Images Processing Using Different Evolutionary Algorithms", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0329.pdf}, url = {}, size = {}, abstract = {The development of image processing methods to discriminate between weed, crop and soil is an important step for Precision Agriculture, the main goal of which is the sitespecific management of crops. The main challenge in terms of image analysis is to achieve an appropriate discrimination in outdoor field images under varying conditions of lighting, soil background texture and crop damage. This work presents several developed computer-vision-based methods for the estimation of percentages of weed, crop and soil in digital images of a crop field. These methods are interchangeable among them, having each one of them a set of input parameters that need to be adjusted differently for each image. Two different evolutionary methods (standard genetic algorithm and NSGAII) have been used to adjust these parameters and find the best method combinations. The proposed approach can reach a correlation with real data of up to 97percent for a set of images acquired from different fields and under different conditions. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Majhi2:2008:cec, author = "Ritanjali Majhi and G. Panda and G. Sahoo and Abhishek Panda and Arvind Choubey", title = "Prediction of S&P 500 and DJIA Stock Indices Using Particle Swarm Optimization Technique", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0330.pdf}, url = {}, size = {}, abstract = {The present paper introduces the Particle Swarm Optimization (PSO) technique to develop an efficient forecasting model for prediction of various stock indices. The connecting weights of the adaptive linear combiner based model are optimized by the PSO so that its mean square error (MSE) is minimized. The short and long term prediction performance of the model is evaluated with test data and the results obtained are compared with those obtained from the multilayer perceptron (MLP) based model. It is in general observed that the proposed model is computationally more efficient, prediction wise more accurate and takes less training time compared to the standard MLP based model. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Sheta:2008:cec, author = "Alaa Sheta and David Rine and Aladdin Ayesh", title = "Development of Software Effort and Schedule Estimation Models Using Soft Computing Techniques", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0334.pdf}, url = {}, size = {}, abstract = {Accurate estimation of the software effort and schedule affects the budget computation. Bidding for contracts depends mainly on the estimated cost. Inaccurate estimates will lead to failure of making a profit, increased probability of project incompletion and delay of the project delivery date. In this paper, we explore the use of Soft Computing Techniques to build a suitable model structure to use improved estimations of software effort for NASA software projects. In doing so, we plan to use Particle Swarm Optimization (PSO) to tune the parameters of the famous COnstructive COst MOdel (COCOMO). We plan also to explore the advantages of Fuzzy Logic to build a set of linear models over the domain of possible software Line Of Code (LOC). The performance of the developed model was evaluated using NASA software projects data set [1]. A comparison between COCOMO tuned-PSO, Fuzzy Logic (FL), Halstead, Walston-Felix, Bailey-Basili and Doty models were provided. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Li10:2008:cec, author = "Ya-Liang Li and Fei Ding and Yu-Xuan Wang", title = "Iterated Function System Based Adaptive Differential Evolution Algorithm", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0335.pdf}, url = {}, size = {}, abstract = {In this paper, we propose a new adaptive Differential Evolution algorithm, in which a simple mechanism based on Iterated Function System is applied to the control parameters F and CR. The performance is reported on a set of benchmark functions, which shows that our algorithm is better than, or at least comparable to the standard DE algorithm and the other adaptive versions of DE algorithm. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Hu2:2008:cec, author = "Rong Hu and Ling Wang and Bin Qian and Fu-zhuo Huang", title = "Differential Evolution Method for Stochastic Flow Shop Scheduling with Limited Buffers", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0336.pdf}, url = {}, size = {}, abstract = {The flow shop scheduling problem (FSSP) with limited buffers constraint is a typical NP-hard combinatorial optimization problem and represents an important area in production scheduling. In this paper, a class of differential evolution (DE) method with the optimal computing budget allocation (OCBA) technique and hypothesis test (HT), namely OHTDE, is proposed for the stochastic flow shop scheduling with limited buffers between consecutive machines to minimize the maximum completion time (i.e., makespan). In the OHTDE, the population-based search mechanism of DE and a special crossover are applied for well exploration and exploitation, and the OCBA technique is used to allocate limited sampling budgets to provide reliable evaluation and identification for good individuals. Meanwhile, HT is also applied to perform a statistical comparison to avoid some repeated search to some extent. The results and comparisons demonstrate the superiority of OHTDE in terms of effectiveness and robustness. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Li11:2008:cec, author = "M. S. Li and T. Y. Ji and Z. Lu and Henry Wu", title = "Optimal Harmonic Estimation Using Dynamic Bacterial Swarming Algorithm", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0337.pdf}, url = {}, size = {}, abstract = {This paper presents a Dynamic Bacterial Swarming Algorithm (DBSA) for harmonic estimation in dynamic environment. DBSA is designed from a dynamic searching framework that combines the underlying mechanisms of bacterial chemotaxis, quorum sensing and environment adaptation. The harmonic estimation process uses DBSA to estimate the phases of the harmonics, alongside a Least Square (LS) method to estimate the amplitudes. A cost function is given as an error between the original signal and the reconstructed signal. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Ashlock3:2008:cec, author = "Daniel Ashlock and Elizabeth Warner", title = "The Geometry of Tartarus Fitness Cases", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0339.pdf}, url = {}, size = {}, abstract = {Tartarus is a standard AI task for grid robots in which boxes must be moved to the walls of a virtual world. There are 320,320 fitness cases for the standard Tartarus task of which 297,040 are valid according to the original statement of the problem. This paper studies different schemes for allocating fitness trials for Tartarus using an agent-based metric on the fitness cases to aid in the design process. This agent-based metric is a tool that permits exploration of the geometry of the space of fitness cases. The information gained from this exploration demonstrates why a scheme designed to yield a superior set of training cases in fact yielded an inferior one. The information gained also suggests a new scheme for allocating fitness trials that decreases the number of trials required to achieve a given fitness of the best agent. This scheme achieves similar fitness to a standard evolutionary algorithm using fewer fitness cases. The space of fitness cases for Tartarus is found, relative to the agent-based metric, to form a hollow sphere with a nonuniform distribution of the fitness cases within the space. The tools developed in this study include a generalisable technique for placing an agent-based metric space structure on the fitness cases of any problem that has multiple fitness cases. This metric space structure can be used to better understand the distribution of fitness cases and so design more effective evolutionary algorithms. }, keywords = {genetic algorithms, genetic programming}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Leong:2008:cec, author = "Wen-Fung Leong and Gary G. Yen", title = "Impact of Tuning Parameters on Dynamic Swarms in PSO-Based Multiobjective Optimization", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0340.pdf}, url = {}, size = {}, abstract = {In this paper, the improvement of two design components (swarm growing strategy and objective space compression and expansion strategy) from the existing multiple swarms MOPSO, namely DSMOPSO, is presented. In addition, sensitivity analysis is conducted to study the impact of the five tuning parameters on its performance through two performance metrics. Simulation results show the improved design is robust with respect to the tuning parameters. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Daneshyari:2008:cec, author = "Moayed Daneshyari and Gary G. Yen", title = "Cultural MOPSO: A Cultural Framework to Adapt Parameters of Multiobjective Particle Swarm Optimization", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0341.pdf}, url = {}, size = {}, abstract = {Multiobjective particle swarm optimization algorithms (MOPSO) have been widely used to solve multiobjective optimization problems. Most of MOPSOs use fixed momentum and acceleration for all particles throughout the evolutionary process. In this paper, we introduce a cultural framework to adapt the flight parameters of the MOPSO namely momentum, personal, and global acceleration for each individual particle based upon the various types of knowledge in belief space, specifically situational knowledge, normative knowledge, and topographical knowledge. Movement of the particles using the adapted parameters helps the MOPSO to perform efficiently and effectively in multiobjective optimization. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Valdez:2008:cec, author = "Fevrier Valdez and Patricia Melin and Oscar Castillo and Oscar Montiel", title = "A New Evolutionary Method with a Hybrid Approach Combining Particle Swarm Optimization and Genetic Algorithms Using Fuzzy Logic for Decision Making", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0342.pdf}, url = {}, size = {}, abstract = {We describe in this paper a new hybrid approach for mathematical function optimization combining Particle Swarm Optimization (PSO) and Genetic Algorithms (GAs) using Fuzzy Logic to integrate the results. The new evolutionary method combines the advantages of PSO and GA to give us an improved PSO+GA hybrid method. Fuzzy Logic is used to combine the results of the PSO and GA in the best way possible. The new hybrid PSO+GA approach is compared with the PSO and GA methods with a set of benchmark mathematical functions. The new hybrid PSO+GA method is shown to be superior than the individual evolutionary methods. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Araújo:2008:cec, author = "Ricardo de A. Araújo and Aranildo R. L. Júnior and Tiago A. E. Ferreira", title = "Morphological-Rank-Linear Time-Lag Added Evolutionary Forecasting Method for Financial Time Series Forecasting", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0344.pdf}, url = {}, size = {}, abstract = {This paper proposes the Morphological-Rank-Linear Time-lag Added Evolutionary Forecasting (MRLTAEF) method for financial time series forecasting, which performs an evolutionary search for the minimum number of relevant time lags necessary to efficiently represent complex time series. It consists of an intelligent hybrid model composed of a Morphological-Rank-Linear (MRL) filter combined with a Modified Genetic Algorithm (MGA) which employs optimal genetic operators in order to accelerate its search convergence. The MGA searches for the particular time lags capable of a fine tuned characterization of the time series and estimates the initial (sub-optimal) parameters of the MRL filter - the mixing parameter (λ), the rank (r), the coefficients of the linear Finite Impulse Response (FIR) filter (b) and the coefficients of the Morphological-Rank (MR) filter (a). Thus, each individual of the MGA population is trained by the averaged Least Mean Squares (LMS) algorithm to further improve the parameters of the MRL filter supplied by the MGA. Initially, the proposed MRLTAEF method chooses the most tuned prediction model for time series representation, thus it performs a behavioral statistical test in the attempt to adjust forecasting time phase distortions that appear in financial time series. Experiments are conducted with the proposed MRLTAEF method using three real world financial time series according to a group of relevant performance metrics and the results are compared to MultiLayer Perceptron (MLP) networks, MRL filters and the previously introduced Time-delay Added Evolutionary Forecasting (TAEF) method. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Araújo2:2008:cec, author = "Ricardo de A. Araújo and Aranildo R. L. Júnior and Tiago A. E. Ferreira", title = "A Quantum-Inspired Intelligent Hybrid Method for Stock Market Forecasting", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0345.pdf}, url = {}, size = {}, abstract = {This work introduces a Quantum-Inspired Intelligent Hybrid (QIIH) method for stock market forecasting. It performs a quantum-inspired evolutionary search for the minimum necessary dimension (time lags) embedded in the problem for determining the characteristic phase space that generates the financial time series phenomenon. The proposed QIIH method consists of a quantum-inspired intelligent hybrid model composed of an Artificial Neural Network (ANN) with a Modified Quantum-Inspired Evolutionary Algorithm (MQIEA), which is able to evolve the complete network architecture and parameters (pruning process), its training algorithm (used to further improve the ANN parameters supplied by the MQIEA) and the particular time lags capable of a fine tuned time series characterization. Initially, the proposed QIIH method chooses the most fitted forecasting model, thus it performs a behavioral statistical test in the attempt to adjust forecasting time phase distortions that appear in financial time series. Furthermore, an experimental analysis is conducted with the proposed QIIH method using three real world stock market time series, and the achieved results are discussed and compared, according to a group of relevant performance metrics, to results found withMultiLayer Perceptron (MLP) networks and the previously introduced Time-delay Added Evolutionary Forecasting (TAEF) method. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Chiang:2008:cec, author = "Cheng-Hsiung Chiang ", title = "A Symbolic Controller Based Intelligent Control System with Quantum Particle Swarm Optimization Based Hybrid Genetic Algorithm", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0346.pdf}, url = {}, size = {}, abstract = {In this paper, a new symbolic controller based intelligent control system is proposed, namely qSyICS, which consists of a symbolic controller, a percepter, and a qAdaptor. The symbolic controller is made up of a number of symbolic rules. The percepter is to detect the control efficiency. Once the sensory information is inefficient or inadaptable, the qAdaptor will be activated; otherwise, the symbolic controller will keep on the controlling assignments. The qAdaptor consisted of the exploration process and symbolic rule generator is firstly to explore the new control actions, and then transforms them into new symbolic rules to update the rule base. The improved hybrid genetic algorithm is proposed to implement the exploration process for searching new actions, namely qHGA. A quantum behavior inspired particle swarm optimization that has the variable-length particles with discrete encoding is proposed to generate the partial initial population of qHGA. An application of robotic path planning is applied to demonstrate the proposed method through comparing with other methods. The simulation results showed that the proposed approach is more efficient than the other approaches. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Sim:2008:cec, author = "Kwang Mong Sim ", title = "An Evolutionary Approach for P-S-Optimizing Negotiation", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0348.pdf}, url = {}, size = {}, abstract = {This work devises an approach for co-evolving negotiation strategies of agents that have different preference criteria such as optimizing price and optimizing negotiation speed. Whereas many works on e-commerce negotiation define utility functions in terms of price only, this work defines a utility function in terms of both price and negotiation speed. Different emphases on these two criteria can be modeled by placing different weights on them. Hence, in this work, negotiation agents can be price-optimizing, speed optimizing, and P-S-optimizing. Additionally, this work is one of the earliest works that adopt an Estimation Distribution Algorithm (EDA) for finding best response strategies for negotiation agents with different preference criteria. Empirical results show that the EDA can evolve price-optimizing, speedoptimizing, and P-S-optimizing strategies that generally achieve high utilities for negotiation agents. Furthermore, empirical results show that the EDA can evolve to a near optimal strategy for price-optimizing negotiation agents. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Merelo-Guervós:2008:cec, author = "Juan Julian Merelo-Guervós and Pedro A. Castillo and JLJ Laredo and A. Mora García and A. Prieto ", title = "Asynchronous Distributed Genetic Algorithms with Javascript and JSON", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0357.pdf}, url = {}, size = {}, abstract = {In a connected world, spare CPU cycles are up for grabs, if you only make its obtention easy enough. In this paper we present a distributed evolutionary computation system that uses the computational capabilities of the ubiquituous web browser. Asynchronous Javascript and JSON (Javascript Object Notation, a serialization protocol) allows anybody with a web browser (that is, mostly everybody connected to the Internet) to participate in a genetic algorithm experiment with little effort, or none at all. Since, in this case, computing becomes a social activity and is inherently impredictable, in this paper we will explore the performance of this kind of virtual computer by solving simple problems such as the Royal Road function and analyzing how many machines and evaluations it yields. We will also examine possible performance bottlenecks and how to solve them, and, finally, issue some advice on how to set up this kind of experiments to maximize turnout and, thus, performance. The experiments show that we we can obtain high, and to a certain point, reliable performance from volunteer computing based on AJAJ, with speedups of up to several (averaged) machines. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Maeda:2008:cec, author = "Yutaka Maeda and Naoto Matsushita ", title = "Combination of Particle Swarm Optimization and Simultaneous Perturbation", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0362.pdf}, url = {}, size = {}, abstract = {In this paper, we propose some different optimization schemes which are combinations of the particle swarm optimization and the simultaneous perturbation optimization method. The proposed schemes can use local information of an objective function and global shape of the function at the same time. These characteristics are from the simultaneous perturbation optimization method and the particle swarm optimization. The schemes have good properties of global search and efficient local search capability. Moreover, the schemes themselves are very simple and easy to implement. These methods only require values of the function similar to the original particle swarm optimization and the simultaneous perturbation method. The proposed schemes are investigated using some test function to know convergence properties such as convergence rate or convergence speed. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Haasdijk:2008:cec, author = "E. Haasdijk and P. Vogt and A. E. Eiben", title = "Social Learning in Population-Based Adaptive Systems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0363.pdf}, url = {}, size = {}, abstract = {The subject of the present investigation is Population-based Adaptive Systems (PAS), as implemented in the NEW TIES platform. In many existing PASs two adaptation mechanisms are combined, (non-Lamarckian) evolution and individual learning, inevitably raising the issue of `forgetful populations': individually learnt knowledge disappears when the individual that learnt it dies. We propose social learning by explicit knowledge transfer to overcome this problem. Our mechanism is based on (1) direct communication among agents in the population, (2) messages carrying rules that the sender agent uses in its controller, and (3) the ability of the recipient agent to incorporate foreign rules into its controller. Thus, knowledge can be disseminated and multiplied within the same generation, making the population a knowledge reservoir for individually acquired knowledge. We present an initial assessment of this idea and show that this social mechanism is capable of efficiently distributing knowledge and improving the performance of the population. }, keywords = {genetic algorithms, genetic programming}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Fu2:2008:cec, author = "Ali Fu and Xiujuan Lei and Xiao Xiao", title = "The Aircraft Departure Scheduling Based on Particle Swarm Optimization Combined with Simulated Annealing Algorithm", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0364.pdf}, url = {}, size = {}, abstract = {Particle swarm optimization combined with simulated annealing algorithm (PSOCSA) was an improved particle swarm optimization algorithm which introduced the simulated annealing (SA) strategy in particle swarm optimization (PSO). It was proposed to solve a mathematical model which is built for aircraft departure sequencing problem in this paper. The correlative implementation techniques and detailed design process of the algorithm were presented. Then the simulation is performed to solve a representative problem using PSOCSA, PSO, and SA. The comparison showed that the PSOCSA algorithm was rational and feasible and more easily converge to the global optimal solution of aircraft departure sequencing problem. Method described in this paper will curtail the consumption of aircraft departure effectively, so it is worth researching it further in the field of airport operations and air traffic control. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Lei:2008:cec, author = "Xiujuan Lei and Ali Fu and Zhongke Shi", title = "The Aircraft Departure Scheduling Based on Second-Order Oscillating Particle Swarm Optimization Algorithm", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0365.pdf}, url = {}, size = {}, abstract = {The second-order oscillating particle swarm optimization (SO-PSO) algorithm, which introduced the second-order oscillating evolutionary equation to the evolutionary equation of PSO, could adjust the particles' global and local search capability and avoid the local optimization. It was proposed to solve a mathematical model which was built for aircraft departure sequencing problem in this paper. The correlative implementation techniques and detailed design process of the algorithm were presented. Then the simulation was performed to solve this sequencing problem using the SO-PSO algorithm. The results showed that the global optimal solution was obtained, so the SO-PSO algorithm was rational and feasible and curtailed the consumption of aircraft departure effectively. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Liu4:2008:cec, author = "Xiaojie Liu ", title = "An Immune Method for Network Security Risk Evaluation", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0366.pdf}, url = {}, size = {}, abstract = {An immune method for real-time computer network security risk evaluation is proposed. The concepts of self, nonself, antigen and immunocyte of computer immune system are defined. The dynamic model of self, marrow model, clone selection, learning scheme, life span of immunocyte are built. A computational model of risk evaluation based on the antibody concentration of memory immunocytes for network security is thus presented. The experiment shows that this method has the features of quantitative calculation and real-time processing ability. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Rahnamayan:2008:cec, author = "Shahryar Rahnamayan and Hamid Reza Tizhoosh", title = "Image Thresholding Using Micro Opposition-Based Differential Evolution (Micro-ODE)", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0368.pdf}, url = {}, size = {}, abstract = {Image thresholding is a challenging task in image processing field. Many efforts have already been made to propose universal, robust methods to handle a wide range of images. Previously by the same authors, an optimization-based thresholding approach was introduced. According to the proposed approach, Differential Evolution (DE) algorithm, minimizes dissimilarity between the input grey-level image and the bi-level (thresholded) image. In the current paper, micro Opposition-Based Differential Evolution (micro-ODE), DE with very small population size and opposition-based population initialization, has been proposed. Then, it is compared with a well-known thresholding method, Kittler algorithm and also with its non-opposition-based version (micro-DE). In overall, the proposed approach outperforms Kittler method over 16 challenging test images. Furthermore, the results confirm that the micro-ODE is faster than micro-DE because of embedding the opposition-based population initialization. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Zhuang2:2008:cec, author = "Ruixin Zhuang and Bin Hu and Zhongxing Ye", title = "Minimal Spanning Tree for Shanghai-Shenzhen 300 Stock Index", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0370.pdf}, url = {}, size = {}, abstract = {This paper uses the ultrametric clustering which is based on time series of stock prices. By calculating the ultrametric distance-matrix of the stocks in Shanghai-Shenzhen 300 Index, a minimum spanning tree (MST) which has certain topological meaning can be drawn. And this MST explains the physical correlation between the topological structure and economic classification of the stocks trading on markets. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Kaylani:2008:cec, author = "A. Kaylani and M. Georgiopoulos and M. Mollaghasemi and G. C. Anagnostopoulos ", title = "MO-GART: Multiobjective Genetic ART Architectures", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0372.pdf}, url = {}, size = {}, abstract = {In this work we present, for the first time, the evolution of ART Neural Network architectures (classifiers) using a multiobjective optimization approach. In particular, we propose the use of a multiobjective evolutionary approach to evolve simultaneously the weights, as well as the topology of three well-known ART architectures; Fuzzy ARTMAP (FAM), Ellipsoidal ARTMAP (EAM) and Gaussian ARTMAP (GAM). We refer to the resulting architectures as MO-GFAM, MOGEAM, or MO-GGAM, and collectively as MO-GART. The major advantage of MO-GART is that it produces a number of solutions for the classification problem at hand that have different levels of merit (accuracy on unseen data (generalization) and size (number of categories created)). MO-GART is shown to be more elegant (does not require user intervention to define the network parameters), more effective (of better accuracy and smaller size), and more efficient (faster to produce the solution networks) than other ART neural network architectures that have appeared in the literature. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Chang3:2008:cec, author = "Pei Chann Chang and Shih Hsin Chen and Qingfu Zhang and Jun Lin Lin", title = "MOEA/D for Flowshop Scheduling Problems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0373.pdf}, url = {}, size = {}, abstract = {Many multiobjective evolutionary algorithms are based Pareto domination, among them NSGA II and SPEA 2 are two very popular ones. MOEA/D is a very recent multiobjective evolutionary algorithm using decomposition. In this paper, we implement MOEA/D for multi-objective flowshop scheduling problems. We study the replacement strategy of neighboring solutions, the determination of the reference point, and compare different decomposition methods. Experimental results demonstrate that MOEA/D outperforms NSGA II and SPEA 2 significantly for the 2-objective and 3-objective benchmark flowshop-scheduling instances. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Dasgupta:2008:cec, author = "Sambarta Dasgupta and Arijit Biswas and Swagatam Das and Ajith Abraham", title = "The Population Dynamics of Differential Evolution: A Mathematical Model", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0375.pdf}, url = {}, size = {}, abstract = {Differential Evolution (DE) is well known as a simple and efficient algorithm for global optimization over continuous spaces. This article provides a simple mathematical model of the underlying evolutionary dynamics of a one-dimensional DE. The model relates the search process of DE with the classical gradient descent search and also analyzes the convergence behavior of a DE population, very near to optima. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Zhou4:2008:cec, author = "Aimin Zhou and Qingfu Zhang and Yaochu Jin and Bernhard Sendhoff", title = "Combination of EDA and DE for Continuous Biobjective Optimization", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0376.pdf}, url = {}, size = {}, abstract = {The Pareto front (Pareto set) of a continuous optimization problem with m objectives is a (m -1) dimensional piecewise continuous manifold in the objective space (the decision space) under some mild conditions. Based on this regularity property in the objective space, we have recently developed several multiobjective estimation of distribution algorithms (EDAs). However, this property has not been used in the decision space. Using the regularity property in both the objective and decision space, this paper proposes a simple EDA for multiobjective optimization. Since the location information has not efficiently used in EDAs, a combination of EDA and differential evolution (DE) is suggested for improving the algorithmic performance. The hybrid method and the pure EDA method proposed in this paper, and a DE based method are compared on several test instances. Experimental results have shown that the algorithm with the proposed strategy is very promising. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Bernardino:2008:cec, author = "H. S. Bernardino and H. J. C. Barbosa and A. C. C. Lemonge and L. G. Fonseca", title = "A New Hybrid AIS-GA for Constrained Optimization Problems in Mechanical Engineering", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0377.pdf}, url = {}, size = {}, abstract = {A genetic algorithm (GA) is hybridized with an artificial immune system (AIS) as an alternative to tackle constrained optimization problems in engineering. The AIS is inspired in the clonal selection principle and is embedded into a standard GA search engine in order to help move the population into the feasible region. The procedure is applied to mechanical engineering problems available in the literature and compared to other alternative techniques. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Angira:2008:cec, author = "Rakesh Angira and Alladwar Santosh", title = "A Modified Trigonometric Differential Evolution Algorithm for Optimization of Dynamic Systems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0378.pdf}, url = {}, size = {}, abstract = {Differential Evolution (DE) is a novel evolutionary algorithm capable of handling non-differentiable, nonlinear and multimodal objective functions. Previous studies have shown that DE is an efficient, effective and robust evolutionary optimization method. Still it takes large computational time for solving the computationally expensive objective functions (for example optimization problems in the areas of computational mechanics, computational fluid dynamics, optimal control etc.) And therefore, an attempt to speed up DE is considered necessary. This paper deals with application and evaluation of a modified version of Trigonometric Differential Evolution (TDE) algorithm. The modification in TDE algorithm is made to further enhance its convergence speed. Further the Modified Trigonometric Differential Evolution (MTDE) algorithm is applied and evaluated for solving dynamic optimization problems encountered in chemical engineering. The performance of MTDE algorithm is compared with that of TDE and original DE algorithms. Results indicate that the MTDE algorithm is efficient and significantly faster than TDE and DE algorithms. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Zhou5:2008:cec, author = "Shang-Ming Zhou and Robert I. John and Xiao-Ying Wang and Jonathan M. Garibaldi and Ian O. Ellis", title = "Compact Fuzzy Rules Induction and Feature Extraction Using SVM with Particle Swarms for Breast Cancer Treatments", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0379.pdf}, url = {}, size = {}, abstract = {Developing a treatment plan for breast cancer patient is a very complex process. In this paper, we propose a scheme of inducing fuzzy rules that characterise breast caner treatment knowledge from data. These fuzzy rules can augment the human experts in the process of medical diagnosis to select optimal treatment for patients. The proposed machine learning scheme applies the particle swarm optimisation technique (PSO) to the construction of an optimal support vector machine (SVM) model for the sake of inducing accurate and parsimonious fuzzy rules and simultaneously reducing input space dimensions, in which a new fittness function that regularises the importance ranks of features with misclassification rate is suggested. The SVM-based fuzzy classifier evades the curse of dimensionality in high-dimensional breast cancer data space in the sense that the number of support vectors, which equals the number of induced fuzzy rules, is not related to the dimensionality. The experiments have shown that not only the classification performance achieved by the proposed fuzzy classifier outperforms the ones achieved by other methods in the literature, but also the input space dimension has been reduced greatly. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Abraham:2008:cec, author = "Ajith Abraham and Arijit Biswas and Sambarta Dasgupta and Swagatam Das", title = "Analysis of Reproduction Operator in Bacterial Foraging Optimization Algorithm", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0380.pdf}, url = {}, size = {}, abstract = {One of the major driving forces of Bacterial Foraging Optimization Algorithm (BFOA) is the reproduction phenomenon of virtual bacteria each of which models one trial solution of the optimization problem. During reproduction, the least healthier bacteria (with a lower accumulated value of the objective function in one chemotactic lifetime) die and the other healthier bacteria each split into two, which then starts exploring the search place from the same location. This keeps the population size constant in BFOA. The phenomenon has a direct analogy with the selection mechanism of classical evolutionary algorithms. In this article, we provide a simple mathematical analysis of the effect of reproduction on bacterial dynamics. Our analysis reveals that the reproduction event contributes to the quick convergence of the bacterial population near optima. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Mitavskiy:2008:cec, author = "Boris Mitavskiy and Jonathan Rowe and Chris Cannings", title = "Preliminary Theoretical Analysis of a Local Search Algorithm to Optimize Network Communication Subject to Preserving the Total Number of Links", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0382.pdf}, url = {}, size = {}, abstract = {A variety of phenomena such as world wide web, social or business interactions are modeled by various kinds of networks (such as the scale free or preferential attachment networks). However, due to the model-specific requirements one may want to rewire the network to optimize the communication among the various nodes while not overloading the number of channels (i.e. preserving the number edges). In the current paper we present a formal framework for this problem and a simple heuristic local search algorithm to cope with it. We estimate the expected single-step improvement of our algorithm, establish the ergodicity of the algorithm (i.e. that the algorithm never gets stuck at a local optima) with probability 1) and we also present a few initial empirical results for the scale free networks. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wen:2008:cec, author = "Jianping Wen and Xiaolan Wu and Kuo Jiang and Binggang Cao", title = "Particle Swarm Algorithm Based on Normal Cloud", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0383.pdf}, url = {}, size = {}, abstract = {This paper presents a novel parameter automation strategy for the particle swarm optimization algorithm; the normal cloud model is used to improve the performance of the particle swarm optimization algorithm. First, the normal cloud model is used to initialize the population; particles are no longer uniformly distributed throughout the search space. Second, one and the same normal cloud is used to nonlinearly, dynamically adjust inertia weight and update two random numbers in velocity update equation. Therefore, three components in the velocity update equation do interact in the PSO search process, which maintains the diversity of the population, provides balance between the global and local search abilities and makes the convergence faster. Experimental results are provided to show that the improved particle swarm optimization algorithm can successfully locate all optima on a small set of benchmark functions. A comparison of the improve algorithm with the standard particle swarm optimization algorithm is also made. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Chen6:2008:cec, author = "H. N. Chen and Y. L. Zhu and K. Y. Hu and T. Ku", title = "Global Optimization Based on Hierarchical Coevolution Model", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0384.pdf}, url = {}, size = {}, abstract = {This paper presents a novel optimization algorithm that we call the particle swarms swarm optimizer (PS2O), which based on a hierarchical coevolution model (HCO model) of symbiotic species. HCO model introduced a number of M species each possesses a number of N individuals to represent the ``biological community''. Both the heterogeneous coevolution and the homogeneous coevolution aspects are simulated in this model to maintain the community biodiversity. This strategy enable the symbiotic species find the optima faster and discourage premature convergence effectively. The experiments compare the performance of PS2O with the canonical PSO, the fully informed particle swarm (FIPS), the unified particle swarm (UPSO) and the Fitness-Distance-Ratio based PSO (FDR-PSO) on a set of 6 benchmark functions. The simulation results show the PS2O algorithm markedly outperforms the four mentioned algorithms on all benchmark functions and has the potential to solve the complex problems with high dimensionality. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Tenne:2008:cec, author = "Yoel Tenne and S. W. Armfield", title = "Metamodel Accuracy Assessment in Evolutionary Optimization", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0385.pdf}, url = {}, size = {}, abstract = {Evolutionary optimization of expensive functions typically uses a metamodel, i.e. a computationally cheaper but inaccurate approximation of the objective function. The success of the optimization search depends on the accuracy of the metamodel hence an integral part of the metamodelling framework is assessing the metamodel accuracy. In this paper we survey a range of accuracy assessment methods such as methods requiring additional sites, hypothesis testing and minimum lossfunction methods. We describe two numerical experiments: the first benchmarks different accuracy assessment methods from which it follows the most accurate methods are LOOCV and the 0.632 bootstrap estimator followed by the 10-CV and lastly the holdout method. The second experiment studies the effect of two different accuracy assessment methods on the performance of a typical metamodel-assisted EA, from which it follows the accuracy assessment method has significant effect on the obtained optimum and hence should be chosen corresponding to the objective function features and dimension.We also discuss several issues related to the performance of accuracy assessment methods in practice. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Xiao:2008:cec, author = "Jing Xiao and YuPing Yan and Ying Lin and Ling Yuan and Jun Zhang", title = "A Quantum-inspired Genetic Algorithm for Data Clustering", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0388.pdf}, url = {}, size = {}, abstract = {The conventional K-Means clustering algorithm must know the number of clusters in advance and the clustering result is sensitive to the selection of the initial cluster centroids. The sensitivity may make the algorithm converge to the local optima. This paper proposes an improved K-Means clustering algorithm based on Quantum-inspired Genetic Algorithm (KMQGA). In KMQGA, Q-bit based representation is employed for exploration and exploitation in discrete 0-1 hyperspace by using rotation operation of quantum gate as well as three genetic algorithm operations (Selection, Crossover and Mutation) of Q-bit. Without knowing the exact number of clusters beforehand, the KMQGA can get the optimal number of clusters as well as providing the optimal cluster centroids after several iterations of the four operations (Selection, Crossover, Mutation, and Rotation). The simulated datasets and the real datasets are used to validate KMQGA and to compare KMQGA with an improved K-Means clustering algorithm based on the famous Variable string length Genetic Algorithm (KMVGA) respectively. The experimental results show that KMQGA is promising and the effectiveness and the search quality of KMQGA is better than those of KMVGA. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Oiso:2008:cec, author = "Masashi Oiso and Yoshiyuki Matsumura and Kazuhiro Ohkura and Noriyuki Fujimoto and Yoshiki Matsuura", title = "Application of Grid Task Scheduling Algorithm R3Q to Evolutionary Multi-Robotics Problem", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0389.pdf}, url = {}, size = {}, abstract = {A computational method for the implementation of an evolutionary multi-robotics (EMR) problem in grid computing environments is discussed. Due to the synchronization requirements of Evolutionary Algorithms (EAs), when the EMR problem is deployed in the grid environment there is a higher waiting time overhead because of medium-grained tasks. The round-robin replication remote work queue (R3Q) is adopted to reduce both the synchronous waiting time and communication time. In the current research, the performance of the grid scheduling is evaluated using uniform computational granularity despite that many problems such as EMR have nonuniform computational granularity. Therefore, in order to evaluate R3Q on nonuniform computational granularity, the cooperative object pushing EMR problem is adopted; and R3Q is compared with grid scheduling algorithms Work Queue (WQ), and list scheduling with round-robin order replication (RR). Our results show that R3Q is effective for tasks which have nonuniform computational granularity. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Li12:2008:cec, author = "Lily D. Li and Xiaodong Li and Xinghuo Yu", title = "A Multi-Objective Constraint-Handling Method with PSO Algorithm for Constrained Engineering Optimization Problems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0390.pdf}, url = {}, size = {}, abstract = {This paper presents a multi-objective constraint handling method incorporating the Particle Swarm Optimization (PSO) algorithm. The proposed approach adopts a concept of Pareto domination from multi-objective optimization, and uses a few selection rules to determine particles' behaviors to guide the search direction. A goaloriented programming concept is adopted to improve efficiency. Diversity is maintained by perturbing particles with a small probability. The simulation results on the three engineering benchmark problems demonstrate the proposed approach is highly competitive. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Maulik:2008:cec, author = "Ujjwal Maulik and Anirban Mukhopadhyay and Sanghamitra Bandyopadhyay and Xuegong Zhang", title = "Multiobjective Fuzzy Biclustering in Microarray Data: Method and a New Performance Measure", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0392.pdf}, url = {}, size = {}, abstract = {Objective of any biclustering algorithm in microarray data is to discover a subset of genes that are expressed similarly in a subset of conditions. The boundaries of biclusters usually overlap as genes and conditions may belong to different biclusters with different membership degrees. Hence the notion of fuzzy sets is useful for discovering such overlapping biclusters. In this article an attempt has been made to develop a multiobjective genetic algorithm based approach for probabilistic fuzzy biclustering that minimizes the residual and maximizes cluster size and expression profile variance. A novel variable string length encoding has been proposed in this regard that encodes multiple biclusters in a single string. Also a new performance measure that reflects how a bicluster is statistically distinguished from the background is proposed. Performance of the proposed algorithm has been compared with some well known biclustering algorithms. Keywords: Fuzzy biclustering, fuzzy K-medoids, mean squared residue, expression profile variance, multiobjective genetic algorithm, variable string length, statistical difference from background. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Lei2:2008:cec, author = "Bin Lei and Wenfeng Li and Fan Zhang", title = "Stable Flocking Algorithm for Multi-Robot Systems Formation Control", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0394.pdf}, url = {}, size = {}, abstract = {The problem of multiple robots system formation using a distributed control method is studied in this paper. The main idea of this paper is that uses swarm flocking control algorithm to implement the ``biods'' model of Reynolds among multi-robots. With the help of graph theory, we propose a provably-stable flocking control law, which ensures that the internal group formation is stabilized to a desired shape, while all the robots' velocities and directions converge to the same. Player/Stage simulation results show that the proposed method can be efficiently applied to multiple robots formation control. With the characteristic of Player/Stage, the algorithm in this paper can be implemented on the real robots with so few or no changes. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Liu5:2008:cec, author = "Li Liu and Wenbo Xu", title = "A Cooperative Artificial Immune Network with Particle Swarm Behavior for Multimodal Function Optimization", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0395.pdf}, url = {}, size = {}, abstract = {Artificial immune network has been receiving particular attention over the last few years. Recent researches have revealed that, without stimulation and cooperation of network cells, lots of redundant explorations waste "resources", which affects searching ability and searching speed. In this paper, a cooperative artificial immune network denoted CoAIN is devised for multimodal function optimization. To explore and exploit searching space efficiently and effectively, the interactions within the network are not only suppression but also cooperation. Network cells cooperate with particle swarm behavior making use of the best position encountered by itself and its neighbor. Numeric benchmark functions were used to assess the performance of CoAIN compared with opt-aiNet, BCA, hybrid GA, and PSO algorithms. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Coensel:2008:cec, author = "Bert De Coensel and Dick Botteldooren and Kenny Debacq and Mats E. Nilsson and Birgitta Berglund", title = "Clustering Outdoor Soundscapes Using Fuzzy Ants", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0399.pdf}, url = {}, size = {}, abstract = {A classification algorithm for environmental sound recordings or ``soundscapes'' is outlined. An ant clustering approach is proposed, in which the behavior of the ants is governed by fuzzy rules. These rules are optimized by a genetic algorithm specially designed in order to achieve the optimal set of homogeneous clusters. Soundscape similarity is expressed as fuzzy resemblance of the shape of the sound pressure level histogram, the frequency spectrum and the spectrum of temporal fluctuations. These represent the loudness, the spectral and the temporal content of the soundscapes. Compared to traditional clustering methods, the advantages of this approach are that no a priori information is needed, such as the desired number of clusters, and that a flexible set of soundscape measures can be used. The clustering algorithm was applied to a set of 1116 acoustic measurements in 16 urban parks of Stockholm. The resulting clusters were validated against visitor's perceptual measurements of soundscape quality. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Oliveto:2008:cec, author = "Pietro S. Oliveto and Jun He and Xin Yao", title = "Analysis of Population-Based Evolutionary Algorithms for the Vertex Cover Problem", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0400.pdf}, url = {}, size = {}, abstract = {Recently it has been proved that the (1+1)-EA produces poor worst-case approximations for the vertex cover problem. In this paper the result is extended to the (1+λ)-EA by proving that, given a polynomial time, the algorithm can only find poor covers for an instance class of bipartite graphs. Although the generalisation of the result to the (u+1)-EA is more difficult, hints are given in this paper to show that this algorithm may get stuck on the local optimum of bipartite graphs as well because of premature convergence. However a simple diversity maintenance mechanism can be introduced into the EA for optimising the bipartite instance class effectively. It is proved that the diversity mechanism combined with one point crossover can change the runtime for some instance classes from exponential to polynomial in the number of nodes of the graph. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Neoh:2008:cec, author = "Siew Chin. Neoh and Arjuna. Marzuki and Norhashimah. Morad and Chee Peng. Lim and Zalina. Abdul Aziz ", title = "An Interactive Genetic Algorithm Approach to MMIC Low Noise Amplifier Design Using A Layered Encoding Structure", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0401.pdf}, url = {}, size = {}, abstract = {In this paper, an interactive genetic algorithm (IGA) approach is developed to optimize design variables for a monolithic microwave integrated circuit (MMIC) low noise amplifier. A layered encoding structure is employed to the problem representation in genetic algorithm to allow human intervention in the circuit design variable tuning process. The MMIC amplifier design is synthesized using the Agilent Advance Design System (ADS), and the IGA is proposed to tune the design variables in order to meet multiple constraints and objectives such as noise figure, current and simulated power gain. The developed IGA is compared with other optimization techniques from ADS. The results showed that the IGA performs better in achieving most of the involved objectives. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wang13:2008:cec, author = "Zhaohua Wang and Jianhua Yin and Weimin Ma", title = "A Reverse Logistics Optimization Model for Hazardous Waste in the Perspective of Fuzzy Multi-Objective Programming Theory", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0403.pdf}, url = {}, size = {}, abstract = {Combining with the characteristic of hazardous waste, this paper develops a multi-objective mathematic model for the location of treatment sites and transfer sites for hazardous wastes. Based on the fuzzy satisfactory levels of objectives, it proposes a two-phase fuzzy algorithm. Through solving the model, it conducts an analysis on the locations and numbers of these sites and how to assign the generation sites to transfer sites. Therefore, a reverse network for hazardous waste is constructed. Finally, it takes Tianjin Economic-technological Develop Area (TEDA) in Tianjin city in China as a case to prove the availability of the fuzzy model. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Martínez-Estudillo:2008:cec, author = "F. J. Martínez-Estudillo and P. A. Gutierrez and C. Hervas and J. C. Fernandez", title = "Evolutionary Learning by a Sensitivity-Accuracy Approach for Multi-Class Problems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0404.pdf}, url = {}, size = {}, abstract = {Performance evaluation is decisive when improving classifiers. Accuracy alone is insufficient because it cannot capture the myriad of contributing factors differentiating the performances of two different classifiers and approaches based on a multi-objective perspective are hindered by the growing of the Pareto optimal front as the number of classes increases. This paper proposes a new approach to deal with multi-class problems based on the accuracy (C) and minimum sensitivity (S) given by the lowest percentage of examples correctly predicted to belong to each class. From this perspective, we compare different fitness functions (accuracy, C, entropy, E, sensitivity, S, and area, A) in an evolutionary scheme. We also present a two stage evolutionary algorithm with two sequential fitness functions, the entropy for the first step and the area for the second step. This methodology is applied to solve six benchmark classification problems. The two-stage approach obtains promising results and achieves a high classification rate level in the global dataset with an acceptable level of accuracy for each class. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Ding2:2008:cec, author = "Nan Ding and Shude Zhou and Hao Zhang and Zengqi Sun", title = "Marginal Probability Distribution Estimation in Characteristic Space of Covariance-Matrix", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0406.pdf}, url = {}, size = {}, abstract = {Marginal probability distribution has been widely used as the probabilistic model in EDAs because of its simplicity and efficiency. However, the obvious shortcoming of the kind of EDAs lies in its incapability of taking the correlation between variables into account. This paper tries to solve the problem from the point view of space transformation. As we know, it seems a default rule that the probabilistic model is usually constructed directly from the selected samples in the space defined by the problem. In the algorithm CM-MEDA, instead, we first transform the sampled data from the initial coordinate space into the characteristic space of covariance-matrix and then the marginal probabilistic model is constructed in the new space. We find that the marginal probabilistic model in the new space can capture the variable linkages in the initial space quite well. The relationship of CM-MEDA with Covariance-Matrix estimation and principal component analysis is also analyzed in this paper. We implement CM-MEDA in continuous domain based on both Gaussian and histogram models. The experimental results verify the effectiveness of our idea. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Cheng4:2008:cec, author = "Hsueh-Chien Cheng and Tsung-Che Chiang and Li-Chen Fu ", title = "Multiobjective Permutation Flowshop Scheduling by an Adaptive Genetic Local Search Algorithm", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0407.pdf}, url = {}, size = {}, abstract = {The multiobjective flowshop problem with makespan and total flow time as objectives is addressed. A genetic local search algorithm is proposed with the ability to allocate the computational resources through the dynamic population size and local search intensity. The proposed method is compared with existing algorithms for flowshop scheduling with a public benchmark problem set. The experimental results show that the proposed method is capable of discovering solutions with better quality and diversity. The proposed method yields the best known nondominated solutions for the commonly studied permutation flowshop benchmarks, and the set of best known solutions is useful for the evaluation of performance of future studies. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Jr.:2008:cec, author = "Maury M. Gouvêa Jr. and Aluizio F. R. Araújo", title = "Population Dynamics Model for Gene Frequency Prediction in Evolutionary Algorithms", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0408.pdf}, url = {}, size = {}, abstract = {The performance of evolutionary algorithms (EAs) may be enhanced whether the choice of some parameters, as mutation rate and crossover method, is made appropriately. Several methods to adjust those parameters have been developed in order to enhance EAs performance. For this reason, it is important to understand EA dynamics. This paper presents a new population dynamics model to describe and predict the diversity at one generation. The formulation is based on the selection probability density function of each individual. The proposed population dynamics is modeled for an infinite population with generational evolution method. The model was tested in several case studies of different population sizes. The results suggest that the prediction error decreases with the population size increasement. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Chen7:2008:cec, author = "Zhenfeng Chen and Yanru Zhong and Jie Li", title = "Parameter Identification of Induction Motors Using Ant Colony Optimization", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0409.pdf}, url = {}, size = {}, abstract = {In this paper, the Ant Colony Optimization (ACO) is introduced and applied to the parameter identification of an induction motor for vector control. The error between the actual stator current output of an induction motor and the stator current output of the model is used as the criterion to correct the model parameters, so as to identify all the parameters of an induction motor. Digital simulations are conducted on speed-varying operation with no load. The ACO is compared with the genetic algorithm (GA) and adaptive genetic algorithm (AGA). Consequently, the ACO is shown to acquire more precise parameter values and need much less computing time than the GA and AGA. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Hernandez-Díaz:2008:cec, author = "Alfredo G. Hernandez-Díaz and Carlos A. Coello Coello and Fatima Perez and Julian Molina", title = "Seeding the Initial Population of a Multi-Objective Evolutionary Algorithm using Gradient-Based Information", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0410.pdf}, url = {}, size = {}, abstract = {In the field of single-objective optimization, hybrid variants of gradient-based methods and evolutionary algorithms have been shown to perform better than an evolutionary method by itself. This same idea has been recently used in Evolutionary Multiobjective Optimization (EMO), obtaining also very promising results. In most cases, gradient information is used along the whole process, which involves a high computational cost, mainly related to the computation of the step lengths required. In contrast, in this paper we propose the use of gradient information only at the beginning of the search process. We will show that this sort of scheme maintains results of good quality while considerably decreasing the computational cost. In our work, we adopt a steepest descent method to generate some nondominated points which are then used to seed the initial population of a multi-objective evolutionary algorithm (MOEA), which will spread them along the Pareto front. The MOEA adopted in our case is the NSGA-II, which is representative of the state-of-the-art in the area. To validate our proposal, we adopt box-constrained continuous problems (the ZDT test suite). The gradients required are approximated using quadratic regressions. Our proposed approach performs a total of 2000 objective function evaluations, which is much lower than the number of evaluations normally adopted with the ZDT test suite in the specialized literature. Our results are compared with respect to the ``pure'' NSGA-II (i.e., without using gradient-based information) so that the potential benefit of these initial solutions fed into the population can be properly assessed. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Ashlock4:2008:cec, author = "Wendy Ashlock ", title = "Evolving Diverse Populations of Prisoner's Dilemma Strategies", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0411.pdf}, url = {}, size = {}, abstract = {It is common for evolved populations of Iterated Prisoner's Dilemma to become homogenous with most of the strategies either identical or similar to each other. As fitness is usually based on play with other members of the population, this favors the evolution of strategies which score well when playing themselves or close mutants of themselves. Also, populations tend to change considerably over time. New strategies arise and take over. A population consisting entirely of a highly cooperative strategy like tit-for-tat can become a population consisting entirely of a highly uncooperative strategy like always-defect. This study uses an experimental setup which incorporates geography in an attempt to evolve a diversity of coexisting strategies. The resulting populations are analyzed using Prisoner's Dilemma fingerprints and found to be both diverse and ``stable'' in the sense that they remain highly cooperative over time. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Bui:2008:cec, author = "Lam T. Bui and James M. Whitacre and Hussein A. Abbass", title = "Performance Analysis of Elitism in Multi-Objective Ant Colony Optimization Algorithms", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0412.pdf}, url = {}, size = {}, abstract = {This paper investigates the effect of elitism on multi-objective ant colony optimization algorithms (MACOs). We use a straightforward and systematic approach in this investigation with elitism implemented through the use of local, global, and mixed non-dominated solutions. Experimental work is conducted using a suite of multi-objective traveling salesman problems (mTSP), each with two objectives. The experimental results indicate that elitism is essential to the success of MACOs in solving multi-objective optimization problems. Further, global elitism is shown to play a particularly important role in refining the pheromone information for MACOs during the search process.Inspired by these results, we also propose an adaptation strategy to control the effect of elitism. With this strategy, the solutions most recently added to the global non-dominated archive are given a higher priority in defining the pheromone information. The obtained results on the tested mTSPs indicate improved performance in the elitist MACO when using the adaptive strategy compared to the original version. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Mason:2008:cec, author = "Jonathan Mason and Ronaldo Menezes", title = "Autonomous Algorithms for Terrain Coverage Metrics, Classification and Evaluation", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0414.pdf}, url = {}, size = {}, abstract = {Terrain coverage algorithms are quite common in the computer science literature and for a good reason: they are able to deal with a diverse set of problems we face. From Web crawling to automated harvesting, from spell checking to area reconnaissance by Unmanned Aerial Vehicles (UAVs), a good terrain coverage algorithm lies at the core of a successful approach to these and other problems. Despite the popularity of terrain coverage, none of the works in the field addresses the important issue of classification and evaluation of these algorithms. It is easy to think that all algorithms (since they are all called terrain coverage) deal with the same problem but this is a fallacy that this paper tries to correct. This paper presents a summary of many algorithms in the field, classifies them based on their goals, introduces metrics to evaluate them, and finally performs the evaluation. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Jassbi:2008:cec, author = "J. Jassbi and S. Khanmohammadi and H. Kharrati", title = "A New Hybrid Method for Determination of Fuzzy Rules and Membership Functions", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0416.pdf}, url = {}, size = {}, abstract = {Fuzzy Logic Controllers are applied to various industrial and non-linear systems, however, their control rules and membership functions are usually obtained by timeconsuming trial and error procedure. This paper presents a hybrid method for determining the fuzzy rules and membership functions simultaneously. The optimization process consists of a Genetic Algorithm (GA) which determines the rule base, and an Extended Kalman Filter (EKF) approach for tuning the parameters of membership functions. The procedure discussed in this study is illustrated on a simple automotive cruise control problem. By comparing nominal and optimized fuzzy controllers, we demonstrate that the hybrid algorithm, as a combination of genetic algorithm and extended Kalman filter, can be an effective tool for improving the performance of a fuzzy controller. In other words, the fuzzy controller thus designed can implement simpler in the real world applications, by using a few fuzzy variables. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Singh2:2008:cec, author = "Hemant Kumar Singh and Amitay Isaacs and Tapabrata Ray and Warren Smith", title = "A Simulated Annealing Algorithm for Constrained Multi-Objective Optimization", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0417.pdf}, url = {}, size = {}, abstract = {In this paper, we introduce a simulated annealing algorithm for constrained Multi-Objective Optimization (MOO). When searching in the feasible region, the algorithm behaves like recently proposed Archived Multi-Objective Simulated Annealing (AMOSA) algorithm [1], whereas when operating in the infeasible region, it tries to minimize constraint violation by moving along Approximate Descent Direction (ADD) [2]. An Archive of non-dominated solutions found during the search is maintained. The acceptance probability of a new point is determined by its feasibility status, and its domination status as compared to the current point and the points in the Archive. We report the performance of the proposed algorithm on a set of seven constrained bi-objective test problems (CTP2 to CTP8), which have been known to pose difficulties to existing multi-objective algorithms. A comparative study of current algorithm with the widely used multi-objective evolutionary algorithm NSGA-II has been included. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Yang7:2008:cec, author = "Zhenyu Yang and Ke Tang and Xin Yao", title = "Multilevel Cooperative Coevolution for Large Scale Optimization", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0418.pdf}, url = {}, size = {}, abstract = {In this paper, we propose a multilevel cooperative coevolution (MLCC) framework for large scale optimization problems. The motivation is to improve our previous work on grouping based cooperative coevolution (EACC-G) [1], which has a hard-to-determine parameter, group size, in tackling problem decomposition. The problem decomposer takes group size as parameter to divide the objective vector into low dimensional subcomponents with a random grouping strategy. In the MLCC, a set of problem decomposers is constructed based on the random grouping strategy with different group sizes. The evolution process is divided into a number of cycles, and at the start of each cycle MLCC uses a self-adapted mechanism to select a decomposer according to its historical performance. Since different group sizes capture different interaction levels between the original objective variables, MLCC is able to selfadapt among different levels. The efficacy of the proposed MLCC is evaluated on the set of benchmark functions provided by CEC'2008 special session [2]. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Chiong:2008:cec, author = "Raymond Chiong and Yang Yaw Chang and Pui Ching Chai and Ai Leong Wong", title = "A Selective Mutation Based Evolutionary Programming for Solving Cutting Stock Problem without Contiguity", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0420.pdf}, url = {}, size = {}, abstract = {The Cutting Stock Problem (CSP) is a combinatorial optimisation problem that involves cutting large stock sheets into smaller pieces. It has attracted vast attention along the years due to its applicability in many industries ranging from steel, glass, wood, plastic to paper manufacturing. A good solution to CSP is thus important as a mean to increase efficiency in these industrial sectors. In this paper, we present a selective mutation based evolutionary programming (SMBEP) for solving CSP without contiguity. We conduct experiments with our novel SMBEP on the benchmark problems of CSP. We show that the performance of our approach is slightly better than the previous results. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Gong2:2008:cec, author = "Dunwei Gong and Jie Yuan and Xiaoping Ma ", title = "Interactive Genetic Algorithms with Large Population Size", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0421.pdf}, url = {}, size = {}, abstract = {Interactive genetic algorithms (IGAs) are effective methods to solve an optimization problem with implicit indices. Whereas it requires direct evaluation of user for each individual and the fact limits the population size for user fatigue problem. While, in general to solve many problems with genetic algorithm, it is desirable to maintain the population size as large as possible. To break the restriction of population size and not increasing the number of individuals being evaluated by user we propose an interactive genetic algorithm with large population size in this paper. The algorithm divides the whole population into several clusters, the maximum number of which changes along with the evolution. User only assigns one representative individual's fitness for each cluster and expresses it with an accurate number. The fitness of other individuals are estimated according to the representative's fitness directly, and are expressed with some intervals, which can maintain the large population size with less number of individuals being evaluated by user. In addition we choose appropriate individuals and crossover point to perform crossover operator. This algorithm is applied in a fashion evolutionary design system and compared it with the above interactive genetic algorithm with small population size, the results effectively validate that the proposed algorithm has good performance in alleviating user fatigue and looking for ``the most satisfactory suits.'' }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Gong3:2008:cec, author = "Tao Gong and Andrea Puglisi and Vittorio Loreto and William S.-Y. Wang", title = "Conventionalization of Linguistic Categories Under Simple Communicative Constraints", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0424.pdf}, url = {}, size = {}, abstract = {The language game approach is widely adopted to study conventionalization of linguistic knowledge. Most of contemporary models concentrate on the dynamics of language games in random or predefined social structures, but neglect the role of communicative constraints. This paper adopts one form of language games, the category game, to discuss whether some simple distance-related communicative constraint may affect the conventionalization of linguistic categories. By comparing the simulation results with those based on another form of language games, the naming game, we point out some essential differences between these two games which cause their distinct performances under the same communicative constraint. This study fills the gap between the dynamics of language games in random structures and that in complex networks, and suggests that internal properties of language games may reversely influence communicative constraints and social structures. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Parsopoulos:2008:cec, author = "K. E. Parsopoulos and V. C. Georgopoulos and M. N. Vrahatis", title = "A Technique for the Visualization of Population-Based Algorithms", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0425.pdf}, url = {}, size = {}, abstract = {A technique for the visualization of stochastic population-based algorithms in multidimensional problems with known global minimizers is proposed. The technique employs projections of the populations in the 2-dimensional vector space spanned by the two extremal eigenvectors of the Hessian matrix of the objective function at a global minimizer. This space condenses information regarding the shape of the objective function around the given minimizer. The proposed approach can provide intuition regarding the behavior of the algorithm in unknown high-dimensional problems. It also provides an alternative visualization framework for problems of any dimension, which alleviates drawbacks of the most popular projection methods. The proposed technique is illustrated for three well-known population-based algorithms, namely, Differential Evolution, Covariance Matrix Adaptation Evolution Strategies and Particle Swarm Optimization, on three test problems of different dimensionality. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Han2:2008:cec, author = "Kyu Y. Han and Brian A. Lail and Fredric M. Ham", title = "Low-Profile Twist Reflector Design Using Genetic Algorithms", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0426.pdf}, url = {}, size = {}, abstract = {A novel low-profile twist reflector is genetically engineered. For a linear polarized incident wave, a perpendicular linear polarized wave can be achieved upon reflection. Unlike the typical twist reflector, which requires at least a quarter-wavelength space between the top metal strip and main reflector, the proposed structure is built on a 0.08λ thick substrate, still enhancing about 20percent bandwidth. Finally, there is good agreement between the measured and the simulated results promising that the twist reflector can be used to alter the antenna polarization response. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Weis:2008:cec, author = "Gerhard Weis and Andrew Lewis and Marcus Randall and Amir Galehdar and David Thiel", title = "Local Search for Ant Colony System to Improve the Efficiency of Small Meander Line RFID Antennas", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0427.pdf}, url = {}, size = {}, abstract = {The efficient design of meander line antennas for RFID devices is a significant real-world problem. Traditional manual tuning of antenna designs is becoming impractical for larger problems. Thus the use of automated techniques, in the form of combinatorial search algorithms, is a necessity. Ant colony system (ACS) is a very efficient meta-heuristic that is commonly used to solve path construction problems. Apart from its own native search capacity, ACS can be dramatically improved by combining it with local search strategies. As shown in this paper, applying local search as a form of structure refinement to RFID meander line antennas delivers effective antenna structures. In particular, we use the operator known as backbite, that has had previous application in the construction of self-avoiding walks and compact polymer chains. Moreover, we apply it in a novel, hierarchical manner that allows for good sampling of the local search space. Its use represents a significant improvement on results obtained previously. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Chuang:2008:cec, author = "Cheng-Long Chuang and Chung-Ming Chen and Grace S. Shieh and Joe-Air Jiang", title = "A Fuzzy Logic Approach to Infer Transcriptional Regulatory Network in Saccharomyces Cerevisiae Using Promoter Site Prediction and Gene Expression Pattern Recognition", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0428.pdf}, url = {}, size = {}, abstract = { A fuzzy logic approach, called FuzzyTRN, to infer transcriptional regulatory networks (TRN) in Saccharomyces cerevisiae is proposed. FuzzyTRN predicts potential regulators and their target genes using sequences analysis on transcription factor binding sites (TFBS) of transcriptional factors (TF) and promoter region of target genes. Those potential regulators and target genes are used to form vertices in the TRN. Furthermore, multiple sets of microarray gene expression data (MGED) are used by FuzzyTRN to predict links in the TRN. FuzzyTRN predicts transcriptional interactions by recognizing expression patterns of genes. In this study, a number of confirmed genetic interactions are used to train FuzzyTRN. 112 indirect genetic interactions that were confirmed by quantitative real-time polymerase chain reaction (qRT-PCR) experiments, and 259 and 86 direct genetic interactions that were collected by TRANSFAC database and literature surveying, were used as training set in this work. A simulation that encompasses 170 TFs and 40 target genes has been conducted and checked against YEASTRACT database to evaluate the performance of the proposed algorithm. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wei3:2008:cec, author = "Ming Wei and Yuanxiang Li and Dazhi Jiang and Yangfan He and Xingyan Huang and Xing Xu", title = "A New Evolutionary Algorithm based on Quantum Statistical Mechanics", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0429.pdf}, url = {}, size = {}, abstract = {A new Evolutionary Algorithm based on quantum statistical mechanics (QSEA) is raised in this paper. In the algorithm, the whole evolutionary system is treated as a quantum statistical system, where quantum coding is adopted to express chromosomes, and superposition of quantum bits is used to simulate the linear superposition state of the system. Quantum system entropy and statistical energy have been defined by analogy with corresponding concepts in quantum statistical mechanics. And the competition between quantum statistical energy and entropy of the system is used to simulate the conflict between 'selection pressure' and 'diversity of population', which helps the algorithm to keep a delicate balance between these two issues,and obtain optimal solution rapidly. Numerical experiments show that this new algorithm has high efficiency and strong ability to get global optimal solution. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Santos:2008:cec, author = "Wellington P. dos Santos and Ricardo E. de Souza and Plínio B. Santos Filho and Francisco M. de Assis", title = "A Dialectical Approach for Classification of DW-MR Alzheimer's Images", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0430.pdf}, url = {}, size = {}, abstract = {Multispectral image analysis is a relatively promising field of research with applications in several areas, such as medical imaging and satellite monitoring. However, a considerable number of current methods of analysis are based on parametric statistics. Alternatively, some methods in Computational Intelligence are inspired by biology and other sciences. Here we claim that Philosophy can be also considered as a source of inspiration. This work proposes the Objective Dialectical Method (ODM), which is a computational intelligent method for classification based on the Philosophy of Praxis. Here, ODM is instrumental in assembling evolvable mathematical tools to analyze multispectral images. In the case study described in this paper, such multispectral images are composed of diffusionweighted (DW) magnetic resonance (MR) images. The results are compared to ground-truth images produced by polynomial networks using a morphological similarity index. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Liu6:2008:cec, author = "Pang-Kai Liu and Chiou-Hwa Yuh and Feng-Sheng Wang", title = "Inference of Genetic Regulatory Networks Using S-System and Hybrid Differential Evolution", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0431.pdf}, url = {}, size = {}, abstract = {The inference of genetic regulatory networks from time-course data is one of the main challenges in systems biology. The ultimate goal of inferred model is to obtain the expressions quantitatively comprehending every detail and principle of biological systems. This study introduces a multiobjective optimization approach to infer a realizable S-system structure for genetic regulatory networks. The work of inference is to minimize simultaneously the concentration error, slope error and interaction measure in order to find a suitable S-system model structure and its corresponding model parameters. Hybrid differential evolution is applied to solve the e-constrained problem, which is converted from the multiobjective optimization problem, for minimizing the interaction measure with subject to the expectation constraints for the concentration and slope error criteria. This approach could avoid assigning a suitable penalty weight for sum of magnitude of kinetic orders for the penalty problem in order to prune the model structure. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Boddhu:2008:cec, author = "Sanjay K. Boddhu and John C. Gallagher", title = "Evolved Neuromorphic Flight Control for a Flapping-Wing Mechanical Insect Model", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0432.pdf}, url = {}, size = {}, abstract = {This paper examines the feasibility of evolving analog neuromorphic devices to control flight in a realistic flapping-wing mechanical insect model. It will summarize relevant prior results in controlling a legged robot and explain why these results are relevant to the problem of winged flight. Following, it will present the outcomes of experiments to evolve flight controllers and discuss the implications of those results and possible future work. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Cheng5:2008:cec, author = "Wang Cheng and Zeng Maimai and Li Jian", title = "Solving Traveling Salesman Problems with Time Windows by Genetic Particle Swarm Optimization", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0433.pdf}, url = {}, size = {}, abstract = {The genetic particle swarm optimization (GPSO) was derived from the original particle swarm optimization (PSO), which is incorporated with the genetic reproduction mechanisms, namely crossover and mutation. To solve traveling salesman problems (TSP), a modified genetic particle swarm optimization (MGPSO) was introduced, where the new solution was generated with local best and individual best solutions with crossover and mutation operators. MGPSO was implemented to the well-known TSP and by comparison with the results of the original PSO, MGPSO has provided much better performance. Furthermore, MGPSO was employed to solve TSP with time windows, where besides minimizing the route, the truck were required to arrive at specifically during a time window, which made the TSP to be a constrained combinatorial optimization. To solve the constraints, the stochastic ranking algorithm was introduced. The approach was experimented with the well-known TSP case. The simulation results have shown its robust and consistent effectiveness. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Taboada:2008:cec, author = "Karla Taboada and Eloy Gonzales and Kaoru Shimada and Kotaro Hirasawa", title = "Genetic Network Programming Based Data Mining Method for Extracting Fuzzy Association Rules", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0434.pdf}, url = {}, size = {}, abstract = {In this paper, a new data mining algorithm is proposed to enhance the capability of exploring interesting knowledge from databases with continuous values. The algorithm integrates Fuzzy Set Theory and ``Genetic Network Programming (GNP)'' to find interesting fuzzy association rules from given transaction data. GNP is a novel evolutionary Optimization technique, which uses directed graph structures as gene instead of strings (Genetic Algorithms) or trees (Genetic Programming), contributing to creating quite compact programs and implicitly memorising past action sequences. We adopt the Fuzzy Set Theory to mine associate rules that can be expressed in linguistic terms, which are more natural and understandable for human beings. The proposed method can measure the significance of the extracted association rules using support, confidence and x2 test, and obtains a sufficient number of important association rules in a short time. Experiments conducted on real world databases are also made to verify the performances of the proposed method. }, keywords = {genetic algorithms, genetic programming}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Liu7:2008:cec, author = "Wen-jie Liu and Han-wu Chen and Zhi-qiang Li and Fang-ying Xiao", title = "Efficient Quantum Secure Direct Communication with Authentication", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0435.pdf}, url = {}, size = {}, abstract = {Two protocols of quantum direct communication with authentication [Phys. Rev. A 73, 042305(2006)], proposed by Lee, Lim and Yang, recently were indicated to be insecure against the authenticator Trent's attacks [Phys. Rev. A 75, 026301(2007)]. In this paper, two novel efficient protocols of quantum direct communication with authentication are presented by using four kinds of Pauli operations (I,σx,iσy,σz). These new protocols can transmit two bits message every GHZ state, instead of one bit in the aforementioned protocols. Analysis shows that they are secure against the inner participant's attacks (such as Trent's attacks) as well as the outer Eve's attacks. Finally, we generalize them to multiparty quantum direction communication. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Zhou6:2008:cec, author = "Xiuling Zhou and Ning Mao and Chengyi Sun and Wenjuan Li ", title = "An Improved CHSO Algorithm for Multi-Objective Optimization Problem", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0436.pdf}, url = {}, size = {}, abstract = {The CHSO algorithm is a fast algorithm for computing the contribution of a point to the hypervolume of the whole set directly. In this paper an improved CHSO is described. And it is explained by theory why not only the points in the first nondominated front, but also the points in the second nondominated front which are dominated only by one of points in the first nondominated front are considered in CHSO while the points in the first nondominated front are considered in HSO. It is shown by experiment that improved CHSO outperforms basic CHSO with reduction running time of approximately 50percent. So improved CHSO can enable hypervolume to be used as diversity or selection mechanism more efficiently. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Hsieh:2008:cec, author = "Sheng-Ta Hsieh and Tsung-Ying Sun and Chan-Cheng Liu and Shang-Jeng Tsai", title = "Solving Large Scale Global Optimization Using Improved Particle Swarm Optimizer", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0439.pdf}, url = {}, size = {}, abstract = {As more and more real-world optimization problems become increasingly complex, algorithms with more capable optimizations are also increasing in demand. For solving large scale global optimization problems, this paper presents a variation on the traditional PSO algorithm, called the Efficient Population Use Strategy for Particle Swarm Optimizer (EPUS-PSO). This is achieved by using variable particles in swarms to enhance the searching ability and drive particles more efficiently. Moreover, sharing principals are constructed to stop particles from falling into the local minimum and make the global optimal solution easier found by particles. Experiments were conducted on 7 CEC 2008 test functions to present solution searching ability of the proposed method. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Victoire:2008:cec, author = "T. Aruldoss Albert Victoire and P. N. Suganthan", title = "Differential Evolution and Evolutionary Programming for Solving Non-Convex Economic Dispatch Problems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0440.pdf}, url = {}, size = {}, abstract = {This article presents a novel and effective algorithm for solving the non-convex economic dispatch problems (EDP) by integrating the Differential Evolution (DE) and Evolutionary Programming (EP) techniques. This Hybrid DE-EP based economic dispatch (DE-EPBED) algorithm uses the strengths of both the techniques to explore the search space to find the best solution. This algorithm incorporates the non-convexity of the EDP while formulating the evaluation function and constraints. To validate the feasibility and effectiveness of the presented algorithm, experiments were carried out on five different test systems. It is demonstrated that, the Hybrid DE-EP algorithm for non-convex EDPs generates quality solutions quickly and reliably. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Lim:2008:cec, author = "Yow Tzu Lim and Pau Chen Cheng and John Andrew Clark and Pankaj Rohatgi", title = "Policy Evolution with Genetic Programming: A Comparison of Three Approaches", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0442.pdf}, url = {}, size = {}, abstract = {In the early days a policy was a set of simple rules with a clear intuitive motivation that could be formalised to good effect. However the world is now much more complex. Subtle risk decisions may often need to be made and people are not always adept at expressing rationale for what they do. Previous research has demonstrated that Genetic Programming can be used to infer statements of policies from examples of decisions made [1]. This allows a policy that may not formally have been documented to be discovered automatically, or an underlying set of requirements to be extracted by interpreting user decisions to posed ``what if'' scenarios. This study compares the performance of three different approaches in using Genetic Programming to infer security policies from decision examples made, namely symbolic regression, IF-THEN rules inference and fuzzy membership functions inference. The fuzzy membership functions inference approach is found to have the best performance in terms of accuracy. Also, the fuzzification and de-fuzzification methods are found to be strongly correlated; incompatibility between them can have strong negative impact to the performance. }, keywords = {genetic algorithms, genetic programming}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Zhang6:2008:cec, author = "Kelvin Xi Zhang and B. F. Francis Ouellette", title = "A New Approach to Predict Interactions Between Integral Membrane Proteins in Yeast", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0443.pdf}, url = {}, size = {}, abstract = {Protein-protein interactions (PPIs) play an extremely important role in performing a variety of biological functions. The interactomes of several model organisms including budding yeast Saccharomyces cerevisiae have recently been studied using experimental techniques such as the yeast two-hybrid assay. However, these techniques are generally biased against integral membrane proteins due to their intrinsic limitations. Given the fact that the interactions between integral membrane proteins cover a large fraction of the whole interactome, we report a study of predicting interactions between integral membrane proteins in yeast by a quantitative model. We integrate protein-protein interaction and domain-domain interaction (DDI) data from disparate sources and apply a log likelihood scoring method on all putative integral membrane proteins in yeast to predict their interactions based on a cut-off threshold. We show that our approach improves on other predictive approaches when tested on a ``gold-standard'' data set and achieves 74.6percent true positive rate at the expense of 0.43percent false positive rate. Furthermore, we find that two integral membrane proteins are more likely to interact with each other if they share more common interaction partners. This study allows us to reach a more extensive understanding of the yeast integral membrane proteins from a network view, which also complements the previous prediction approaches based on the genomic context. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Al-Hammadi:2008:cec, author = "Yousof Al-Hammadi and Uwe Aickelin and Julie Greensmith", title = "DCA for Bot Detection", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0444.pdf}, url = {}, size = {}, abstract = {Ensuring the security of computers is a nontrivial task, with many techniques used by malicious users to compromise these systems. In recent years a new threat has emerged in the form of networks of hijacked zombie machines used to perform complex distributed attacks such as denial of service and to obtain sensitive data such as password information. These zombie machines are said to be infected with a 'bot' - a malicious piece of software which is installed on a host machine and is controlled by a remote attacker, termed the 'botmaster of a botnet'. In this work, we use the biologically inspired Dendritic Cell Algorithm (DCA) to detect the existence of a single bot on a compromised host machine. The DCA is an immune-inspired algorithm based on an abstract model of the behaviour of the dendritic cells of the human body. The basis of anomaly detection performed by the DCA is facilitated using the correlation of behavioural attributes such as keylogging and packet flooding behaviour. The results of the application of the DCA to the detection of a single bot show that the algorithm is a successful technique for the detection of such malicious software without responding to normally running programs. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Esmin:2008:cec, author = "A. A. A. Esmin and D. L. Pereira and F. P. A. de Araújo", title = "Study of Different Approach to Clustering Data by Using the Particle Swarm Optimization Algorithm", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0446.pdf}, url = {}, size = {}, abstract = {This paper proposes two new data clustering approaches using the Particle Swarm Optimization Algorithm (PSO). It is shown how the PSO can be used to find centroids of a user specified number of clusters. The proposed approaches are an attempt to improve the Merwe and Engelbrecht method using different fitness functions and considering the situation where data is uniformly distributed. The data clustering PSO algorithm, using the original and proposed fitness functions is evaluated on well known data sets. Notable improvements on the results were achieved by the modifications, this shows the potential of the PSO, not only on data clustering but also on the several areas it can be applied. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Tinós:2008:cec, author = "Renato Tinós and Shengxiang Yang", title = "Evolutionary Programming with q-Gaussian Mutation for Dynamic Optimization Problems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0447.pdf}, url = {}, size = {}, abstract = {The use of evolutionary programming algorithms with self-adaptation of the mutation distribution for dynamic optimization problems is investigated in this paper. In the proposed method, the q -Gaussian distribution is employed to generate new candidate solutions by mutation. A real parameter q, which defines the shape of the distribution, is encoded in the chromosome of individuals and is allowed to evolve. Algorithms with self-adapted mutation generated from isotropic and anisotropic distributions are presented. In the experimental study, the q -Gaussian mutation is compared to Gaussian and Cauchy mutation on three dynamic optimization problems. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Sato:2008:cec, author = "Yuji Sato and Ryosuke Suzuki and Yosuke Akatsuka", title = "Formation Dependency in Event-Driven Hybrid Learning Classifier Systems for Soccer Video Games", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0449.pdf}, url = {}, size = {}, abstract = {In this paper, we discuss dependencies on player formation when using a classifier system in a decision algorithm for agents in a soccer game. Our aim is to respond to the changing environment of video gaming that has resulted from the growth of the Internet, and to provide bug-free programs in a short time. We have already proposed a bucket brigade algorithm and a procedure for choosing what to learn depending on the frequency of events with the aim of facilitating real-time learning while a game is in progress. We have also proposed a hybrid system configuration that combines existing algorithm strategies with a classifier system, and we have reported on the effectiveness of this hybrid system. In this paper, we pit players in several different formations against each other and show that the proposed system is able to learn regardless of the differences in formation. We also show that by performing simulations ahead of time, it is possible to investigate formations that will be effective against an opponent's formation. Finally, by investigating changes in frequency and success rates for each type of play due to changes in formation, we show that it is possible to acquire a team strategy for the current formation through learning. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Ren:2008:cec, author = "Zhigang Ren and Zuren Feng and Liangjun Ke and Hong Chang", title = "A Fast and Efficient Ant Colony Optimization Approach for the Set Covering Problem", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0450.pdf}, url = {}, size = {}, abstract = {In this paper, we present an ant colony optimization (ACO) approach to solve the set covering problem. A constraint-oriented solution construction method is proposed. The main difference between it and the existing method is that, while adding a column to the current partial solution, it randomly selects an uncovered row and only considers the columns covering the row, but not all the unselected columns as candidate solution components. This decreases the number of candidate solution components and therefore accelerates the run speed of the algorithm. Moreover, a simple but effective local search procedure, which aims at eliminating redundant columns and replacing some columns with more effective ones, is developed to improve the quality of solutions constructed by ants while keeping their feasibility. The proposed algorithm has been tested on a number of benchmark instances. Computational results indicate that it is capable of producing high quality solutions and performs better than the existing ACO-based algorithms. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Yao:2008:cec, author = "Chen Yao and Huo Jia-Zhen and Li Hu ", title = "Optimal Model, Algorism and Decision Support System of Bulk Ship Loading Problem", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0451.pdf}, url = {}, size = {}, abstract = {Ship loading planning Problem is a kind of 3/V/D/R combinational optimization problem. In this paper, a new mathematical 0-1 optimal model is proposed for the loading multi-sized bulk cargo to ship, a hybrid genetic algorithm is constructed to find a satisfying solution and a Decision Support System for solving a real-world problem is designed, developed and implemented. The system has successfully improved the rented ship's loading efficiency of a Chinese steel plant in Shanghai. The model and hybrid genetic algorithm will serve as references for the other problems in loading area. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Ji2:2008:cec, author = "Zhen Ji and Jiarui Zhou and Huilian Liao and Q. H. Wu", title = "Requantization Codebook Design Using Particle-Pair Optimizer", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0452.pdf}, url = {}, size = {}, abstract = {A new algorithm of optimal requantization codebook design using particle-pair optimizer (PPO) is proposed to provide an effective way for image transmission over multispeed communication system with minimal transmission delay. PPO is used for optimal codebook design in the first and second quantization respectively. In the second quantization, global distortion is used as the fitness value instead of second quantization distortion. Simulation results demonstrated that the proposed algorithm is able to achieve higher PSNR value with less transmission delay in comparison with conventional codebook optimization strategies. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Li13:2008:cec, author = "Cuimin Li and Tomoyuki Hiroyasu and Mitsunori Miki ", title = "Mesh Dependency of Stress-Based Crossover for Structural Topology Optimization", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0453.pdf}, url = {}, size = {}, abstract = {This paper presents a genetic algorithm (GA) with a stress-based crossover (SX) operator to obtain a solution without ``checkerboard'' patterns for multi-constrained topology optimization problems. SX is based on the element stress. On one hand, smaller mesh size is required to improve the accuracy of structure analysis results. On the other hand, the computation cost of genetic algorithms for structural topology optimization problems (STOPs) increases with a more detailed mesh. Therefore, it is necessary to discuss the mesh dependency of SX for STOPs. Here, the mesh dependency of SX has been investigated through experiments with four different sized meshes. Furthermore, a comparison of evolutionary structural optimization (ESO) and SX is discussed. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Numnark:2008:cec, author = "Somrak Numnark and Worasait Suwannik", title = "Improving the Performance of LZWGA by Using a New Mutation Method", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0454.pdf}, url = {}, size = {}, abstract = {LZW encoding in Genetic Algorithm (LZWGA) encodes a chromosome in a format that can be decompressed by Lempel-Ziv-Welch (LZW) algorithm. This encoding reduces the size of the chromosome and enabled the algorithm to solve a very large problem. This paper proposes a novel mutation in LZWGA. The result shows that the new method can solve OneMax and Trap problem 46.3percent faster. Moreover, this method can reduce the size of the compressed chromosome by 54.8percent. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Long:2008:cec, author = "Fei Long and Fuchun Sun and Fengge Wu", title = "A QoS Routing Based on Heuristic Algorithm for Double-Layered Satellite Networks", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0455.pdf}, url = {}, size = {}, abstract = {Double-Layered Satellite Networks (DLSNs) that consist of low earth orbit (LEO) and medium earth orbit (MEO) satellites are becoming increasingly important since they have higher coverage and better service than single-layered satellite networks. One of the challenges in DLSNs is the development of specialized and efficient routing algorithms. In this paper, virtual topology grouping strategy is improved, and a routing scheme based on heuristic algorithm is proposed to satisfy the QoS requirements of the applications. Three typical heuristic algorithms-Ant Colony Algorithm, Taboo Search Algorithm and Genetic Algorithm are used in the routing scheme for avoiding package loss and link congestion. Simulation results show that heuristic routing algorithm can provide more QoS guarantees than shortest path first (SPF) algorithm on package loss probability and link congestion. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Tai:2008:cec, author = "K. Tai and N. F. Wang and Y. W. Yang", title = "Target Geometry Matching Problem with Conflicting Objectives for Multiobjective Topology Design Optimization Using GA", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0456.pdf}, url = {}, size = {}, abstract = {Genetic algorithms (GA) do have some advantages over gradient-based methods for solving topology design optimization problems. However, their success depends largely on the geometric representation used. In this work, an enhanced morphological representation of geometry is applied and evaluated to be efficient and effective in producing good results via a target matching problem: a simulated topology and shape design optimization problem where a `target' geometry set is first predefined as the Pareto optimal solutions and a multiobjective optimization problem formulated such that the design solutions will evolve and converge towards the target geometry set. As the objectives (and constraints) are conflicting, the problem is challenging and an adaptive constraint strategy is also incorporated in the GA to improve convergence towards the true Pareto front. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Chow:2008:cec, author = "Chi Kin Chow and Shiu Yin Yuen", title = "A Non-Revisiting Particle Swarm Optimization", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0457.pdf}, url = {}, size = {}, abstract = {In this article, a non-revisiting particle swarm optimization (NrPSO) is proposed. NrPSO is an integration of the non-revisiting scheme and a standard particle swarm optimization (PSO). It guarantees that all updated positions are not evaluated before. This property leads to two advantages:(1) it undisputedly reduces the computation cost on evaluating a time consuming and expensive objective function and;(2) It helps prevent premature convergence. The non-revisiting scheme acts as a self-adaptive mutation. Particles genericly switch between local search and global search. In addition, since the adaptive mutation scheme of NrPSO involves no parameter, comparing with other variants of PSO which involve at least two performance sensitive parameters, the performance of NrPSO is more reliable. The simulation results show that NrPSO outperforms four variants of PSOs on optimizing both uni-modal and multi-modal functions with dimensions up to 40. We also illustrate that the overhead and archive size of NrPSO are insignificant. Thus NrPSO is practical for real world applications. In addition, it is shown that the performance of NrPSO is insensitive to the specific chosen values of parameters. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Chow2:2008:cec, author = "Chi Kin Chow and Shiu Yin Yuen", title = "A Non-Revisiting Particle Swarm Optimization", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0458.pdf}, url = {}, size = {}, abstract = {In this article, a non-revisiting particle swarm optimization (NrPSO) is proposed. NrPSO is an integration of the non-revisiting scheme and a standard particle swarm optimization (PSO). It guarantees that all updated positions are not evaluated before. This property leads to two advantages: (1) it undisputedly reduces the computation cost on evaluating a time consuming and expensive objective function and; (2) It helps prevent premature convergence. The non-revisiting scheme acts as a self-adaptive mutation. Particles genericly switch between local search and global search. In addition, since the adaptive mutation scheme of NrPSO involves no parameter, comparing with other variants of PSO which involve at least two performance sensitive parameters, the performance of NrPSO is more reliable. The simulation results show that NrPSO outperforms four variants of PSOs on optimizing both uni-modal and multi-modal functions with dimensions up to 40. We also illustrate that the overhead and archive size of NrPSO are insignificant. Thus NrPSO is practical for real world applications. In addition, it is shown that the performance of NrPSO is insensitive to the specific chosen values of parameters. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Durillo:2008:cec, author = "Juan J. Durillo and Antonio J. Nebro and Carlos A. Coello Coello and Enrique Alba", title = "A Comparative Study of the Effect of Parameter Scalability in Multi-Objective Metaheuristics", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0460.pdf}, url = {}, size = {}, abstract = {Some real-world optimization problems have hundreds or even thousands of decision variables. However, the effect that the scalability of parameters has in modern multiobjective metaheuristic algorithms has not been properly studied (the current benchmarks are normally adopted with ten to thirty decision variables). In this paper, we adopt a benchmark of parameter-wise scalable problems (the ZDT test problems) and analyze the behavior of six multi-objective metaheuristics on these test problems when using a number of decision variables that goes from 8 up to 2048. The computational effort required by each algorithm in order to reach the true Pareto front is also analyzed. Our study concludes that a particle swarm algorithm provides the best overall performance, although it has difficulties in multifrontal problems. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Lin:2008:cec, author = "Zhiyong Lin and Zhifeng Hao and Xiaowei Yang", title = "Evolutionary Support Center Machine", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0461.pdf}, url = {}, size = {}, abstract = {Support Vector Machines (SVMs) are powerful tools in machine learning community, but it is not easy to select suitable parameters for them. And, very often SVMs show slow speeds in test phase due to their large number of support vectors. To remedy SVMs deficiencies, we propose a novel SVM-like method, which is called evolutionary support center machine (ESCM) in this paper. The key idea behind ESCM is to apply evolutionary algorithm to construct the separation hyperplane with the similar form to those constructed by SVMs in an incremental way. ESCM can not only optimize the support centers and tune the kernel parameters adaptively, but also control the number of support centers appropriately. Numerical experiments on several UCI benchmarks verify the efficiency of ESCM. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Zhang7:2008:cec, author = "Cheng Zhang and Suling Jia and Fajie Wei", title = "Artificial Ant Colony Foraging Simulation and Emergent Property Analysis", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0462.pdf}, url = {}, size = {}, abstract = {Based on Wilensky's ant colony foraging model and considering additional nature rules including multi-species, competition and evolution mechanism, the model called AntcolonySim was promoted. Simulations based on AntcolonySim under different conditions were carried out as well as emergent properties including foraging predominance and competition predominance were analyzed quantificationally. AntcolonySim has simple rules which can simulate nature ant colony foraging realistically. The model is also a reference for discussing emergent behaviors in complex systems by bottom interactional agents. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Hasan:2008:cec, author = "S. M. Kamrul Hasan and Ruhul Sarker and David Cornforth", title = "GA with Priority Rules for Solving Job-Shop Scheduling Problems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0464.pdf}, url = {}, size = {}, abstract = {The Job-Shop Scheduling Problem (JSSP) is considered as one of the difficult combinatorial optimization problems and treated as a member of NP-complete problem class. In this paper, we consider JSSPs with an objective of minimizing makespan while satisfying a number of hard constraints. First, we develop a genetic algorithm (GA) based approach for solving JSSPs. We then introduce a number of priority rules such as partial reordering, gap reduction and restricted swapping to improve the performance of the GA. We run the GA incorporating these rules in a number of different ways. We solve 40 benchmark problems and compared their results with that of a number of well-known algorithms. We obtain optimal solutions for 27 problems, and the overall performance of our algorithms is quite encouraging. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Harding:2008:cec, author = "Simon Harding", title = "Evolution of Image Filters on Graphics Processor Units Using Cartesian Genetic Programming", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0465.pdf}, url = {}, size = {}, abstract = {Graphics processor units are fast, inexpensive parallel computing devices. Recently there has been great interest in harnessing this power for various types of scientific computation, including genetic programming. In previous work, we have shown that using the graphics processor provides dramatic speed improvements over a standard CPU in the context of fitness evaluation. In this work, we use Cartesian Genetic Programming to generate shader programs that implement image filter operations. Using the GPU, we can rapidly apply these programs to each pixel in an image and evaluate the performance of a given filter. We show that we can successfully evolve noise removal filters that produce better image quality than a standard median filter. }, keywords = {genetic algorithms, genetic programming, Cartesian Genetic Programming}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Atashpaz-Gargari:2008:cec, author = "Esmaeil Atashpaz-Gargari and Farzad Hashemzadeh and Caro Lucas", title = "Designing MIMO PIID Controller Using Colonial Competitive Algorithm: Applied to Distillation Column Process", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0466.pdf}, url = {}, size = {}, abstract = {In this paper, a colonial competitive algorithm is applied to the problem of designing a multivariable PID controller. The goal is to design a controller to decouple the controlled process, and to track the desired inputs by outputs of the process as much as possible. The method is used to design a multi variable controller for a typical distillation column process. Also a GA and an analytical method are used to design the controller parameters. Comparison results among these methods show that the controller obtained by colonial competitive algorithm has better performance than the others. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Komatsu:2008:cec, author = "Takanori Komatsu and Seiji Yamada", title = "How Does Appearance of Agents Affect how People Interpret the Agents' Attitudes -Experimental Investigation on Expressing the Same Information from Agents Having Different Appearance", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0467.pdf}, url = {}, size = {}, abstract = {An experimental investigation of how the appearance of agents affects interpretations people make of the agents' attitudes is described. We conducted a psychological experiment where participants were presented artificial sounds that can make people estimate specific agents' primitive attitudes from three kinds of agents, e.g., Mindstorms robot, AIBO robot, and a normal laptop PC. Specifically, the participants were asked to select the appropriate attitude based on the sounds expressed by these three agents. The results showed that the participants had higher correct interpretation rates when a PC presented the sounds, while they had lower rates when Mindstorms and AIBO robots presented the sounds, even though these agents expressed information that was completely the same. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Castillo:2008:cec, author = "P. A. Castillo and J. J. Merelo and M. Moreto and F. J. Cazorla and M. Valero and A. M. Mora and S. A. McKee", title = "Evolutionary System for Prediction and Optimization of Hardware Architecture Performance", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0468.pdf}, url = {}, size = {}, abstract = {The design of computer architectures is a very complex problem. The multiple parameters make the number of possible combinations extremely high.Many researchers have used simulation, although it is a slow solution since evaluating a single point of the search space can take hours. In this work we propose using evolutionary multilayer perceptron (MLP) to compute the performance of an architecture parameter settings. Instead of exploring the search space, simulating many configurations, our method randomly selects some architecture configurations; those are simulated to obtain their performance, and then an artificial neural network is trained to predict the remaining configurations performance. Results obtained show a high accuracy of the estimations using a simple method to select the configurations we have to simulate to optimize the MLP. In order to explore the search space, we have designed a genetic algorithm that uses the MLP as fitness function to find the niche where the best architecture configurations (those with higher performance) are located. Our models need only a small fraction of the design space, obtaining small errors and reducing required simulation by two orders of magnitude. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Tseng:2008:cec, author = "Vincent S. Tseng and Chun-Hao Chen and Pai-Chieh Huang and Tzung-Pei Hong", title = "A Cluster-Based Genetic Approach for Segmentation of Time Series and Pattern Discovery", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0470.pdf}, url = {}, size = {}, abstract = {In the past, we proposed a time series segmentation approach by combining the clustering technique, the discrete wavelet transformation and the genetic algorithm to automatically find segments and patterns from a time series. In this paper, we propose an enhanced approach to solve the problems that may occur during the evolution process. Two factors, namely the density factor and the distortion factor, are used to solve them. The distortion factor is used to avoid the distortion of the segments and the density factor is used to avoid generation of meaningless patterns. The fitness value of a chromosome is then evaluated by the distances of segments and these two factors. Experimental results on a financial dataset also show the effectiveness of the proposed approach. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Aguilar:2008:cec, author = "Jose Aguilar and Luís Hernandez and Anny Olivar", title = "Design and Implementation of a Patterns Recognition System for Analysis of Biological Liquids", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0471.pdf}, url = {}, size = {}, abstract = {Given the great amount of data that are generated of the experiments that are made to analyze information of the extracted chemical fluids from the brain of a rodent, arises the necessity to design and to implement data mining systems to process this data. In this work is proposed a Fuzzy System for the Analysis of Biological Liquids (FSABL) that allows to analyze and to process the data, and this way, to know a series of disorders products of alterations, storage, and liberation of the Neurotransmitters. The FSABL is constructed under the paradigm of the Classifier Systems. Our system has been tested to determine the variation of the glutamate Neurotransmitter in the cerebral tonsil of the rats. It discovers and evaluates new rules, and it generates new solutions associated to clinical disorders. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wu2:2008:cec, author = "Zhifeng Wu and Houkuan Huang and Xiong Zhang and Bei Yang and Hongbin Dong", title = "Adaptive Equalization Using Differential Evolution", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0472.pdf}, url = {}, size = {}, abstract = {Adaptive equalization technology requires a long training sequence to update the parameters of the taps by gradient descent method step by step. In order to decrease the number of training sequence, this paper proposes an improved version of the classical differential evolution algorithm for adaptive equalizer to estimate the parameters, in which two trial vectors are created by crossover operator. The modified algorithm speeds up the convergence rate and improves the convergence precision through the evolution of multi-generation in the situation of a short training set. Compared with the traditional least mean squares (LMS) algorithm and the classical differential evolution (CDE) algorithm, the modified algorithm can switch to data transmission mode from the training mode much earlier; at the same time improve the efficiency of the transmission greatly. The simulation results have confirmed that the proposed algorithm achieves the faster convergence rate, the lower misadjustment and the less symbol error rate than the LMS algorithm and CDE algortihm in 4-PAM and 16-QAM signal systems. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Lukasiewycz:2008:cec, author = "Martin Lukasiewycz and Michael Glaß and Jürgen Teich", title = "A Feasibility-Preserving Local Search Operator for Constrained Discrete Optimization Problems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0474.pdf}, url = {}, size = {}, abstract = {Meta-heuristic optimization approaches are commonly applied to many discrete optimization problems. Many of these optimization approaches are based on a local search operator like, e.g., the mutate or neighbor operator that are used in Evolution Strategies or Simulated Annealing, respectively. However, the straightforward implementations of these operators tend to deliver infeasible solutions in constrained optimization problems leading to a poor convergence. In this paper, a novel scheme for a local search operator for discrete constrained optimization problems is presented. By using a sophisticated methodology incorporating a backtracking-based ILP solver, the local search operator preserves the feasibility also on hard constrained problems. In detail, an implementation of the local serach operator as a feasibility-preserving mutate and neighbor operator is presented. To validate the usability of this approach, scalable discrete constrained testcases are introduced that allow to calculate the expected number of feasible solutions. Thus, the hardness of the testcases can be quantified. Hence, a sound comparison of different optimization methodologies is presented. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wang14:2008:cec, author = "Feng Wang and Yuanxiang Li and Kangshun Li and Zhiyi Lin", title = "A New Circuit Representation Method for Analog Circuit Design Automation", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0475.pdf}, url = {}, size = {}, abstract = {The Analog circuits are very important in many high-speed applications such as communications. Since the size of analog circuit is becoming larger and more complex, the design is becoming more and more difficult. This paper proposes a new circuit representation method based on a two layer evolutionary scheme with Genetic Programming (TLGP), which uses a divide-and-conquer approach to evolve the analog circuits. This representation has the desirable property which is more helpful to generate expectant circuit graphs. And it is capable of generating various kinds of circuits by evolving the circuits with dynamical size, circuit topology, and component values. The experimental results on the designs of the voltage amplifier and the low-pass filter show that this method is efficient. }, keywords = {genetic algorithms, genetic programming}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Mostaghim:2008:cec, author = "Sanaz Mostaghim and Jürgen Branke and Andrew Lewis and Hartmut Schmeck", title = "Parallel Multi-Objective Optimization using Master-Slave Model on Heterogeneous Resources", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0476.pdf}, url = {}, size = {}, abstract = {In this paper, we study parallelization of multiobjective optimization algorithms on a set of heterogeneous resources based on the Master-Slave model. The Master-Slave model is known to be the simplest parallelization paradigm, where a master processor sends function evaluations to several slave processors. The critical issue when using the standard methods on heterogeneous resources is that in every iteration of the optimization, the master processor has to wait for all of the computing resources (including the slow ones) to deliver the evaluations. In this paper, we study a new algorithm where all of the available computing resources are efficiently used to perform the multi-objective optimization task independent of the speed (fast or slow) of the computing processors. For this we propose a hybrid method using Multi-objective Particle Swarm optimization and Binary search methods. The new algorithm has been tested on a scenario containing heterogeneous resources and the results show that not only does the new algorithm perform well for parallel resources, but also when compared to a normal serial run on one computer. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Khanmohammadi:2008:cec, author = "S. Khanmohammadi and G. Alizadeh and J. Jassbi and M. Pourmahmood", title = "A New Artificial Intelligence Approach for 2D Path Planning for Underwater Vehicles Avoiding Static and Energized Obstacles", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0477.pdf}, url = {}, size = {}, abstract = {Optimal trajectories in energetic environment for underwater vehicles can be computed using a numerical solution of the optimal control problem (OCP). An underwater vehicle is modeled with the six dimensional nonlinear and coupled equations of motion, controlled by DC motors in all degrees of freedom. An energy performance index that should be minimized may be considered. This leads to a Two Point Boundary Value Problem (TPBVP). The resulting TPBVP is generally solved using iterative methods. In this paper, the applications of two different intelligent algorithms are briefly studied and compared versus the generally acceptable conjugate gradient penalty (CGP) method for the OCP. Genetic algorithm (GA) and particle swarm optimization (PSO) methods are applied to solve OCP. Two approaches for performance index minimization, using GA and PSO, are proposed. CGP method is used to solve the TPBVP, by applying Euler-Lagrange equation. The simulation results show that the trajectories obtained by the intelligent methods were better than that of conjugate gradient penalty. After analyzing the simple path planning problem, the problem energetic environments consisting some energy sources is propounded. The optimal paths are found using GA and PSO algorithms. The problem of collision avoidance in an energetic environment is solved and energy avoidance paths are computed. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Watchareeruetai:2008:cec, author = "Ukrit Watchareeruetai and Yoshinori Takeuchi and Tetsuya Matsumoto and Noboru Ohnishi", title = "Transformation of Redundant Representations of Linear Genetic Programming into Canonical Forms for Efficient Extraction of Image Features", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0478.pdf}, url = {}, size = {}, abstract = {Recently, evolutionary computation (EC) has been adopted to search for effective feature extraction programs for given image recognition problems. For this approach, feature extraction programs are constructed from a set of primitive operations (POs), which are usually general image processing and pattern recognition operations. In this paper, we focus on an approach based on a variation of linear genetic programming (LGP). We describe the causes of redundancies in LGP based representation, and propose a transformation that converts the redundant LGP representation into a canonical form, in which all redundancies are removed. Based on this transformation, we present a way to reduce computation time, i.e., the evolutionary search that avoids executions of redundant individuals. Experimental results demonstrate a success in computation time reduction; around 7-62percent of total compuation time can be reduced. Also, we have experimented with an evolutionary search that prohibits existence of redundant individuals. When selection pressure is high enough, its search performance is better than that of conventional evolutionary search. }, keywords = {genetic algorithms, genetic programming}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Sharma:2008:cec, author = "Deepak Sharma and Kalyanmoy Deb and N. N. Kishore", title = "Towards Generating Diverse Topologies of Path Tracing Compliant Mechanisms Using A Local Search Based Multi-Objective Genetic Algorithm Procedure", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0479.pdf}, url = {}, size = {}, abstract = {A new bi-objective optimization problem is formulated for generating the diverse topologies of compliant mechanisms tracing a user-defined path. Motivation behind the present study is to generate the compliant mechanisms which perform the same task of tracing a prescribed trajectory near minimum-weight solution. Therefore, the constraint are imposed at each precision point representing a prescribed path for accomplishing the tracing task. An additional constraint on stress is also included for the feasible designs. The study starts with a single objective analysis of minimum-weight of compliant mechanism and the obtained topology is referred as the reference design. Thereafter, a bi-objective optimization problem is solved by considering the objectives as minimization of weight of structure and maximization of diversity of structure with respect to the reference design. Here, the diversity is evaluated by finding the dissimilarity in the bit value at each gene position of the binary strings of the reference design and a structure evolved from the GA population.A local search based multi-objective genetic algorithm (MOGA) optimization procedure is used in which the NSGAII is used as a global search and optimization algorithm. A parallel computing is employed in the study for evaluating non-linear geometric FE analysis and also for the NSGA-II operations. After the NSGA-II run, a few solutions are selected from the non-dominated front and the local search is applied on them. With the help of a given optimization procedure, compliant mechanism designs tracing curvilinear and straight line trajectories are evolved and presented in the study. In both examples, compliant mechanisms are designed to have any arbitrary support and loading regions. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Kwong:2008:cec, author = "K. M. Kwong and James N. K. Liu and P. W. Chan and Raymond Lee", title = "Using LIDAR Doppler Velocity Data and Chaotic Oscillatory-Based Neural Network for the Forecast of Meso-Scale Wind Field", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0481.pdf}, url = {}, size = {}, abstract = {Current research based on various approaches including the use of numerical prediction models, statistical models and machine learning models have provided some encouraging results in the area of long-term weather forecasting. But at the level of meso-scale and even micro-scale severe weather phenomena (involving very short-term chaotic perturbations) such as turbulence and wind shear phenomena, these approaches have not been so successful. This paper focuses on the use of chaotic oscillatory-based neural networks for the study of a meso-scale weather phenomenon, namely, wind shear, a challenging and complex meteorological phenomena which has a vital impact on aviation safety. Using LIDAR data collected at the Hong Kong International Airport via the Hong Kong Observatory, we are able to forecast the Doppler velocities with reasonable accuracy and validate our prediction model. Preliminary results are promising and provide room for further research into its potential for application in aviation forecasting. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wu3:2008:cec, author = "Ling Wu and Hang-Yu Wang and Fa-Xing Lu and Peifa Jia", title = "An Anytime Algorithm Based on Modified GA for Dynamic Weapon-Target Allocation Problem", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0482.pdf}, url = {}, size = {}, abstract = {An anytime algorithm based on modified genetic algorithm (GA) for dynamic WTA problem, subject to temporal constraints, is developed in the paper. In the algorithm the weapons are assigned to targets one by one before the deadline of each target comes. After a target is assigned with some weapon, the target is replaced by a new one in all chromosomes in the population while the optimization process will not undergo any restart. The algorithm has three main advantages: (1) a new target can be dynamically accommodated in the allocation process without losing previous optimizing information, (2) the quality of the pairing decisions may be improved in the evolving process with a prolonged computation time, and (3) it optimally deploys weapons to targets where a weapon can be assigned to more than one target asynchronously without missing any deadline of the targets, under the precondition that the weapon can be allocated to only one target at one time. The feasibility and the validity of the modified GA are verified in simulations. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Xin:2008:cec, author = "Chun-lin Xin and Wei-min Ma and Bin Liu", title = "Online Quantity Flexibility Contract Model and its Competitive Analysis", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0483.pdf}, url = {}, size = {}, abstract = {The literatures related to online Quantity Flexibility contract model (or various applications) is quite extensive. The common denominator of all previous theoretical work on the subject is based on the traditional ``average case analysis''. In other word, analyses are typically made under the assumption that the market demand function follows a particular stochastic process that may or may not be known to the online player. But in some situation this leads to the very difficult questions as to how the distribution was selected and what evidence suggests that this distribution is either typical or representative. In this paper we use the competitive ratio optimality criterion to restudy this model and some interesting results are obtained. We present a QF strategy and get the optimal competitive ratio. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Brest:2008:cec, author = "Janez Brest and Aleš Zamuda and Borko Bošković and Mirjam Sepesy Mausšec and Viljem šumer ", title = "High-Dimensional Real-Parameter Optimization using Self-Adaptive Differential Evolution Algorithm with Population Size Reduction", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0484.pdf}, url = {}, size = {}, abstract = {In this paper we investigate a Self-Adaptive Differential Evolution algorithm (jDEdynNP-F) where F and CR control parameters are self-adapted and a population size reduction method is used. Additionally the proposed jDEdynNPF algorithm uses a mechanism for sign changing of F control parameter with some probability based on the fitness values of randomly chosen vectors, which are multiplied by the F control parameter (scaling factor) in the mutation operation of DE algorithm. The performance of the jDEdynNP-F algorithm is evaluated on the set of 7 benchmark functions provided for the CEC'2008 special session on high-dimensional real-parameter optimization. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Ast:2008:cec, author = "Jelmer van Ast and Robert Babuška and Bart De Schutter", title = "Ant Colony Optimization for Optimal Control", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0485.pdf}, url = {}, size = {}, abstract = {Ant Colony Optimization (ACO) has proven to be a very powerful optimization heuristic for Combinatorial Optimization Problems (COPs). It has been demonstrated to work well when applied to various NP-complete problems, such as the traveling salesman problem. In this paper, an ACO approach to optimal control is proposed. This approach requires that a continuous-time, continuous-state model of the system, together with a finite action set, is formulated as a discrete, nondeterministic automaton. The control problem is then translated into a stochastic COP. This method is applied to the time-optimal swing-up and stabilization of a pendulum. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Chen8:2008:cec, author = "Jun Ying Chen and Zheng Qin and Ji Jia", title = "A PSO-Based Subtractive Clustering Technique for Designing RBF Neural Networks", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0487.pdf}, url = {}, size = {}, abstract = {When designing radial basis function neural networks, the central task is to set parameters of radial basis functions. In this paper, subtractive clustering is improved by particle swarm optimization (PSO) to automatically select the number and locations of radial basis functions. Subtractive clustering is used to find center prototypes and then PSO fines their locations iteratively. Comparative experiments were executed between subtractive clustering and PSO-based subtractive clustering proposed in this paper for designing RBF neural networks on several datasets. The experimental results suggest that the PSO-based subtractive clustering algorithm can be successfully applied to design RBF neural networks with competitive classification accuracy and small number of radial basis functions. The RBF neural networks evolved by PSO-based subtractive clustering have stronger generalization ability than the ones evolved by subtractive clustering. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Qiu:2008:cec, author = "Xuan Qiu and Shenshan Qiu", title = "Convergence Analysis of the Brain-State-in-a-Box(BSB) Model with Delay", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0488.pdf}, url = {}, size = {}, abstract = {In this paper, theoretical analysis proves the convergence properties of the Brain-state-in-a-Box (BSB) models with delay. We propose a convergence theorem of the BSB with delay, generalized the BSB without delay, while all previous studies on this model without delay assumed that symmetric and quasi-symmetric. We have performed a detailed convergence analysis of this network and found convergence theorem under proper assumptions of the weight matrices of this network: ones is non-symmetric and the other is row diagonal dominant. Meanwhile, the updating process is presented by a newly given updating rule. Theoretical analysis demonstrates that the BSB with delay performs much better than the original one in updating to an equilibrium point, and its updating rate is four times higher than that of the original BSB. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Zhang8:2008:cec, author = "Junqi Zhang and Zhongmin Xiao and Ying Tan and Xingui He ", title = "Hybrid Particle Swarm Optimizer with Advance and Retreat Strategy and Clonal Mechanism for Global Numerical Optimization", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0489.pdf}, url = {}, size = {}, abstract = {A novel particle swarm optimization algorithm based on advance and retreat strategy and clone mechanism(ARC-PSO) is proposed in this paper. It is well known that the advance-and-retreat strategy is a simple and effective method of one-dimensional search. We use the advance-and-retreat strategy to endow the clones with faster speed to find nearby local basins before next clonal operation. Furthermore, in the next clonal operation, the search space is enlarged greatly and the diversity of clones is increased. When the fitness value turns better after last ``flying'', the cloned particle advances. On the contrary, the cloned particle retreats then searches in the reverse direction of the last ''flying'' with a small step-size of the previous velocity. Thus, the swarm has strong optimization ability. Comparisons among the proposed ARC-PSO, the conventional standard particle swarm optimization(SPSO) and the pure clone particle swarm optimization(CPSO) on thirteen benchmark test functions are presented in this paper. Experimental results show that the proposed ARC-PSO is capable of speeding up the evolution process significantly and improving the performance of global optimizer greatly. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Meshoul:2008:cec, author = "S. Meshoul and M. Batouche", title = "Aligning Images with Multiple Objectives", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0491.pdf}, url = {}, size = {}, abstract = {Most high level interpretation tasks in image analysis rely on image registration (alignment) process. Basically, image registration consists in finding the geometric transformation that best aligns two or several images. In this paper, we focus on mono-modality image alignment. The core task to do in this case is to put into correspondence two sets of data points assuming the presence of noise and outliers. The novelty of the proposed method consists in the fact that we cast the problem as a multi-objective optimization task for which a quantum evolutionary algorithm is defined to carry out the optimization process. The advantage of such process is to get at the end of the process, a set of solutions from which the best alignment is derived using mutual information measure. Experiments show that good and promising results have been obtained. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Griffioen:2008:cec, author = "A. R. Griffioen and S. K. Smit and A. E. Eiben", title = "Learning Benefits Evolution if Sex Gives Pleasure", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0492.pdf}, url = {http://www.cs.vu.nl/~gusz/papers/2008-CEC-Griffioen-Smit-Eiben.pdf}, size = {}, abstract = {In this paper the effects of individual learning on an evolving population of situated agents are investigated. We work with a novel type of system where agents can decide autonomously (by their controllers) if/when they reproduce and the bias in the agent controllers for the mating action is adaptable by individual learning. Our experiments show that in such a system reinforcement learning with the straightforward rewards system based on energy makes the agents lose their interest in mating. In other words, we see that learning frustrates evolution, killing the whole population on the long run. This effect can be counteracted by introducing a specially designated positive mating reward, pretty much like an orgasm in Nature.With this twist individual learning becomes a positive force. It can make the otherwise disappearing population viable by keeping agents alive that did not yet learn the task at hand. This hiding effect proves positive for it provides a smooth road for the population to adapt and learn the task with a lower risk of extinction. }, keywords = {genetic algorithms, genetic programming}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Chen9:2008:cec, author = "Chuanliang Chen and Rongfang Bie and Ping Guo", title = "Combining LPP with PCA for Microarray Data Clustering", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0493.pdf}, url = {}, size = {}, abstract = {DNA Microarray technique has produced large amount of gene expression data. To analyze these data, many excellent machine learning techniques have been proposed in recent related work. In this paper, we try to perform the clustering of microarray data by combining the recently proposed Locality Preserving Projection (LPP) method with PCA, i.e. PCA-LPP. The comparison between PCA and PCA-LPP is performed based on two clustering algorithms, K-means and agglomerative hierarchical clustering. As we already known, clustering with the components extracted by PCA instead of the original variables does improve cluster quality. Moreover, our empirical study shows that by using LPP to perform further process the dimensions of components extracted by PCA can be further reduced and the quality of the clusters can be improved greatly meanwhile. Particularly, the first few components obtained by PCA-LPP capture more information of the cluster structure than those of PCA. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Chang4:2008:cec, author = "Chia-Hung Chang and Bor-Sen Chen and Yung-Jen Chuang", title = "Robust Model Matching Control of Immune Systems Under Environmental Disturbances: Fuzzy Dynamic Game Approach", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0494.pdf}, url = {}, size = {}, abstract = {A robust model matching control of immune response is proposed for therapeutic enhancement to match a prescribed immune response under uncertain initial states and environmental disturbances, including continuous intrusion of exogenous pathogens. The worst-case effect of all possible environmental disturbances and uncertain initial states on the matching for a desired immune response is minimized for the enhanced immune system, i.e. a robust control is designed to track a prescribed immune model response from the minimax matching perspective. This minimax matching problem could be transformed to an equivalent dynamic game problem. The exogenous pathogen and environmental disturbances are considered as a player to maximize (worsen) the matching error when the therapeutic control agents are considered as another player to minimize the matching error. Since the innate immune system is highly nonlinear, it is not easy to solve the robust model matching control problem by the nonlinear dynamic game method directly. A fuzzy model is proposed to interpolate several linearized immune systems at different operation points to approximate the innate immune system via smooth fuzzy membership functions. With the help of fuzzy approximation method, the minimax matching control problem of immune systems could be easily solved by the proposed fuzzy dynamic game method via the linear matrix inequality (LMI) technique with the help of Robust Control Toolbox in Matlab. Finally, an in silico example is given to illustrate the design procedure and to confirm the efficiency and efficacy of the proposed method. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Yang8:2008:cec, author = "Cheng-San Yang and Li-Yeh Chuang and Chao-Hsuan Ke and Cheng-Hong Yang", title = "Boolean Binary Particle Swarm Optimization for Feature Selection", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0495.pdf}, url = {}, size = {}, abstract = {Feature selection is the process of choosing a subset of features from an original set. This subset should be necessary, reasonably represent the original data, and useful for identification classification. The task of feature selection is to search for an optimal solution in a- usually large- search space. However, if the search space too large, difficulties can occur during the search process, often resulting in a considerable increase in computational time. A particle swarm optimization algorithm (PSO) is a relatively new evolutionary computation technique, which has previously been used to implement feature selection. However, particle swarm optimization, like other evolutionary algorithms, tends to converge at a local optimum early. In this paper, we introduce a Boolean function which improves on the disadvantages of standard particle swarm optimization and use it to implement a feature selection for six microarray data sets. The experimental results show that the proposed method selects a smaller number of feature subsets and obtains better classification accuracy than standard PSO. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Ostaszewski:2008:cec, author = "Marek Ostaszewski and Pascal Bouvry and Franciszek Seredynski", title = "An Approach to Intrusion Detection by Means of Idiotypic Networks Paradigm", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0498.pdf}, url = {}, size = {}, abstract = {In this paper we present a novel intrusion detection architecture based on Idiotypic Network Theory (INIDS), that aims at dealing with large scale network attacks featuring variable properties, like Denial of Service (DoS). The proposed architecture performs dynamic and adaptive clustering of the network traffic for taking fast and effective countermeasures against such high-volume attacks. INIDS is evaluated on the MIT'99 dataset and outperforms previous approaches for DoS detection applied to this set. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Kotecha:2008:cec, author = "Ketan Kotecha and Apurva Shah", title = "Adaptive Scheduling Algorithm for Real-Time Operating System", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0499.pdf}, url = {}, size = {}, abstract = {EDF (Earliest Deadline First) has been proved to be optimal scheduling algorithm for single processor realtime operating systems when the systems are preemptive and underloaded. The limitation of this algorithm is, its performance decreases exponentially when system becomes slightly overloaded. Authors have already proved ability of ACO (Ant Colony Optimization) based scheduling algorithm for real-time operating system which is optimal during underloaded condition and it gives outstanding results in overloaded condition. The limitation of this algorithm is, it takes more time for execution compared to EDF algorithm. In this paper, an adaptive scheduling algorithm is proposed which is combination of both of these algorithms. Basically the new algorithm uses EDF algorithm but when the system becomes overloaded, it will switch to ACO based scheduling algorithm. Again, when the overload disappears, the system will switch to EDF algorithm. Therefore, the proposed algorithm takes the advantages of both algorithms and overcomes the limitations of each other. The proposed algorithm along with EDF algorithm and ACO based scheduling algorithm, is simulated for real-time system and the results are obtained. The performance is measured in terms of Success Ratio and Effective CPU Use. Execution Time taken by each scheduling algorithm is also measured. From analysis and experiments it reveals that the proposed algorithm is fast as well as very efficient in both underloaded and overloaded conditions. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Caponio:2008:cec, author = "Andrea Caponio and Ferrante Neri and Giuseppe L. Cascella and Nadia Salvatore", title = "Application of Memetic Differential Evolution Frameworks to PMSM Drive Design", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0500.pdf}, url = {}, size = {}, abstract = {This paper proposes the application of Memetic Algorithms employing Differential Evolution as an evolutionary framework in order to achieve optimal design of the control system for a permanent-magnet synchronous motor. Two Memetic Differential Evolution frameworks have been considered in this paper and their performance has been compared to a standard Differential Evolution, a standard Genetic Algorithm and a Memetic Algorithm presented in literature for solving the same problem. All the algorithms have been tested on a simulation of the whole system (control system and plant) using a model obtained through identification tests. Numerical results show that the Memetic Differential Evolution frameworks seem to be very promising in terms of convergence speed and has fairly good performance in terms of final solution detected for the realworld problem under examination. In particular, it should be remarked that the employment of a meta-heuristic local search component during the early stages of the evolution seems to be very beneficial in terms of algorithmic efficiency. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Oliveira:2008:cec, author = "Tatyana B. S. de Oliveira and Liang Zhao and Katti Faceli and Andre C. P. L. F. de Carvalho", title = "Data Clustering Based on Complex Network Community Detection", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0501.pdf}, url = {}, size = {}, abstract = {Data clustering is an important technique to extract and understand relevant information in large data sets. In this paper, a clustering algorithm based on graph theoretic models and community detection in complex networks is proposed. Two steps are involved in this processing: The first step is to represent input data as a network and the second one is to partition the network into subnetworks producing data clusters. In the network partition stage, each node has a randomly assigned initial angle and it is gradually updated according to its neighbors angle agreement. Finally, a stable state is reached and nodes belonging to the same cluster have similar angles. This process is repeated, each time a cluster is chosen and results in an hierarchical divisive clustering. Simulation results show two main advantages of the algorithm: the ability to detect clusters in different shapes, densities and sizes and the ability to generate clusters with different refinement degrees. Besides of these, the proposed algorithm presents high robustness and efficiency in clustering. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Shir:2008:cec, author = "Ofer M. Shir and Thomas Bäck and Herschel Rabitz and Marc J. J. Vrakking", title = "On the Evolution of Laser Pulses under a Dynamic Quantum Control Environment", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0502.pdf}, url = {}, size = {}, abstract = {This paper introduces the optimization of a Quantum Control application, the so-called molecular alignment problem, subject to a dynamic environment. Given the relative simplicity of optimized pulse-shapes in the low-intensity variant of the problem, versus the high complexity of the optimized pulse-shapes in the high-intensity case, a dynamic-intensity environment is simulated in a noise-free calculation. Specific Evolution Strategies, natural candidates for optimization in dynamic environments, are applied to this task. The calculations reveal the evolution of the pulse-shapes and their underlying evolving structures, that allow a complete physical interpretation. The combination of an optimization in a dynamic environment with the examination of the intermediate optimized solutions offers a sharper physics view of the problem, and accomplishes a fruitful interdisciplinary study. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Neri:2008:cec, author = "Ferrante Neri and Ville Tirronen", title = "On Memetic Differential Evolution Frameworks: A Study of Advantages and Limitations in Hybridization", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0505.pdf}, url = {}, size = {}, abstract = {This paper aims to study the benefits and limitations in the hybridization of the Differential Evolution with local search algorithms. In order to perform this study, the performance of three Memetic Algorithms employing a Differential Evolution as an evolutionary framework and several local search algorithms adaptively coordinated by means of a fitness diversity logic have been analyzed. The performance of a standard Differential Evolution whose parameter setting has been executed only after fine tuning has also been taken into account in the comparison. The comparative analysis has been performed on a set of various test functions. Numerical results show that the Memetic Algorithms without any extensive parameter tuning are still competitive with the finely tuned plain Differential Evolution. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Woldemariam:2008:cec, author = "Kumlachew M. Woldemariam and Gary G. Yen", title = "Vaccine Enhanced Artificial Immune System for Multimodal Function Optimization", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0506.pdf}, url = {}, size = {}, abstract = {This paper proposes the use of vaccine to promote exploration in the search space for solving multimodal function optimization problems using artificial immune system. In this method, first we divide the decision space into equal subspaces. Vaccine is then extracted randomly from each subspace. A few of these antigens are then injected into the algorithm to enhance the exploration of global and local optima. The vaccine is introduced in the form of suppressed antibodies. The goal of this process is to allocate the available antibodies at unexplored areas. Using this biologically motivated notion we design the vaccine enhanced artificial immune system for multimodal function optimization. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Saffari:2008:cec, author = "A. Saeed Saffari and R. Mohammad and T. Akbarzadeh and Mahmoud Naghibzadeh", title = "A Novel Approach to Distributed Routing by Super-AntNet", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0508.pdf}, url = {}, size = {}, abstract = {Various forms of swarm intelligence are inspired by social behavior of insects that live collectively. AntNet is a form of such social algorithms, but it has a scalability problem with growing network size. If every node sends only one ant to each destination node and there are N nodes in the network, the total number of ants that are sent is N(N-1). In addition with increasing overhead for large networks, most of the ants are often lost for distant destinations. Furthermore, due to long travel times, ants that do arrive may carry outdated information. In this paper, a novel hierarchical algorithm is proposed to resolve this scalability problem of AntNet. The proposed Super-AntNet divides a large scale network into several small networks that are chosen based their internal traffic patterns. A separate ant colony is then assigned to each of these networks. A Super-Ant Colony is then responsible to coordinate data routing among the colonies. Performance of Super-AntNet is compared with those of standard AntNet as well as two other conventional routing algorithms such as Distance Vector (DV) and Link State (LS) in terms of end-toend delay, throughput, packet loss ratio, increased overhead, as well as jitter. Application to a 16-node network indicates the superiority of the proposed algorithm. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Peng:2008:cec, author = "Chen Peng and Meng Anbo and Zhao Chunhua", title = "Particle Swarm Optimization in Multi-Agent System for the Intelligent Generation of Test Papers", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0509.pdf}, url = {}, size = {}, abstract = {Agent-oriented design is one of the most active areas in the field of deployment of web-based distance education, and test is a popular measurement tool of learners' knowledge in order to verify the learner's level of understanding and select corresponding educational strategy. In this paper, an innovative approach to seamless integration of the particle swarm optimization (PSO) and multi-agent system (MAS) is proposed. In order to generate a test paper automatically, a modified genetic particle swarm optimization (GPSO) is presented, in which the values of parameters will be decreased linearly with the number of iterations for improving the late convergence rate. For the implementation of GPSO based on multi-agent system, a core agents TPAgent(TPA) is provided to undertake the operations of GPSO and will control the evolution operations of each generation of population. To keep communication between different nodes at a minimum cost, fitness evaluation tasks are implemented by the TPAgents at local nodes, only the local minimum fitness and the corresponding best particle are sent to center node so as to get the global best particle in the parallel computing environment. For avoiding the prematurity, the global best particle will be dispatched to remote node randomly. Based on the JADE, a prototype system is setup, and the simulation results show that the proposed approach is feasible and robust. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Lewis:2008:cec, author = "Andrew Lewis and David Ireland", title = "Automated Solution Selection in Multi-Objective Optimisation", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0510.pdf}, url = {}, size = {}, abstract = {This paper proposes an approach to the solution of multi-objective optimisation problems that delivers a single, preferred solution. A conventional, population-based, multiobjective optimisation method is used to provide a set of solutions approximating the Pareto front. As the set of solutions evolves, an approximation to the Pareto front is derived using a Kriging method. This approximate surface is traversed using a single objective optimisation method, driven by a simple, aggregated objective function that expresses design preferences. The approach is demonstrated using a combination of multi-objective particle swarm optimisation (MOPSO) and the Simplex method of Nelder and Mead, applied to several, standard, multi-objective test problems. Good, compromise solutions meeting user-defined design preferences are delivered without manual intervention. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Vitela:2008:cec, author = "J. E. Vitela and O. Castaños", title = "A Real-Coded Niching Memetic Algorithm for Continuous Multimodal Function Optimization", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0511.pdf}, url = {}, size = {}, abstract = {In this work we extend the sequential niching technique of Beasley et at. for multiple optimal determination, incorporating a local search to improve accuracy. In the proposed method a sequence of GA runs make use of a derating function and of niching and clearing techniques to promote the occupation of different niches in the function to be optimized. The algorithm searches the solution space eliminating from the fitness landscape previously located peaks forcing the individuals to converge into unoccupied niches. Unlike other algorithms the efficiency of this sequential niching memetic algorithm (SNMA) is not highly sensitive to the niche radius. Performance measurements with standard test functions used by other researchers, show that the SNMA proposed outperforms other algorithms in accurately locating all optima, both global and local, in the search space. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Katada:2008:cec, author = "Yoshiaki Katada and Jun Nakazawa ", title = "Investigation of Simply Coded Evolutionary Artificial Neural Networks on Robot Control Problems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0512.pdf}, url = {}, size = {}, abstract = {One of the advantages of evolutionary robotics over other approaches in embodied cognitive science would be its parallel population search. Due to the population search, it takes a long time to evaluate all robot in a real environment. Thus, such techniques as to shorten the time are required for real robots to evolve in a real environment. This paper proposes to use simply coded evolutionary artificial neural networks for robot control to make genetic search space as small as possible and investigates the performance of them using simulated robots. Two types of genetic algorithm (GAs) are employed, one is the standard GA and the other is an extended GA, to achieve higher final fitnesses as well as achieve high fitnesses faster. The results suggest the benefits of the proposed method. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Shum:2008:cec, author = "Dennis T. F. Shum and Raymond S. T. Lee and James N. K. Liu", title = "Chaotic Weatherman; the Design and Implementation of a Chaotic Weather Prediction System", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0513.pdf}, url = {}, size = {}, abstract = {Chaos Theory describes systems that are extremely sensitive to initial conditions. Assuming that weather is a chaotic phenomenon, chaos theory may provide suitable weather prediction models. The study describes chaos theory-based weather prediction model for forecasting short range severe rainstorms in Hong Kong. The model uses a Lee oscillator, which is based on chaos theory, as a transfer function in a Multi-Layer Perceptron model. The proposed model was compared with three non-linear time series models, RBF, SVM and MLP, and was more accurate by 4.518percent, 4.902percent and 3.791percent respectively. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Yu4:2008:cec, author = "L. Yu and P. N. Suganthan", title = "Empirical Comparison of Niching Methods on Hybrid Composition Functions", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0514.pdf}, url = {}, size = {}, abstract = {In this paper, we compare the performance of three popular niching genetic algorithms namely deterministic crowding, restricted tournament selection, and clearing by a set of hybrid composition test functions originally proposed for the special session on real parameter optimization at CEC 2005. The number of function evaluations is used as the main control parameter for an unbiased comparison instead of using the generation count as done frequently in the previous comparative studies. Results are given in tables and graphs to show the searching ability, accuracy, and computation time requirement of each method. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Li14:2008:cec, author = "Zhiqiang Li and Hanwu Chen and Baowen Xu and Wenjie Liu and Xiaoyu Song and Xilin Xue", title = "Fast Algorithm for 4-qubit Reversible Logic Circuits Synthesis", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0515.pdf}, url = {}, size = {}, abstract = {Owing to the exponential nature of the memory or run-time complexity, many existing methods can only synthesize 3-qubit circuits, however, [14] can achieve 12 steps for the CNP (Controlled-Not gate, NOT gate and Peres gate) library in 4-qubit circuit synthesis with mini-length by using an enhanced bi-directional synthesis approach. We mainly absorb the ideas of our 3-qubit synthesis algorithms based on Hash table and present a novel and efficient algorithm which can construct almost all optimal 4-qubit reversible logic circuits with various types of gates and mini-length cost based on constructing the shortest coding and the specific topological compression, whose lossless compression ratios of the space of n-qubit circuits is near 2×n!. Our algorithm has created all 3120218828 optimal 4-qubit circuits whose length is less than 9 for the CNT(Toffoli gate) library, and it can quickly achieve 16 steps through cascading created circuits. To the best of our knowledge, there are no other algorithms to achieve the contribution. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Ghandar:2008:cec, author = "Adam Ghandar and Zbigniew Michalewicz and Thuy-Duong Tô and Ralf Zurbruegg", title = "The Performance of an Adaptive Portfolio Management System", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0516.pdf}, url = {}, size = {}, abstract = {This paper describes the operation and performance of a computational intelligence rulebase system that manages a portfolio of stocks according to investment objectives. We present an overview of several improvements to the system presented in previous papers and provide detailed results from applying the system in representative scenarios toward determining the robustness of the approach. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Yuen:2008:cec, author = "Shiu Yin Yuen and Chi Kin Chow", title = "Applying Non-Revisiting Genetic Algorithm to Traveling Salesman Problem", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0518.pdf}, url = {}, size = {}, abstract = {In [1], we propose non-revisiting genetic algorithm (NrGA) and apply it to a set of bench mark real valued test functions. NrGA has the advantage that it is non-revisiting, i.e. a visited point will not be visited again. This provides an automatic mechanism for diversity maintenance which does not suffer from premature convergence. Another advantage is that it supports a parameter-less adaptive mutation mechanism. In this paper, we show how NrGA can be adapted to a real world combinatorial optimization problem - the famous traveling salesman problem (TSP). Comparison with genetic algorithm (GA) (with revisits and standard mutation) is made. It is shown that NrGA gives superior performance compared to GA. Moreover, it gives the same stable performance using different types of mutation operators. Moreover, turning off GA's mutation operator but only use the NrGA inherent parameter-less adaptive mutation gives the best performance. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Fukunaga:2008:cec, author = "Alex S. Fukunaga ", title = "A New Grouping Genetic Algorithm for the Multiple Knapsack Problem", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0519.pdf}, url = {}, size = {}, abstract = {The Multiple Knapsack Problem (MKP) is the problem of assigning (packing) objects of various weights and values (profits) to a set of containers (bins) of various capacities, in order to maximize the total profit of the items assigned to the containers. We propose a new genetic algorithm for the MKP which searches a space of undominated candidate solutions. We compare the new algorithm to previous heuristics for the MKP, as well as alternative evolutionary algorithms, and show experimentally that our new algorithm yields the best performance on difficult instances where item weights and profits are highly correlated. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Rungrattanaubol:2008:cec, author = "Jaratsri Rungrattanaubol and Pensiri Tongpadungrod", title = "Sensing Positions Optimisation of a Distributive Tactile Sensor Using Principal Component Analysis", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0520.pdf}, url = {}, size = {}, abstract = {This paper describes a method to optimise sensing positions of a two-dimensional distributive tactile sensor for determining an applied load position from surface deflections. The distributive approach relies on coupling between sensing positions that capture a pattern of response to contacting load. The paper describes an experimental arrangement and the corresponding mathematical model that incorporates surface's response induced by a contact. The size of the experimental rig is 250 mm × 340 mm. The determination of an applied load position is completed through a back propagation neural network as an interpretation algorithm using surface deflections as input data. The average Euclidean error using 16 inputs from measurement was approximately 23 mm when sensing positions were at an equal pitch. Optimisation was achieved using principal component analysis as a tool to evaluate the performance. The number of inputs was simulated surface deflection at 4-16 positions. It was found that the number of sensing elements converged accordingly to the number of principal components (eigenvalues) used in optimisation. In terms of performance, the errors ranged from approximately 23.9-15.8 mm and 20.5-14.0 mm when inputs were mathematically derived from 4-16 non-optimised and optimised sensing positions respectively. Optimisation was an effective method to enhance the accuracy in determining an applied load position, in particular with a smaller number of sensing elements. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Tan2:2008:cec, author = "C. H. Tan and J. H. Ang and K. C. Tan and A. Tay ", title = "Online Adaptive Controller for Simulated Car Racing", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0521.pdf}, url = {}, size = {}, abstract = {An adaptive game AI has the potential of tailoring a uniquely entertaining and meaningful game experience to a specific player. An online adaptive AI should be able to profile its opponent efficiently during the early phase of the game and adapts its own playing style to the level of the player so that the player feels entertained playing against it. This paper presents an online adaptive algorithm that uses ideas from evolutionary computation to match the skill level of the opponent during the game. The proposed algorithms demonstrated using a car racing simulator is capable of matching its opponents in terms of both mean score and winning percentages. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Foo:2008:cec, author = "Cherhan Foo and Michael Kirley", title = "An Analysis of the Effects of Clustering in Graph-Based Evolutionary Algorithms", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0523.pdf}, url = {}, size = {}, abstract = {Recently, there has been increased interest in combining work from the complex networks domain with evolutionary computation to solve challenging search and optimization problems. Typically, individuals in the evolving population occupy a node in a graph (or network) and are only allowed to mate with individuals within their local neighbourhood. The use of specific graph topologies have been shown to alter the population dynamics, which in turn impacts on the ability of the algorithm to find (near)-optimal solutions for a given problem. In this paper, we continue this line of research. Here, we have analyzed the impact of clustering on the performance of graph-based evolutionary models. We have constructed a range of alternative graphs to act as scaffolding for the evolving population by systematically rewiring some of the edges/links in a regular lattice. Significantly, we have kept the mean node degree constant in all graphs. Two different problems defined on a binary string with regulated levels of epistasis;- the NK Landscape problem and the hierarchical if and only if (H-IFF) problem;- were used to examine the efficacy of our model. Simulation results show that the clustering coefficient of the underlying graph has a significant impact on the ability of a graph-based evolutionary algorithm to solve a given problem. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Onoda:2008:cec, author = "Takashi Onoda and Norihiko Ito and Yamasaki Hironobu", title = "Unusual Condition Monitoring based on Support Vector Machines for Hydroelectric Power Plants", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0524.pdf}, url = {}, size = {}, abstract = {Kyushu Electric Power Co., Inc. collects different sensor data and weather information to maintain the safety of hydroelectric power plants while the plants are running. It is very rare to occur trouble condition in equipment of hydroelectric power plants. And in order to collect the trouble condition data, it is hard to construct experimental power generation plant and hydroelectric power plant. In this situation, we have to find trouble condition sign. In this paper, we consider that the rise inclination of unusual condition data gives trouble condition sign. This paper shows results of detecting unusual condition data of bearing vibration from the collected different sensor data and weather information by using one class support vector machine and analyzing the trend of generating unusual condition data by using a support vector machine. The result shows that our approach may be useful for unusual condition data detection in bearing vibration and maintaining hydroelectric power plants. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Ghosh:2008:cec, author = "Madhumala Ghosh and Amit Konar and L. C. Jain and Uday K. Chakraborty", title = "Behavioral Analysis of Co-operative/Competitive Antibody Dynamics", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0528.pdf}, url = {}, size = {}, abstract = {The paper presents an analysis of chaos, limit cycles and stability in the antigen-antibody interactive dynamics. Both co-operation and competition of antibodies are considered in the dynamics. The classical approach of Lyapunov has been employed here for the stability analysis of the dynamics. Computer simulations have been undertaken to support the results of the analysis. Both temporal behaviors of the antibodies and their phase portraits have been given to study their chaotic, limit cyclic and stable behavior. Results of stability analysis of the dynamics have been applied in a garbage cleaning problem by a mobile robot. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Talukder:2008:cec, author = "A. K. M. Khaled Ahsan Talukder and Michael Kirley", title = "A Pareto Following Variation Operator For Evolutionary Dynamic Multi-objective Optimization", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0529.pdf}, url = {}, size = {}, abstract = {Tracking the Pareto-front in a dynamic multiobjective optimization problem (MOP) is a challenging task. Evolutionary algorithms are a representative meta-heuristic capable of meeting this challenge. Typically, the stochastic variation operators used in an evolutionary algorithm work in decision (or design) variable space, thus there are no guarantees that the new individuals produced are non-dominated and/or are unique in the population. In this paper, we introduce a novel variation operator that manipulates the values in both objective space and design variable space in such a way that it can avoid re-exploration of dominated solutions. The proposed operator, inspired by the theory of dynamic system identification, is based on integral transformation. Here, we approximate the next expected Pareto-front, and from this expected front, we generate corresponding correct decision variables. We show empirically that our algorithm can approximate the Pareto-optimal set for given static benchmark MOP's and that it can track changes in the Pareto-front for particular dynamic MOP's. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Gao2:2008:cec, author = "Yanping Gao and Hong Yu and Xinzhong Cui and Yi Xie", title = "Implementation of a New Algorithm for the Various Pattern and Language in the Workflow Management System", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0532.pdf}, url = {}, size = {}, abstract = {Workflow Management Systems (WfMS) allow organizations to streamline and automate business processes and reengineer their structure. One important requirement for this type of system is the management and computation of the Quality of Service (QoS) of processes and workflows. Currently, a range of Web processes and workflow languages exist. Each language can be characterized by the set of patterns they support. Developing and implementing a suitable and generic algorithm to compute the QoS of processes that have been designed using different languages is a difficult task. This is because some patterns are specific to particular process languages and new patterns may be introduced in future versions of a language. In this paper, we describe an adaptive algorithm implemented to cope with these two problems. The algorithm is called adaptive since it can be dynamically changed as the patterns of a process language also change. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Deb:2008:cec, author = "Kalyanmoy Deb and Karthik Sindhya", title = "Deciphering Innovative Principles for Optimal Electric Brushless D.C. Permanent Magnet Motor Design", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0534.pdf}, url = {}, size = {}, abstract = {This paper shows how a routine design optimization task can be enhanced to decipher important and innovative design principles which shall provide far-reaching knowledge about the problem at hand. Although the ‘innovization' task for this purpose was proposed by the first author elsewhere, the application to a brushless D.C. permanent magnet motor design is the first real application of the innovization concept to a discrete optimization problem. The model for cost and peak-torque objectives and associated constraints are borrowed from an existing study. The extent of knowledge gained in designing high-performing yet low-cost motors achieved in this study is phenomenal and should motivate other practitioners to pursue similar studies in other design and optimization related activities. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Kaji:2008:cec, author = "Hirotaka Kaji and Kokolo Ikeda and Hajime Kita", title = "Acceleration of Parametric Multi-Objective Optimization by an Initialization Technique for Multi-Objective Evolutionary Algorithms", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0535.pdf}, url = {}, size = {}, abstract = {Most real world problems can be formulated as Multi-objective Optimization Problems (MOPs) because they have various competing objectives. Engine calibration, which is the tuning process of controller parameters in automotive engine development, is such a problem. In the engine calibration, a set of MOPs depending on plural operating conditions such as engine speed have to be optimized one at a time. In this paper, such a problem composed by MOPs parameterized by condition variables as subproblems is called Parametric MOP (PMOP). We can solve the PMOP by applying Multi-Objective Evolutionary Algorithms (MOEAs) to each MOP separately. However, the calculation cost of PMOP becomes quite expensive in real world applications. To accelerate the evolutionary multiobjective optimization of PMOPs, we propose an initialization method of MOEAs for PMOPs. This method uses an interpolation of plural Pareto approximation populations of different conditions obtained in the past for an initial population of succeeding MOPs. The effectiveness of the proposed method is demonstrated through a numerical experiment. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Misra:2008:cec, author = "B. B. Misra and S. Dehuri and P. K. Dash and G. Panda", title = "Reduced Polynomial Neural Swarm Net for Classification Task in Data Mining", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0536.pdf}, url = {}, size = {}, abstract = {In this paper, we proposed a reduced polynomial neural swarm net (RPNSN) for the task of classification. Classification task is one of the most studied tasks of data mining. In solving classification task of data mining, the classical algorithm such as Polynomial Neural Network (PNN) takes large computation time because the network grows over the training period (i.e. the partial descriptions (PDs) in each layer grows in successive generations). Unlike PNN our proposed network needs to generate the partial description for a single layer. Particle swarm optimization (PSO) technique is used to select a relevant set of PDs as well as features, which are then fed to the output layer of our proposed net which contain only one neuron. The selection mechanism used here is a kind of wrapper approach. Performance of this model is compared with the results obtained from PNN. Simulation result shows that the performance of RPNSN is encouraging for harnessing its power in data mining area and also better in terms of processing time than the PNN model. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Takahama:2008:cec, author = "Tetsuyuki Takahama and Setsuko Sakai", title = "Reducing Function Evaluations in Differential Evolution using Rough Approximation-Based Comparison", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0537.pdf}, url = {}, size = {}, abstract = {In this study, we propose to use a rough approximation model, which is an approximation model with low accuracy and without learning process, to reduce the number of function evaluations effectively. Although the approximation errors between the true function values and the approximation values estimated by the rough approximation model are not small, the rough model can estimate the order relation of two points with fair accuracy. In order to use this nature of the rough model, we propose estimated comparison which omits the function evaluations when the result of comparison can be judged by approximation values. The advantage of the estimated comparison method is shown by comparing the results obtained by Differential Evolution (DE) and DE with estimated comparison method in various types of benchmark functions. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Takama:2008:cec, author = "Yasufumi Takama and Hiroki Namba and Yoshihiro Iwase and Yuki Muto", title = "Application of TV Program Recommendation to Communication Support between Human and Partner Robot", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0539.pdf}, url = {}, size = {}, abstract = {This paper studies the communication support between human and partner robot based on TV program recommendation. It is expected in near future that various robots designed for supporting humans will come into our daily lives. In order for such robots to coexist with humans, those should be recognized as ``partners'' for us. Therefore, studying communication between humans and robots is very important. In this paper, we focus on the communication under the situation of watching TV, because TV is bound up with the ordinary, natural rhythms of our daily life. If a robot can estimate our interests/preference by observing our watching behavior and have conversation about the topics we might be interested in, we could recognize the robot as a partner. This paper applies TV program recommendation to communication support between human and partner robot. First, the paper proposes a method for generating user profile for TV program recommendation based on fuzzy inference. The method does not estimate user's interest in a TV program only from its watching time as most of existing methods do, but also from user's utterances by applying sentiment analysis. Experiments are performed with test subjects, and the results show the proposed method can generate a user profile that can reflect user's interests. The paper also introduces a prototype system for studying communication support between human and partner robot under TV watching environment, in which the proposed profile generation method is implemented. The system architecture as well as how it works for TV program recommendation is described. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(He2:2008:cec, author = "Huimin He and Haiyan Du and Yongjin Liu and Fangping Li and Yi Xie", title = "New Coding Method to Reduce the Database Size and Algorithm with Significant Efficiency in Association Rules", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0540.pdf}, url = {}, size = {}, abstract = {The problem of discovery association rules in large databases is considered. An encoding method for converting large databases to small one is proposed. Significant efficiency is obtained by applying some modified known algorithm on our proposed database layout. In addition, a new algorithm based on the proposed encoding method is introduced. Using some properties of numbers our database converts itemset to numerical domain. Our implementation indicates that the proposed layout made the size of database significantly smaller. Also the time to find association rules is reduced. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Jeurissen:2008:cec, author = "Roland Jeurissen and Jan van den Berg", title = "Optimized Index Tracking using a Hybrid Genetic Algorithm", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0543.pdf}, url = {}, size = {}, abstract = {Assuming the market is efficient, an obvious portfolio management strategy is passive where the challenge is to track a certain benchmark like a stock index such that equal returns and risks are achieved. A tracking portfolio consists of a (usually small) weighted subset of stock funds. The weights are supposed to be positive here which means that short selling is not allowed. We investigate an approach for tracking the Dutch AEX index where an optimal tracking portfolio is determined. The optimal weights of a portfolio are found by minimizing the tracking error for a set of historical returns and covariances. The overall optimal portfolio is found using a hybrid genetic algorithm where each chromosome represents a specific subset of the stocks from the index, the fitness function of each chromosome corresponds to the minimized tracking error achievable with that subset, and the optimal portfolio is the tracking portfolio with highest fitness achievable. We show the experimental setup and the simulation results, including the out-of-sample performance of the optimal tracking portfolio. The hybrid genetic algorithms used appear to be robust in finding the optimal tracking portfolio and the performance of this portfolio on the out-of-sample data set is approximately four times better than that of randomly selected portfolios with optimized stock weights. By choosing a dedicated crossover operator, the hybrid genetic algorithm appears to find the optimal tracking portfolio using, on average, less than 23 generations only. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Yu5:2008:cec, author = "Zhiwen Yu and Dingwen Wang and Hau-San Wong", title = "Nearest Neighbor Evolutionary Algorithm for Constrained Optimization Problem", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0544.pdf}, url = {}, size = {}, abstract = {Although there exist a lot of approaches to solve constrained optimization problem, few of them makes use of the knowledge obtained in the searching process. In the paper, a new algorithm called nearest neighbor evolutionary algorithm (NNE) is proposed to solve the constrained optimization problem. NNE not only performs global search and local search in the searching process, but also considers the knowledge obtained in the searching process. NNE also avail itself of the elitist strategy and keeps the best individuals for the next generation. The results in the experiments show that NNE not only achieves good performance in a lot of constrained optimization problems, but also outperforms most of state-ofart approaches in most of constrained optimization problems, such as ASCHEA and SEMS. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Hu3:2008:cec, author = "Laihong Hu and Fuchun Sun and Hualong Xu and Huaping Liu and Fengge Wu", title = "On-Orbit Long-Range Maneuver Transfer via EDAs", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0546.pdf}, url = {}, size = {}, abstract = {Long-range maneuver transfer consumes the most fuel and time of the rendezvous process, and it is a multivariable, multi-extremum optimization problem, which is difficult to solve using traditional optimization algorithms. This paper researched the mathematical model of long-range maneuver transfer of spacecraft with impulse thrust, and optimized the parameters of orbit transfer based on a class of novel stochastic optimization algorithms, estimation of distribution algorithms (EDAs) with minimum fuel-time consumption being the optimization objective, and compared with Genetic Algorithms (GAs). Simulation results showed that EDAs were effective method for solving long-range maneuver transfer. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Lee:2008:cec, author = "Ki-Baek Lee and Jong-Hwan Kim", title = "Mass-Spring-Damper Motion Dynamics-Based Particle Swarm Optimization", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0547.pdf}, url = {}, size = {}, abstract = {Mass-spring-damper motion dynamics-based particle swarm optimization (MMD-PSO) is a novel optimization paradigm based on motion dynamic model which consists of mass, spring and damper. In MMD-PSO some particles, which are located fitter places than other particles, drop their anchor and connect springs and dampers between the anchors and all the particles. These connections influence the movements of the particles so as to proceed to fitter places attracted by the anchors. To demonstrate the effectiveness of MMD-PSO, several experiments are carried out on numerical optimization problems with complex test functions. The results show that proposed MMD-PSO is more powerful than original PSO and PSO mass-spring analogy in terms of robustness and convergence speed with no tuning parameters. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Park:2008:cec, author = "Hyungmin Park and Jong-Hwan Kim", title = "Potential and Dynamics-Based Particle Swarm Optimization", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0548.pdf}, url = {}, size = {}, abstract = {The Particle Swarm Optimization (PSO) algorithm is a robust stochastic evolutionary computation technique based on the movement and intelligence of swarms. This paper proposes a novel PSO algorithm, based on the potential field and the motion dynamics model. It is assumed that particles form potential fields and each particle has its own mass. The potential filed and mass are modeled by the particles' fitness value. By using these fitness based models, the proposed algorithm performs well, in particular, in avoiding the local minima compare to the original PSO. The proposed PDPSO successfully solves minimization problems of complex test functions. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Khatir:2008:cec, author = "Mehrdad Khatir and Amir Hossein Jahangir and Hamid Beigy", title = "Investigating the Baldwin Effect on Cartesian Genetic Programming Efficiency", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0549.pdf}, url = {}, size = {}, abstract = {Cartesian Genetic Programming (CGP) has an unusual genotype representation which makes it more efficient than Genetic programming (GP) in digital circuit design problem. However, to the best of our knowledge, all methods used in evolutionary design of digital circuits deal with rugged, complex search space, which results in long running time to obtain successful evolution. Therefore, employing a method to guide evolution in these spaces can facilitate achieving more reasonable results. It has been claimed that a two-step evolutionary scenario caused by benefit and cost of learning called Baldwin effect can guide evolution in the biology and artificial life. Therefore, we have been motivated to examine this effect on CGP. We observe using this scenario the success rate and evolution time of CGP improves dramatically especially when size of chromosomes increases. }, keywords = {genetic algorithms, genetic programming, Cartesian Genetic Programming, Baldwin Effect, Phenotypic Plasticity, Digital Circuit, Reinforcement Learning.}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wang15:2008:cec, author = "Feng Wang and Yuanxiang Li and Li Liang and Kangshun Li", title = "Triangular Arbitrage in Foreign Exchange Rate Forecasting Markets", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0550.pdf}, url = {}, size = {}, abstract = {The non-existence of triangular arbitrage in an efficient foreign exchange markets is widely believed. In this paper, we deploy a forecasting model to predict foreign exchange rates and apply the triangular arbitrage model to evaluate the possibility of an arbitrage opportunity. Surprisingly, we substantiate the existence of triangular arbitrage opportunities in the exchange rate forecasting market even with transaction costs. This also implies the inefficiency of the market and potential market threats of profit-seeking investors. In our experiments, Neural Network based model with back-propagation (BP-NN) is used for exchange rate forecasting. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(MacNish:2008:cec, author = "Cara MacNish and Xin Yao", title = "Direction Matters in High-Dimensional Optimisation", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0552.pdf}, url = {}, size = {}, abstract = {Directional biases are evident in many benchmarking problems for real-valued global optimisation, as well as many of the evolutionary and allied algorithms that have been proposed for solving them. It has been shown that directional biases make some kinds of problems easier to solve for similarly biased algorithms, which can give a misleading view of algorithm performance. In this paper we study the effects of directional bias for highdimensional optimisation problems. We show that the impact of directional bias is magnified as dimension increases, and can in some cases lead to differences in performance of many orders of magnitude. We present a new version of the classical evolutionary programming algorithm, which we call unbiased evolutionary programming (UEP), and show that it has markedly improved performance for high-dimensional optimisation. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Xu4:2008:cec, author = "Yiliang Xu and Meng Hiot Lim and Yew-Soon Ong", title = "Automatic Configuration of Metaheuristic Algorithms for Complex Combinatorial Optimization Problems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0554.pdf}, url = {}, size = {}, abstract = {We report our work on the algorithmic development of an evolutionary methodology for automatic configuration of metaheuristic algorithms for solving complex combinatorial Optimization problems.We term it Automatic Configuration Engine for Metaheuristics (ACEM). We first propose a novel Left Variation s- Right Property (LVRP) tree structure to manage various metaheuristic procedures and properties. With LVRP tree, feasible configurations of metaheuristics can be easily specified. An evolutionary learning algorithm is then proposed to evolve the internal context of the trees based on pre-selected training set. Guided by a user-defined satisfaction function of the candidate algorithms, it converges to the optimal or a very good algorithm. The experimental comparison with two recent state-of-the-art algorithms for solving the quadratic assignment problem (QAP) shows that ACEM produces an hybrid-genetic algorithm with human-competitive or even better performance. }, keywords = {genetic algorithms, genetic programming}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Lin2:2008:cec, author = "Ying Lin and Jun Zhang and Lu-kai Lan ", title = "A Contour Method in Population-Based Stochastic Algorithms", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0555.pdf}, url = {}, size = {}, abstract = {Inspired by the contours in topography, this paper proposes a contour method for the population-based stochastic algorithms to solve the problems with continuous variables. Relying on the existed population, the contour method explores the landscape of the fitness function in the search space, which leads to effective speculation about the positions of the potential optima. The contour method is embedded into every generation of the simple genetic algorithm (SGA) for efficiency examination. The genetic algorithm with the contour method is first realized in a two-dimensional space, where the contours in topography can be directly used. Then the proposed contour method is modified to adapt high dimensional space. Numerical optimization experiments are carried out on ten benchmark functions of two and thirty dimensions. Results show that the genetic algorithm with the contour method can outperform the SGA in both solution quality and convergence speed. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Chen10:2008:cec, author = "Angela H. L. Chen and Chiuh-Cheng Chyu", title = "A Memetic Algorithm for Maximizing Net Present Value in Resource-Constrained Project Scheduling Problem", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0557.pdf}, url = {}, size = {}, abstract = {In this study, we develop a model that considers monetary issues in resource-constrained environments, and involves scheduling project activities to maximize net present value. This problem is recognized as the ''resource-constrained project scheduling problem with discounted cash flows (RCPSPDCF),'' which is strongly NP-hard. All resources considered are both types of renewable and nonrenewable; the duration of each activity depends on the amount of resources allocated to its execution. Efforts are made by considering a two-stage method applying mode selection rules at the first stage and the memetic algorithm at the second stage. Results are shown in a comparative study which demonstrates the effectiveness of using memetic algorithm in maximizing project net present value; as well as, a combination of mode selection rules which provide a high probability of giving the best solution. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Jiang3:2008:cec, author = "He Jiang and Jifeng Xuan and Xianchao Zhang", title = "An Approximate Muscle Guided Global Optimization Algorithm for the Three-Index Assignment Problem", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0558.pdf}, url = {}, size = {}, abstract = {The Three-Index Assignment Problem (AP3) is a famous NP-hard problem with wide applications. Since it's intractable, many heuristics have been proposed to obtain near optimal solutions in reasonable time. In this paper, a new meta-heuristic was proposed for solving the AP3. Firstly, we introduced the conception of muscle (the union of optimal solutions) and proved that it is intractable to obtain the muscle under the assumption that P≠ NP. Moreover, we showed that the whole muscle can be approximated by the union of local optimal solutions. Therefore, the Approximate Muscle guided Global Optimization (AMGO) is proposed to solve the AP3. AMGO employs a global optimization strategy to search in a search space reduced by the approximate muscle, which is constructed by a multi-restart scheme. During the global optimization procedure, the running time can be dramatically saved by detecting feasible solutions and extracting poor partial solutions. Extensive experimental results on the standard AP3 benchmark indicated that the new algorithm outperforms the state-of-the-art heuristics in terms of solution quality. Work of this paper not only provides a new meta-heuristic for NP-hard problems, but shows that global optimization can provide promising results in reasonable time, by restricting it to a fairly reduced search space. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Li15:2008:cec, author = "Gang Li and Tak-Ming Chan and Kwong-Sak Leung and Kin-Hong Lee", title = "An Estimation of Distribution Algorithm for Motif Discovery", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0559.pdf}, url = {}, size = {}, abstract = {The problem of Transcription Factor Binding Sites identification or motif discovery is to identify the motif binding sites in the cis-regulatory regions of DNA sequences. The biological experiments are expensive and the problem is NP-hard computationally. We have proposed Estimation of Distribution Algorithm for Motif Discovery (EDAMD). We use Bayesian analysis to derive the fitness function to measure the posterior probability of a set of motif instances, which can be used to handle a variable number of motif instances in the sequences. EDAMD adopts a Gaussian distribution to model the distribution of the sets of motif instances, which is capable of capturing the bivariate correlation among the positions of motif instances. When a new Position Frequency Matrix (PFM) is generated from the Gaussian distribution, a new set of motif instances is identified based on the PFM via the Greedy Refinement operation. At the end of a generation, the Gaussian distribution is updated with the sets of motif instances. Since Greedy Refinement assumes a single motif instance on a sequence, a Post Processing operation based on the fitness function is used to find more motif instances after the evolution. The experiments have verified that EDAMD is comparable to or better than GAME and GALF on the real problems tested in this paper. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Ishibuchi:2008:cec, author = "Hisao Ishibuchi and Noritaka Tsukamoto and Yusuke Nojima", title = "Evolutionary Many-Objective Optimization: A Short Review", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0560.pdf}, url = {}, size = {}, abstract = {Whereas evolutionary multiobjective optimization (EMO) algorithms have successfully been used in a wide range of real-world application tasks, difficulties in their scalability to many-objective problems have also been reported. In this paper, first we demonstrate those difficulties through computational experiments. Then we review some approaches proposed in the literature for the scalability improvement of EMO algorithms. Finally we suggest future research directions in evolutionary many-objective optimization. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Shaobin:2008:cec, author = "Zhan Shaobin and Chen Shengbo and Bao Yunfei", title = "Building Grid Service on Atmospheric Radiative Transfer Simulation of Remote Sensing Data", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0561.pdf}, url = {}, size = {}, abstract = {The radiance leaving the earth-atmosphere system which can be sensed by a satellite borne radiometer is the sum of radiation emission from the earth surface and each atmospheric level that are transmitted to the top of the atmosphere. It can be separated from the radiance at the top the atmospheric level measured by radiometer. However, it is very difficult to measure the atmospheric radiance, especially the synchronous measurement with the satellite. Thus some atmospheric radiative transfer models (ARTM) have been developed to provide many options for modeling atmospheric radiation transport, the newly atmospheric ARTM, MODTRAN, will be researched after the atmospheric radiative transfer is described. And the simulation procedures and the applications to atmospheric transmittance, retrieval of atmospheric elements, and surface parameters, will also be presented. At the same time, the powerful computing resource was required which the urgent requirement of disposal plentiful data. When test area of atmospheric radiative transfer extends the state, even all of country, the computing capability in single computer fall short of demand. So we introduce the conception- ''Grid Service'', it can solve the problem commendably. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Lin3:2008:cec, author = "Ying Lin and Jian Huang and Jun Zhang", title = "New Evaluation Criteria for the Convergence of Continuous Evolutionary Algorithms", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0562.pdf}, url = {}, size = {}, abstract = {The first hitting time (FHT) plays an important role in convergence evaluation for evolutionary algorithms. However, the current criteria of the FHT are mostly under a hypothesis that never has been testified: the FHT subjects to the normal distribution. Aiming at more convincible evaluations, this paper investigates the distribution of the FHT through a goodness-of-fit test and discovers an unexpected result. Based on this result, this paper proposes a new set of criteria, which uses two types of relative frequency histograms. This paper validates the proposed criteria on the optimization problem of benchmark functions by the standard genetic algorithm (SGA) and the particle swarm optimization (PSO). The experiments show that the proposed criteria are effective to evaluate the convergent speed and the convergent stability of the evolutionary algorithms. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Zhang9:2008:cec, author = "Jun Zhang and Ying Lin ", title = "A Particle Swarm Optimizer with Lifespan for Global Optimization on Multimodal Functions", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0563.pdf}, url = {}, size = {}, abstract = {The particle swarm optimizer (PSO) is a popular computing technique of swarm intelligence, known for its fast convergence speed and easy implementation. All the particles in the traditional PSO must learn from the best-so-far solution, which makes the best solution the leader of the swarm. This paper proposes a variation of the traditional PSO, named the PSO with lifespan (LS-PSO), in which the lifespan of the leader is adjusted according to its power of leading the swarm towards better solutions. When the lifespan is exhausted, a new solution is produced and it will conditionally replace the original leader depending on its leading power. Experiments on six benchmark multimodal functions show that the proposed algorithm can significantly improve the performance of the traditional PSO. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wang16:2008:cec, author = "Luyi Wang and Hiroyuki Ishida and Tomoyuki Hiroyasu and Mitsunori Miki", title = "Examination of Multi-Objective Optimization Method for Global Search Using DIRECT and GA", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0566.pdf}, url = {}, size = {}, abstract = {A number of multi-objective genetic algorithms (MOGAs) have been developed to obtain Pareto optimal solu- tions for multi-objective optimization problems. However, as these methods involve probabilistic algorithms, there is no guarantee that the global search will be conducted in the design variable space. In such cases, there are unsearched areas in the design variable space, and the obtained Pareto solutions may not be truly optimal. In this paper, we propose an optimization method called NSDIRECT-GA to conduct a global search over the design variable space as much as possible, which improves the reliability of the obtained Pareto solutions. The effectiveness of NSDIRECT-GA was examined through numerical experiments. NSDIRECT-GA can obtain not only Pareto solutions, but also grasp the landscape of the search space, which results in higher reliability of the obtained solutions compared to MOGAs. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Huang6:2008:cec, author = "Tao Huang and Jian Huang and Jun Zhang", title = "An Orthogonal Local Search Genetic Algorithm for the Design and Optimization of Power Electronic Circuits", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0567.pdf}, url = {}, size = {}, abstract = {In this paper, an orthogonal local search genetic algorithm (OLSGA) is proposed for the design and optimization of power electronic circuits. The genetic algorithm is accelerated with a fast local search operator that automatically adjusts the search direction and the step size. An experimental design method called orthogonal design is used to determine the most promising direction of the potential region in the local search. In each generation, the step size is adaptively expanded or shrunk according to whether there is a newly improvement in the given local region. As a result, with proper direction and step size, the local search operator is able to stride forward and provide better exploitation ability to speed up the convergence rate of the genetic algorithm. The proposed method is applied to design and optimize a buck regulator. The results in comparison with other published results indicate that our proposed algorithm is effective and efficient. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Zhong:2008:cec, author = "Wen-Liang Zhong and Jian Huang and Jun Zhang", title = "A Novel Particle Swarm Optimization for the Steiner Tree Problem in Graphs", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0568.pdf}, url = {}, size = {}, abstract = {The Steiner tree problem (STP) in graphs is a special but essential case of multiple destination routing (MDR) problems, which focuses on finding a minimal spanning tree (MST) that connecting the source and destinations. It has been proved to be an NP-hard problem. Particle swarm optimization (PSO) is an important swarm intelligent algorithm with fast convergence speed and easy implementation. In this paper, a novel discrete PSO for the STP (DPSO-STP), with the concept that the particle is guided by social and self cognition, is proposed. Different from the standard PSO, the DPSO-STP includes four parts: (1) two preprocessing operations are introduced, which are to construct a complete graph and to calculate each node's total distance from itself to the source and destination nodes; (2) the position of a particle is represented as a binary string, where 1 stands for the selected nodes and 0 denotes the opposite; (3) several novel update operations, including new mutation factor c3, are adopted for the binary string; (4) when generating a MST from a binary string, a modified Prim's algorithm and a trimming strategy are employed. The experiments based on the benchmarks from category B, C of STP in the OR-library have been carried out to demonstrate the effectiveness of the proposed algorithm. Compared with traditional heuristic algorithms, such as shortest path heuristic (SPH), average distance heuristic (ADH), etc., the DPSO obtains more promising results. And it also performs better than the other iteration based algorithm, with much less computation. The discussion to extend the algorithm to other MDR problems is also given. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Kattan:2008:cec, author = "Ahmad Kattan and Riccardo Poli", title = "Evolutionary Lossless Compression with GP-ZIP", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0569.pdf}, url = {}, size = {}, abstract = {In this paper we propose a new approach for applying Genetic Programming to loss-less data compression based on combining well-known lossless compression algorithms. The file to be compressed is divided into chunks of a predefined length, and GP is asked to find the best possible compression algorithm for each chunk in such a way to minimise the total length of the compressed file. This technique is referred to as ''GP-zip''. The compression algorithms available to GP-zip (its function set) are: Arithmetic coding (AC), Lempel-Ziv-Welch (LZW), Unbounded Prediction by Partial Matching (PPMD), Run Length Encoding (RLE), and Boolean Minimisation. In addition, two transformation techniques are available: Burrows-Wheeler Transformation (BWT) and Move to Front (MTF). In experimentation with this technique, we show that when the file to be compressed is composed of heterogeneous data fragments (as is the case, for example, in archive files), GP-zip is capable of achieving compression ratios that are superior to those obtained with well-known compression algorithms. }, keywords = {genetic algorithms, genetic programming}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Osmera:2008:cec, author = "Pavel Osmera and Ondrej Popelka and Petr Pivonka", title = "Two Level Parallel Grammatical Evolution", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0570.pdf}, url = {}, size = {}, abstract = {This paper describes a Two Level Parallel Grammatical Evolution (TLPGE) that can evolve complete programs using a variable length linear genome to govern the mapping of a Backus Naur Form grammar definition. To increase the efficiency of Grammatical Evolution (GE) the influence of backward processing was tested and a second level with differential evolution was added. The significance of backward coding (BC) and the comparison with standard coding of GEs is presented. The new method is based on parallel grammatical evolution (PGE) with a backward processing algorithm, which is further extended with a differential evolution algorithm. Thus a two-level optimisation method was formed in attempt to take advantage of the benefits of both original methods and avoid their difficulties. Both methods used are discussed and the architecture of their combination is described. Also application is discussed and results on a real-word application are described. }, keywords = {genetic algorithms, genetic programming, grammatical evolution}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Scriven:2008:cec, author = "Ian Scriven and David Ireland and Andrew Lewis and Sanaz Mostaghim and Jürgen Branke", title = "Asynchronous Multiple Objective Particle Swarm Optimisation in Unreliable Distributed Environments", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0571.pdf}, url = {}, size = {}, abstract = {This paper examines the performance characteristics of both asynchronous and synchronous parallel particle swarm optimisation algorithms in heterogeneous, fault-prone environments. Algorithm convergence is measured as a function of both iterations completed and time elapsed, allowing the two particle update mechanisms to be comprehensively evaluated and compared in such an environment. Asynchronous particle updates are shown to negatively impact the convergence speed in regards to iterations completed, however the increased parallel efficiency of the asynchronous model appears to counter this performance reduction, ensuring the asynchronous update mechanism performs comparably to the synchronous mechanism in fault-free environments. When faults are introduced, the synchronous update method is shown to suffer significant performance drops, suggesting that at least partly asynchronous algorithms should be used in real-world environments where faults can regularly occur. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Lu3:2008:cec, author = "Xiaojun Lu and Guowu Yang and Jianping Li and Xiaoyu Song and William N. N. Hung", title = "The Probability Logics for Nanoscale Inverters Cascade", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0572.pdf}, url = {}, size = {}, abstract = {Device failure is an important consideration in nano-scale design. This paper presents a probabilistic logic model to compute the probability distribution of the nano gate states. The characterization is based on markov random field and statistical physics. The basic logic gates are probabilistically characterized. The effectiveness of the method is demonstrated by an inverter and the inverter casecade. Our analysis shows that the device probability distribution highly depends on the system structures and other performance parameters. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Li16:2008:cec, author = "Yamin Li and Jinru Ma and Qiuxia Zhao", title = "Two Improvements in Genetic Programming for Image Classification", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0573.pdf}, url = {}, size = {}, abstract = {A new classification algorithm for multi-image classification in genetic programming (GP) is introduced, which is the centred dynamic class boundary determination with quick-decreasing power value of arithmetic progression. In the classifier learning process using GP for multi-image classification, different sets of power values are tested to achieve a more suitable range of margin values for the improvement of the accuracy of the classifiers. In the second development, the program size is introduced into the fitness function to control the size of program growth during the evolutionary learning process. The approach is examined on a Chinese character image data set and a grass leaves data set, both of which have four or more classes. The experimental results show that while dealing with complicated problems of multi-image classification, the new approach can be used for more accurate classification and work better than the previous algorithms of either static or dynamic class boundary determination. With the fitness function, the size of the programs in the population can be controlled effectively and shortened considerably during evolution. Thus, the readability of the programs could be seemingly improved. }, keywords = {genetic algorithms, genetic programming}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Albrecht:2008:cec, author = "Andreas A. Albrecht and Peter C. R. Lane and Kathleen Steinhofel", title = "Combinatorial Landscape Analysis for {\it k}-SAT Instances", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0574.pdf}, url = {}, size = {}, abstract = {Over the past ten years, methods from statistical physics have provided a deeper inside into the average complexity of hard combinatorial problems, culminating in a rigorous proof for the asymptotic behaviour of the k-SAT phase transition threshold by Achlioptas and Peres in 2004. On the other hand, when dealing with individual instances of hard problems, gathering information about specific properties of instances in a pre-processing phase might be helpful for an appropriate adjustment of local search-based procedures. In the present paper, we address both issues in the context of landscapes induced by k-SAT instances: Firstly, we use a sampling method devised by Garnier and Kallel in 2002 for approximations of the number of local maxima in landscapes generated by individual k-SAT instances and a simple neighbourhood relation. The objective function is given by the number of satisfied clauses. Secondly, we outline a method for obtaining upper bounds for the average number of local maxima in k-SAT instances which indicates some kind of phase transition for the neighbourhood-specific ratio m/n = Θ(2k/k). }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Piccand:2008:cec, author = "Sebastien Piccand and Michael O'Neill and Jacqueline Walker", title = "On the Scalability of Particle Swarm Optimisation", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0577.pdf}, url = {}, size = {}, abstract = {Particle swarm has proven to be competitive to other evolutionary algorithms in the field of optimization, and in many cases enables a faster convergence to the ideal solution. However, like any optimization algorithm it seems to have difficulties handling optimization problems of high dimension. Here we first show that dimensionality is really a problem for the classical particle swarm algorithms. We then show that increasing the swarm size can be necessary to handle problem of high dimensions but is not enough. We also show that the issue of scalability occurs more quickly on some functions. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Zhang10:2008:cec, author = "Xiao-hang Zhang and Jun Wu and Xue-cheng Yang and Ting-jie Lu", title = "Extracting Meaningful Patterns for Time Series Classification", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0578.pdf}, url = {}, size = {}, abstract = {An import area in machine learning is multivariate time series classification. In this paper we present a novel algorithm which extracts some meaningful patterns from time series data and then uses traditional machine learning algorithm to create classifier. During the stage of pattern extraction, the Gini function is used to evaluate the patterns and the starting position and the length of each pattern are automatically determined. We also apply sampling method to reduce the search space and improve efficiency. The common datasets are used to check our algorithm which is compared with the naïve algorithms. The results show that a lot of improvement can be gained in terms of interpretability, simplicity of the model and also in terms of accuracy. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Liu8:2008:cec, author = "Julie Yu-Chih Liu and Pei-Chann Chang", title = "Constraints for Data Operations in Extended Possibility-Based Databases", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0579.pdf}, url = {}, size = {}, abstract = {This paper considers the data operation for multidatabases in an extended possibility-based data model. Owing to the complexity of the data model considered, inconsistent redundancy of tuples may occur when database relations being operated are associated with different resemblance relations on a given domain. This work first demonstrates the inconsistency problem, and then presents the notions of consistency constraints for multi-database design. Under the constraints, the extended possibility-based databases using different resemblance relations can preserve consistent redundancy. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Tsai:2008:cec, author = "Sientang Tsai and Wei-Yeh Chen and Rumin Yang", title = "Molecular Solutions for the Set-Partition Problem on DNA-Based Computing", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0581.pdf}, url = {}, size = {}, abstract = {Suppose that a finite set S has q elements, and each element in S is a positive integer. The set-partition problem is to determine whether there is a subset T ⊆ S such that Σx ∈ Tx = ∑x ∈ Τx, where Τ = {x|x ∈ S and x ∉ T}. This paper shows that biological operations can be applied to solve the set-partition problem. In order to perform this goal, we offer two DNA-based algorithms, an unsigned parallel adder and a parallel Exclusive-OR (XOR) operation, that formally verify our designed molecular solutions for solving the set-partition problem. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Godley:2008:cec, author = "Paul Godley and Julie Cowie and David Cairns and John McCall and Catherine Howie", title = "Optimisation of Cancer Chemotherapy Schedules Using Directed Intervention Crossover Approaches", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0582.pdf}, url = {}, size = {}, abstract = {This paper describes two directed intervention crossover approaches that are applied to the problem of deriving optimal cancer chemotherapy treatment schedules. Unlike traditional uniform crossover (UC), both the calculated expanding bin (CalEB) method and targeted intervention with stochastic selection (TInSSel) approaches actively choose an intervention level and spread based on the fitness of the parents selected for crossover. Our results indicate that these approaches lead to significant improvements over UC when applied to cancer chemotherapy scheduling. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Passow:2008:cec, author = "Benjamin N. Passow and Mario Gongora and Simon Coupland and Adrian A. Hopgood", title = "Real-Time Evolution of an Embedded Controller for an Autonomous Helicopter", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0584.pdf}, url = {}, size = {}, abstract = {In this paper we evolve the parameters of a proportional, integral, and derivative (PID) controller for an unstable, complex and nonlinear system. The individuals of the applied genetic algorithm (GA) are evaluated on the actual system rather than on a simulation of it, thus avoiding the ''reality gap''. This makes implicit a formal model identification for the implementation of a simulator. This also calls for the GA to be approached in an unusual way, where we need to consider new aspects not normally present in the usual situations using an unnaturally consistent simulator for fitness evaluation. Although elitism is used in the GAs, no monotonic increase in fitness is exhibited by the algorithm. Instead, we show that the GA's individuals converge towards more robust solutions. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wang17:2008:cec, author = "D. Wang and G. S. Ng and C. Quek ", title = "A Novel Hybrid Intelligent System: Genetic Algorithm and Rough Set Incorporated Neural Fuzzy Inference System", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0585.pdf}, url = {}, size = {}, abstract = {This paper proposes a novel hybrid intelligent system denoted as genetic algorithm and rough set incorporated neural fuzzy inference system (GARSINFIS). Its network structure dynamically changes along with the evolving genetic algorithm based rough set clustering (GARSC) technique. When input data set is applied, only the most essential information is retained in the clustering result, as knowledge reduction is done using rough set approximations and the most optimal solution is selected by genetic algorithm. The system not only obtains promising accuracy but also possesses a great level of interpretability to meet the increasing need of understanding the inference process. In terms of TSK type of fuzzy inference system, better structural interpretability is typically manifested as employing less number of input features, less number of rules, less number of fuzzy membership functions in each feature, and less complex rules in both antecedent and consequent parts. Extensive simulations on various data sets were conducted, and the performance of GARSINFIS was benchmarked against other well established neural and neural-fuzzy systems. Experimental results have shown that GARSINFIS performs well in both accuracy and interpretability. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Akhtar:2008:cec, author = "Junaid Akhtar and Mian M. Awais and Basit B. Koshul", title = "Evolutionary Algorithms Based on Non-Darwinian Theories of Evolution", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0586.pdf}, url = {}, size = {}, abstract = {One name that comes to mind in connection with the word evolution is Darwin. One evolutionist however, who is rarely talked about, especially in the Artificial Intelligence community, is Peirce. The Darwinian model is based on the concepts of absolute chance, mechanistic laws, and inexplicable interaction between the two. In contrast, Peirce's framework posits a dynamic interaction between possibility, necessity and regularity to describe the process of evolution. The theory of evolution proposed by Peirce is superior to the one proposed by Darwin because it is more general and it has greater explanatory power. Peirce's insights are significant enough to be used to improve the existing evolutionary algorithms. It was observed during our literature review that almost all evolutionary algorithms are fundamentally based on Darwinian principles of evolution. The present paper highlights the differences between Darwinian and Peircian evolutionary theories and provides the theoretical foundation for developing a novel Peirce based Evolutionary Algorithm. Preliminary experiments have been conducted and results seem very promising. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Ramstein:2008:cec, author = "Gerard Ramstein and Nicolas Beaume and Yannick Jacques", title = "A Grammatical Swarm for Protein Classification", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0587.pdf}, url = {}, size = {}, abstract = {We present a Grammatical Swarm (GS) for the Optimization of an aggregation operator. This combines the results of several classifiers into a unique score, producing an optimal ranking of the individuals. We apply our method to the identification of new members of a protein family. Support Vector Machine and Naive Bayes classifiers exploit complementary features to compute probability estimates. A great advantage of the GS is that it produces an understandable algorithm revealing the interest of the classifiers. Due to the large volume of candidate sequences, ranking quality is of crucial importance. Consequently, our fitness criterion is based on the Area Under the ROC Curve rather than on classification error rate. We discuss the performances obtained for a particular family, the cytokines and show that this technique is an efficient means of ranking the protein sequences. }, keywords = {genetic algorithms, genetic programming, grammatical evolution}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Samways:2008:cec, author = "Neale Samways and Yaochu Jin and Xin Yao and Bernhard Sendhoff", title = "Toward a Gene Regulatory Network Model for Evolving Chemotaxis Behavior", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0588.pdf}, url = {}, size = {}, abstract = {Inspired from bacteria, a gene regulatory network model for signal transduction is presented in this paper. After describing experiments on stabilizing the population size for sustained open-ended evolution, we examine the ability of the model to evolve gradient-following behavior resembling bacterial chemotaxis. Under the conditions defined in this paper, an overwhelming chemotaxis behavior does not seem to emerge. Further experimentation suggests that chemotaxis is selectively favored, however, it is shown that the gradient information, which is critical for evolving chemotaxis, is heavily degraded under the current regime. It is hypothesized that lack of consistent gradient information results in the selection of nonchemotaxis behavior. Future work on revising the model as well as the environmental setups is discussed. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Smit:2008:cec, author = "S. K. Smit and A. R. Griffioen and M. C. Schut", title = "A Controller Architecture for the Evolution of State-Persistent Controllers: Behaviour Oriented Decision Tree (BODT)", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0589.pdf}, url = {}, size = {}, abstract = {In this paper we present a new controller architecture. A central design choice is that the controller can be easily modified or changed by evolution. Our aim is to initially endow the agents with as little knowledge as possible and to let them evolve their controllers autonomously. One particular aspect of the controller that we will investigate in this research is the evolution of state persistent controllers. With this is meant controllers that that can carry out multiple tasks. Without state persistence, agents may suffer from so-called ``unfocused attention'': the case where an agent is caught in the middle between tasks and interchangeably executes these partially, but can and will never fully commit to either one and therefore never accomplish any. We will present the state-persistent controller architecture and demonstrate this property in an experiment. }, keywords = {genetic algorithms, genetic programming}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Kuyucu:2008:cec, author = "Tüze Kuyucu and Martin Trefzer and Andrew Greensted and Julian Miller and Andy Tyrrell", title = "Fitness Functions for the Unconstrained Evolution of Digital Circuits", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0590.pdf}, url = {}, size = {}, abstract = {This work is part of a project that aims to develop and operate integrated evolvable hardware systems using unconstrained evolution. Experiments are carried out on an evolvable hardware platform featuring both combinatorial and registered logic as well as sequential feedback loops. In order to be able to accurately assess the transient output of the system and at the same time speed up evolution, new fitness evaluation methods are introduced. These bitwise and hierarchical fitness evaluation methods are adapted and further developed specifically for hardware implementation. It is shown that the newly developed approaches are particularly powerful in coping with two important issues: computational ambiguities, which generally occur when evaluating binary strings, and transient effects resulting from measuring hardware output. On two combinatorial problems it is shown that the new fitness functions improve the performance of evolution and allow stable solutions to be found more reliably. The experiments are carried out with a recently developed hardware platform called reconfigurable integrated system array (RISA). }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Ni:2008:cec, author = "Bing Ni and M. H. Wong and K. S. Leung ", title = "N-SAMSAM : A Simple and Faster Algorithm for Solving Approximate Matching in DNA Sequences", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0591.pdf}, url = {}, size = {}, abstract = {This work proposes a novel algorithm to do approximate matching in a database consisting of multiple sequences. We apply Agrep algorithm in an indexing structure, the r-cut numerical substring array (r-NSA). The structure basically indexes all the substrings of length r. The advantage of using the r-NSA is two-fold: (1) The space requirement of the r-NSA is much smaller than that of the other existing indexing structures, such as the generalized suffix tree. (2) We propose an algorithm to apply Agrep in the r-NSA, in which the substrings are processed sequentially. Since the common substrings are processed only once, the cost of our algorithm is smaller than that of the full scanning search by Agrep. Consequently, the matching time of our algorithm is also reduced. We design experiments to validate and compare the performance of our algorithm against the full scanning search by Agrep. We define the speed-up of our algorithm as the time required by the full scanning search by Agrep over that of our algorithm. We use eight sets of real DNA sequences in our experiments, and the results show that our algorithm achieves significant speed-up. We also investigate the speed-up of difference data sets, and analyze their differences in detail. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Johnson:2008:cec, author = "Colin G. Johnson ", title = "Multi-Level Neutrality in Optimization", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0592.pdf}, url = {}, size = {}, abstract = {This paper explores the idea of neutrality in heuristic optimization algorithms. In particular, the effect of having multiple levels of neutrality in representations is explored. Two experiments using a fitness-adaptive walk algorithm are carried out: the first is concerned with function optimization with Random Boolean Networks, the second with a tunable neutral mapping applied to the hierarchical if-and-only-if function. In both of these cases it is shown that a two-level neutral mapping can be found that performs better than both nonneutral mappings and mappings with a single level of neutrality. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Laredo:2008:cec, author = "J. L. J. Laredo and P. A. Castillo and A. M. Mora and J. J. Merelo", title = "Exploring Population Structures for Locally Concurrent and Massively Parallel Evolutionary Algorithms", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0594.pdf}, url = {}, size = {}, abstract = {In this paper we present the Gossip-based Evolvable Agent Model (GossEvAg) within the context of parallel fine-grained Evolutionary Algorithms (EAs). It extends the Cellular Evolutionary Algorithm (CEA) definition with two novel features designed to work on Peer-to-Peer (P2P) networks: every individual is self-scheduled in a single thread and dynamically self-organizes its neighbourhood via newscasting, a gossip protocol. As a consequence of such multi-threading model, each Evolvable Agent (EvAg) updates asynchronously its state at random depending on the underlying platform scheduler. In order to assess the effects of asynchrony and the gossip protocol, we perform an experimental evaluation of the model for a set of discrete optimization problems. As a baseline for comparison we use two canonical genetic algorithms (GA): A steady-state GA (ssGA) and a generational GA (gGA). We also test two more topologies for the EvAg, a complete graph topology which allows panmixia and a Watts-Strogatz topology which has shown good theoretical and empirical results in related papers. We found that leaving the management of the EvAg to the underlying platform scheduler has an interesting emerging feature: the model is able to scale seamlessly in desktop computers without any effort from the practitioner.We measure how the algorithm speed scales by conducting the experiments in a Single and a Dual-Core Processor architectures. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Tasgetiren:2008:cec, author = "Fatih Tasgetiren and Quan-Ke Pan and Yun-Chia Liang", title = "A Discrete Differential Evolution Algorithm for Single Machine Total Weighted Tardiness Problem with Sequence Dependent Setup Times", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0595.pdf}, url = {}, size = {}, abstract = {In this paper, a discrete differential evolution algorithm with the reference local search is presented to solve the single machine total weighted tardiness problem with sequence dependent setup times. In addition, To facilitate the greedy job insertion into a partial solution, newly designed speed-up methods are presented for the insertion move as a further and novel contribution to the single machine tardiness related scheduling with sequence dependent setup times literature. To evaluate its performance, the discrete differential evolution algorithm is tested on a set of benchmark instances from the literature. Through the analyses of experimental results, highly effective performance of the discrete differential evolution algorithm is shown against the best known solutions from the literature, especially, against the very recent newly designed particle swarm optimization algorithm and ant colony algorithm of Anghinolfi & Paolucci [European Journal of Operational Research 2007; doi: 10.1016/j.ejor.2007.10.044, Available Online] and Anghinolfi & Paolucci [to appear in the International Journal of Operations Research 2007], respectively. Ultimately, 46 out of 120 aggregated best known solutions so far in the literature are further improved. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Brownlee:2008:cec, author = "Alexander E. I. Brownlee and John A. W. McCall and Qingfu Zhang and Deryck F. Brown", title = "Approaches to Selection and their Effect on Fitness Modelling in an Estimation of Distribution Algorithm", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0596.pdf}, url = {}, size = {}, abstract = {Selection is one of the defining characteristics of an evolutionary algorithm, yet inherent in the selection process is the loss of some information from a population. Poor solutions may provide information about how to bias the search toward good solutions. Many Estimation of Distribution Algorithms (EDAs) use truncation selection which discards all solutions below a certain fitness, thus losing this information. Our previous work on Distribution Estimation using Markov networks (DEUM) has described an EDA which constructs a model of the fitness function; a unique feature of this approach is that because selective pressure is built into the model itself selection becomes optional. This paper outlines a series of experiments which make use of this property to examine the effects of selection on the population. We look at the impact of selecting only highly fit solutions, only poor solutions, selecting a mixture of highly fit and poor solutions, and abandoning selection altogether. We show that in some circumstances, particularly where some information about the problem is already known, selection of the fittest only is suboptimal. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Patricio:2008:cec, author = "Miguel A. Patricio and J. García and A. Berlanga and Jose M. Molina", title = "Solving Video-Association Problem with Explicit Evaluation of Hypothesis Using EDAs", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0598.pdf}, url = {}, size = {}, abstract = {In this work the data association problem in visual tracking is formulated as a combinatorial hypotheses search with a heuristic evaluation function taking into account structural and specific information such as distance, shape, colour, etc. In order to guarantee real time performance, the search process has a time limit to explore alternative solutions. This time defines the upper bound of the number of evaluations depending on the efficiency of the search algorithm. Estimation Distribution Algorithms (EDA) is proposed as an efficient Evolutionary Computation technique to search in this hypothesis space. Then, an exhaustive comparison of the performance of alternative algorithms is carried out considering complex representative situations in real video sets. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Ashlock5:2008:cec, author = "Daniel A. Ashlock and Kenneth M. Bryden and Steven Corns", title = "Small Population Effects and Hybridization", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0599.pdf}, url = {}, size = {}, abstract = {This paper examines the confluence of two lines of research that seek to improve the performance of evolutionary computation systems through management of information flow. The first is hybridisation; the second is using small population effects. Hybridisation consists of restarting evolutionary algorithms with copies of bestof- population individuals drawn from many populations. Small population effects occur when an evolutionary algorithm's performance, either speed or probability of premature convergence, is improved by use of a very small population. This paper presents a structure for evolutionary computation called a blender which performs hybridisation of many small populations. The blender algorithm is tested on the PORS and Tartarus tasks. Substantial and significant effects result from varying the size of the small populations used and from varying the frequency with which hybridisation is performed. The major effect results from changing the frequency of hybridization; the impact of population size is more modest. The parameter settings which yield best performance of the blender algorithm are remarkably consistent across all seven sets of experiments performed. Blender performance is found to be superior to other algorithms for six cases of the PORS problem. For Tartarus, blender performs well, but not as well as the previous hybridization experiments that motivated its development. }, keywords = {genetic algorithms, genetic programming}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Gallagher:2008:cec, author = "John C. Gallagher and Kshitij S. Deshpande and Mitch Wolff", title = "An Adaptive Neuromorphic Chip for Augmentative Control of Air Breathing Jet Turbine Engines", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0602.pdf}, url = {}, size = {}, abstract = {Continuous Time Recurrent Neural Network Evolvable Hardware (CTRNN-EH) has been proposed as an enabling control technology for electromechanical devices. In addition to being able to learn control laws tabula rasa, CTRNNs can learn how to augment existing, trusted, controllers to add new capabilities without breaking existing operation. The ability to augment would be most useful in situations in which significant patching of existing controllers is needed to address contingencies not seen at design time and in which traditional design processes might be too slow to deliver quickly. In this paper, we will discuss the use of CTRNN-EH to augment a standard FADEC controller for an air-breathing jet turbine engine. We will show how we were able to extend the FADEC to properly control thrust under unusual loading conditions that were not considered at design time. Following, we will discuss future applications. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Dorronsoro:2008:cec, author = "Bernabe Dorronsoro and Enrique Alba and Gabriel Luque and Pascal Bouvry", title = "A Self-Adaptive Cellular Memetic Algorithm for the DNA Fragment Assembly Problem", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0604.pdf}, url = {}, size = {}, abstract = {The DNA fragment assembly problem is to reconstruct a DNA chain from multiple fragments that have previously been sequenced in a laboratory. This is a critical step in any genomic project, since the resulting chains are the basis of all the work. Therefore, the quality of these chains is a prime importance to the correct development of the project. The methods typically applied to this problem usually encounter difficulties on large instances, so more efficient techniques are necessary. In this context, this work proposes a new method combining a general purpose metaheuristic (an advanced cellular genetic algorithm which automatically regulates the intensity of the search) with a local search method specifically designed for this problem (PALS). This local search method (recently published) finds very accurate solutions in very short times. As a result, our proposal is a very accurate and efficient hybrid technique clearly outperforming the other existing ones. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Tayarani:2008:cec, author = "N. M. H. Tayarani and T. M. R. Akbarzadeh", title = "Magnetic Optimization Algorithms a New Synthesis", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0605.pdf}, url = {}, size = {}, abstract = {A novel optimization algorithm is proposed here that is inspired by the principles of magnetic field theory. In the proposed Magnetic Optimization Algorithm (MOA) the possible solutions are magnetic particles scattered in the search space. Each magnetic particle has a measure of mass and magnetic field according to its fitness. The fitter magnetic particles are those with higher magnetic field and higher mass. These particles are located in a lattice-like environment and apply a force of attraction to their neighbors. The proposed cellular structure allows a better exploitation of local neighborhoods before they move towards the global best, hence it increases population diversity. Experimental results on 14 numerical benchmark functions show that MOA in some benchmark functions can work better than GA and PSO. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Tayarani2:2008:cec, author = "N. M. H. Tayarani and T. M. R. Akbarzadeh", title = "A Cellular Structure and Diversity Preserving Operator in Quantum Evolutionary Algorithms", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0606.pdf}, url = {}, size = {}, abstract = {A Diversity Preserving Cellular Quantum Evolutionary Algorithm (DPCQEA) is proposed in which the quantum individuals are located in a specific topology and interact only with their neighbors. The proposed cellular structure aims to provide a better exploitation of local neighborhoods before moving towards a global best, hence it increases population diversity. This paper also proposes a new operator for diversity preservation in the population. In standard QEA the diversity in the population decreases across the generations. Decreasing the diversity of the population decreases the exploration performance of the algorithm and causes possible algorithm trapping in the local optima. In the proposed algorithm, only the fittest of converged q-individuals from among similar individuals are preserved, while others are reinitialized. A criterion is then proposed to measure convergence and similarity among individuals. Experimental results on Knapsack Problem, Trap Problem as well as 14 Numerical benchmark functions show that DPCQEA consistently exceeds the performance of QEA. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Morales-Reyes:2008:cec, author = "Alicia Morales-Reyes and Evangelos F. Stefatos and Ahmet T. Erdogan and Tughrul Arslan", title = "Fault Tolerant Cellular Genetic Algorithm", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0607.pdf}, url = {}, size = {}, abstract = {This paper presents a cellular Genetic Algorithm (cGA) which aims at realizing a fault tolerant platform based on the inherent ability of cGAs to deal with Single Hard Errors (SHE) that could permanently affect the operation of a system. To attain this objective it is indispensable to control the parameters of the cGA which directly affect the efficiency and accuracy of its search process. Among the overall set of parameters, the migration rate and frequency, the grid size, and the shape and size of local neighbourhoods have a remarkable effect on the cGA performance. By appropriately controlling these parameters, the complex search space (presenting multi-peak fitness-function) associated with the practical case study of the investigation herein presented, is conveniently explored in terms of efficiency and efficacy. Initially, fitness score registers have been identified as critical for proper system's operation. In case, SHEs occur at these registers, the algorithm will ignore possible good solutions and rapidly spread bad individuals. Experiments results show the faults effects regarding convergence time, search rate and results accuracy, as well as the cGA improvement on faulty scenarios when migration is applied following different selection and replacement criteria or increasing selection intensity through different local neighbourhoods configurations. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wong:2008:cec, author = "Phillip Wong and Mengjie Zhang", title = "SCHEME: Caching Subtrees in Genetic Programming", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0608.pdf}, url = {}, size = {}, abstract = {This paper introduces SCHEME (Sub-tree Caching using a Hashing for Equivalence MEthod), a method of caching program subtrees while taking into consideration algebraic equivalences between these programs. By using hashing in order to estimate algebraic equivalence between subtrees, we develop a hash table based caching mechanism which is easily integrated with the standard GP system. Experiments are performed on two regression and four classification tasks of varying difficulty. The results suggest that using SCHEME significantly reduces the number of node evaluations performed during the GP runs, which in turn leads to a faster GP training process. }, keywords = {genetic algorithms, genetic programming}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Epitropakis:2008:cec, author = "M. G. Epitropakis and V. P. Plagianakos and M. N. Vrahatis", title = "Balancing the Exploration and Exploitation Capabilities of the Differential Evolution Algorithm", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0609.pdf}, url = {}, size = {}, abstract = {The hybridisation and composition of different Evolutionary Algorithms to improve the quality of the solutions and to accelerate execution is a common research practice. In this paper we propose a hybrid approach that combines Differential Evolution mutation operators in an attempt to balance their exploration and exploitation capabilities. Additionally, a self-balancing hybrid mutation operator is presented, which favours the exploration of the search space during the first phase of the Optimization, while later opts for the exploitation to aid convergence to the optimum. Extensive experimental results indicate that the proposed approaches effectively enhance DE's ability to accurately locate solutions in the search space. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Vergidis2:2008:cec, author = "Theodoros Vergidis and Kostas Vergidis and Ashutosh Tiwari", title = "The Evaluation Line: A Posteriori Preference Articulation Approach", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0610.pdf}, url = {}, size = {}, abstract = {This paper presents the concept of the evaluation line - a posteriori preference articulation approach for evaluating Pareto-optimal solutions using high level preference criteria. The evaluation line is an approach based on analytical geometry. It is sketched using a weighted function that prioritizes the different objectives by assigning unique weights. It then evaluates each Pareto-optimal solution based on its point-line distance from the line. Based on its function, the evaluation line leans appropriately to demonstrate preference towards one or more objectives and its function is extended to the n-objective space. The evaluation line is compared with the classical weighted sum approach. The comparison demonstrates the relativity of the two approaches but also highlights the strength of the evaluation line in the cases of nonconvex Pareto-optimal fronts. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Stefatos:2008:cec, author = "Evangelos F. Stefatos and Tughrul Arslan and Alister Hamilton", title = "Evolutionary Techniques for Precise and Real-Time Implementation of Low-Power FIR Filters", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0611.pdf}, url = {}, size = {}, abstract = {This paper presents an evolutionary based reconfigurable framework that aims at implementing and reconfiguring precise and low-power FIR filters within short amount of time. Five evolutionary techniques are evaluated for their efficiency to drive the evolution of FIR filters upon the same custom reconfigurable hardware substrate. From a hardware perspective, our architecture composes a novel topology that achieves hardware economy and does not introduce hardware dependencies between different coefficients within the targeted coefficient-set. Three novel evolutionary techniques are proposed that guarantee accurate, prompt and low-power implementation of FIR filters. Each evolutionary technique mainly emphasises on one or two out of the three investigated parameters (accuracy, power-consumption and real-time adaptation) and hence the designer can select one of these techniques, based on the nature and the needs of the targeted application. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Graff:2008:cec, author = "Daniel Graff and Ronaldo Menezes and Robert Tolksdorf", title = "On the Performance of Swarm-Based Tuple Organization in Linda Systems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0612.pdf}, url = {}, size = {}, abstract = {Coordination systems have been gaining popularity since the early 80s with the introduction of the Linda coordination model. Soon after its introduction researchers and practitioners alike started to realize that coordination is ubiquitous to any distributed systems but unfortunately partially responsible for the inefficiency found in these systems-coordination deals with costly issues such as process communication and synchronization. In the beginning of this decade, researchers looked for alternatives for implementing more efficient means of coordination; they turned to Swarm Intelligence; the first of these approaches was called Swarmlinda. Performance of Linda-based coordination systems is directly related to the issue of system entropy (tuple organization). Swarmlinda approaches for tuple organization are investigated in this paper using a simulator. After a careful study on the performance of Swarmlinda, we introduce modifications on the algorithm so as to achieve better entropic levels. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Someya:2008:cec, author = "Hiroshi Someya ", title = "Theoretical Parameter Value for Appropriate Population Variance of the Distribution of Children in Real-coded GA", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0613.pdf}, url = {}, size = {}, abstract = {The purposes of this paper are to discuss theoretical parameter value for crossover operators in real-coded GAs (RCGAs) and to bridge the gap between earlier related studies. Crossover operator in RCGAs has at least one parameter that forms its probability distribution function. The appropriateness of the value for this parameter affects optimization performance of RCGA. To obtain suitable parameter value, some manners have been reported. However, they have confused us by their several differences, such as the reference point often used as the first choice to be tuned. This paper has theoretically introduced a constraint that explains that these manners are essentially identical. Parameter values determined under this constraint have been empirically confirmed that they satisfy requirements of the manners. Experiments on several test functions have supported that such parameter values are suitable for the reference point. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Almosallam:2008:cec, author = "Ibrahim A. Almosallam and Yi Shang", title = "A New Adaptive Framework for Collaborative Filtering Prediction", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0615.pdf}, url = {}, size = {}, abstract = {Collaborative filtering is one of the most successful techniques for recommendation systems and has been used in many commercial services provided by major companies including Amazon, TiVo and Netflix. In this paper we focus on memory-based collaborative filtering (CF). Existing CF techniques work well on dense data but poorly on sparse data. To address this weakness, we propose to use z-scores instead of explicit ratings and introduce a mechanism that adaptively combines global statistics with item-based values based on data density level. We present a new adaptive framework that encapsulates various CF algorithms and the relationships among them. An adaptive CF predictor is developed that can self adapt from user-based to item-based to hybrid methods based on the amount of available ratings. Our experimental results show that the new predictor consistently obtained more accurate predictions than existing CF methods, with the most significant improvement on sparse data sets. When applied to the Netflix Challenge data set, our method performed better than existing CF and singular value decomposition (SVD) methods and achieved 4.67percent improvement over Netflix's system. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wijesinghe:2008:cec, author = "Gayan Wijesinghe and Shahrul Badariah Mat Sah and Vic Ciesielski", title = "Grid vs. Arbitrary Placement of Tiles for Generating Animated Photomosaics", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0617.pdf}, url = {}, size = {}, abstract = {A traditional photo-mosaic is a still image where a larger picture is created by selectively arranging small picture tiles on a blank, gridded canvas. We show interesting and engaging animations can be generated from an evolutionary search for the final photomosaic image. We then investigate two different tile placement strategies for generating the animations. In the first strategy tiles can only be placed in fixed cells in a 2 dimensional grid and it is not possible for tiles to overlap. This strategy is implemented with a genetic algorithm. In the second strategy, which is implemented using genetic programming, the tiles can be placed in any position and at an arbitrary rotation. It is possible for one tile to be placed on top of another so a method for dealing with overlap is needed. We have investigated three methods for dealing with overlap. The second strategy generates more engaging animations but at considerably increased computational cost. We conclude that evolutionary search can be used to produce very engaging animations in which a target image gradually emerges from an initial random collection of tiles.}, keywords = {genetic algorithms, genetic programming}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Pinto:2008:cec, author = "Pedro C. Pinto and Andreas Nägele and Mathäus Dejori and Thomas A. Runkler and João M. C. Sousa", title = "Learning of Bayesian Networks by a Local Discovery Ant Colony Algorithm", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0618.pdf}, url = {}, size = {}, abstract = {Bayesian networks (BNs) are knowledge representation tools capable of representing dependence or independence relationships among random variables that compose a problem domain. Bayesian networks learned from data sets are receiving increasing attention within the community of researchers of uncertainty in artificial intelligence, due to their capacity to provide good inference models and to discover the structure of complex domains. One approach to learning BNs from data is to use a scoring metric to evaluate the fitness of any given candidate network for the database, and apply an optimization procedure to explore the set of candidate networks. Among the most frequently used optimization methods for this purpose is greedy search, either deterministic or stochastic. This article proposes a hybrid Bayesian network learning algorithm MMACO, based on the local discovery algorithm Max-Min Parents and Children (MMPC) and ant colony optimization (ACO). MMPC is used to construct the skeleton of the Bayesian network and then ACO is used to orientate its edges, thus returning the final structure. We apply MMACO (Max-Min ACO) to several sets of benchmark networks and show that it outperforms greedy search (GS) and simulated annealing (SA) algorithms. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Yang9:2008:cec, author = "Erfu Yang and Nick H. Barton and Tughrul Arslan and Ahmet T. Erdogan", title = "A Novel Shifting Balance Theory-Based Approach to Optimization of an Energy-Constrained Modulation Scheme for Wireless Sensor Networks", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0619.pdf}, url = {}, size = {}, abstract = {This paper presents a new approach to optimization of an energy-constrained modulation scheme for wireless sensor networks by taking advantage of a novel bio-inspired optimization algorithm. The algorithm is inspired by Wright's shifting balance theory (SBT) of evolution in population genetics. The total energy consumption of an energy-constrained modulation scheme is minimized by using the new SBT-based optimization algorithm. The results obtained by this new algorithm are compared with other popular optimization algorithms. Numerical experiments are performed to demonstrate that the SBT-based algorithm could be used as an efficient optimizer for solving the optimization problems arising from currently emerging energy-efficient wireless sensor networks. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Tapia:2008:cec, author = "Jose Juan Tapia and Edgar E. Vallejo", title = "A Clustering Genetic Algorithm for Inferring Protein-Protein Functional Interactions from Phylogenetic Profiles", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0620.pdf}, url = {}, size = {}, abstract = {This paper explores the capabilities of genetic algorithms for clustering genomic data. We conducted a series of computational experiments on reconstructing proteinprotein interactions from phylogenetic profiles. We validated the proposed model using experimentally confirmed functional associations and known bacterial operons. Experimental results demonstrated that clustering genetic algorithms produce competitive results with respect to traditional clustering methods. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Li17:2008:cec, author = "Rui Li and Michael T. M. Emmerich and Jeroen Eggermont and Ernst G. P. Bovenkamp and Jouke Dijkstra and Johan H. C. Reiber", title = "Metamodel-Assisted Mixed Integer Evolution Strategies and Their Application to Intravascular Ultrasound Image Analysis", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0623.pdf}, url = {}, size = {}, abstract = {This paper discusses mixed integer evolution strategies (MIES) assisted by metamodels based on radial basis function networks (RBFN). The goal is to make MIES more suitable for optimization with time consuming evaluation functions. A novelty of the presented research is that RBFN are studied for metamodeling in heterogeneous (mixed-integer) parameter spaces. A heterogeneous metric (HEOM) is adopted that is in conformity with the design of the MIES. In addition, crossvalidation based optimization techniques are suggested for adjusting hyper-parameters of the model and avoid singularities. Empirical studies on prediction of random sets indicate good prediction capabilities of the proposed RBFN for functional landscapes of moderate dimension/smoothness. The influence of the training set size as well as of the dimension on computational complexity and accuracy of the RBFN is investigated. In the metamodel-assisted MIES, a RBFN metamodel is build and updated after each generation. The metamodel is used for selecting a small subset of offspring individuals from a bigger set of variations and thereby increase the number of promising solutions in the offspring population. The algorithm is designed in a way that in case of failure of the metamodel (e.g. ''random'' predictions) the metamodel-assisted MIES behaves like a standard MIES. Experimental results, both on artificial test problems and a real world application, namely the optimization of feature detectors in ultrasound images, indicate a clear acceleration that can be achieved by using heterogeneous RBFN. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Pietro:2008:cec, author = "Anthony Di Pietro and Luigi Barone and Lyndon While", title = "On the Behaviour of Evolutionary Strategies for Problems with Varying Noise Strength", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0624.pdf}, url = {}, size = {}, abstract = {For many real-world applications of evolutionary computation, the fitness function is obscured by random noise which may vary throughout the search space. Previously, we presented algorithms that were significantly better than naive resampling, but found (perhaps counter-intuitively) that for some problems it is better to use a higher resampling rate where the noise strength is lower and vice versa. This paper analyses why this is the case, and explores how the evolutionary process works differently on these problems. We show why it is often the case that using a high resampling rate in high noise regions is ineffective and it is instead better to use these samples in low noise regions. We conclude that when applying a basic evolutionary strategy to this class of problems, it is only better to use higher resampling rates where the noise strength is higher if it is too difficult to reach a good solution without searching in or through the high noise regions. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Isaacs:2008:cec, author = "Amitay Isaacs and Tapabrata Ray and Warren Smith", title = "Blessings of Maintaining Infeasible Solutions for Constrained Multi-Objective Optimization Problems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0625.pdf}, url = {}, size = {}, abstract = {The most common approach to handling constraints in a constrained optimization problem has been the use of penalty functions. In recent years non-dominance based ranking methods have been applied for an efficient handling of constraints. These techniques favor the feasible solutions over the infeasible solutions, thus guiding the search through the feasible space. Usually the optimal solutions of the constrained optimization problems are spread along the constraint boundary. In this paper we propose a constraint handling method that maintains infeasible solutions in the population to aid the search of the optimal solutions through the infeasible space. The constraint handling method is implemented in Constraint Handling Evolutionary Algorithm (CHEA), which is the modified Non-dominated Sorting Genetic Algorithm II (NSGAII) [1]. The original constrained minimization problem with k objectives is reformulated as an unconstrained minimization problem with k+1 objectives, where an additional objective function is the number of constraint violations. In CHEA, the infeasible solutions are ranked higher than the feasible solutions, thereby focusing the search for the optimal solutions near the constraint boundaries through infeasible region. CHEA simultaneously obtains the solutions to the constrained as well as the unconstrained optimization problem. The performance of CHEA is compared with NSGA-II on the set of CTP test problems. For a fixed number of function evaluations, CHEA converges to the Pareto optimal solutions much faster than NSGA-II. It is observed that retaining even a small number of infeasible solutions in the population, CHEA is able to prevent the search from prematurely converging to a sub-optimal Pareto front. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Huang7:2008:cec, author = "Hui-Ling Huang and Kuan-Wei Chen and Shinn-Jang Ho and Shinn-Ying Ho", title = "Inferring S-system Models of Genetic Networks from a Time-Series Real Data Set of Gene Expression Profiles", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0628.pdf}, url = {}, size = {}, abstract = {It is desirable to infer cellular dynamic regulation networks from gene expression profiles to discover more delicate and substantial functions in molecular biology, biochemistry, bioengineering, and pharmaceutics. The S-system model is suitable to characterize biochemical network systems and capable of analyzing the regulatory system dynamics. To cope with the problem ``multiplicity of solutions'', a sufficient amount of data sets of time-series gene expression profiles were often used. An efficient newly-developed method iTEA was proposed to effectively obtain S-system models from a large number (e.g., 15) of simulated data sets with/without noise. In this study, we propose an extended optimization method (named iTEAP) based on iTEA to infer the S-system models of genetic networks from a time-series real data set of gene expression profiles (using SOS DNA microarray data in E.coli as an example). The algorithm iTEAP generated additionally multiple data sets of gene expression profiles by perturbing the given data set. The results reveal that (1) iTEAP can obtain S-system models with high-quality profiles to best fit the observed profiles; (2) the performance of using multiple data sets is better than that of using a single data set in terms of solution quality, and 3) the effectiveness of iTEAP using a single data set is close to that of iTEA using two real data sets. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Zhang11:2008:cec, author = "Jingqiao Zhang and Viswanath Avasarala and Arthur C. Sanderson and Tracy Mullen", title = "Differential Evolution for Discrete Optimization: An Experimental Study on Combinatorial Auction Problems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0629.pdf}, url = {}, size = {}, abstract = {Differential evolution (DE) mutates solution vectors by the weighted difference of other vectors using arithmetic operations. As these operations cannot be directly extended to discrete combinatorial space, DE algorithms have been traditionally applied to optimization problems where the search space is continuous. In this paper, we use JADE, a self-adaptive DE algorithm, for winner determination in Combinatorial Auctions (CAs) where users place bids on combinations of items. To adapt JADE to discrete optimization, we use a rank-based representation schema that produces only feasible solutions and a regeneration operation that constricts the problem search space. It is shown that JADE compares favorably to a local stochastic search algorithm, Casanova, and a genetic algorithm based approach, SGA. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Zhang12:2008:cec, author = "Jingqiao Zhang and Arthur C. Sanderson", title = "Self-Adaptive Multi-Objective Differential Evolution with Direction Information Provided by Archived Inferior Solutions", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0630.pdf}, url = {}, size = {}, abstract = {We propose a new self-adaptive differential evolution algorithm for multi-objective optimization problems. To address the challenges in multi-objective optimization, we introduce an archive to store recently explored inferior solutions whose difference with the current population is used as direction information about the optimum, and also consider a fairness measure in calculating crowding distances to prefer the solutions whose distances to nearest neighbors are large and close to be uniform. As a result, the obtained solutions can spread well over the computed non-dominated front and the front can be moved fast toward the Pareto-optimal front. In addition, the control parameters of the algorithm are adjusted in a self-adaptive manner, avoiding parameter tuning for problems of different characteristics. The proposed algorithm, named JADE2, achieves better or at least competitive results compared to NSGA-II and GDE3 for a set of twenty-two benchmark problems. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Neshatian:2008:cec, author = "Kourosh Neshatian and Mengjie Zhang", title = "Genetic Programming for Performance Improvement and Dimensionality Reduction of Classification Problems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0631.pdf}, url = {}, size = {}, abstract = {In this paper, Genetic programming (GP) is used to construct a new set of high level features based on the original attributes of a classification problem with the goal of improving the classification performance and reducing the dimensionality. A non-wrapper approach is taken and a new fitness function is proposed based on the Renyi entropy. The GP system uses a variable terminal pool which is constructed by the class-wise orthogonal transformations of the original features. The performance measure is classification accuracy on 12 benchmark problems using constructed features in a decision tree classifier. The performance over difficult problems has been improved by constructing features for compound classes. This approach is compared with the principle component analysis (PCA) method and the results show that the new approach outperforms the PCA method on most of the problems in terms of classification performance and dimensionality reduction. }, keywords = {genetic algorithms, genetic programming}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Larson:2008:cec, author = "Eric C. Larson and Gary G. Yen", title = "Facial Feature Analysis in Dynamic Bandwidth Environments: A Genetic Approach", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0633.pdf}, url = {}, size = {}, abstract = {Facial feature tracking for model-based coding has evolved over the past decades. Of particular interest is its application in very low bit rate coding in which optimization is used to analyze head and shoulder sequences. We present the results of a computational experiment in which we apply a combination of non-dominated sorting genetic algorithm (NSGAII) and a deterministic search to find optimal facial animation parameters at many bandwidths, simultaneously. As objective functions are concerned, peak signal-to-noise ratio is chosen to be maximized while the total number of facial animation parameters is chosen to be minimized. Particularly, the algorithm is tested for efficiency and reliability. The results show that the overall methodology works effectively, but that a better error assessment function is needed. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Au:2008:cec, author = "Chun-Kit Au and Ho-Fung Leung", title = "On the Behavior of Cooperative Coevolution in Dynamic Environments", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0634.pdf}, url = {}, size = {}, abstract = {This paper investigates the behavior of cooperative coevolutionary algorithms (CCEAs) under dynamic environments. The backgroud of dynamic optimization and the approaches used in evolutionary algorithms (EAs) to address dynamic environments are first briefly reviewed. Two common approaches, including hypermutations and random immigrants, are incorporated into CCEAs to solve two dynamic problems: one moving peak problem and two moving peaks problem. The performance on these two problems under different change severities and different change periods are empirically compared with those of the EA counterparts. Experimental results indicate that using cooperative coevolutionary approach can generally provide a better performance than the EA conterparts. In particular, CCEA with the use of random immigrants consistently outperforms other algorithms we study. The reasons behind these observations are analyzed by studying the best-of-generation fitness against generations and the trajectories of best-of-generation individuals when tracking the moving optima in the search space. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Soliman:2008:cec, author = "Omar S. Soliman and Lam T. Bui", title = "A Self-Adaptive Strategy for Controlling Parameters in Differential Evolution", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0635.pdf}, url = {}, size = {}, abstract = {The Differential Evolution (DE) is a stochastic population-based search method for global optimization over continuous spaces. This paper presents an efficient strategy for self-adapting control parameters in Differential Evolution to solve real-parameter optimization problems. The proposed strategy introduces an adaptive mechanism at the individual level based on Cauchy distribution(CD) where the step length and crossover rate are self-adapted during the evolution process. This strategy is to use attractive features of CD, which has thick tails that enable it to generate considerable changes more frequently and to escape a local optima for multi-modal optimization problems. Detailed performance comparisons of a DE using the proposed strategy on wide range of fifteen standard benchmark test problems are carried out. The obtained results showed that the performance of the DE had been improved with the proposed self-adaptive strategy. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Martí:2008:cec, author = "Luis Martí and Jesús García and Antonio Berlanga and Jose M. Molina ", title = "Model-Building Algorithms for Multiobjective EDAs: Directions for Improvement", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0637.pdf}, url = {}, size = {}, abstract = {In order to comprehend the advantages and shortcomings of each model-building algorithm they should be tested under similar conditions and isolated from the MOEDA it takes part of. In this work we will assess some of the main machine learning algorithms used or suitable for model-building in a controlled environment and under equal conditions. They are analyzed in terms of solution accuracy and computational complexity. To the best of our knowledge a study like this has not been put forward before and it is essential for the understanding of the nature of the model-building problem of MOEDAs and how they should be improved to achieve a quantum leap in their problem solving capacity. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Matsui:2008:cec, author = "Shouichi Matsui and Seiji Yamada", title = "A Genetic Algorithm for Optimizing Hierarchical Menus", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0639.pdf}, url = {}, size = {}, abstract = {Hierarchical menus are widely used as a standard user interface in modern applications that use GUIs. The performance of the menu depends on many factors: structure, layout, colours and so on. There has been extensive research on novel menus, but there has been little work on improving performance by optimizing the menu's structure. This paper proposes algorithms based on the genetic algorithm (GA) and the simulated annealing (SA) for optimizing the performance of menus. The algorithms aim to minimize the average selection time of menu items by considering the user's pointer movement and search/decision time. We will show the results on a static hierarchical menu of a cellular phone as an example where a small screen and limited input device are assumed. We will also show performance comparison of GA-based algorithm and the SA-based one by using wide variety of the useage patterns. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Xie:2008:cec, author = "Huayang Xie and Mengjie Zhang and Peter Andreae", title = "An Analysis of the Distribution of Swapped Subtree Sizes in Tree-based Genetic Programming", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0641.pdf}, url = {}, size = {}, abstract = {This paper analyses the distribution of swapped subtree sizes involved in crossover events in approximations of an optimal crossover operator that allows the root node to be crossed over. The goal is to examine how the offspring search space can be effectively reduced for given parents. It concludes that good crossover events have a strong preference for the roots of the parent programs and for nodes with small sub-trees. This paper also quantifies the ability of crossover to optimise offspring fitness, and concludes that this ability is far below what was expected. }, keywords = {genetic algorithms, genetic programming}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Aghdam:2008:cec, author = "Mehdi Hosseinzadeh Aghdam and Nasser Ghasem-Aghaee and Mohammad Ehsan Basiri", title = "Application of Ant Colony Optimization for Feature Selection in Text Categorization", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0642.pdf}, url = {}, size = {}, abstract = {Feature selection is commonly used to reduce dimensionality of datasets with tens or hundreds of thousands of features. A major problem of text categorisation is the high dimensionality of the feature space; therefore, feature selection is the most important step in text categorisation. This paper presents a novel feature selection algorithm that is based on ant colony Optimization. Ant colony Optimization algorithm is inspired by observation on real ants in their search for the shortest paths to food sources. Proposed algorithm is easily implemented and because of use of a simple classifier in that, its computational complexity is very low. The performance of proposed algorithm is compared to the performance of information gain and CHI algorithms on the task of feature selection in Reuters-21578 dataset. Simulation results on Reuters-21578 dataset show the superiority of the proposed algorithm. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wu4:2008:cec, author = "Xiaolan Wu and Binggang Cao and Jianping Wen and Zhanbin Wang ", title = "Application of Particle Swarm Optimization for Component Sizes in Parallel Hybrid Electric Vehicles", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0643.pdf}, url = {}, size = {}, abstract = {This paper describes an approach for the optimization of parallel Hybrid Electric Vehicle (HEV) component sizing using Particle Swarm Optimization (PSO) algorithm. In this study, the fitness function is defined to minimize the vehicle engine fuel consumption (FC) and emissions. The driving performance requirements are then considered as constraints. Finally, the optimization process is performed over the test procure TEST_CYCLE_HYWT, in which a vehicle model named ADVISOR is used as the analysis tool. The results from the computer simulation show the effectiveness of the approach and reduction in FC, emissions while ensuring that the vehicle performance is not sacrificed }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Du:2008:cec, author = "Zhihua Du and Yiwei Wang and Zhen Ji", title = "Gene Clustering Using an Evolutionary Algorithm", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0644.pdf}, url = {}, size = {}, abstract = {Microarray technology enables the study of measuring gene expression levels for thousands of genes simultaneously. Cluster analysis of gene expression profiles has been applied for analyzing the function of gene because co-expressed genes are likely to share the same biological function. K-MEANS is one of well-known clustering methods. However, it requires a precise estimation of number of clusters and it has to assign all the genes into clusters. Other main problems are sensitive to the selection of an initial clustering and easily becoming trapped in a local minimum. We present a new clustering method for microarray gene data, called ppoCluster. It has two steps: (1) Estimate the number of clusters (2) Take sub-clusters resulting from the first step as input, and bridge a variation of traditional Particle Swarm Optimization (PSO) algorithm into K-MEANS for particles perform a parallel search for an optimal clustering. Our results indicate that ppoCluster is generally more accurate than K-MEANS and FKM. It also has better robustness for it is less sensitive to the initial randomly selected cluster centroids. And it outperforms comparable methods with fast convergence rate and low computation load. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Xu5:2008:cec, author = "Jinhong Xu and Weijun Xu and Jinling Li and Yucheng Dong", title = "Competitive Algorithms About Online Reverse Auctions", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0645.pdf}, url = {}, size = {}, abstract = {Similar to the concept of on-line auctions presented by Ron Lavi and Noam Nisan [3], this paper discusses pricing algorithms for on-line reverse auction which bidders arrive one by one and on-line buyer must be required to make a decision immediately about each bid as it is received. For online buyer in a reverse auction, we propose on-line mean pricing algorithm and on-line randomized pricing algorithm, and then prove that the two algorithms are competitive and incentive compatible. Moreover, as the bid prices concentrated in a small domain, by competitive analysis for the two algorithms, we find their merits which can avoid the results of purchasing failure or more cost caused by reservation price algorithm. Finally, an example is obtained to illustrate their application. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Lee2:2008:cec, author = "Chi-Ho Lee and Ye-Hoon Kim and Jong-Hwan Kim", title = "Multiobjective Evolutionary Algorithm Reinforcing Specific Objective", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0646.pdf}, url = {}, size = {}, abstract = {This paper proposes a multiobjective evolutionary algorithm (MOEA) for the problem with many objectives, where each objective is more strengthened. In the real world applications, satisfying as many objectives as possible somewhat at the same time can be less preferred than optimizing each specific objective individually. To solve this kind of problems, this paper proposes the complement of (1-k) dominance and the pruning method considering objective deviation to get a set of nondominated solutions with specifically optimized objectives. Promoting the specificity of objective improves the optimization performance on problems with many objectives. In experimental results, proposed algorithm shows improved performance compared with the state-of-the-art MOEAs such as SPEA, SPEA2 and NSGA2. The performance is measured in terms of the solution set coverage and the closeness to the true Pareto front. Also, diversity metric is applied to verify the spread of nondominated set. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Li18:2008:cec, author = "Boyang Li and Yew-Soon Ong and Minh Nghia Le and Chi Keong Goh", title = "Memetic Gradient Search", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0648.pdf}, url = {}, size = {}, abstract = {This paper reviews the different gradient-based schemes and the sources of gradient, their availability, precision and computational complexity, and explores the benefits of using gradient information within a memetic framework in the context of continuous parameter optimization, which is labeled here as Memetic Gradient Search. In particular, we considered a quasi-Newton method with analytical gradient and finite differencing, as well as simultaneous perturbation stochastic approximation, used as the local searches. Empirical study on the impact of using gradient information showed that Memetic Gradient Search outperformed the traditional GA and analytical, precise gradient brings considerable benefit to gradient-based local search (LS) schemes. Though gradient-based searches can sometimes get trapped in local optima, memetic gradient searches were still able to converge faster than the conventional GA. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Alfaro-Cid:2008:cec, author = "E. Alfaro-Cid and P. A. Castillo and A. Esparcia and K. Sharman and J. J. Merelo and A. Prieto and J. L. J. Laredo", title = "Comparing Multiobjective Evolutionary Ensembles for Minimizing Type I and II Errors for Bankruptcy Prediction", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0649.pdf}, url = {}, size = {}, abstract = {In many real world applications type I (false positive) and type II (false negative) errors have to be dealt with separately, which is a complex problem since an attempt to minimise one of them usually makes the other grow. In fact, a type of error can be more important than the other, and a trade-off that minimises the most important error type must be reached. In the case of the bankruptcy prediction problem the error type II is of greater importance, being unable to identify that a company is at risk causes problems to creditors and slows down the taking of measures that may solve the problem. Despite the importance of type II errors, most bankruptcy prediction methods take into account only the global classification error. In this paper we propose and compare two methods to optimise both error types in classification: artificial neural networks and function trees ensembles created through multiobjective Optimization. Since the multiobjective Optimization process produces a set of equally optimal results (Pareto front) the classification of the test patterns in both cases is based on the non-dominated solutions acting as an ensemble. The experiments prove that, although the best classification rates are obtained using the artificial neural network, the multiobjective genetic programming model is able to generate comparable results in the form of an analytical function. }, keywords = {genetic algorithms, genetic programming}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Sathe:2008:cec, author = "Madan Sathe and Günter Rudolph and Kalyanmoy Deb", title = "Design and Validation of a Hybrid Interactive Reference Point Method for Multi-Objective Optimization", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0650.pdf}, url = {}, size = {}, abstract = {This paper offers a classification of the main representatives of interactive classical and evolutionary methods. After a crossfertilization of these two fields a new hybrid interactive reference point method is designed. The method combines the reference point idea with the relative speed of a (1+1)- EA and is implemented with a graphical user interface. Finally, it is validated on two well-known real-world test problems. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Greeff:2008:cec, author = "Marde Greeff and Andries P. Engelbrecht", title = "Solving Dynamic Multi-Objective Problems with Vector Evaluated Particle Swarm Optimisation", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0651.pdf}, url = {}, size = {}, abstract = {Many optimisation problems are multi-objective and change dynamically. Many methods use a weighted average approach to the multiple objectives. This paper introduces the usage of the vector evaluated particle swarm optimiser (VEPSO) to solve dynamic multi-objective optimisation problems. Every objective is solved by one swarm and the swarms share knowledge amongst each other about the objective that it is solving. Not much work has been done on using this approach in dynamic environments. This paper discusses this approach as well as the effect of the population size and the response methods to a detected change on the performance of the algorithm. The results showed that more non-dominated solutions, as well as more uniformly distributed solutions, are found when all swarms are re-intialised when a change is detected, instead of only the swarm(s) optimising the specific objective function(s) that has changed. Furthermore, an increase in population size results in a higher number of non-dominated solutions found, but can lead to solutions that are less uniformly distributed. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Scriven2:2008:cec, author = "Ian Scriven and Andrew Lewis and David Ireland and Junwei Lu", title = "Decentralised Distributed Multiple Objective Particle Swarm Optimisation Using Peer-to-Peer Networks", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0652.pdf}, url = {}, size = {}, abstract = {This paper describes a distributed particle swarm optimisation algorithm (PSO) based on peer-to-peer computer networks. A number of modifications are made to the more traditional synchronous PSO algorithm to allow for fully decentralised, scalable and fault-tolerent operation. The modified algorithm uses staggered propagation of objective-space knowledge between sub-swarms to eliminate the need for a centralised data store. Analytical test functions are used to examine the performance of the proposed algorithm and its variations in comparison with a basic synchronous PSO implementation. The results clearly show the feasibility of decentralised particle swarm optimisation. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Mohemmed:2008:cec, author = "Ammar W. Mohemmed and Mengjie Zhang ", title = "Evaluation of Particle Swarm Optimization Based Centroid Classifier with Different Distance Metrics", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0653.pdf}, url = {}, size = {}, abstract = {The Nearest Centroid Classifier (NCC) is based on finding the arithmetic means of the classes from the training instances and unseen-class instances are classified by measuring the distance to these means. It may work well if the classes are well separated which is not the case for many practical datasets. In this paper, particle swarm optimization (PSO) is used to find the centroids under an objective function to minimize the error of classification. Three different measures are investigated namely the Euclidean distance, the Mahalanobis distance and a Weighted distance to represent the distance function. The performance is tested on eight practical datasets. Simulation results show that the PSO based centroid classifier improves the classification results especially for datasets that the basic NCC does not handle well. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Luo2:2008:cec, author = "Wenjian Luo and Peng Guo and Xufa Wang", title = "On Convergence of Evolutionary Negative Selection Algorithms for Anomaly Detection", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0654.pdf}, url = {}, size = {}, abstract = {Evolutionary Negative Selection Algorithms (ENSAs) are proposed by combining negative selection model and evolutionary operators. In this paper, the convergence of ENSAs with two different mutation operators is analyzed. The first mutation operator is that only one bit of a detector is selected and flipped with a high probability. The second mutation operator is that every bit of a detector has a positive probability to be flipped. The analysis results show that the ENSAs with different mutation operators have different convergent properties. Especially, the shape of the self set will affect the convergence of ENSAs with the first mutation operator. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Zhang13:2008:cec, author = "Min Zhang and Wenjian Luo and Xingxin Pei and Xufa Wang", title = "The Self-Adaption Strategy for Parameter ε in ε-MOEA", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0655.pdf}, url = {}, size = {}, abstract = {A novel self-adaption strategy for the parameter ε in ε-MOEA is proposed in this paper based on the analyses of the relationship between the value of εand the maximum number of non-dominated solutions. Then this novel strategy is applied in ε-MOEA and tested on 10 common benchmark functions. The experimental results demonstrate that even if without the good initial value for the parameter ε, ε-MOEA with this self-adaption strategy (named Algorithm 1) is able to approximately obtain the expected number of non-dominated solutions, which are very close to and uniformly distributed on the Pareto-optimal front. Furthermore, the genetic drift phenomenon in Algorithm 1 is discussed. Two cases of genetic drift are pointed out, and one case can be fixed up by a simple approach proposed in this paper. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Srivastava:2008:cec, author = "Kamal Srivastava and Reeti Sharma", title = "A Hybrid Simulated Annealing Algorithm for the Bipartite Crossing Number Minimization Problem", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0657.pdf}, url = {}, size = {}, abstract = {The bipartite crossing number of a bipartite graph is the minimum number of crossings of edges when the partitions are placed on two parallel lines and edges are drawn as straight line segments between the lines. In this paper, a simulated annealing algorithm is designed which exploits the relationship between the linear arrangement problem and the Bipartite Crossing Number Minimization Problem. The initial ordering of the vertices is provided by the spectral sequencing technique. Extensive tests on several benchmark graphs show that in a majority of the cases there is a considerable improvement in the crossing number when compared with the best known results. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Pan:2008:cec, author = "Quan-Ke Pan and Fatih Tasgetiren and Yun-Chia Liang and P. N. Suganthan", title = "Upper Bounds on Taillard's Benchmark Suite for the No-Wait Flowshop Scheduling Problem with Makespan Criterion", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0659.pdf}, url = {}, size = {}, abstract = {In this paper, the discrete particle swarm optimization (DPSO) algorithm is employed to solve the no-wait flowshop scheduling problem with the makespan criterion for Taillard's benchmark suite [1]. As known, there exist 31 benchmark instances provided by Carlier [2], Heller [3], and Revees [4] for the makespan criterion. However, these benchmarks are relatively small in size and easy to be solved even by a simple descent algorithm. Since there is a lack of a sound benchmark suite for the no-wait flowshop scheduling problem with the makespan criterion, the DPSO algorithm presented by the authors [5] is applied to the 110 benchmark instances of Taillard by treating them as the no-wait flowshop problem instances with the makespan criterion. The DPSO algorithm is hybridized with the variable neighborhood descent (VND) algorithm to further improve the solution quality. Ultimately, we carried out extensive runs and provide the upper bounds for the future researchers to test their algorithms. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Huynh:2008:cec, author = "Hieu Trung Huynh and Yonggwan Won", title = "Hematocrit Estimation from Compact Single Hidden Layer Feedforward Neural Networks Trained by Evolutionary Algorithm", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0661.pdf}, url = {}, size = {}, abstract = {Hematocrit is expressed as the percentage of red blood cells in the whole blood; it is the most highly influencing factor for measuring glucose in the whole blood by handheld devices. This paper presents hematocrit estimation from transduced current curves by using single hidden layer feedforward neural networks (SLFNs). These transduced current curves are produced by glucose-oxidase reaction in electrochemical biosensors which is used in glucose measurements. Points of the current curve sampled at frequency of 10Hz are used as the input features for the networks. Applying neural networks to hematocrit estimation has also proposed in our previous works. However, in this paper, the SLFN is trained by evolutionary least-squares extreme learning machine (ELS-ELM) algorithm in which the input weights and hidden layer biases are determined based on the differential evolution (DE). Experimental results show that the accuracy of hematocrit estimation on ELS-ELM can be improved, from which it can be used to reduce the dependency of hematocrit in measurement of glucose values by handheld devices. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Yan2:2008:cec, author = "Yang Yan and Hongfeng Wang and Dingwei Wang and Shengxiang Yang and Dazhi Wang", title = "A Multi-Agent Based Evolutionary Algorithm in Non-stationary Environments", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0662.pdf}, url = {}, size = {}, abstract = {In this paper, a multi-agent based evolutionary algorithm (MAEA) is introduced to solve dynamic optimization problems. The agents simulate living organism features and co-evolve to find optimum. All agents live in a lattice like environment, where each agent is fixed on a lattice point. In order to increase the energy, agents can compete with their neighbors and can also acquire knowledge based on statistic information. In order to maintain the diversity of the population, the random immigrants and adaptive primal dual mapping schemes are used. Simulation experiments on a set of dynamic benchmark problems show that MAEA can obtain a better performance in non-stationary environments in comparison with several peer genetic algorithms. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Biermann:2008:cec, author = "D. Biermann and K. Weinert and T. Wagner", title = "Model-Based Optimization Revisited: Towards Real-World Processes", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0663.pdf}, url = {}, size = {}, abstract = {The application of empirically determined surrogate models provides a standard solution to expensive optimization problems. Over the last decades several variants based on DACE (Design and Analysis of Computer Experiments) have provided excellent optimization results in cases where only a few evaluations could be made. In this paper these approaches are revisited with respect to their applicability in the optimization of production processes, which are in general multiobjective and allow no exact evaluations. The comparison to standard methods of experimental design shows significant improvements with respect to prediction quality and accuracy in detecting the optimum even if the experimental outcomes are highly distorted by noise. The universally assumed sensitivity of DACE models to nondeterministic data can therefore be refuted. Additionally, a practical example points out the potential of applying EC-methods to production processes by means of these models. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Smart:2008:cec, author = "Will Smart and Mengjie Zhang", title = "Empirical Analysis of Schemata in Genetic Programming using Maximal Schemata and MSG", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0665.pdf}, url = {}, size = {}, abstract = {Plenteous research studies schemata in Genetic Programming (GP), though little of it is been empirical, due to the vast numbers of typical schemata in even small populations. In this research, we define maximal schemata, and extend our Trips algorithm to the more general Max-Schema-Growth (MSG) algorithm, applicable to a wider range of schema forms (Trips only handles standard fragment schemata). We present MSG specialised to work with unordered-fragments schemata (tree-fragments with unordered functions), and compare the number of maximal schemata found of these two forms. For most maximal fragments, another maximal fragment was also found that differed only by the orders of function node arguments. We conclude that maximal unordered-fragments may represent a greater range of common patterns between programs than standard maximal fragments, though the greater reach comes at a price with a severe increase in the time taken by the algorithm. }, keywords = {genetic algorithms, genetic programming}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wu5:2008:cec, author = "Yanghui Wu and John McCall and Paul Godley and Alexander Brownlee and Julie Cowie", title = "Bio-Control in Mushroom Farming Using a Markov Network EDA", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0666.pdf}, url = {}, size = {}, abstract = {In this paper we present an application of an Estimation of Distribution Algorithm (EDA) that uses a Markov network probabilistic model. The application is to the problem of bio-control in mushroom farming, a domain which admits bang-bang-control solutions. The problem is multiobjective and uses a weighted fitness function. Previous work on this problem has applied genetic algorithms (GA) with directed intervention crossover schemes aimed at effective biocontrol at an efficient level of intervention. Here we compare these approaches with the EDA Distribution Estimation Using Markov networks (DEUMd). DEUMd constructs a probabilistic model using Markov networks. Our experiments compare the quality of solutions produced by DEUMd with the GA approaches and also reveal interesting differences in the search dynamics that have implications for algorithm design. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Chicano:2008:cec, author = "Francisco Chicano and Enrique Alba", title = "Finding Liveness Errors with ACO", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0668.pdf}, url = {}, size = {}, abstract = {Model Checking is a well-known and fully automatic technique for checking software properties, usually given as temporal logic formulae on the program variables. Most of model checkers found in the literature use exact deterministic algorithms to check the properties. These algorithms usually require huge amounts of memory if the checked model is large. We propose here the use of an algorithm based on ACOhg, a new kind of Ant Colony Optimization model, to search for liveness property violations in concurrent systems. This algorithm has been previously applied to the search for safety errors with very good results and we apply it here for the first time to liveness errors. The results state that our algorithmic proposal, called ACOhg-live, is able to obtain very short error trails in faulty concurrent systems using a low amount of resources, outperforming by far the results of Nested-DFS, the traditional algorithm used for this task in the model checking community and implemented in most of the explicit state model checkers. This fact makes ACOhg-live a very suitable algorithm for finding liveness errors in large faulty concurrent systems, in which traditional techniques fail because of the model size. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Johansson:2008:cec, author = "Ulf Johansson and Rikard Konig and Tuve Lofstrom and Lars Niklasson", title = "Increasing Rule Extraction Accuracy by Post-Processing GP Trees", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0669.pdf}, url = {}, size = {}, abstract = {Genetic programming (GP), is a very general and efficient technique, often capable of outperforming more specialised techniques on a variety of tasks. In this paper, we suggest a straightforward novel algorithm for post-processing of GP classification trees. The algorithm iteratively, one node at a time, searches for possible modifications that would result in higher accuracy. More specifically, the algorithm for each split evaluates every possible constant value and chooses the best. With this design, the post-processing algorithm can only increase training accuracy, never decrease it. In this study, we apply the suggested algorithm to GP trees, extracted from neural network ensembles. Experimentation, using 22 UCI datasets, shows that the post-processing results in higher test set accuracies on a large majority of datasets. As a matter of fact, for two setups of three evaluated, the increase in accuracy is statistically significant. }, keywords = {genetic algorithms, genetic programming}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Pant:2008:cec, author = "Millie Pant and Radha Thangaraj and Crina Grosan and Ajith Abraham", title = "Improved Particle Swarm Optimization with Low-Discrepancy Sequences", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0670.pdf}, url = {}, size = {}, abstract = {Quasirandom or low discrepancy sequences, such as the Van der Corput, Sobol, Faure, Halton (named after their inventors) etc. are less random than a pseudorandom number sequences, but are more useful for computational methods which depend on the generation of random numbers. Some of these tasks involve approximation of integrals in higher dimensions, simulation and global optimization. Sobol, Faure and Halton sequences have already been used [7, 8, 9, 10] for initializing the swarm in a PSO. This paper investigates the effect of initiating the swarm with another classical low discrepancy sequence called Vander Corput sequence for solving global optimization problems in large dimension search spaces. The proposed algorithm called VC-PSO and another PSO using Sobol sequence (SO-PSO) are tested on standard benchmark problems and the results are compared with the Basic Particle Swarm Optimization (BPSO) which follows the uniform distribution for initializing the swarm. The simulation results show that a significant improvement can be made in the performance of BPSO, by simply changing the distribution of random numbers to quasi random sequence as the proposed VC-PSO and SO-PSO algorithms outperform the BPSO algorithm by noticeable percentage, particularly for problems with large search space dimensions. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Lee3:2008:cec, author = "Dong-Hyun Lee and Jong-Hwan Kim", title = "Evolutionary Personalized Robotic Doll: GomDoll", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0671.pdf}, url = {}, size = {}, abstract = {Genetic robot is one of artificial creatures and has its own genome in which each chromosome consists of many genes that contribute to defining its personality. By using the concept of genetic robot, this paper proposes personalized robotic doll by applying evolutionary process to generate unique propensity, defined by its genome. A genome population is evolved such that it customizes the genome satisfying a propensity desired by user based on Big Five personality dimensions. Robotic doll has emotion and motivation to reflect its internal state and to provide human friendly interaction. To demonstrate the effectiveness of this scheme, a bear-like robotic doll, GomDoll, is developed and the evolved genome is implanted to it to see its manner of internal and external responses to stimuli. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Lee4:2008:cec, author = "Donghoon Lee and Kunsu Kim and Tae Bok Yoon and Jee-Hyong Lee", title = "Design of Web Page Evaluation System Using Ajax and Neural Networks", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0672.pdf}, url = {}, size = {}, abstract = {Web page evaluation is an important issue in the Internet. The page view count is a widely used criterion for the web page evaluation because of its easiness. But, the evaluation methods based on the page view count cannot reflect whether the web page content corresponds with users' needs because users click a page after looking at only the title or the small part of the page. If the page content does not satisfy a user, the user generally does not spend much time nor take any actions to look at the page so therefore we developed an Ajax Log System. Using this system, we collect users' visiting time and action on web pages such as clicks, scrolling, etc. Users are not interrupted while Ajax works. But the collected data are continuous values. We cannot determine adaptive criteria to each user data. To solve this problem, the evaluation module of the system is based on the neural network. The system with neural network learns users' action pattern while reading useful web pages and evaluates the usefulness of web pages from users' actions. Our system can more accurately find pages which satisfy users than a search engine. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Li19:2008:cec, author = "Huan Li and Beibei Huang and Jinhu Lü", title = "Dynamical Evolution Analysis of the Object-Oriented Software Systems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0673.pdf}, url = {}, size = {}, abstract = {Software evolution and update play a vital role in software engineering. It has many advantages, such as improving the efficiency of programming, reducing the cost of maintenance and promoting the development of software systems. This paper further analyzes the evolution and update processes of three typical kinds of real-world object-oriented software systems by using the tools of complex networks. It discovers some underlying dynamical evolution characteristics and rules of the object-oriented software systems. These results are very useful for the design and development of the objectoriented software systems. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Voß:2008:cec, author = "Thomas Voß and Nicola Beume and Günter Rudolph and Christian Igel", title = "Scalarization Versus Indicator-Based Selection in Multi-Objective CMA Evolution Strategies", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0675.pdf}, url = {}, size = {}, abstract = {While scalarization approaches to multicriteria optimization become infeasible in the case of many objectives, for few objectives the benefits of population-based methods compared to a set of independent single-objective optimization trials on scalarized functions are not obvious. The multi-objective covariance matrix adaptation evolution strategy (MO-CMA-ES) is a powerful algorithm for real-valued multi-criteria optimization. This population-based approach combines mutation and strategy adaptation from the elitist CMA-ES with multi-objective selection. We empirically compare the steady-state MO-CMA-ES with different scalarization algorithms, in which the elitist CMA-ES is used as single-objective optimizer. Although only bicriteria benchmark problems are considered, the MO-CMA-ES performs best in the overall comparison. However, if the scalarized problems have a structure that can easily be exploited by the CMA-ES and that is less apparent in the vector-valued fitness function, the CMA-ES with scalarization outperforms the population-based approach. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Carvalho:2008:cec, author = "Danilo F. Carvalho and Carmelo J. A. Bastos-Filho", title = "Clan Particle Swarm Optimization", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0677.pdf}, url = {}, size = {}, abstract = {Particle Swarm Optimization (PSO) has been used to solve many different types of optimization problems. By applying PSO to problems where the feasible solutions are too much difficult to find, new ways of solving the problems are required. Many variations on the basic PSO form have been explored, targeting the velocity update equation. Other approaches attempt to change the structure of the swarm. In this paper a Clan PSO topology is proposed for improving the PSO degree of convergence focusing on the distribution of the particles in the search space. A comparison with star, ring, and Four Clusters topologies was performed. Our simulation results have shown that the proposed topology achieves better degrees of convergence than the cluster-based one. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Tseng2:2008:cec, author = "Lin-Yu Tseng and Chun Chen", title = "Multiple Trajectory Search for Large Scale Global Optimization", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0678.pdf}, url = {}, size = {}, abstract = {In this paper, the multiple trajectory search (MTS) is presented for large scale global optimization. The MTS uses multiple agents to search the solution space concurrently. Each agent does an iterated local search using one of three candidate local search methods. By choosing a local search method that best fits the landscape of a solution's neighborhood, an agent may find its way to a local optimum or the global optimum. We applied the MTS to the seven benchmark problems designed for the CEC 2008 Special Session and Competition on Large Scale Global Optimization. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Liu9:2008:cec, author = "Wudong Liu and Qingfu Zhang and Edward Tsang and Botond Virginas", title = "Tchebycheff Approximation in Gaussian Process Model Composition for Multi-Objective Expensive Black Box", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0679.pdf}, url = {}, size = {}, abstract = {Black-box expensive function is ubiquitous in real world problems. Much research has been done on scalar objective optimization for such problems with great success. Comparatively, very little work has been done in multi-objective optimization. In many cases, it is not straightforward to convert methods from scalar objective optimization to multi-objective optimization due to the complexities incurred by Pareto domination.}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Miconi:2008:cec, author = "Thomas Miconi ", title = "Evosphere: Evolutionary Dynamics in a Population of Fighting Virtual Creatures", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0681.pdf}, url = {}, size = {}, abstract = {It is often suggested that traditional models of artificial evolution, based on explicit, human-defined fitness functions, are fundamentally more restricted and less creative than natural evolution, in which no such constraint exists. After a discussion and refinement of this statement, we suggest a classification of evolutionary systems according to their evolutionary ``creativity''. We describe an environment, called Evosphere, in which a population of 3D creatures interact, fight with each other, and evolve freely on the surface of a ``microplanet''. We demonstrate the onset of natural selection and adaptive evolution within this virtual world, both by visual inspection and statistical analysis. We show that the introduction of reproductively isolated species enriches the dynamics of the system, leading to simple evolutionary feedbacks among species. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Fan:2008:cec, author = "Kai Fan and Anthony Brabazon and Conall O'Sullivan and Michael O'Neill", title = "Benchmarking the Performance of the Real-Valued Quantum-Inspired Evolutionary Algorithm", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0682.pdf}, url = {}, size = {}, abstract = {Following earlier claims that Quantum-inspired Evolutionary Algorithm (QIEA) may offer advantages in high dimensional environments, this paper tests a real-valued QIEA on a series of benchmark functions of varying dimensionality in order to examine its scalability. The results are compared with those from a genetic algorithm using both a binary and real-valued representation. The results show that the QIEA obtains highly competitive results versus the genetic algorithm, while substantially outperforming both versions of the Genetic Algorithm (GA) in terms of running time. This suggests that QIEA may have substantial utility for real-world high dimensional problems. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Hooshyar:2008:cec, author = "B. Hooshyar and A. Rahmani and M. Shenasa", title = "A Genetic Algorithm to Time- Cost Trade off in Project Scheduling", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0683.pdf}, url = {}, size = {}, abstract = {One of the key important issues in project management is trade off between cost and time in such a way that the project is completed in the shortest time and minimum cost. Genetic Algorithm is a practical approach for such optimization problems. In this paper, an algorithm is presented to solve the Time-Cost Tradeoff Problem (TCTP) using genetic algorithm. In this algorithm in order to search the problem space two control variables are used. Also an intelligent mutation operator is presented to approximate to the project's optimal point. Comparing this algorithm with Siemens classical algorithm shows the higher speed of proposed algorithm because in this algorithm many computations of paths in project's network graph are not needed. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(O'Neill:2008:cec, author = "Michael O'Neill and Anthony Brabazon", title = "Self-Organizing Swarm (SOSwarm) for Financial Credit-Risk Assessment", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0684.pdf}, url = {}, size = {}, abstract = {This paper applies a self-organizing Particle Swarm algorithm, SOSwarm, for the purposes of credit-risk assessment. SoSwarm can be applied for unsupervised clustering and for classification. In the algorithm, input vectors are projected into a lower dimensional map space producing a visual representation of the input data in a manner similar to a self-organizing map (SOM). However, unlike SOM, the nodes (particles) in this map react to input data during the learning process by modifying their velocities using an adaptation of the Particle Swarm Optimization velocity update step. The utility of SoSwarm is tested by applying it to two important credit-risk assessment problems drawn from the domain of finance, namely the prediction of corporate bond ratings and the prediction of corporate failure. The results obtained on the financial benchmark problems are highly-competitive against those of traditional classification methodologies. The paper makes a further contribution showing that the canonical SOM can be explored within the PSO paradigm. This highlights an important linkage between the heretofore distinct literatures of SOM and PSO. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Benjamin:2008:cec, author = "Simon C. Benjamin", title = "Evolutionary Route to Computation in Self-Assembled Nanoarrays", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0685.pdf}, url = {}, size = {}, abstract = {Ordered nanoarrays, i.e. regular patterns of quantum structures at the nanometre scale, can now be synthesised in a range of systems. In this paper I study a form of array computation where the internal dynamics are driven by intrinsic cell-cell interactions and global optical pulses addressing entire structure indiscriminately. The array would need to be ' wired' to conventional technologies only at its boundary. Any self assembled array would have a unique set of defects, therefore I employ an ab initio evolutionary process to subsume such flaws without any need to determine their location or nature. The approach succeeds for various forms of physical interaction within the array. }, keywords = {genetic algorithms, genetic programming}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Naeini:2008:cec, author = "Armin Tavakoli Naeini and Maziar Palhang", title = "Evolving a Multiagent Coordination Strategy Using Genetic Network Programming for Pursuit Domain", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0686.pdf}, url = {}, size = {}, abstract = {The design and development of strategies to coordinate the actions of multiple agents is a central research issue in the field of Multiagent Systems (MAS). It is nearly impossible to identify or prove the existence of the best coordination strategy. In most cases a coordination strategy is chosen for a domain, if it is reasonably good.In this paper, we propose a new design methodology using Genetic Network Programming (GNP) to evolve a coordination strategy for a well-known and difficult-to-solve multi agent problem named pursuit domain where cooperation of agents is required. Genetic Network Programming (GNP) is a newly developed evolutionary computation inspired from Genetic Programming (GP). While GP uses a tree structure as genes of an individual, GNP uses a directed graph type structure. We show the effectiveness of proposed methodology through simulations. In addition, the comparison of the performances between GNP and GP is carried out. The results show that performance of GNP solution is significantly superior to GP solution. }, keywords = {genetic algorithms, genetic programming}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Lai:2008:cec, author = "K. Robert Lai and Bo-Ruei Kao and Yi-Yuan Chiang", title = "Fuzzy Constraint-Directed Negotiation Mechanism as a Framework for Multi-agent Scheduling", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0688.pdf}, url = {}, size = {}, abstract = {This paper presents a fuzzy constraint-directed negotiation mechanism for agent-based scheduling. Scheduling problem is modeled as a set of fuzzy constraint satisfaction problems (FCSP), interlinked together by inter-agent constraints. Each FCSP represents the perspective of participants and is governed by agents. Negotiation process is considered as a global consistency enforcing via iterative constraint adjustment and relaxation. To facilitate convergence and improve solution quality for a particular performance measure, sharing metascheduling information during negotiation is applied. Experimental results suggest that the proposed approach not only can obtain a high quality schedule in a cost-effective manner, but also provides superior performance in all criteria to other negotiation models for agent-based scheduling. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Zielinski:2008:cec, author = "Karin Zielinski and Matthias Joost and Rainer Laur and Bernd Orlik", title = "Comparison of Differential Evolution and Particle Swarm Optimization for the Optimization of a PI Cascade Control", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0689.pdf}, url = {}, size = {}, abstract = {PI cascade controllers are often used in control applications due to their simplicity. Because of uncertain and varying system parameters, a robust control is needed. However, known methods to generate robust controllers often lead to complicated control structures. Unfortunately, there are no analytical solutions to generate robust controllers with a fixed simple structure like the PI cascade. Therefore, easy-to-use optimization algorithms are needed. In this paper it is shown that for a practical approach using recommended parameter settings from literature both Differential Evolution and Particle Swarm Optimization can be used for the optimization of a PI cascade control. A performance comparison shows similar results, so both of them are useful to field engineers who apply optimization algorithms to real-world problems and are often inexperienced concerning optimization. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Helmi:2008:cec, author = "B. Hoda Helmi and Adel T. Rahmani", title = "An AIS Algorithm for Web Usage Mining with Directed Mutation", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0690.pdf}, url = {}, size = {}, abstract = {This paper presents a model based on artificial immune system for mining Web usage data. One of the new features of the proposed model is directed mutation that is designed to avoid the random nature of mutation that make the system nondeterministic, besides that the model presents a new method for learning new unseen antigens instead of using the hypermutation which its computational cost is high. In the proposed algorithm each gene in the antigen has its own strength so strong genes are recognized more powerfully. Experimental results show that by exerting the directed mutation and considering item weights in noisy data like Web log data the quality of extracted antibodies are improved and by using the new method for learning new antigens, outliers can't penetrate to set of antibodies. Like the natural immune system, the strongest advantage of immune based learning is its ease of adaptation to the dynamic environment. By introducing the new features, a model which is shown to be more robust and better able to adapt to the dynamic environments such as Web than the similar models is proposed. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Binh:2008:cec, author = "Huynh Thi Thanh Binh and Nguyen Xuan Hoai and R. I. (Bob) McKay", title = "A New Hybrid Genetic Algorithm for Solving the Bounded Diameter Minimum Spanning Tree Problem", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0692.pdf}, url = {}, size = {}, abstract = {In this paper, a new hybrid genetic algorithm - known as HGA - is proposed for solving the Bounded Diameter Minimum Spanning Tree (BDMST) problem. We experiment with HGA on two sets of benchmark problem instances, both Euclidean and Non-Euclidean. On the Euclidean problem instances, HGA is shown to outperform the previous best two Genetic Algorithms (GAs) reported in the BDMST literature, while on the Non-Euclidean problem instance, HGA performs comparably with these two GAs. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Chu2:2008:cec, author = "Ying Chu and Hua Mi and Huilian Liao and Zhen Ji and Q. H. Wu", title = "A Fast Bacterial Swarming Algorithm For High-Dimensional Function Optimization", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0693.pdf}, url = {}, size = {}, abstract = {A novel Fast Bacterial Swarming Algorithm (FBSA) for high-dimensional function optimization is presented in this paper. The proposed algorithm combines the foraging mechanism of E-coli bacterium introduced in Bacterial Foraging Algorithm (BFA) with the swarming pattern of birds in block introduced in Particle Swarm Optimization (PSO). It incorporates the merits of the two bio-inspired algorithms to improve the convergence for high-dimensional function optimization. A new parameter called attraction factor is introduced to adjust the bacterial trajectory according to the location of the best bacterium (bacterium with best fitness value). An adaptive step length is adopted to improve the local search ability. The algorithm has been evaluated on standard high-dimensional benchmark functions in comparison with BFA and PSO respectively. The simulation results have demonstrated the fast convergence ability and the improved optimization accuracy of FBSA. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Patnaik:2008:cec, author = "Awhan Patnaik and L. Behera", title = "Evolutionary Multiobjective Optimization Based Control Strategies for an Inverted Pendulum On a Cart", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0694.pdf}, url = {}, size = {}, abstract = {We report the design and implementation of three different multiobjective optimization based control strategies for the cart pole system: (1) a multiobjective version of the classic quadratic regulator problem, (2) a multiobjective formulation of a standard H∞ controller and (3) a mixed norm H2 /H ∞ controller design problem in a multiobjective setting. The optimization problems have been solved using an elitist Pareto dominance based multiobjective genetic algorithm developed by the authors. Input saturation and bounds on state variables have been incorporated in the problem. It is shown by way of an example that the solution to the scalarized version of muliobjective linear regulator design problem is contained in the set of solutions of the vector objective formulation of the same multiobjective design problem. Finally the validity of the solutions was tested on a real cart pole regulator system. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Keller:2008:cec, author = "Robert E. Keller and Riccardo Poli", title = "Toward Subheuristic Search", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0695.pdf}, url = {}, size = {}, abstract = {In previous work, we have introduced an effective, resource-efficient and self-adapting hyperheuristic that uses Genetic Programming (GP) as its method of search in the space of domain-specific metaheuristics. GP employs user-provided, local heuristics from which it produces these metaheuristics (MHs). Here, we show that the hyperheuristic performs even better when working at the subheuristic level, i.e., when building MHs from generic components and specific elementary operations. In particular, this approach supports efficiency of the better MHs. Specifically, these MHs do not excessively iterate local search steps, i.e., their good performance comes from smart patterns of calls of the provided, basic components. Also, a moderate reduction of the maximum allowed MH size does not reduce performance significantly. }, keywords = {genetic algorithms, genetic programming}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Zhang14:2008:cec, author = "Zhenya Zhang and Hongmei Cheng and Shuguang Zhang and Qiansheng Fang", title = "Clustering Aggregation Based on Genetic Algorithm for Documents Clustering", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0698.pdf}, url = {}, size = {}, abstract = {Clustering aggregation problem is a kind of formal description for clustering ensemble problem and technologies for the solving of clustering aggregation problem can be used to construct clustering division with better clustering performance when the clustering performances of each original clustering division are fluctuant or weak. In this paper, an approach based on genetic algorithm for clustering aggregation problem, named as GeneticCA, is presented. To estimate the clustering performance of a clustering division, clustering precision is defined and features of clustering precision are discussed. In our experiments about clustering performances of GeneticCA for document clustering, hamming neural network is used to construct clustering divisions with fluctuant and weak clustering performances. Experimental results show that the clustering performance of clustering division constructed by GeneticCA is better than clustering performance of original clustering divisions with clustering precision as criterion. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Tseng3:2008:cec, author = "Chun-Shun Tseng and Ya-Yun Jheng and Sih-Yin Shen and Jung-Hua Wang", title = "Fast Symmetric Keys Generation via Mutual Imitating Process", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0699.pdf}, url = {}, size = {}, abstract = {This paper presents an eavesdropper-proof algorithm that is capable of fast generating symmetric (secret) keys. Instead of literally exchanging secret keys, both the sender and receiver adopt an imitating process based on an improved Hebbian rule that uses identical random inputs to separately train on their reciprocal outputs to generate a pair of exactly identical secret key strings. Important parameters are elaborately characterized to implement a fast information transmission for ephemeral key exchanging. We show the possible performance optimization can be achieved by coordinating the parameters. One essential feature of the proposed algorithm is that even an eavesdropper who acquires entire structure of the algorithm and the transmission data still has no chance to decrypt the encrypted message, thus ensuring security in the subsequent encryption task. Moreover, computation load is well bounded in an acceptable range despite the increasing key length. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(smith:2008:cec, author = "James F. {Smith, III}", title = "Co-Evolving Fuzzy Decision Trees and Scenarios", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0700.pdf}, url = {}, size = {}, abstract = {A co-evolutionary data mining algorithm has been invented that automatically generates decision logic in the form of fuzzy decision trees (FDTs). The algorithm initially uses a genetic program (GP) to mine a database of scenarios to automatically create the fuzzy logic. This is followed by the application of a genetic algorithm (GA) that is used to search for pathological scenarios (PS) that result in unsatisfactory performance by the fuzzy logic found by the GP. The fuzzy logic found in the previous step by the GP along with failure criteria (FC) is used to form the fitness function for the GA. If the GA fails to find pathological scenarios then the co-evolution ends; otherwise, the new scenarios are appended to the GP's database followed by GP based data mining and a GA scenario search. A detailed description of the co-evolution of a fuzzy decision tree for real-time control of unmanned air vehicles is provided. The fitness functions for the GP, terminal set, function set, and methods of accelerating convergence are included. The fitness function for the GA and a method of representing scenarios as chromosomes are given. Simulations related to validation of the fuzzy logic are discussed. }, keywords = {genetic algorithms, genetic programming}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Syberfeldt:2008:cec, author = "Anna Syberfeldt and Henrik Grimm and Amos Ng and Robert I. John", title = "A Parallel Surrogate-Assisted Multi-Objective Evolutionary Algorithm for Computationally Expensive Optimization Problems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0701.pdf}, url = {}, size = {}, abstract = {This paper presents a new efficient multiobjective evolutionary algorithm for solving computationallyintensive optimization problems. To support a high degree of parallelism, the algorithm is based on a steady-state design. For improved efficiency the algorithm uses a surrogate to identify promising candidate solutions and filter out poor ones. To handle the uncertainties associated with the approximative surrogate evaluations, a new method for multi-objective optimization is described which is generally applicable to all surrogate techniques. In this method, basically, surrogate objective values assigned to offspring are adjusted to consider the error of the surrogate. The algorithm is evaluated on the ZDT benchmark functions and on a real-world problem of manufacturing optimization. In assessing the performance of the algorithm, a new performance metric is suggested that combines convergence and diversity into one single measure. Results from both the benchmark experiments and the realworld test case indicate the potential of the proposed algorithm. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Yang10:2008:cec, author = "Shengxiang Yang and Renato Tinós", title = "Hyper-Selection in Dynamic Environments", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0702.pdf}, url = {}, size = {}, abstract = {In recent years, several approaches have been developed for genetic algorithms to enhance their performance in dynamic environments. Among these approaches, one kind of methods is to adapt genetic operators in order for genetic algorithms to adapt to a new environment. This paper investigates the effect of the selection pressure on the performance of genetic algorithms in dynamic environments. A hyper-selection scheme is proposed for genetic algorithms, where the selection pressure is temporarily raised whenever the environment changes. The hyper-selection scheme can be combined with other approaches for genetic algorithms in dynamic environments. Experiments are carried out to investigate the effect of different selection pressures on the performance of genetic algorithms in dynamic environments and to investigate the effect of the hyper-selection scheme on the performance of genetic algorithms in combination with several other schemes in dynamic environments. The experimental results indicate that the effect of the hyperselection scheme depends on the problem under consideration and other schemes combined in genetic algorithms. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Zhang15:2008:cec, author = "Zhenya Zhang and Hongmei Cheng and Wanli Chen and Qiansheng Fang", title = "Correlation Clustering Based on Genetic Algorithm for Documents Clustering", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0703.pdf}, url = {}, size = {}, abstract = {Correlation clustering problem is a NP hard problem and technologies for the solving of correlation clustering problem can be used to cluster given data set with relation matrix for data in the given data set. In this paper, an approach based on genetic algorithm for correlation clustering problem, named as GeneticCC, is presented. To estimate the performance of a clustering division, data correlation based clustering precision is defined and features of clustering precision are discussed in this paper. Experimental results show that the performance of clustering division for UCI document data set constructed by GeneticCC is better than clustering performance of other clustering divisions constructed by SOM neural network with clustering precision as criterion. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Rani:2008:cec, author = "B. Padmaja Rani and B. Vishnu Vardhan and A. Kanaka Durga and A. Vinaya Babu", title = "Analysis of N-Gram Model on Telugu Document Classification", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0704.pdf}, url = {}, size = {}, abstract = {Document classification is one of the recent areas of research evolved as a result of exponential growth in the quantum electronic form of documents. Various document representation methods based on linguistic knowledge are revisited in Literature. Adaptability of N-gram models on various languages is the recent trend. In this paper an attempt is made to analyze character N-gram model on Telugu documents. Tokenization of syllables and the associated complexity of Telugu script is described. A combination of Bayes probabilistic classifier and character N-gram model is discussed in this paper. The performance of the proposed classifier is evaluated in terms of overall accuracy. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Saxena:2008:cec, author = "Dhish Kumar Saxena and Kalyanmoy Deb", title = "Dimensionality Reduction of Objectives and Constraints in Multi-Objective Optimization Problems: A System Design Perspective", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0705.pdf}, url = {}, size = {}, abstract = {The notion of Optimal System Design [12] holds that in order to ' truly ' maximize/minimize an objective function, the feasible set needs to be optimized. Inspired by it, the attempt in our recent work [11] was to incorporate constraint-reduction in our earlier proposed procedures on dimensionality reduction of objectives [4,10]. In that, while targetting constrained single-objective optimization problems (SOPs), we could arrive at a critical set of constraints and also their importance based rank-ordering. This information was used to study the shift from the constrained to the unconstrained optima. The methodology above was based on treating the apriori stated constraints as objectives besides the original-objective, and on applying [4,10] to this combined objective set-but- without constraints. In this work, the endeavor is to extend the above notion to the realm of multi-objective optimization problems (MOPs). Towards it, while we hire much from the above methodology, we make a fundamental shift, in that, we retain the a priori stated constraints, while evaluating the combined objective set. The motivation for this shift lies, in that, it allows more effective realization of the notion of System Design than the approach in [11]. Reasonable effort has been spent on establishing this argument. Incorporating this change, a procedure for simultaneous reduction in objectives and constraints (for both SOPs, MOPs) is proposed, which also defines a realizable path towards Optimal System Design. Finally, the procedure is demonstrated on two test problems and one real world problem. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Siwik:2008:cec, author = "Leszek Siwik and Piotr Sikorski", title = "Efficient Constrained Evolutionary Multi-Agent System for Multi-Objective Optimization", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0706.pdf}, url = {}, size = {}, abstract = {Evolutionary Multi-agent System approach for optimization (especially for multi-objective optimization) is a very promising computational model. Its computational as well as implemental simplicity causes that approaches based on EMAS model can be widely used for solving optimization tasks. It turns out that introducing some additional mechanisms into basic EMAS-causes that EMAS-based system can be successfully applied for solving constrained multi-objective optimization tasks-and what is important results obtained by proposed approach are better/not worse than results obtained by NSGAII or SPEA2 algorithms. In the course of this paper some extensions that can be introduced into EMAS in order to constrained multi-objective optimization are presented. What is important-any new additional mechanisms do not have to be introduced into EMAS to solve constrained optimization tasks-the only extensions causing that EMAS-based model becomes an efficient and simple both in conception as well as in implementation-is an appropriate strategy regarding the flow among agents crucial non-renewable resource which is usually called life energy. In this paper, both the idea as well as preliminary results of Constrained Evolutionary Multi- Agent System (conEMAS) for Multi-objective Optimization are presented. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(McClintock:2008:cec, author = "James McClintock and Gary G. Yen", title = "A Two-Tiered, Agent Based Approach for Autonomous, Evolutionary Texture Generation", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0707.pdf}, url = {}, size = {}, abstract = {This paper proposes a two-tiered, evolutionary architecture for computer based synthesis of textures. In this architecture, a traditional tree based texture generation system is controlled by a set of evolutionary agents. The main contribution of this work is that the user is able to choose the degree of interaction and control they exert over the system. Evolutionary agents are designed to contain information about desirable image features, and they evolve based on user feedback. The agents in turn control the main evolutionary engine for generating textures. This system allows the computer to continue working when the designer leaves without limiting the designer's ability to control the texture generation process when they are available to interact with the system. An experimental implementation is developed to verify the utility of the proposed architecture for texture synthesis. Results show significant improvements in the average user ranking of the agents as the genetic algorithm progresses. }, keywords = {genetic algorithms, genetic programming}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Periyasamy:2008:cec, author = "Sathish Periyasamy and Alex Gray and Peter Kille", title = "The Epigenetic Algorithm", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0708.pdf}, url = {}, size = {}, abstract = {Evolutionary Computation (EC) paradigms are inspired by the optimization strategies used by biological systems. While these strategies can be found in every level of biological organization, almost all of the EC techniques which comprise techniques from Evolutionary Algorithm (EA) to Swarm Intelligence (SI) have been inspired by organism level optimization strategies. While EA is based on trans-generational genetic adaptation of organisms (biologically inspired), SI is mainly based on intra-generational collective behavioral adaptation of organisms (socially inspired). This paper describes the optimization strategies that bio-molecules use both for intra-generational and trans-generational adaptation of biological cells. These adaptive strategies which are known as epigenetic mechanisms emerged long before any other biological strategy and form the basis for Epigenetic Algorithms (EGA). Further, the paper proposes an intra-generational EGA based on bio-molecular degradation and autocatalysis which are ubiquitous cellular processes and are pivotal for the adaptive dynamics and evolution of intelligent cellular organization. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Song:2008:cec, author = "Andy Song", title = "Fast Video Analysis by Genetic Programming", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0709.pdf}, url = {}, size = {}, abstract = {Genetic programming has been applied to various types of vision tasks. This paper extends the use of this powerful problem solving method to a more complex but more common domain, video analysis. We present the methodology as well as the experiments on two video analysis tasks: segmenting texture regions and detecting moving objects. The advantages of GP in this domain can be shown by this study. Firstly GP methods are less dependent on knowledge from domain experts. One methodology is suitable for both tasks. Secondly GP can generate fast video frame analysers which are highly desirable or even critical in real time vision applications. }, keywords = {genetic algorithms, genetic programming}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Sheri:2008:cec, author = "Guleng Sheri and David W. Corne", title = "The Simplest Evolution/Learning Hybrid: LEM with KNN", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0710.pdf}, url = {}, size = {}, abstract = {The Learnable Evolution Model (LEM) was introduced by Michalski in 2000, and involves interleaved bouts of evolution and learning. Here we investigate LEM in (we think) its simplest form, using k-nearest neighbour as the 'learning' mechanism. The essence of the hybridisation is that candidate children are filtered, before evaluation, based on predictions from the learning mechanism (which learns based on previous populations). We test the resulting 'KNNGA' on the same set of problems that were used in the original LEM paper. We find that KNNGA provides very significant advantages in both solution speed and quality over the unadorned GA. This is in keeping with the original LEM paper's results, in which the learning mechanism was AQ and the evolution/learning interface was more sophisticated. It is surprising and interesting to see such beneficial improvement in the GA after such a simple learning-based intervention. Since the only application-specific demand of KNN is a suitable distance measure (in that way it is more generally applicable than many other learning mechanisms), LEM methods using KNN are clearly recommended to explore for large-scale Optimization tasks in which savings in evaluation time are necessary. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Carrano:2008:cec, author = "Eduardo G. Carrano and Bruno B. Souza and Oriane M. Neto", title = "An Immune Inspired Memetic Algorithm for Power Distribution System Design under Load Evolution Uncertainties", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0712.pdf}, url = {}, size = {}, abstract = {This work proposes an Immune Inspired Memetic Algorithm for the expansion planning of electric distribution systems. This algorithm is based on a Clonal Selection Algorithm and a Local Search Method which is built using network distance concepts abstracted from continuous spaces. The memetic algorithm is intended to find not only the optimal solution for the design conditions, but a whole set of viable solutions, that can be considered as alternatives under perturbed operation conditions. Those alternatives are used for handling with load evolution uncertainties, which are inherently related with long term evaluation of the distribution system. The post-optimization analysis of solutions has been made using a Monte Carlo Simulation and a Multiobjective Sensitivity Analysis, in order to estimate their robustness under perturbed load conditions. The results achieved by the proposed algorithm in a practical problem indicate that this method can be more suitable for designing distribution system under load evolution uncertainties. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Carrano2:2008:cec, author = "Eduardo G. Carrano and Ricardo H. C. Takahashi and Walmir M. Caminhas", title = "A Genetic Algorithm for Multiobjective Training of ANFIS Fuzzy Networks", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0713.pdf}, url = {}, size = {}, abstract = {The achievement of approximation models may constitute a complex computational task, in the cases of models with non-linear relation between parameters and data. This problem becomes even harder when the system to be modeled is subject to noisy data, since the simple minimization of error over a training data set can give rise to misleading models that fit both the system structure and the noise (the phenomenon of model overfit). This paper proposes a multiobjective genetic algorithm for guiding the training of ANFIS fuzzy networks. This algorithm considers the complexity of network jointly with the error over the training set as relevant objectives, that should be minimized. Results obtained in three regression problems are presented to show the generalization capacity of models constructed with the proposed methodology. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Fraga:2008:cec, author = "Luis Gerardo de la Fraga and Israel Vite Silva ", title = "Direct 3D Metric Reconstruction from Two Views Using Differential Evolution", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0714.pdf}, url = {}, size = {}, abstract = {To obtain a 3D metric reconstruction from two images taken with a same camera without previous calibration, it is necessary to estimate the intrinsic camera parameters and the orientation and position of the two views with respect to the camera. At the present time, there are several algorithms to estimate camera parameters from two views, all of them are based on the epipolar geometry and the estimation of the fundamental matrix. However, it is well known there are some configurations where the fundamental matrix can not be estimated, called critical configurations. In this article we present a novel method to retrieve directly the camera parameters, and orientation and position parameters for two views, from points taken over the two images, using the Differential Evolution (DE) algorithm. This method exploits the reprojection error as the cost function for DE, instead of computing the fundamental matrix. Experimental results show our method recovers 3D points, intrinsic, and orientation and position parameters on non-critical configurations and in the critical configuration of pure translation.We used simulated and real images to prove its effectiveness and robustness. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Togelius:2008:cec, author = "Julian Togelius and Faustino Gomez and Jürgen Schmidhuber", title = "Learning What to Ignore: Memetic Climbing in Topology and Weight Space", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0715.pdf}, url = {}, size = {}, abstract = {We present the memetic climber, a simple searchalgorithm that learns topology and weights of neural networkson different time scales. When applied to the problem of learningcontrol for a simulated racing task with carefully selectedinputs to the neural network, the memetic climber outperformsa standard hill-climber. When inputs to the network are lesscarefully selected, the difference is drastic. We also present twovariations of the memetic climber and discuss the generalizationof the underlying principle to population-based neuroevolutionalgorithms.}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Jin2:2008:cec, author = "Nanlin Jin ", title = "Genetic Algorithm-Based Ecosystem for Heather Management", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0716.pdf}, url = {}, size = {}, abstract = {This paper applies Genetic Algorithms (GA) to simulate a heather moorland ecosystem. We investigate, in this ecosystem how to manage heather for the benefits of survival and reproduction of grouse. A GA candidate solution is a grid, representing spatial relationship of three types of heather. From solutions provided by GA, we have found that the diversity of neighborhood and its distribution are essential. The evenly diversified heather distributions emerge as the best fit solutions for grouse's needs. We compared this finding with data collected from the field work. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Pilat:2008:cec, author = "Marcin L. Pilat and Christian Jacob", title = "Creature Academy: A System for Virtual Creature Evolution", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0717.pdf}, url = {}, size = {}, abstract = {In this paper, we present Creature Academy, a virtual laboratory that allows for the evolution of form and function within simulated physical 3D environments. Creature Academy can be used to explore evolutionary mechanisms, design, learning and other processes studied in artificial life simulations. Our system allows to perform hierarchical evolutionary experiments and ecosystem-inspired setups to investigate bodied creatures that interact, compete, adapt, and evolve. As a first proof of concept, we use Creature Academy to evolve morphologies and motion strategies of virtual creatures that walk and jump. We then present results that compare hierarchical evolution scenarios to generate creatures that excel in both walking and jumping, demonstrating how to evolve from creature specialists to generalists. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Arami:2008:cec, author = "Arash Arami and Bijan Rahmizadeh Rofoee and Caro Lucas", title = "Multiple Heterogeneous Ant Colonies with Information Exchange", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0719.pdf}, url = {}, size = {}, abstract = {The method of Multiple Heterogeneous Ant Colonies with Information Exchange (MHACIE) is presented in this paper with emphasis on the speed of finding the optimal solution and the corresponding computational complexity. The proposed method which is inspired by biology and psychology has a structure composed of several ant colonies. These colonies participate in solving problems in a concurrently manner and also exchange information with each other in communicational steps. Each ant colony is considered as an intelligent agent with behavioral traits. These behavioral traits play a key role in the solving procedure, in interrelation circumstances and in installation of relations. Faster solutions have been achieved using different employments of agents in the algorithm structure. Experimental results show the superiority of Multiple Heterogeneous Ant Colonies algorithm in comparison to the standard ant colony system (ACS) and particle swarm optimization (PSO) algorithms on different benchmarks. A dynamic, control engineering benchmark is also provided in order to gain a more complete evaluation of the proposed algorithm. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Rohling:2008:cec, author = "Greg Rohling ", title = "Methods for Decreasing the Number of Objective Evaluations for Independent Computationally Expensive Objective Problems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0720.pdf}, url = {}, size = {}, abstract = {In this paper, three new methods for pushing solutions toward a desired region of the objective space more quickly are explored; hypercube distance scaling, dynamic objective thresholding, and hypercube distance objective ordering. These methods are applicable for problems that do not require the entire Pareto front and that require an independent computationally expensive computation for each objective. The performance of these methods is evaluated with the multiple objective 0/1 Knapsack problem. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Li20:2008:cec, author = "Hui Li and Dario Landa-Silva", title = "Evolutionary Multi-Objective Simulated Annealing with Adaptive and Competitive Search Direction", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0721.pdf}, url = {}, size = {}, abstract = {In this paper, we propose a population-based implementation of simulated annealing to tackle multi-objective optimisation problems, in particular those of combinatorial nature. The proposed algorithm is called Evolutionary Multi-objective Simulated Annealing Algorithm (EMOSA), which combines local and evolutionary search by incorporating two distinctive features. The first feature is to tune the weight vectors of scalarizing functions (i.e., search directions) for selection during local search using a two-phase strategy. The second feature is the competition between members of the current population with similar weight vectors. We compare the proposed algorithm to three other multi-objective simulated annealing algorithms and also to the Pareto archived evolutionary strategy (PAES). Experiments are carried out on a set of bi-objective travelling salesman problem (TSP) instances with convex or nonconvex Pareto-optimal fronts. Our experimental results demonstrate that the two-phase tuning of weight vectors and the competition between individuals make a significant contribution to the improved performance of EMOSA. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Siwik2:2008:cec, author = "Leszek Siwik and Szymon Natanek", title = "Elitist Evolutionary Multi-Agent System in Solving Noisy Multi-Objective Optimization Problems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0722.pdf}, url = {}, size = {}, abstract = {Evolutionary Multi-Agent System approach for optimization (for multi-objective optimization in particular) is a promising computational model. Its computational as well as implemental simplicity cause that approaches based on EMAS model can be widely used for solving optimization tasks. It turns out that introducing some additional mechanisms into basic EMAS—such as presented in the course of this paper elitist extensions cause that results obtained with the use of proposed elEMAS (elitist Evolutionary Multi-Agent System) approach are as high-quality results as results obtained by such famous and commonly used algorithms as NSGA-II or SPEA2. Apart from the computational simplicity especially important and interesting aspects of EMAS-based algorithms it is characteristic for them a kind of soft selection which can be additionally easily adjusted depending on a particular situation—in particular it is possible to introduce auto-adapting selection into such systems. Such a kind of selection seems to be especially important and valuable in solving optimization tasks in uncertain or ``noised'' environments. In the course of this paper the model and experimental results obtained by elEMAS system in solving noisy multi-objective optimization problems are presented and the general conclusion is as follows: EMAS-based optimization system seems to be more effective alternative than classical (i.e. non agent-based) evolutionary algorithms for multi-objective optimization, in particular, in uncertain environment, it seems to be better alternative than NSGA-II algorithm. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Penrod:2008:cec, author = "Nathan A. Penrod and Sushil J. Louis and David Carr and Bobby D. Bryant", title = "Neuro-Evolving Maintain-Station Behavior for Realistically Simulated Boats", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0723.pdf}, url = {}, size = {}, abstract = {We evolve a neural network controller for a boat that learns to maintain a given bearing and range with respect to a moving target in the Lagoon 3D game environment. Simulating realistic physics makes maneuvering boats difficult and thus makes an evolutionary approach an attractive alternative to hand coded methods. We evolve the weights of simple recurrent neural networks trained with a fitness function designed to combine multiple fitness objectives based on speed, heading, and position to create a robust maintain station behavior. Results with an enforced subpopulation neural-evolution genetic algorithm indicate that we can consistently evolve robust maintain controllers for realistically simulated boats in Lagoon. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Pilato:2008:cec, author = "Christian Pilato and Daniele Loiacono and Fabrizio Ferrandi and Pier Luca Lanzi", title = "High-Level Synthesis with Multi-Objective Genetic Algorithm: A Comparative Encoding Analysis", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0724.pdf}, url = {}, size = {}, abstract = {The high-level synthesis process involves three interdependent and NP-complete optimization problems: (i) the operation scheduling, (ii) the resource allocation, and (iii) the controller synthesis. Evolutionary algorithms have been effectively applied to high level synthesis in presence conflicting design objectives for finding good tradeoffs in the design space. However, so far the design space exploration has been performed using single-objective evolutionary algorithms with an ad hoc fitness function to achieve the desired tradeoff between the objectives. Recently we proposed a framework based on multi-objective genetic algorithms to perform a fully automated design space exploration. In this paper we focus on the choice of the solution representations that can be used to perform the design space exploration with multi-objective genetic algorithms. In particular we consider two specific representations and compare them on a set of benchmark problems. Our results suggest that they have different biases on the search space that make them more effective in different problems and design subspaces. Accordingly, we present a preliminary investigation on a new representation that exploits the advantages of both of them. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Bader-El-Den:2008:cec, author = "Mohamed Bader-El-Den and Riccardo Poli", title = "Analysis and Extension of the Inc* on the Satisfiability Testing Problem", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0725.pdf}, url = {}, size = {}, abstract = {Inc (star) is a general algorithm that can be used in conjunction with any local search heuristic and that has the potential to substantially improve the overall performance of the heuristic. The general idea of the algorithm is the following. Rather than attempting to directly solve a difficult problem, the algorithm dynamically chooses a smaller instance of the problem, and then increases the size of the instance only after the previous simplified instances have been solved, until the full size of the problem is reached. Genetic programming is used to discover new strategies for Inc*. Preliminary experiments on the satisfiability problem (SAT) problem have shown that Inc* is a competitive approach. In this paper we enhance Inc* and we experimentally test it on larger set of benchmarks, including big instances of SAT. Furthermore, we provide an analysis of the algorithm's behaviour. }, keywords = {genetic algorithms, genetic programming}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Loiacono:2008:cec, author = "Daniele Loiacono and Pier Luca Lanzi", title = "Computed Prediction in Binary Multistep Problems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0726.pdf}, url = {}, size = {}, abstract = {Computed prediction was originally devised to tackle problems defined over real-valued domains. Recent experiments on Boolean functions showed that the concept of computed prediction extends beyond real values and it can also be applied to solve more typical classifier system benchmarks such as Boolean multiplexer and parity functions. So far however, no result has been presented for other well known classifier system benchmarks, i.e., binary multistep problems such as the woods environments. In this paper, we apply XCS with computed prediction to woods environments and show that computed prediction can also tackle this class of problems. Our results demonstrate that (i) XCS with computed prediction converges to optimality faster than XCS, (ii) it solves problems that may be too difficult for XCS and (iii) it evolves solutions that are more compact than those evolved by XCS. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Siwik3:2008:cec, author = "Leszek Siwik and Szymon Natanek", title = "Solving Constrained Multi-Criteria Optimization Tasks Using Elitist Evolutionary Multi-Agent System", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0727.pdf}, url = {}, size = {}, abstract = {Introducing elitism into Evolutionary Multi-Agent System for multi-objective optimization proofed to be smooth both conceptually and in realization. Simultaneously it allowed for obtaining results with comparable high quality to such referenced algorithms as Non-dominated Sorting Genetic Algorithm (NSGA-II) or Strength Pareto Evolutionary Algorithm (SPEA2). What is more, applying mentioned agent-based computational paradigm for solving multi-criteria optimization tasks in ``noisy'' environments mainly because of—characteristic for EMAS-based approach—a kind of soft selection allowed for obtaining better solutions than mentioned referenced algorithms. From the above observations the following conclusion can be drown: Evolutionary Multi-Agent System (EMAS) (and being the subject of this paper Elitist Evolutionary Multi-Agent System (elEMAS) in particular) seems to be promising computational model in the context of multi-criteria optimization tasks. In previous works however the possibility of applying elEMAS for solving constrained multi-objective optimization task has not been investigated. It is obvious however that in almost all real-life problems constraints are a crucial part of Multi-Objective Optimization Problem (MOOP) definition and it is nothing strange that among (evolutionary) algorithms for multi-objective optimization a special attention is paid to techniques and algorithms for constrained multi-objective optimization and a variety—more or less effective—algorithms have been proposed. Thus, the question appears if effective constrained multi-objective optimization with the use of Elitist Evolutionary Multi-Agent System is possible. In the course of this paper preliminary answer for that question is given. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Jewajinda:2008:cec, author = "Yutana Jewajinda and Prabhas Chongstitvatana", title = "FPGA Implementation of a Cellular Univariate Estimation of Distribution Algorithm and Block-Based Neural Network as an Evolvable Hardware", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0729.pdf}, url = {}, size = {}, abstract = {This paper presents a hardware implementation of evolvable block-based neural network (BBNN) amd a kind of EDAs called cellular compact genetic algorithm (CCGA) in FPGA. The CCGA and BBNN have cellular-like and array-like structures which are suitable for hardware implementation. The implemented hardware demonstrates the completely intrinsic online evolution in hardware without software running on microprocessors. This work contributes to the field of evolvable hardware by proposing CCGA and a layer-based architecture for integration of BBNN and CCGA as a kind of evolvable hardware. In addition, the proposed CCGA efficiently solves the scalable issues by scaling up to the size of BBNN. The presented approach demonstrates a new kind of evolvable hardware. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Lukac:2008:cec, author = "Martin Lukac and Marek Perkowski", title = "Evolutionary Approach to Quantum Symbolic Logic Synthesis", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0730.pdf}, url = {}, size = {}, abstract = {In this paper we present an evolutionary approach to the quantum symbolic logic synthesis that was introduced in [1]. We use a Genetic Algorithm to synthesize quantum circuits from examples, allowing to synthesize functions that are both completely and incompletely specified. The symbolic synthesis is implemented in the GA so as to verify our approach. The Occam Razor principle, fundamental to inductive learning as well as to logic synthesis, is satisfied in this approach by seeking circuits of reduced complexity. The GA is tested on a set of benchmark functions representing single output quantum circuits as well as multiple entangled-qubit state generators. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wierstra:2008:cec, author = "Daan Wierstra and Tom Schaul and Jan Peters and Juergen Schmidhuber", title = "Natural Evolution Strategies", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0731.pdf}, url = {}, size = {}, abstract = {This paper presents Natural Evolution Strategies (NES), a novel algorithm for performing real-valued 'black box' function optimization: optimizing an unknown objective function where algorithm-selected function measurements constitute the only information accessible to the method. Natural Evolution Strategies search the fitness landscape using a multivariate normal distribution with a self-adapting mutation matrix to generate correlated mutations in promising regions. NES shares this property with Covariance Matrix Adaption (CMA), an Evolution Strategy (ES) which has been shown to perform well on a variety of high-precision optimization tasks. The Natural Evolution Strategies algorithm, however, is simpler, less ad-hoc and more principled. Self-adaptation of the mutation matrix is derived using a Monte Carlo estimate of the natural gradient towards better expected fitness. By following the natural gradient instead of the 'vanilla' gradient, we can ensure efficient update steps while preventing early convergence due to overly greedy updates, resulting in reduced sensitivity to local suboptima. We show NES has competitive performance with CMA on unimodal tasks, while outperforming it on several multimodal tasks that are rich in deceptive local optima. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Ahmadi:2008:cec, author = "Abbas Ahmadi and Fakhri Karray and Mohamed Kamel", title = "Model Order Selection for Multiple Cooperative Swarms Clustering Using Stability Analysis", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0732.pdf}, url = {}, size = {}, abstract = {Extracting different clusters of the given data is an appealing topic in swarm intelligence applications. This paper introduces multiple cooperative swarms and single swarm clustering approaches and provides mathematical descriptions explaining why the former approach outperform the other one. Moreover, the stability analysis is proposed to obtain the model order of the data using multiple cooperative swarms clustering approach. The proposed clustering approach is evaluated using three data sets and its performance is compared with that of other clustering techniques. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Esmaeili:2008:cec, author = "Afshin Esmaeili and Christian Jacob", title = "Evolutionary Exploration of Boolean Networks", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0733.pdf}, url = {}, size = {}, abstract = {Random Boolean networks (RBNs) are abstract models of gene regulatory networks that govern gene expression in cells. We have developed an evolutionary model to explore the dynamic states of random Boolean networks using heuristic optimization methods. The generic behavior of random Boolean networks is investigated as the evolutionary process works its way through different generations, identifying attractors that have been suggested to resemble cell types. We investigate several fitness functions to tune RBNs with respect to the number of attractors and other network parameters such as excess graph, attractor cycle length, network sensitivity and average basin entropy. We show that by imposing particular constraints on the evolutionary model we can generate ensembles of more stable networks, which are less sensitive to perturbations. Therefore, we demonstrate that an evolutionary approach can be useful for the generation of RBN ensembles, that is sets of regulatory networks that share particular properties. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Siwik4:2008:cec, author = "Leszek Siwik and Przemyslaw Sroka and Marek Psiuk", title = "Flock-Based Evolutionary Multi-Agent System in Solving Noisy Multi-Objective Optimization Problems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0734.pdf}, url = {}, size = {}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Romero:2008:cec, author = "Andres Romero and Fernando Nino and Gerardo Quintana", title = "An Artificial Immune System Model for Knowledge Extraction and Representation", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0735.pdf}, url = {}, size = {}, abstract = {This paper presents an approach to knowledge extraction and representation based on an artificial immune system. The main idea is to extract the important concepts from a set of text documents, and find the relations between such concepts. At the end, a graph representation is obtained, which is intended to present a picture of the documents' contents. Some experiments were carried out in order to validate the proposed approach, and very promising results were obtained. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Caporale:2008:cec, author = "Guglielmo Maria Caporale and Antoaneta Serguieva and Hao Wu", title = "A Mixed-Game Agent-Based Model for Simulating Financial Contagion", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0736.pdf}, url = {}, size = {}, abstract = {Over the past two decades, financial market crises with similar features have occurred in different regions of the world. Unstable cross-market linkages during financial crises are referred to as financial contagion. We simulate the transmission of financial crises in the context of a model of market participants adopting various strategies; this allows testing for financial contagion under alternative scenarios. Using a minority game approach, we develop an agent-based multinational model and investigate the reasons for contagion. Although contagion has been extensively investigated in the financial literature, it has not been studied yet through computational intelligence techniques. Our simulations shed light on parameter values and characteristics which can be exploited to detect contagion at an earlier stage, hence recognising financial crises with the potential to destabilise cross-market linkages. In the real world, such information would be extremely valuable to develop appropriate risk management strategies. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Flores-Mendoza:2008:cec, author = "Jorge Isacc Flores-Mendoza and Efren Mezura-Montes", title = "Dynamic Adaptation and Multiobjective Concepts in a Particle Swarm Optimizer for Constrained Optimization", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0738.pdf}, url = {}, size = {}, abstract = {In this paper, we propose a novel approach to solve constrained optimization problems based on particle swarm optimization (PSO). First, an empirical comparison of the most popular PSO variants is presented as to select the most convenient among them. After that, the PSO variant chosen is improved in: (1) its parameter control with a dynamic proposal as to promote a better exploration of the search space and to avoid premature convergence and (2) its constraint-handling mechanism by using multiobjective concepts as to promote a better approach to the feasible region. The algorithm is tested on a set of 13 well-known benchmark problems and the obtained performance is compared against some PSO variants and state-of- the-art approaches. Based on the results presented some conclusions are drawn and the future work is established. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Junior:2008:cec, author = "Aranildo R. L. Junior and Tiago A. E. Ferreira and Ricardo de A. Araújo", title = "An Experimental Study with a Hybrid Method for Tuning Neural Network for Time Series Prediction", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0739.pdf}, url = {}, size = {}, abstract = {This paper presents an study of a new Hybrid method based on the Greedy Randomized Adaptive Search Procedure(GRASP) and Evolutionary Strategies(ES) concepts for tuning the structure and parameters of an Artificial Neural Network (ANN). It consists of an ANN trained and adjusted by this new method, which searches for the minimum number of (and their specific) relevant time lags for a correct time series representation, the parameters configuration and the weights of the ANN until the learning performance in terms of fitness value is good enough, which found, for an optimal or sub-optimal forecasting model. An experimental analysis is presented with the proposed method using three relevant time series, and its results are discussed according to five well-known performance measures, showing the effectiveness and robustness of the proposed method. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Prieto:2008:cec, author = "Camilo E. Prieto and Fernando Niño and Gerardo Quintana", title = "A Goalkeeper Strategy in Robot Soccer Based on Danger Theory", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0740.pdf}, url = {}, size = {}, abstract = {Artificial Immune Systems (AIS) have been successfully modeled and implemented in several engineering applications. In this work, a goalkeeper strategy in robot soccer based on Danger Theory is proposed. Danger Theory is a recent immune theory which has not been widely applied so far. The proposed strategy is implemented and evaluated using middle league SIMUROSOT from FIRA. Experiments carried out yielded promising results. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Beume:2008:cec, author = "Nicola Beume and Holger Danielsiek and Christian Eichhorn and Boris Naujoks and Mike Preuss", title = "Measuring Flow as Concept for Detecting Game Fun in the Pac-Man Game", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0741.pdf}, url = {}, size = {}, abstract = {Popular games often have a high-quality graphic design but quite simple-minded non player characters (NPC). Recently, Computational Intelligence (CI) methods have been discovered as suitable methods to revive NPC, making games more interesting, challenging, and funny. We present a fairly large study of human players on the simple arcade game Pac- Man, controlling the ghosts behaviors by simple strategies, neural networks or evolutionary algorithms. The player's fun is of course a subjective experience, but we presume that it is related to the psychological flow concept. We deal with the question whether flow is a more reliable measure than asking human players directly for the fun experienced during the game. In order to detect flow, we introduce a measure based on the interaction time fraction between the human-controlled Pac- Man and the ghosts, and compare the outcome to the results of a fun measure suggested by Yannakakis and Hallam [1]. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Kaylani2:2008:cec, author = "A. Kaylani and M. Georgiopoulos and M. Mollaghasemi and G. C. Anagnostopoulos ", title = "Efficient Evolution of ART Neural Networks", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0742.pdf}, url = {}, size = {}, abstract = { Genetic algorithms have been used to evolve several neural network architectures. In a previous effort, we introduced the evolution of three well known ART architects; Fuzzy ARTMAP (FAM), Ellipsoidal ARTMAP (EAM) and Gaussian ARTMAP (GAM). The resulting architectures were shown to achieve competitive generalization and exceptionally small size. A major concern regarding these architectures, and any evolved neural network architecture in general, is the added overhead in terms of computational time needed to produce the finally evolved network. In this paper we investigate ways of reducing this computational overhead by reducing the computations needed for the calculation of the fitness value of the evolved ART architectures. The results obtained in this paper can be directly extended to many other evolutionary neural network architectures, beyond the studied evolution of ART neural network architectures. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Beume2:2008:cec, author = "Nicola Beume and Tobias Hein and Boris Naujoks and Georg Neugebauer and Nico Piatkowski", title = "To Model or Not to Model: Controlling Pac-Man Ghosts without Incorporating Global Knowledge", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0743.pdf}, url = {}, size = {}, abstract = {The creation of interesting opponents for human players in computer games is an interesting and challenging task. In contrast to up-to-date computer games, e.g. real time strategy games, learning of non-player-character strategies for older games seems to be easier and not that time-consuming. This way, older games, like the famous arcade game Pac-Man, serve as a test bed for the creation of strategies that are fun to play against. The paper at hand uses computational intelligence methods to accomplish this challenge, namely evolutionary algorithms (EA) and artificial neural networks (ANN). The latter are trained on a model of the game whereas the EA learn good behavior by playing. The performance of these two approaches is compared on the original Pac-Man level as well as on other maps with different properties to test the ability of generalizing the learned strategies. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Das:2008:cec, author = "Swagatam Das and Sudeshna Sil and Uday K. Chakraborty", title = "Kernel-Based Clustering of Image Pixels with Modified Differential Evolution", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0744.pdf}, url = {}, size = {}, abstract = {A modified Differential Evolution (DE) algorithm is presented for clustering the pixels of an image in its intensity space. The algorithm requires no prior information about the number of naturally occurring clusters in the image. It employs a kernel-induced similarity measure instead of the conventional sum-of-squares distance. Use of the kernel function makes it possible to partition data that is linearly non-separable and non hyper-spherical in the original input space, into homogeneous groups in a transformed high-dimensional feature space. A novel chromosome representation scheme is adopted for selecting the optimal number of clusters from several possible choices. Extensive performance comparison over a test-suit of five gray scale images (with ground truth) indicates that the proposed algorithm has an edge over a few state-of-the-art algorithms for automatic multi-class image segmentation. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Liu10:2008:cec, author = "Li Liu and Wenxin Liu and David A. Cartes and Nian Zhang", title = "Real Time Implementation of Particle Swarm Optimization Based Model Parameter Identification and an Application Example", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0745.pdf}, url = {}, size = {}, abstract = {Particle Swarm Optimization (PSO) has been widely used in optimization problems. If an identification problem can be transformed into an optimization problem, PSO can be used to identify the unknown parameters in the model. Currently, most PSO based identification or optimization applications can only be applied offline. The difficulties of online implementation mainly come from the unavoidable simulation time to evaluate a candidate solution. In this paper, the techniques for faster than real time simulation are introduced and the hardware implementation details of PSO algorithm are presented. We demonstrate the performance of the described approach by applying it to parameter identification of permanent magnet synchronous motor. The method can be easily implemented using dSPACE®. controller and other hardware controllers. The techniques can also be extended to other online identification and optimization problems. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Chang5:2008:cec, author = "Yaw-Jen Chang and Jui-Ju Tsai ", title = "Process Optimization Based on Neural Network Model and Orthogonal Arrays", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0746.pdf}, url = {}, size = {}, abstract = {This paper presents a systematic and cost-effective approach for process optimization with minimal experimental runs. Based on the experimental design scheme of orthogonal arrays, artificial neural network is used to establish the process model. Moreover, Taguchi-genetic algorithm (TGA) is used to search for the global optimum of the fabrication conditions. The procedure starts planning and conducting the initial experiment with fewer levels. By adding experimental points selected from augmented orthogonal arrays, the process model is corrected. This step is continued until the termination condition has been reached. Then, the optimum given by Taguchi-genetic algorithm is the final solution. The proposed approach provides an effective and economical solution for process optimization. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Yu6:2008:cec, author = "Tina Yu and Dave Wilkinson and Julian Clark and Morgan Sullivan", title = "Evolving Finite State Transducers to Interpret Deepwater Reservoir Depositional Environments", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0748.pdf}, url = {}, size = {}, abstract = {Predicting oil recovery efficiency of deep water reservoirs is a challenging task. One approach to characterise and predict the producibility of a reservoir is by analysing its depositional information. In a deposition-based stratigraphic interpretation framework, one critical step is the identification and labelling of the stratigraphic components in the reservoir according to their depositional information. This interpretation process is labour intensive and can produce different results depending on the stratigrapher who performs the analysis. To relieve stratigrapher's workload and to produce more consistent results, this research developed a methodology to automate this process using various computational intelligent techniques. Using a well log data set, we demonstrated that the developed methodology and the designed work flow can produce finite state transducer models that interpret deepwater reservoir depositional environments adequately. }, keywords = {genetic algorithms, genetic programming}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Bichot:2008:cec, author = "Charles-Edmond Bichot ", title = "A New Meta-Method for Graph Partitioning", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0750.pdf}, url = {}, size = {}, abstract = {In this paper, a new meta-method based on the physical nuclear process is presented. This meta-method called Fusion-Fission is applied to the two different class of graph partitioning problems. This paper presents results found by this method in comparison with results of classical methods for an air traffic management problem, an image segmentation problem and applied to classical benchmarks. All of these applications of the Fusion-Fission method are successful and the results found by this method outperform state-of-the-art graph partitioning packages both on classical benchmarks and on the air traffic management problem. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Mora:2008:cec, author = "A. M. Mora and J. J. Merelo and P. A. Castillo and J. L. J. Laredo and C. Cotta", title = "Influence of Parameters on the Performance of a MOACO Algorithm for Solving the Bi-Criteria Military Path-finding Problem", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0753.pdf}, url = {}, size = {}, abstract = {This paper presents a statistical parameter analysis of the ant colony optimization algorithm that was implemented to solve the bi-criteria military path-finding problem. Three parameters have been studied using analysis of variance (ANOVA) in order to identify their influence in the results and the most suitable values for them: number of ants, number of iterations and exploration/exploitation factor. In addition, a mean analysis has been performed in order to complete the conclusions obtained. The study has yielded optimal values for the parameters under study, and some internal relationships between them have been identified. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Ponweiser:2008:cec, author = "Wolfgang Ponweiser and Tobias Wagner and Markus Vincze", title = "Clustered Multiple Generalized Expected Improvement: A Novel Infill Sampling Criterion for Surrogate Models", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0755.pdf}, url = {}, size = {}, abstract = {Surrogate model-based optimization is a well-known technique for optimizing expensive black-box functions. By applying this function approximation, the number of real problem evaluations can be reduced because the optimization is performed on the model. In this case two contradictory targets have to be achieved: increasing global model accuracy and exploiting potentially optimal areas. The key to these targets is the criterion for selecting the next point, which is then evaluated on the expensive black-box function – the 'infill sampling criterion'. Therefore, a novel approach – the 'Clustered Multiple Generalized Expected Improvement' (CMGEI) – is introduced and motivated by an empirical study. Furthermore, experiments benchmarking its performance compared to the state of the art are presented. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Avery:2008:cec, author = "Phillipa M. Avery and Garrison W. Greenwood and Zbigniew Michalewicz", title = "Coevolving Strategic Intelligence", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0758.pdf}, url = {}, size = {}, abstract = {Strategic decision making done in parallel with the opposition makes it difficult to predict the opposition's strategy. An important aspect in deciding a move is evaluating your opponent's past moves and using them to predict future movement. In the game of TEMPO this is done through the purchase of intelligence, which gives you a relative view of your opponent's choices. The research presented here seeks to evaluate the way this intelligence is used in the current game, and present an alternative method of representation. This alternate mechanism is then used in a coevolutionary system to obtain a computer player that will self-learn the importance of using opposition data in strategic decision making. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Shyang:2008:cec, author = "Woei Shyang and Charles Lakos and Zbigniew Michalewicz and Sven Schellenberg", title = "Experiments in Applying Evolutionary Algorithms to Software Verification", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0759.pdf}, url = {}, size = {}, abstract = {Complex concurrent systems present a significant challenge for software verification. If those systems are safetycritical, the need for software verification becomes particularly pressing, given the serious consequences of unforeseen defects. Complex concurrent systems are characterised by extremely large state spaces. The use of testing techniques for verification means that very little of the state space is explored. On the other hand, model-checking techniques exhaustively examine the state space, but will be stymied by the actual size. In this paper, we discuss some preliminary experiments on the application of evolutionary algorithms to software verification. This approach does not explore the whole state space, but does use heuristics to guide the search through the most promising parts of the state space for locating errors. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Koppen:2008:cec, author = "Mario Koppen and Yutaka Kinoshita and Kaori Yoshida", title = "Auxiliary Objectives for the Evolutionary Multi-Objective Principal Colour Extraction from Logo Images", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0760.pdf}, url = {}, size = {}, abstract = {In this paper, we present an approach to the selection of principal colours for the class of logo images. The approach is using multiple objectives that can be assigned to a colour set, qualifying the selected colours as being principal colours of the image. Since all these objectives have a different preference, and have different computational complexity and granularity, it is not useful to put them all together into a single objective vector. Instead, a three stages procedure is proposed. The first stage optimizes only objectives of high relevance, and lower computational effort. Here, evolutionary multi-objective optimization is used. The second stage re-evaluates the Pareto set of the first stage according to an additional set of objectives. Finally, one solution of the Pareto set from the second stage is selected according to a single objective of highest preference. As suitable objectives for the first stage, the average minimum distance of the colour set to the image pixels, together with the average number of pixel that are closer than a threshold have been found. The approach was studied on a number of logo images, and it could reconstruct the logo images of good visual quality from the found principal colours in the majority of the cases. The experiments also show that the result is usually improved by searching for more principal colours than are present in the logo image, and by repeating the process to find also small, but notable detail structures. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Bate:2008:cec, author = "Iain Bate and Dimitar Kazakov", title = "New Directions in Worst-Case Execution Time Analysis", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0761.pdf}, url = {}, size = {}, abstract = {Most software engineering methods require some form of model populated with appropriate information. Realtime systems are no exception. A significant issue is that the information needed is not always freely available and derived it using manual methods is costly in terms of time and money. Previous work showed how machine learning information derived during software testing can be used to derive loop bounds as part of the Worst-Case Execution Time analysis problem. In this paper we build on this work by investigating the issue of branch prediction. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Siebel:2008:cec, author = "Nils T. Siebel and Sven Grünewald and Gerald Sommer", title = "Creating Edge Detectors by Evolutionary Reinforcement Learning", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0764.pdf}, url = {}, size = {}, abstract = {In this article we present results from experiments where a edge detector was learned from scratch by EANT2, a method for evolutionary reinforcement learning. The detector is constructed as a neural network that takes as input the pixel values from a given image region—the same way that standard edge detectors do. However, it does not have any perimage parameters. A comparison between the evolved neural networks and two standard algorithms, the Sobel and Canny edge detectors, shows very good results. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Qian:2008:cec, author = "Xiaoxue Qian and Xiangrong Zhang and Licheng Jiao and Wenping Ma ", title = "Unsupervised Texture Image Segmentation Using Multiobjective Evolutionary Clustering Ensemble Algorithm", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0765.pdf}, url = {}, size = {}, abstract = {Multiobjective evolutionary clustering approach has been successfully used in data clustering. In this paper, we propose a novel unsupervised machine learning algorithm namely multiobjective evolutionary clustering ensemble algorithm (MECEA) to perform the texture image segmentation. MECEA comprises two main phases. In the first phase, MECEA uses a multiobjective evolutionary clustering algorithm to optimize two complementary clustering objectives: one based on compactness in the same cluster, and the other based on connectedness of different clusters. The output of the first phase is a set of Pareto solutions, which correspond to different tradeoffs between two clustering objectives, and different numbers of clusters. In the second phase, we make use of the meta-clustering algorithm (MCLA) to combine all the Pareto solutions to get the final segmentation. The segmentation results are evaluated by comparing with three known algorithms: K-means, fuzzy K-means (FCM), and evolutionary clustering algorithm (ECA). It is shown that MECEA is an adaptive clustering algorithm, which outperforms the three algorithms in the experiments we carried out. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wesolkowski:2008:cec, author = "S. Wesolkowski and Z. Zhu", title = "Optimizing the Stochastic Fleet Estimation Model Using Evolutionary Computation", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0766.pdf}, url = {}, size = {}, abstract = {We introduce an evolutionary computation framework using genetic algorithms to optimize the Stochastic Fleet Estimation (SaFE) model. SaFe is a Monte Carlo-based model which generates a vehicle fleet based on the set of requirements that the fleet is supposed to accomplish. A genetic algorithm framework is used in order to alternate solutions between different plausible sets of platforms. We use SaFE coupled with a simple cost evaluation based on the output of SaFe as the genetic algorithm's cost function. Results showing a decrease in fleet cost are shown and analyzed. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Davarynejad:2008:cec, author = "M. Davarynejad and M.-R. Akbarzadeh T and Carlos A. Coello Coello", title = "Auto-Tuning Fuzzy Granulation for Evolutionary Optimization", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0767.pdf}, url = {}, size = {}, abstract = {Much of the computational complexity in employing evolutionary algorithms as optimization tool is due to the fitness function evaluation that may either not exist or be computationally very expensive. With the proposed approach, the expensive fitness evaluation step is replaced by an approximate model. An intelligent guided technique via an adaptive fuzzy similarity analysis for fitness granulation is used to decide on use of expensive function evaluation and dynamically adapt the predicted model. In order to avoid tuning parameters in this approach, a fuzzy supervisor as autotuning algorithm is employed with three inputs. The proposed method is then applied to three traditional optimization benchmarks with four different choices for the dimensionality of the search apace. Effect of number of granules on rate of convergence is also studied. In comparison with standard application of evolutionary algorithms, statistical analysis confirms that the proposed approach demonstrates an ability to reduce the computational complexity of the design problem without sacrificing performance. Furthermore, the auto-tuning of the fuzzy supervisory removes the need for exact parameter determination. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Zhou7:2008:cec, author = "Qing Zhou and Xuebin Yang", title = "Modeling and Simulation of Contestable Market Based on Classifier Systems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0768.pdf}, url = {}, size = {}, abstract = {The contestable markets theory provides a new view of business behaviors and other basic economic problems, such as economies of scale and natural monopolies. In order to study the potential competition from new entrants in the contestable market, a competing frame of agents was designed, and a contestable market model integrated with the theory of complex adaptive systems (CAS) was built. We designed the corresponding dynamic competition mechanism, operational mechanism and especially the classifier system evolutionary learning mechanism. By constructing emulator based on SWARM, the dynamic behavior of retailer agent was simulated and then the results of the multi-agent system were analyzed. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Ishibuchi2:2008:cec, author = "Hisao Ishibuchi and Yasuhiro Hitotsuyanagi and Yusuke Nojima", title = "Scalability of Multiobjective Genetic Local Search to Many- Objective Problems: Knapsack Problem Case Studies", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0769.pdf}, url = {}, size = {}, abstract = {It is well-known that Pareto dominance-based evolutionary multiobjective optimization (EMO) algorithms do not work well on many-objective problems. This is because almost all solutions in each population become non-dominated with each other when the number of objectives is large. That is, the convergence property of EMO algorithms toward the Pareto front is severely deteriorated by the increase in the number of objectives. Currently the design of scalable EMO algorithms is a hot issue in the EMO community. In this paper, we examine the scalability of multiobjective genetic local search (MOGLS) to many-objective problems using a hybrid algorithm of NSGA-II and local search. Multiobjective knapsack problems with 2, 4, 6, 8, and 10 objectives are used in computational experiments. It is shown by experimental results that the performance of NSGA-II is improved by the hybridization with local search independent of the number of objectives in the range of 2 to 10 objectives. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Togelius2:2008:cec, author = "Julian Togelius and Renzo {De Nardi} and Alberto Moraglio", title = "Geometric PSO + GP = Particle Swarm Programming", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0771.pdf}, url = {}, size = {}, abstract = {Geometric particle swarm Optimization (GPSO) is a recently introduced formal generalisation of traditional particle swarm Optimization (PSO) that applies naturally to both continuous and combinatorial spaces. In this paper we apply GPSO to the space of genetic programs represented as expression trees, uniting the paradigms of genetic programming and particle swarm Optimization. The result is a particle swarm flying through the space of genetic programs. We present initial experimental results for our new algorithm. }, keywords = {genetic algorithms, genetic programming}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Zhao2:2008:cec, author = "Yaou Zhao and Yuehui Chen and Meng Pan and Qiang Zhu", title = "A Region Reproduction Algorithm for Global Numerical Optimization", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0772.pdf}, url = {}, size = {}, abstract = {This paper introduces a novel numerical stochastic optimization algorithm called Region Reproduction Algorithm (RRA) to solve global numerical optimization problems. The algorithm firstly generates some regions in space which the individual in the population exists. Then we evaluate the regions according to the fitness of the individuals in them. The number of offspring in the region is reproduced by the fitness in the regions. With the algorithm goes on, there would be more offspring in the superior regions than the poorer regions. Because the algorithm is more concerned in the superior regions, it has more probability to find the optimal solution than traditional algorithms. Experiments show that the algorithm is more effective and stable in terms of the solution quality and standard deviation compared with other existing methods, such as GA, PSO, Canonical PSO and EO. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Doerr:2008:cec, author = "Benjamin Doerr and Edda Happ ", title = "Directed Trees: A Powerful Representation for Sorting and Ordering Problems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0773.pdf}, url = {}, size = {}, abstract = {We present a simple framework for dealing with search spaces consisting of permutations. To demonstrate its usefulness, we build upon it a simple (1+1)-evolutionary algorithm for one of the most fundamental problems in computer science, namely the problem of sorting n pairwise comparable items. We give a rigorous proof that the optimization time is at most O(n2) with high probability. Our experimental evaluation shows that it is much better, namely around O(n log n). This compares favorably with the currently best (1+1)-EAs for sorting, for which an optimization time of O(n2 log n) was proven (Scharnow, Tinnefeld and Wegener (2004)) and one of similar order is observed experimentally in this work. Our approach has the particular advantage that it does distinguish between wrong and unexplored information. This allows to retrieve partial, correct information even before the optimal solution has been found. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Yao2:2008:cec, author = "Z. Yao and J. Liu and Y.-G. Wang", title = "Fusing Genetic Algorithm and Ant Colony Algorithm to Optimize Virtual Enterprise Partner Selection Problem", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0774.pdf}, url = {}, size = {}, abstract = {The partner selection in virtual enterprises organization is one of the key issues corporate enterprises experience nowadays. Based on the model of Ant Colony Optimization Algorithm (ACA) in virtual enterprise partner selection, in this paper, we fuse the genetic algorithm into ACA, called fusion algorithm, in order to improve the effect of the partner selection. The fusion algorithm has two steps: (1) it uses the GA to optimize the model of partner selection and takes advantages of rapid convergence of GA in initial search periods. (2) When GA search speed has become slow, the ACA takes over the search process, in which it uses the candidates produced by the GA as the seeds of pheromone used by ACA. By experimental comparison with GA optimization and ACA optimization, it shows that the fusion algorithm has performed better than the GA and ACA optimization, respectively, in both speed and accuracy under our selected numerical case. The fusion algorithm presented in this study may be applicable to similar business problems. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Seo:2008:cec, author = "Yoonho Seo and Chiung Moon and Young-Hoon Moon and Taioun Kim and Sung Shick Kim", title = "Adapting Genetic Algorithm and Tabu Search Approaches for Unidirectional AGV Flowpath Design Problems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0775.pdf}, url = {}, size = {}, abstract = {In this paper we suggest an evolutionary computational approach by applying a combination of a genetic algorithm and a tabu search to obtain a good solution for relatively large unidirectional automated guided vehicle flowpath design problems. Unidirectional flowpaths are used to lessen the traffic control loads for large fleets of vehicles and to increase the efficiency in use of space. The flow path design is one of the most important steps in efficient vehicle systems design. We use an genetic algorithm to obtain partially directed networks, which are then completed and afterwards improved by a tabu search. A set of computational experiments is conducted to show the efficiency of the proposed solution procedure and the results are reported. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Fujii:2008:cec, author = "Seiya Fujii and Tomoharu Nakashima and Hisao Ishibuchi", title = "A Study on Constructing Fuzzy Systems for High-Level Decision Making in a Car Racing Game", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0776.pdf}, url = {}, size = {}, abstract = {In this paper, we examine the performance of fuzzy rule-based systems in a car racing domain. Fuzzy rulebased systems are used for high-level decision making of a car agent. We examine two methods that generate a set of training patterns for constructing fuzzy rule-based systems. We also examine the effect of sensory information on the high-level decision making. The performance of four types of fuzzy rule-based systems are compared in a series of computational experiments. The analysis of using different types of sensory information and different methods for generating training patterns is also performed. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Ang:2008:cec, author = "J. H. Ang and E. J. Teoh and C. H. Tan and K. C. Goh and K. C. Tan", title = "Dimension Reduction Using Evolutionary Support Vector Machines", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0777.pdf}, url = {}, size = {}, abstract = {This paper presents a novel approach of hybridising two conventional machine learning algorithms for dimension reduction. Genetic Algorithm (GA) and Support Vector Machines (SVMs) are integrated effectively based on a wrapper approach. Specifically, the GA component searches for the best attribute set using principles of evolutionary process, after which the reduced dataset is presented to the SVMs. Simulation results show that GA-SVM hybrid is able to produce good classification accuracy and a high level of consistency. In addition, improvements are made to the hybrid by using a correlation measure between attributes as a fitness measure to replace the weaker members in the population with newly formed chromosomes. This correlation measure injects greater diversity and increases the overall fitness of the population }, keywords = {genetic algorithms, genetic programming}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Hu4:2008:cec, author = "X. B. Hu and E. Di Paolo and L. Barnett", title = "Ripple-Spreading Model and Genetic Algorithm for Random Complex Networks: Preliminary Study", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0778.pdf}, url = {}, size = {}, abstract = {Recently complex network theory has been broadly applied in various domains. How to effectively and efficiently optimize the topology of complex networks remains largely an unsolved fundamental question. When applied to the network topology optimization, Genetic Algorithms (GAs) are often confronted with permutation representation, memory-inefficiency and stochastic modeling problems, as well as difficulties in the design of problem-specific evolutionary operators. This paper, inspired by the natural ripple spreading phenomenon, reports a deterministic model of random complex networks. Unlike existing stochastic models, the topology of a random network can be thoroughly determined by some ripple-spreading related parameters in the new model. Therefore, the network topology can be improved by optimize these ripple-spreading related parameters. As a result, no purpose-designed GA is required, but a very basic binary GA, compatible to all classic evolutionary operators, can be applied in a straightforward way. Preliminary simulation results demonstrate the potential of the proposed ripple-spreading model and GA for the topology optimization of random complex networks. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Tang2:2008:cec, author = "Ke Tang and Zai Wang and Xianbin Cao and Jun Zhang", title = "A Multi-Objective Evolutionary Approach to Aircraft Landing Scheduling Problems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0779.pdf}, url = {}, size = {}, abstract = {Scheduling aircraft landings has been a complex and challenging problem in air traffic control for long time. In this paper, we propose to solve the aircraft landing scheduling problem (ALSP) using multi-objective evolutionary algorithms (MOEAs). Specifically, we consider simultaneously minimizing the total scheduled time of arrival and the total cost, and formulate the ALSP as a 2-objective optimization problem. A MOEA named Multi-Objective Neighborhood Search Differential Evolution (MONSDE) is applied to solve the 2-objective ALSP. Besides, a ranking scheme named non-dominated average ranking is also proposed to determine the optimal landing sequence. Advantages of our approaches are demonstrated on two example scenarios. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Guo:2008:cec, author = "Yuanping Guo and Xianbin Cao and Jun Zhang", title = "Multiobjective Evolutionary Algorithm with Constraint Handling for Aircraft Landing Scheduling", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0780.pdf}, url = {}, size = {}, abstract = {Aircraft landing scheduling is a multiobjective optimization problem with lots of constraints, which is difficult to be dealt with by traditional multiobjective evolutionary algorithms with general constraint handling strategies such as constraint-dominate definition. In this paper we pertinently designed an effective constraint handling method, and then presented a multiobjective evolutionary algorithm using the constraint handing method to solve the aircraft landing scheduling problem. Experiments show that our method is able to locate the feasible region in the search space, obtain the jagged Pareto front, and thereby provide efficient schedule for aircraft landing. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Mallipeddi:2008:cec, author = "R. Mallipeddi and P. N. Suganthan", title = "Empirical Study on the Effect of Population Size on Differential Evolution Algorithm", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0781.pdf}, url = {}, size = {}, abstract = {In this paper, we investigate the effect of population size on the quality of solutions and the computational effort required by the Differential evolution (DE) Algorithm. A set of 5 problems chosen from the problem set of CEC 2005 Special Session on Real-Parameter Optimization are used to study the effect of population sizes on the performance of the DE. Results include the effects of various population sizes on the 10 and 30-dimensional versions of each problem for two different mutation strategies. Our study shows a significant influence of the population size on the performance of DE as well as interactions between mutation strategies, population size and dimensionality of the problems. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Jr.2:2008:cec, author = "Maury M. Gouvêa Jr. and Aluizio F. R. Araújo", title = "Diversity Control Based on Population Heterozygosity Dynamics", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0782.pdf}, url = {}, size = {}, abstract = {Maintaining the population diversity in genetic algorithms (GAs), or minimize its loss, may benefit the evolutionary process in several ways. The premature convergence may lead the GA to a non-optimal result, that is, converging to a local optimum. Specially in dynamic problems, the diversity preservation is a crucial issue. In this work, a study of different diversity models based on several works has been made. From these models a diversity reference-model has been created in order to enhance diversity-reference adaptive control (DRAC) [20] performance. This new version of DRAC method was evaluated in case studies using a dynamic test functions presented in [26]. The validation of the proposed adaptive parameter control method was performed comparing its performance with SGA and other diversity-based algorithm. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Dreżewski:2008:cec, author = "Rafa Dreżewski and Leszek Siwik", title = "Agent-Based Multi-Objective Evolutionary Algorithm with Sexual Selection", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0783.pdf}, url = {}, size = {}, abstract = {Evolutionary algorithms are (meta-)heuristic techniques used in the case of search, optimization, and adaptation problems, which cannot be solved with the use of traditional methods. Sexual selection mechanism helps to maintain the population diversity in evolutionary algorithms. In this paper the agent-based realization of multi-objective evolutionary algorithm with sexual selection mechanism is presented. The system is evaluated with the use of Zitzler's test problems and compared to "classical" multi-objective evolutionary algorithms. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Vaccaro:2008:cec, author = "James Vaccaro and Clark Guest", title = "Automated Dynamic Planning and Execution for a Partially Observable Game Model: Tsunami City Search and Rescue", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0784.pdf}, url = {}, size = {}, abstract = {This paper addresses the problem of autonomous dynamic planning and execution (ADP&E) for partially observable model environments. There are three accomplish-ments illustrated in this paper: (1) develop an ADP&E implementation framework for planning and executing in partially observable model environments, (2) design and implement a methodology for adapting planner parameters to improve the overall planning process, and (3) demonstrate the utility of the planning process on a large complex application (i.e., city search and rescue operations). }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Tavares:2008:cec, author = "Jorge Tavares and Francisco B. Pereira and Ernesto Costa", title = "Golomb Rulers: A Fitness Landscape Analysis", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0785.pdf}, url = {}, size = {}, abstract = {Fitness landscape analysis techniques are used to better understand the influence of genetic representations and associated variation operators when solving a combinatorial optimization problem. Several representations for the Optimal Golomb Ruler problem are examined. Common mutation operators such as bit-flip mutation are employed to generate fitness landscapes to study the genetic representations. Furthermore, additional experiments are made to observe the effects of adding heuristics and local improvements to the encodings. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Duro:2008:cec, author = "João António Duro and Jose Valente de Oliveira", title = "Particle Swarm Optimization Applied to the Chess Game", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0786.pdf}, url = {}, size = {}, abstract = {To the best of the authors' knowledge this paper investigates for the first time the applicability of particle swarm optimization (PSO) to a chess player agent endowing it with learning abilities, i.e. allowing the agent to improve its performance based on its experience. A minimax algorithm with alpha beta pruning is used to select the next move of the chess agent. The performance of the agent strongly depends on the heuristic evaluation function available to the minimax algorithm. In this work, board features such as material strength, piece mobility, pawn structure, king safety and control of the centre are used in a parameterized board evaluation function whose weights are optimized using PSO. The simulation results included, illustrate both the feasibility of the proposed approach and reveals that on average, PSO can provide faster learning results than simulated annealing under similar experimental conditions, especially in the presence of bounded computing time. Unfortunately, results also show that, for this application, PSO is highly sensitive to initial conditions. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Xie2:2008:cec, author = "Huayang Xie and Mengjie Zhang and Peter Andreae and Mark Johnston", title = "Is the Not-Sampled Issue in Tournament Selection Critical?", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0787.pdf}, url = {}, size = {}, abstract = {The standard tournament selection samples individuals with replacement. The sampling-with-replacement strategy has its advantages but also has issues. One of the commonly recognised issues is that it is possible to have some individuals not sampled at all during the selection phase. The not-sampled issue aggravates the loss of program diversity. However, it is not clear how the issue affects Genetic Programming (GP) search. This paper investigates the importance of the issue. The theoretical and experimental results show that the issue can be solved and the loss of diversity contributed by not-sampled individuals can be minimised. However, doing so does not appears to significantly improve a GP system. Our conclusion is that the not-sampled issue does not seriously affect the selection performance in the standard tournament selection. }, keywords = {genetic algorithms, genetic programming}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Zamuda:2008:cec, author = "Aleš Zamuda and Janez Brest and Borko Bošković and Viljem Zumer ", title = "Large Scale Global Optimization Using Differential Evolution with Self-Adaptation and Cooperative Co-Evolution", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0788.pdf}, url = {}, size = {}, abstract = {In this paper, an optimization algorithm is formulated and its performance assessment for large scale global optimization is presented. The proposed algorithm is named DEwSAcc and is based on Differential Evolution (DE) algorithm, which is a floating-point encoding evolutionary algorithm for global optimization over continuous spaces. The original DE is extended by log-normal self-adaptation of its control parameters and combined with cooperative co-evolution as a dimension decomposition mechanism. Experimental results are given for seven high-dimensional test functions proposed for the Special Session on Large Scale Global Optimization at 2008 IEEE World Congress on Computational Intelligence. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Tsang:2008:cec, author = "Jeffrey Tsang ", title = "Evolving Trajectories of the N-Body Problem", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0791.pdf}, url = {}, size = {}, abstract = { The N-body problem in k dimensions is the task of determining the time evolution of a system of kN second order ordinary differential equations according to Newton's inverse square law. It comes up in astrophysics as an approximation to celestial systems. Separately, evolved art is the use of evolutionary computation to create artistic works, visual or otherwise. This study attempts to use the trajectories of 4-rotationally symmetric 2-dimensional N-body initial conditions computed under leapfrog integration as visual art. The integration routine inevitably accumulates roundoff error; the initial conditions are evolved separately to both minimize and maximize the number of timesteps before the system becomes unstable. Unexpectedly, genes evolved to maximize the number of timesteps can reach thousands of times the number from random genes; evolving to minimize creates configurations declared unstable in the first timestep. Visual inspection of the pictures obtained also reveals common motifs among high and low fitness genes: two co-circling planets for high fitness, circling close to the center and being far off for low fitness. Some genes do not follow the motifs and are considered visually appealing by the author. The fitness landscape under this representation is highly multimodal with lots of sharp peaks and troughs, and mostly flat outside. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Whigham:2008:cec, author = "Peter A. Whigham and Grant Dick", title = "Exploring the Use of Ancestry as a Unified Network Model of Finite Population Evolution", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0792.pdf}, url = {}, size = {}, abstract = {The evolution of a population is determined by many factors, including the geographic separation of individuals in the population (spatial structure), parent selection via assortative mating (biasing who breeds with whom), environmental gradients, founder effects, disturbance, selection, stochastic effects characterised as genetic drift and so on. Ultimately the interest in studying a population of organisms is about characterising parent selection over time. This paper will examine the evolution of a population under the neutral conditions of genetic drift and for a simple selection model. For drift two conditions are considered: the first is for a range of spatial (geographic) constraints defined by a network; the second is through the use of a tagging system that models assortative mate selection. A simple selection model for the OneMax problem is used to illustrate the response of a population to selection pressure. An ancestry network is constructed representing the shared parent interactions over time. This structure is analyzed as a method for characterising the interactions of a population. The approach demonstrates a unified model to characterise population dynamics, independent of the underlying evolutionary constraints. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Goh:2008:cec, author = "C. K. Goh and Y. S. Ong and K. C. Tan and E. J. Teoh", title = "An Investigation on Evolutionary Gradient Search for Multi-Objective Optimization", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0793.pdf}, url = {}, size = {}, abstract = {Evolutionary gradient search is a hybrid algorithm that exploits the complementary features of gradient search and evolutionary algorithm to achieve a level of efficiency and robustness that cannot be attained by either techniques alone. Unlike the conventional coupling of local search operators and evolutionary algorithm, this algorithm follows a trajectory based on the gradient information that is obtain via the evolutionary process. In this paper, we consider how gradient information can be obtained and used in the context of multi-objective optimization problems. The different types of gradient information are used to guide the evolutionary gradient search to solve multi-objective problems. Experimental studies are conducted to analyze and compare the effectiveness of various implementations. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Nicolau:2008:cec, author = "Miguel Nicolau and Marc Schoenauer", title = "Evolving Scale-Free Topologies Using a Gene Regulatory Network Model", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0795.pdf}, url = {}, size = {}, abstract = {A novel approach to generating scale-free network topologies is introduced, based on an existing artificial Gene Regulatory Network model. From this model, different interaction networks can be extracted, based on an activation threshold. By using an Evolutionary Computation approach, the model is allowed to evolve, in order to reach specific network statistical measures. The results obtained show that, when the model uses a duplication and divergence initialisation, such as seen in nature, the resulting regulation networks not only are closer in topology to scale-free networks, but also exhibit a much higher potential for evolution. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wu6:2008:cec, author = "Ling Wu and Changfeng Xing and Faxing Lu and Peifa Jia", title = "An Anytime Algorithm Applied to Dynamic Weapon-Target Allocation Problem with Decreasing Weapons and Targets", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0796.pdf}, url = {}, size = {}, abstract = {In this paper, an anytime algorithm based on the modified genetic algorithm (GA) is presented to solve the dynamic weapon-target allocation (DWTA) problem. With this algorithm, the targets are assigned with weapons one-by-one according to when each target flies away from the launching zone of the weapons, which is also a deadline for completion of pairing weapon for the target. In this paper, the modified GA is applied to the case that the targets are reduced by continuous interception until all of them are neutralized, and along with it in the process, the number of weapons put in use is decreasing. This is a marginal case for an integrated DWTA problem, and the investigation on it provides a basis for solving more complex DWTA problem. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Lanzi:2008:cec, author = "Pier Luca Lanzi and Daniele Loiacono and Matteo Zanini", title = "Evolving Classifier Ensembles with Voting Predictors", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0797.pdf}, url = {}, size = {}, abstract = {In XCS with computed prediction, namely XCSF, the classifier prediction parameter is replaced by a parametrized prediction function. So far, the works on the computed prediction in XCSF has been limited to evolve a single type of prediction function at once. Recently, several works studied and extended the computed prediction in XCSF. However, it is still not clear how the most adequate prediction function should be chosen for a given problem. In this paper we introduce XCSF with voting predictors that extends XCSF to let it select best prediction function to use in each problem subspace. We compared XCSFV to XCSF on several problems. Our results suggest that XCSFV performs as well as XCSF with the best prediction function in all the tested problems. In addition, XCSFV finds the most accurate prediction function in each problem subspace }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(O'Neill2:2008:cec, author = "Michael O'Neill and Anthony Brabazon and Erik Hemberg", title = "Subtree Deactivation Control with Grammatical Genetic Programming in Dynamic Environments", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0798.pdf}, url = {}, size = {}, abstract = {We investigate the usefulness of a sub-tree deactivation control mechanism which is open to evolutionary learning. It is hypothesised that this representation confers an adaptive advantage in dynamic environments over the standard subtree representation adopted in Genetic Programming. Results presented on benchmark dynamic problem instances provides evidence to support that such an adaptive advantage exists. }, keywords = {genetic algorithms, genetic programming, grammatical evolution}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Hemberg:2008:cec, author = "Erik Hemberg and Michael O'Neill and Anthony Brabazon", title = "Grammatical Bias and Building Blocks in Meta-Grammar Grammatical Evolution", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0802.pdf}, url = {}, size = {}, abstract = {This paper describes and tests the utility of a meta Grammar approach to Grammatical Evolution (GE). Rather than employing a fixed grammar as is the case with canonical GE, under a meta Grammar approach the grammar that is used to specify the construction of a syntactically correct solution is itself allowed to evolve. The ability to evolve a grammar in the context of GE means that useful bias towards specific structures and solutions can be evolved and directly incorporated into the grammar during a run. This approach facilitates the evolution of modularity and reuse both on structural and symbol levels and consequently could enhance both the scalability of GE and its adaptive potential in dynamic environments. In this paper an analysis of the extent that building block structures created in the grammars are used in the solution is undertaken. It is demonstrated that building block structures are incorporated into the evolving grammars and solutions at a rate higher than would be expected by random search. Furthermore, the results indicate that grammar design can be an important factor in performance. }, keywords = {genetic algorithms, genetic programming, grammatical evolution}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Mishra:2008:cec, author = "K. K. Mishra and Brajesh Kumar Singh and Akash Punhani and Lavkush Sharma", title = "Optimizing Melting Rate and Fuel Consumption of Rotary Furnace Using NSGA - II", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0803.pdf}, url = {}, size = {}, abstract = {In this paper we will study one multi objective optimization problem, which is related to small-scale foundry. Rotary furnace is used in smallscale foundry to melt the metal. To increase the production of a foundry we have to increase melting rate of the rotary furnace. We will use NSGA-II to maximize the melting rate of rotary furnace by minimizing the amount of fuel used. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(O'Neill3:2008:cec, author = "Michael O'Neill and Anthony Brabazon", title = "Evolving a Logo Design Using Lindenmayer Systems, Postscript and Grammatical Evolution", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0804.pdf}, url = {}, size = {}, abstract = {We present an application of Grammatical Evolution to the exploration of Lindenmayer systems. The resulting L-systems are expressed in the Postscript language, and as such a Postscript grammar was provided as input to the Grammatical Evolution algorithm. The system takes the form of an interactive evolutionary algorithm, with a human-in-the-loop acting as the fitness function for the generated L-systems. The motivation for this research was to evolve a logo for the UCD Natural Computing Research and Applications group, and to this end the study was a success. }, keywords = {genetic algorithms, genetic programming, grammatical evolution, L-System}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Ast2:2008:cec, author = "Jelmer van Ast and Robert Babuskay and Bart De Schutter", title = "A General Modeling Framework for Swarms", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0806.pdf}, url = {}, size = {}, abstract = {Swarms are characterized by the ability to generate complex behavior from the coupling of simple individuals. While the swarm approach to distributed systems of moving agents is gradually finding a way to engineering applications, a true successful demonstration of an engineered swarm is still missing. One of the reasons for this is the gap between the complexity of the swarms studied in fundamental research and the complexity needed for the application to interesting control problems. In the majority of the research on swarm intelligent systems, the moving agents in the swarm are modeled as simple reactive agents. This model comprises too little intelligence to fully exploit the potential of swarms. In this paper, a general comprehensive swarm framework is introduced and related to the established state of the art. Such a framework is novel and it is a first and important step in the development and analysis of more complex and intelligent swarms. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Keller2:2008:cec, author = "Robert E. Keller and Riccardo Poli", title = "Self-Adaptive Hyperheuristic and Greedy Search", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0809.pdf}, url = {}, size = {}, abstract = {In previous work, we have introduced an effective and resource-efficient hyperheuristic that uses Genetic Programming as its search heuristic on the space of heuristics. Here, we show that the hyperheuristic performs better than purely greedy and even only mostly greedy flavours of hill climbing. We also introduce a generic principle that allows the hyperheuristic to automatically find good parameter values for its effective and efficient search. }, keywords = {genetic algorithms, genetic programming}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Harvey:2008:cec, author = "Nicholas Harvey and Robert Luke and James M. Keller and Derek Anderson ", title = "Speedup of Fuzzy Logic Through Stream Processing on Graphics Processing Units", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0811.pdf}, url = {}, size = {}, abstract = {As the size and operator complexity of a fuzzy logic system increases, computational tractability becomes a problem. There is a significant amount of parallelism in both the creation of the fuzzy rule base and in fuzzy inference. Traditional processors (CPUs) cannot take full advantage of this natural parallelism. Graphics Processing Units (GPUs) speed up rule construction and inference by using up to 128 processing units operating in parallel. Normally, these processors are used to perform high speed graphics calculations for video games, movies, and other areas of intense graphical work. In this paper, a method is discussed for speeding up fuzzy logic by structuring it into a format such that it resembles the standard rendering procedure for a graphics pipeline based on rasterization. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Jin3:2008:cec, author = "Nanlin Jin and Mette Termansen and Klaus Hubacek ", title = "Genetic Algorithms for Dynamic Land-Use Optimization", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0812.pdf}, url = {}, size = {}, abstract = {This paper concerns the use of Genetic Algorithms designed to optimize agricultural land use based on economic criteria. The agricultural areas considered are heather moorland areas in the UK where sheep farming competes with grouse farming and the land is managed differently for each activity. Additionally, there are tenant farmers who rent land for fixed periods and are more interested in short term economic gain and landlords who are more concerned with land value and capability and economic returns in the longer term. This paper explores the application of Genetic Algorithms (GAs) to what we call an inter-temporal optimization. Inter-temporal optimization aims to maximize outcomes for a period of time, not for a time point. GAs are shown to be able to cope with two important features of intertemporal optimization: (1) dynamics; (2) optimizing areas of landscape. These two features make it difficult for traditional approaches such as econometrics and mathematical dynamic programming to tackle such an optimization problem. This paper exemplifies GA's capabilities by tackling an intertemporal optimization problem in land-use decision making. We use GA to represent land-use decisions, to simulate economic and biologic dynamics, and to optimize decisionmakers' objectives in inter-temporal optimization. Experimental results indicate that a long-term inter-temporal optimization smoothes the impacts of dynamics and reduces the number of decision changes. We also compare the experimental results versus the predictions made by agricultural experts. We have found that a GA system forecasts land-use changes in line with experts' predictions. This work demonstrates how GA successfully deals with dynamics for inter-temporal optimization. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Mo:2008:cec, author = "Hongwei Mo and Lifang Xu", title = "Research of Immune Network Clone Optimization Algorithm", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0813.pdf}, url = {}, size = {}, abstract = {Immune clone selection algorithm is a kind of optimization algorithm based on the theory of clone selection. It cannot keep diversity at the end of antibody evolutionary because many antibodies with similar affinity appear. So it leads the algorithm to premature. In order to improve its performance, we use the mechanisms of immune network and crossover operators in genetic algorithm to design immune network clone optimization algorithm(INCOA), which adopts different clone strategies in different processes. The test results show the efficiency of the proposed algorithm in solving optimization problem. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Diedrich:2008:cec, author = "Florian Diedrich and Frank Neumann", title = "Using Fast Matrix Multiplication in Bio-Inspired Computation for Complex Optimization Problems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0814.pdf}, url = {}, size = {}, abstract = {Population-based search heuristics such as evolutionary algorithms or ant colony optimization have been widely used to tackle complex problems in combinatorial optimization. In many cases these problems involve the optimization of an objective function subject to a set of constraints which is very large. In this paper, we examine how population-based search heuristics can be sped up by making use of fast matrix multiplication algorithms. First, we point out that this approach is applicable to the wide class of problems which can be expressed as an Integer Linear Program (ILP). Later on, we investigate the speedup that can be gained by the proposed approach in our experimental studies for the multidimensional knapsack problem. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Mussetta:2008:cec, author = "Marco Mussetta and Paola Pirinoli and Stefano Selleri and Riccardo E. Zich", title = "Development and Validation of Differentiated and Undifferentiated Meta-PSO Techniques for Electromagnetics", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0815.pdf}, url = {}, size = {}, abstract = {Some variations of the Particle Swarm Optimization are here proposed in order to increase the efficiency of the search over the solution space with a negligible overhead in the algorithm complexity and speed. The recently developed Differentiated and Undifferentiated Meta-PSO Technique have been compared in terms of capability and speed of convergence by their application to different test functions; analyses of the optimization technique performances are provided, with respect to the standard PSO convergence rate. Moreover, this paper presents the application of the developed procedures to the optimization of a linear array antenna for mobile dual-band applications. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Mei:2008:cec, author = "Florence Choong Chiao Mei and Somnuk Phon-Amnuaisuk and Mohammad Yusoff Alias", title = "Adaptive GA: An Essential Ingredient in High-Level Synthesis", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0816.pdf}, url = {}, size = {}, abstract = {High-level synthesis, a crucial step in VLSI and System on Chip (SoC) design, is the process of transforming an algorithmic or behavioural description into a structural specification of the architecture realizing the behaviour. In the past, researchers have attempted to apply GAs to the HLS domain. This is motivated by the fact that the search space for HLS is large and GAs are known to work well on such problems. However, the process of GA is controlled by several parameters, e.g. crossover rate and mutation rate that largely determine the success and efficiency of GA in solving a specific problem. Unfortunately, these parameters interact with each other in a complicated way and determining which parameter set is best to use for a specific problem can be a complex task requiring much trial and error. This inherent drawback is overcome in this paper where it presents two adaptive GA approaches to HLS, the adaptive GA operator probability (AGAOP) and adaptive operator selection (AOS) and compares the performance to the standard GA (SGA) on eight digital logic benchmarks with varying complexity. The AGAOP and AOS are shown to be far more robust than the SGA, providing fast and reliable convergence across a broad range of parameter settings. The results show considerable promise for adaptive approaches to HLS domain and opens up a path for future work in this area. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Zhao3:2008:cec, author = "S. Z. Zhao and J. J. Liang and P. N. Suganthan and M. F. Tasgetiren", title = "Dynamic Multi-Swarm Particle Swarm Optimizer with Local Search for Large Scale Global Optimization", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0817.pdf}, url = {}, size = {}, abstract = {In this paper, the performance of dynamic multi-swarm particle swarm optimizer (DMS-PSO) on the set of benchmark functions provided for the CEC2008 Special Session on Large Scale optimization is reported. Different from the existing multi-swarm PSOs and local versions of PSO, the sub-swarms are dynamic and the sub-swarms' size is very small. The whole population is divided into a large number sub-swarms, these sub-swarms are regrouped frequently by using various regrouping schedules and information is exchanged among the particles in the whole swarm. The Quasi-Newton method is combined to improve its local searching ability. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Folleco:2008:cec, author = "Andres Folleco and Taghi M. Khoshgoftaar and Jason Van Hulse and Lofton Bullard ", title = "Software Quality Modeling: The Impact of Class Noise on the Random Forest Classifier", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0818.pdf}, url = {}, size = {}, abstract = {This study investigates the impact of increasing levels of simulated class noise on software quality classification. Class noise was injected into seven software engineering measurement datasets, and the performance of three learners, random forests, C4.5, and Naive Bayes, was analyzed. The random forest classifier was used for this study because of its strong performance relative to well-known and commonly-used classifiers such as C4.5 and Naive Bayes. Further, relatively little prior research in software quality classification has considered the random forest classifier. The experimental factors considered in this study were the level of class noise and the percent of minority instances injected with noise. The empirical results demonstrate that the random forest obtained the best and most consistent classification performance in all experiments. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Huang8:2008:cec, author = "Dong Huang and Cyril Leung and Chunyan Miao", title = "Memetic Algorithm for Dynamic Resource Allocation in Multiuser OFDM Based Cognitive Radio Systems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0819.pdf}, url = {}, size = {}, abstract = {Cognitive Radio (CR) is a novel concept for improving spectrum use in wireless communication systems by permitting secondary (unlicensed) users to access those frequency bands which are not currently being used by primary (licensed) users. A CR user has the ability to change its transmit parameters rapidly according to the environment it senses. Orthogonal frequency division multiplexing (OFDM) modulation is a good candidate for CR systems due to its flexibility in allocating resources among secondary users. In this paper, the design of a fast and efficient method for dynamically allocating subcarriers, transmit powers and bits to secondary users in a multiuser (MU) OFDM-based CR system is considered. A memetic algorithm (MA) is proposed and shown to provide an improved performance over previously reported algorithms. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Jin4:2008:cec, author = "Shuai Jin and Zhaohan Sheng", title = "Modeling and Simulation Research on Diffusion of the Public Voice", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0820.pdf}, url = {}, size = {}, abstract = {The public opinion formation is becoming of a strategic importance at all levels of society. Public voice, a consequence of an asymmetrical informational structure, is interwoven by viewpoints of many individuals involved in the issue. The control and possible handling to manipulate information are now major issues in social organizations, including economy, politics, fashion, and even personal affairs. This paper focuses on simulation of the public voice diffusing based on the social mechanisms at individual level. At first, an analysis was performed to establish a multi-agent-based model analogous to a cellular automata model; it incorporated the variables of individual characters and other individuals' impact to describe the thinking process of individuals, when they were confronted with the public voice. The attributes and hypothesizes referred in the model would be detailed in this section. Then another analyses followed further examine the functions of some parameters and mechanisms in the public voice forming, based on simulation experiments and sensitivity analysis. Finally, exploratory discussions and limitations on the model were presented. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Dhahri:2008:cec, author = "H. Dhahri and Adel. M. Alimi and F. Karray", title = "The Modified Particle Swarm Optimization for the Design of the Beta Basis Function Neural Networks", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0821.pdf}, url = {}, size = {}, abstract = {This paper proposes and describes an effective use of the Heuristic optimization. The focus of this research is on a hybrid method combining two heuristic optimization techniques; Differential evolution algorithms (DE) and particle swarm optimization (PSO), to train the Beta Basis Function neural network (BBFNN). Denoted as PSO-DE, this hybrid technique incorporates concepts from DE and PSO and creates individuals in a new generation not only by crossover and mutation operations as found in DE but also by mechanisms of PSO. The results of various experimental studies using the Mackey time prediction have demonstrated the superiority of the hybrid PSO-DE approach over the other four search techniques in terms of solution quality and convergence rates. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Tirronen:2008:cec, author = "Ville Tirronen and Ferrante Neri and Kirsi Majava and Tommi Kärkkäinen", title = "The "Natura Non Facit Saltus'' Principle in Memetic Computing", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0822.pdf}, url = {}, size = {}, abstract = {This paper proposes the employment of continuous probability distributions instead of step functions for adaptive coordination of the local search in fitness diversity based Memetic Algorithms. Two probability distributions are considered in this study: the beta and exponential distributions. These probability distributions have been tested within two memetic frameworks present in literature. Numerical results show that employment of the probability distributions can be beneficial and improve performance of the original Memetic Algorithms on a set of test functions without varying the balance between the evolutionary and local search components. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Atyabi:2008:cec, author = "Adham Atyabi and Somnuk Phon-Amnuaisuk and Chin Kuan Ho", title = "Cooperative Learning of Homogeneous and Heterogeneous Particles in Area Extension PSO", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0823.pdf}, url = {}, size = {}, abstract = {Particle Swarm Optimization with Area Extension (AEPSO) is a modified PSO that performs better than basic PSO in static, dynamic, noisy, and real-time environments. This paper investigates the effectiveness of cooperative learning AEPSO in a simulated environment. The environment is a 2D landscape planted with various types of bombs with arbitrary explosion times and locations. The simulated-robots' task (i.e., swarm particles) is to disarm these bombs. Different bombs must be disarmed with appropriate robots (i.e., disarming skills and bomb types must correspond) and the robots (hereafter, referred to as agents) do not have full observations of the environment due to uncertainties in their perceptions. In this study, each agent has the ability to disarm different type of bombs in heterogeneous scenario while each agent has the ability to disarm all types of bombs in homogeneous scenario. We found that AEPSO shows reliable performance in both heterogeneous and homogeneous scenarios as compared to the basic PSO. We also found that the proposed cooperative learning is robust in environment where agents' perception are distorted with noise. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Plant:2008:cec, author = "William R. Plant and Gerald Schaefer and Tomoharu Nakashima", title = "An Overview of Genetic Algorithms in Simulation Soccer", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0824.pdf}, url = {}, size = {}, abstract = {This paper discusses the use of genetic algorithms and genetic programming within the simulation soccer domain. Genetic algorithms (GAs) are based on the Darwinian theory of evolution and provide techniques to execute an effective search on a large range of potential solutions to a specific problem. Genetic Programming (GP) uses GA concepts to evolve a computer program. We show how GAs and GP have been applied to the challenging real-time and noisy domain of RoboCup simulation soccer. Among others, genetic approaches can be used to find appropriate actions for a soccer agent during a game, to improve different aspects of team strategy as well as to strengthen the ability of a player or a team in training exercises. }, keywords = {genetic algorithms, genetic programming}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Kononova:2008:cec, author = "Anna V. Kononova and Derek B. Ingham and Mohamed Pourkashanian", title = "Simple Scheduled Memetic Algorithm for Inverse Problems in Higher Dimensions: Application to Chemical Kinetics", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0825.pdf}, url = {}, size = {}, abstract = {This paper proposes a scheme for the hybridisation of an Evolution Strategy framework and periodically scheduled Nelder-Mead algorithm. This relatively simple hybridisation scheme turns out to be efficient for the optimisation problems in higher dimensions. The efficiency of the proposed method is tested for a complex engineering problem, namely an inverse problem of chemical kinetics. An extensive parameter analysis and tuning are presented. Numerical results show the superiority of the proposed methods in comparison with some popular metaheuristics and some tailored algorithms presented in the literature for solving the problem under investigation. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Cheeneebash:2008:cec, author = "Jayrani Cheeneebash and Jose Antonio Lozano and Harry Coomar Shumsher Rughooputh", title = "A Multi-Objective Approach to the Channel Assignment Problem", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0826.pdf}, url = {}, size = {}, abstract = {With the rapid growth of mobile communications, solving the channel assignment problem has now become a new challenge in research. In this paper, we present the Channel Assignment Problem (CAP) from a multi-objective approach. From this new idea, the communication system can be easily managed in case of an unexpected rise in demand in some particular cells. We carry out the experiments with the Philadelphia problem. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wang18:2008:cec, author = "Yu Wang and Bin Li", title = "A Restart Univariate Estimation of Distribution Algorithm: Sampling Under Mixed Gaussian and Levy Probability Distribution", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0827.pdf}, url = {}, size = {}, abstract = {A univariate EDA denoted as ''LSEDA-gl'' for large scale global optimization (LSGO) problems is proposed in this paper. Three efficient strategies: sampling under mixed Gaussian and Levy probability distribution, Standard Deviation Control strategy and restart strategy are adopted to improve the performance of classical univariate EDA on LSGO problems. The motivation of such work is to extend EDAs to LSGO domain reasonably. Comparison among LSEDA-gl, EDA with standard deviation control strategy only (EDA-STDC) and similar EDA version ''continuous univariate marginal distribution algorithm'' UMDAc is carried out on classical test functions. Based on the general comparison standard, the strengths and weaknesses of the algorithms are discussed. Besides, LSEDA-gl is tested on 7 functions with 100, 500, 1000 dimensions provided in the CEC'2008 Special Session on LSGO. This work is also expected to provide a comparison result for the CEC'2008 special session. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Siebel2:2008:cec, author = "Nils T. Siebel and Gerald Sommer", title = "Learning Defect Classifiers for Visual Inspection Images by Neuro-Evolution Using Weakly Labelled Training Data", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0828.pdf}, url = {}, size = {}, abstract = {This article presents results from experiments where a detector for defects in visual inspection images was learned from scratch by EANT2, a method for evolutionary reinforcement learning. The detector is constructed as a neural network that takes as input statistical data on filter responses from a bank of image filters applied to an image region. Training is done on example images with weakly labelled defects. Experiments show good results of EANT2 in an application area where evolutionary methods are rare. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Li21:2008:cec, author = "Yingrong Li and Anastasiya Kolesnikova and Won Don Lee", title = "A New Classification Approach for Handling New Outcomes", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0829.pdf}, url = {}, size = {}, abstract = {Classification is an important technique in the field of Data Mining and Machine Learning. The classifier can predict the class of unknown data based on their given attribute values. In ubiquitous computing environment, a great deal information can be obtained from various sensors. However, with the time going on, new sensor may be recruited. The recruited new sensors may bring new outcomes to the existing attribute. How to handle the new outcomes is a difficult issue. This paper first presents the problem and meanwhile a new method for handling new outcomes is proposed. The old rule is generated from the old data with fewer outcomes and modified and combined with the new data smoothly. In the method, the old rule can improve the performance of classifier constructed only from the new data set. The experiments show that the proposed approach is effective in handling new outcomes. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Ji3:2008:cec, author = "Zhengping Ji and Matthew D. Luciw and Juyang Weng", title = "Epigenetic Sensorimotor Pathways and Its Application to Developmental Object Learning", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0830.pdf}, url = {}, size = {}, abstract = {A pathway in the central nervous system (CNS) is a path through which nervous signals are processed in an orderly fashion. A sensorimotor pathway starts from a sensory input and ends at a motor output, although almost all pathways are not simply unidirectional. In this paper, we introduce a simple, biologically inspired, unified computational model – Multi-layer In-place Learning Network (MILN), with a design goal to develop a recurrent network, as a function of sensorimotor signals, for open-ended learning of multiple sensorimotor tasks. The biologically motivated MILN provides automatic feature derivation and pathway refinement from the temporally real-time inputs. The work presented here is applied in the challenging application field of developing reactive behaviours from a video camera and a (noisy) radar range sensor for a vehicle-based robot in open, natural driving environments. An internal model of the agent's experience of the environments is created and refined from the ground-up using a cell-centered model, based on the genomic equivalence principle. The outputs can be imposed by a teacher, at the same time as the learning is active. At any time instant, sensory information from the radar allows the system to focus its visual analysis on relatively small areas within the image plane (attention selection), in a computationally efficient way, suitable for real-time training. This system was trained with data from 10 different city and highway road environments, and cross validation shows that MILN was able to correctly recognize above 95percent of the radarextracted images from the multiple environments. The in-place learning mechanism compares with other learning algorithms favorably, as results of a comparison indicate that in-place learning is the only one to fit all the specified criteria of development of a general-purpose sensorimotor pathway. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Maxville:2008:cec, author = "Valerie Maxville and Chiou Peng Lam and Jocelyn Armarego", title = "Supporting Component Selection with a Suite of Classifiers", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0833.pdf}, url = {}, size = {}, abstract = {Software selection involves the assimilation of information and results for each candidate to enable a comparison for decisions to be made. The processes and tools developed assist with software selection to enhance quality, documentation and repeatability. The CdCE process aims to retain and document the information used in selection to assist decisions and to document them for reference as the system evolves. This paper describes the CdCE process and our approach to assist the shortlisting of candidates through a suite of classifiers. The application of the suite is illustrated using a selection and evaluation case study. Applying this approach helps retain the multidimensional nature of the selection process and enhances user awareness in the decision making process. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Luitel:2008:cec, author = "Bipul Luitel and Ganesh K. Venayagamoorthy", title = "Differential Evolution Particle Swarm Optimization for Digital Filter Design", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0834.pdf}, url = {}, size = {}, abstract = {In this paper, swarm and evolutionary algorithms have been applied for the design of digital filters. Particle swarm optimization (PSO) and differential evolution particle swarm optimization (DEPSO) have been used here for the design of linear phase finite impulse response (FIR) filters. Two different fitness functions have been studied and experimented, each having its own significance. The first study considers a fitness function based on the passband and stopband ripple, while the second study considers a fitness function based on the mean squared error between the actual and the ideal filter response. DEPSO seems to be promising tool for FIR filter design especially in a dynamic environment where filter coefficients have to be adapted and fast convergence is of importance. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Kawada:2008:cec, author = "Kazuo Kawada and Toru Yamamoto", title = "Design of an Evolutionary Controller and Its Application", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0839.pdf}, url = {}, size = {}, abstract = {PID control schemes still continue to be widely used for most industrial control systems. This is mainly because PID controllers have simple control structures, and are simple to maintain and tune. However, it is difficult to find a set of suitable control parameters in the case of time-varying and/or nonlinear systems. For such a problem, the robust controller has been proposed. Although it is important to choose the suitable nominal model in designing the robust controller, it is not usually easy. In this paper, a new robust PD controller design scheme is proposed, in which the suitable nominal model is designed using a real-coded genetic algorithm. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Jiao:2008:cec, author = "Jun Jiao and Wu-Wei Chen and Kwong-Sak Leung and Shao-Wen Li and Ji-Xian Wang", title = "Intelligent Variable Structure Control for Automated Guided Vehicle", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0840.pdf}, url = {}, size = {}, abstract = {Aiming at Automated Guided Vehicle (AGV) dynamic model characteristics, a Variable Structure Control based on genetic algorithm (GA) and least square-support vector machine (LS-SVM) was designed. Parameters, predetermined by conventional reaching law, were regulated by LS-SVM online. It was shown that system shattering is eliminated. Simulation results indicated that this method possesses the advantages of higher precision, greater adaptability and robustness, as compared to the conventional Variable Structure Control methods. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Miyagawa:2008:cec, author = "Eiji Miyagawa and Toshimichi Saito", title = "Particle Swarm Optimizers with Grow-and-Reduce Structure", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0841.pdf}, url = {}, size = {}, abstract = {This paper presents an improved version of PSO having grow-and-reduce structure. When a particle is trapped into a local optimum, a new particle is born at a position away from the trap and is connected to some/all of existing particles. If a particle can not escape from the trap, the particle is deleted in order to suppress excessive swarm grows. We have adopted three basic population topology: complete graph, ring and tree. Performing basic numerical experiments, the algorithm performance is investigated. The results suggest that the ''growand- reduce'' is very effective for escape from a trap and the tree topology has effective flexibility to realize the optimization. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wu7:2008:cec, author = "Chuansheng Wu and Jinrong He and Xiufen Zou", title = "A Genetic Algorithm Approach for Selecting Tikhonov Regularization Parameter", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0842.pdf}, url = {}, size = {}, abstract = {This paper presents a Genetic Algorithm approach for selecting a Tikhonov regularization parameter. In using Tikhonov parameters regularization for solving ill problems, in terms of Inverse problems of the first category, we could first apply discrete regularization method to transfer it into linear algebraic equations, and then get regular solutions by solving of Euler equations which is of minimum functional equivalence for Tikhonov. As to the selection of regularization parameter, this paper choose a Genetic Algorithm approach, which takes Morozov deviation equation as fitness function for Genetic Algorithm approach, and dynamically selects regularization parameter by designing genetic operation like crossover, mutation and genetic selection. Numerical results show that it is a feasible as well as an effective approach for selecting regularization parameter. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Fieldsend:2008:cec, author = "Jonathan E. Fieldsend and Richard M. Everson", title = "On the Efficient Use of Uncertainty when Performing Expensive ROC Optimisation", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0843.pdf}, url = {}, size = {}, abstract = {When optimising receiver operating characteristic (ROC) curves there is an inherent degree of uncertainty associated with the operating point evaluation of a model parameterisation x. This is due to the finite amount of training data used to evaluate the true and false positive rates of x. The uncertainty associated with any particular x can be reduced, but only at the computation cost of evaluating more data. Here we explicitly represent this uncertainty through the use of probabilistically non-dominated archives, and show how expensive ROC optimisation problems may be tackled by only evaluating a small subset of the available data at each generation of an optimisation algorithm. Illustrative results are given on data sets from the well known UCI machine learning repository. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Zhihui:2008:cec, author = "Huang Zhihui and Kan Shulin", title = "Based on MES for Implement Optimization of Production Scheduling of Auto Electronic Parts Manufacture", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0848.pdf}, url = {}, size = {}, abstract = {Characteristic of Auto electronic parts production industry are introduced at first. And with the background of the practical demands of Auto electronic parts production industry, with model of Browser/Server (B/S) and program as Microsoft.NET, Visual C++, SQLSever2000 was introduced. Meanwhile, this architecture of Manufacturing Execution System (MES) design theory and database structural charts of the whole system are presented. Information integration and production scheduling optimization function of MES implement information fusion and combination between PCS and ERP, realization the optimization control and management of the Auto electronic parts enterprise production process, which improves enterprise's synthesis competition ability. The two layers integrated automation system production indexes and scheduling as the design core realization optimization of production scheduling indexes. The MES that adopting this method is achieved the integration of management and control. This system has been applied to the Auto electronic parts plant and the production efficiency is increased. The successful implementation of this system has explored a new method for the overall optimization of the large systems in discrete manufacturing enterprise. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Chiu:2008:cec, author = "Hsiao-Ya Chiu ", title = "Designing a Satisfaction-Oriented Option Analysis Framework to Support Organization Decisions on Online Training Project", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0851.pdf}, url = {}, size = {}, abstract = {Online training or web-based training has been widely adopted by organizations. Due to the high cost of time, money and human resources, it is important for decision makers to superintend those projects' performance. However, existing frameworks for evaluating such project's performance are rare. In an attempt to help decision makers monitor their online training projects, this study proposes a satisfaction-oriented framework to evaluate nonprofit-oriented projects' performance using an option pricing approach. This framework can be also seamlessly applied to any IT project that has both quantitative and qualitative factors which require evaluation under uncertainties. In order to construct an ideal evaluation framework, this study proposes a satisfaction-oriented option analysis framework that can be applied to evaluate both quantitative and qualitative measurements on the same scale. Meanwhile, this study constructs a measurement framework that integrates Kirkpatrick's and Black-Scholes models with theoretical groundings to support performance evaluation. At the end of this paper provides an empirical study that demonstrates the analytical procedures to apply the proposed framework to real world applications. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Montero:2008:cec, author = "Elizabeth Montero and María Cristina Riff and Daniel Basterrica", title = "Improving MMAS Using Parameter Control", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0852.pdf}, url = {}, size = {}, abstract = {Tuning parameters values in metaheuristics is a time consuming task. Techniques to control parameters during the execution have been successfully applied into evolutionary algorithms. The key idea is that the algorithm themselves computes its parameters values according to its current state of the search. In this paper, we propose a strategy to include parameters control on ants based algorithms. We have tested our approach to solve hard instances of the travel salesman problem using MMAS. The tests shown that in some cases, it is possible to obtain better results than the reported ones for the same algorithm, by including a parameter control strategy. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Riff:2008:cec, author = "María-Cristina Riff and Teddy Alfaro and Xavier Bonnaire and Carlos Grandón", title = "EA-MP: An Evolutionary Algorithm for a Mine Planning Problem", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0854.pdf}, url = {}, size = {}, abstract = {In this paper we introduce an evolutionary algorithm for solving a copper mine planning problem. In the last 10 years this real-world problem has been tackled using linear integer programming and constraint programming. However, because it is a large scale problem, the model must be simplified by relaxing many constraints in order to obtain a near-optimal solution in a reasonable time. We now present an algorithm which takes into account most of the problem constraints and it is able to find better feasible solutions than the approach that has been used until now. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Iclanzan:2008:cec, author = "David Iclanzan and D. Dumitrescu", title = "How Can Artificial Neural Networks Help Making the Intractable Search Spaces Tractable", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0855.pdf}, url = {}, size = {}, abstract = {In this paper, we propose the incorporation of Artificial Neural Network (ANN) based supervised and unsupervised Machine Learning techniques into the evolutionary search, in order to detect strongly connected variables. The cost of extending a search method with an ANN based learning skill is relatively low, the memory requirements and model building cost being at most linearithmic in the number of variables. As a case study, we show how these mechanisms can enable the simple (1+1) Evolutionary Algorithm to efficiently solve hard problems, which are provably intractable using just fixed representation and problem independent operators. Furthermore, simulation results show, that on test suites characterized by strong variable coupling, the ANN extended (1+1) Evolutionary Algorithm qualitatively outperform the best known, full-featured, population based Estimation of Distribution Algorithms. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Moioli:2008:cec, author = "Renan C. Moioli and Patricia A. Vargas and Fernando J. Von Zuben", title = "Towards the Evolution of an Artificial Homeostatic System", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0856.pdf}, url = {}, size = {}, abstract = {This paper presents an artificial homeostatic system (AHS) devoted to the autonomous navigation of mobile robots, with emphasis on neuro-endocrine interactions. The AHS is composed of two modules, each one associated with a particular reactive task and both implemented using an extended version of the GasNet neural model, denoted spatially unconstrained GasNet model or simply non-spatial GasNet (NSGasNet). There is a coordination system, which is responsible for the specific role of each NSGasNet at a given operational condition. The switching among the NSGasNets is implemented as an artificial endocrine system (AES), which is based on a system of coupled nonlinear difference equations. The NSGasNets are synthesized by means of an evolutionary algorithm. The obtained neuro-endocrine controller is adopted in simulated and real benchmark applications, and the additional flexibility provided by the use of NSGasNet, together with the existence of an automatic coordination system, guides to convincing levels of performance. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Handoko:2008:cec, author = "S. D. Handoko and C. K. Kwoh and Y. S. Ong and M. H. Lim", title = "A Study on Constrained MA Using GA and SQP: Analytical vs. Finite-Difference Gradients", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0857.pdf}, url = {}, size = {}, abstract = {Many deterministic algorithms in the context of constrained optimization require the first-order derivatives, or the gradient vectors, of the objective and constraint functions to determine the next feasible direction along which the search should progress. Although the second-order derivatives, or the Hessian matrices, are also required by some methods such as the sequential quadratic programming (SQP), their values can be approximated based on the first-order information, making the gradients central to the deterministic algorithms for solving constrained optimization problems. In this paper, two ways of obtaining the gradients are compared under the framework of the simple memetic algorithm (MA) employing genetic algorithm (GA) and SQP. Despite the simplicity and straightforwardness of the finite-difference gradients, faster convergence rate can be achieved when the analytical gradients can be made available. The savings on the number of function evaluations as well as the amount of time taken to solve some benchmark problems are presented along with some discussions. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Wang19:2008:cec, author = "N. F. Wang and Y. W. Yang and K. Tai", title = "Optimization of Structures Under Load Uncertainties Based on Hybrid Genetic Algorithm", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0859.pdf}, url = {}, size = {}, abstract = {This paper describes a technique for design under uncertainty based on hybrid genetic algorithm. In this work, the proposed hybrid algorithm integrates a simple local search strategy with a constrained multi-objective evolutionary algorithm. The local search is integrated as the worst-casescenario technique of anti-optimization. When anti-optimization is integrated with structural optimization, a nested optimization problem is created, which can be very expensive to solve. The paper demonstrates the use of a technique alternating between optimization (general genetic algorithm) and anti-optimization (local search) which alleviates the computational burden. The method is applied to the optimization of a simply supported structure, to the optimization of a simple problem with conflicting objective functions. The results obtained indicate that the approach can produce good results at reasonable computational costs. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Mallipeddi2:2008:cec, author = "R. Mallipeddi and P. N. Suganthan", title = "Evaluation of Novel Adaptive Evolutionary Programming on Four Constraint Handling Techniques", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0860.pdf}, url = {}, size = {}, abstract = {This paper presents empirical studies carried out to evaluate the performance of different constraint handling methods on Constrained Real-Parameter Optimization using a novel adaptive Evolutionary Programming (EP). Twenty five runs have been conducted for each of the 13 test problems considered. Our experimental results show that no single Constraint Handling method can be the best for all problems i.e, each Constraint Handling method is suitable only for a subset of problems. We also show that the novel adaptive EP proposed in this paper has improved performance over the Classical EP (CEP). }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Mantere:2008:cec, author = "Timo Mantere and Janne Koljonen", title = "Solving and Analyzing Sudokus with Cultural Algorithms", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0862.pdf}, url = {}, size = {}, abstract = {This paper studies how cultural algorithm suits to solving and analyzing Sudoku puzzles. Sudoku is a number puzzle that has recently become a worldwide phenomenon. It can be regarded as a combinatorial problem, but when solved with evolutionary algorithms it can also be handled as a constraint satisfaction or multi-objective optimization problem. The objectives of this study were (1) to test if a cultural algorithm with a belief space solves Sudoku puzzles more efficiently than a normal permutation genetic algorithm, (2) to see if the belief space gathers information that helps analyze the results and improve the method accordingly, (3) to improve our previous Sudoku solver presented in CEC2007. Experiments showed that proposed the cultural algorithm performed slightly better than the previous genetic algorithm based Sudoku solver. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Bhattacharya:2008:cec, author = "Maumita Bhattacharya ", title = "DPGA: A Simple Distributed Population Approach to Tackle Uncertainty", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0863.pdf}, url = {}, size = {}, abstract = {Evolutionary algorithms (EA) have been widely accepted as efficient optimizers for complex real life problems [2]. However, many real life optimization problems involve time-variant noisy environment, which pose major challenges to EA-based optimization. Presence of noise interferes with the evaluation and the selection process of EA and adversely affects the performance of the algorithm [6]. Also presence of noise means fitness function can not be evaluated and it has to be estimated instead. Several approaches have been tried to overcome this problem, such as introduction of diversity (hyper mutation, random immigrants, special operators) or incorporation of memory of the past (diploidy, case based memory) [5]. In this paper we propose a method, DPGA (distributed population genetic algorithm) that uses a distributed population based architecture to simulate a distributed, self-adaptive memory of the solution space. Local regression is used in each sub-population to estimate the fitness. Specific problem category considered is that of optimization of functions with time variant noisy fitness. Successful applications to benchmark test problems ascertain the proposed method's superior performance in terms of both adaptability and accuracy. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Santana:2008:cec, author = "Roberto Santana and Pedro Larrañaga and Jose A. Lozano", title = "Component Weighting Functions for Adaptive Search with EDAs", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0865.pdf}, url = {}, size = {}, abstract = {This paper introduces the component weighting approach as a general optimization heuristic to increase the likelihood of escaping from local optima by dynamically modifying the fitness function. The approach is tested on the optimization of the simplified hydrophobic-polar (HP) protein problem using estimation of distribution algorithms (EDAs). We show that the use of component weighting together with statistical information extracted from the set of selected solutions considerably improve the results of EDAs for the HP problem. The paper also elaborates on the use of probabilistic modeling for the definition of dynamic fitness functions and on the use of combinations of models. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Caputo:2008:cec, author = "D. Caputo and F. Grimaccia and M. Mussetta and R. E. Zich", title = "An Enhanced GSO Technique for Wireless Sensor Networks Optimization", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0866.pdf}, url = {}, size = {}, abstract = {Sensor networks are an emerging field of research which combines many challenges of modern computer science, wireless communication and mobile computing. They present significant systems challenges involving the use of large numbers of resource-constrained nodes operating essentially unattended and exposed to potential local communication failures. The physical constraints of a sensor network, especially in terms of energy, are an intrinsically complex problem and request to take into account many parameters at the same time; in this paper we investigate the possibility of using evolutionary algorithms to optimize the lifetime of a network with a limited power supply. The Genetical Swarm Optimization (GSO) is a recently introduced hybrid technique between GA and PSO. It has developed in order to exploit in the most effective way the uniqueness and peculiarities of these classical optimization approaches, and it can be used to solve combinatorial optimization problems. In this paper the authors present an enhancement of this technique for application in the maximization of the lifetime a wireless sensor network. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(He3:2008:cec, author = "Pei He and Lishan Kang and Ming Fu", title = "Formality Based Genetic Programming", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0867.pdf}, url = {}, size = {}, abstract = {Genetic programming (GP) is an illogical method for automatic programming. It shows creativity in discovering a desired program to solve problem, but in essence bases its searching principle on software testing. This paper is dedicated to establishing a novel GP which combines classical GP and formal approaches like Hoare's logic, model checking, and automaton, etc. The result indicates these methods can collaborate in the framework pretty well. As has been demonstrated by the experiment, they work in a way that preserves their advantages while each compensates for the deficiencies of the other. So, once an approximate program is obtained, we can say with certainty it is correct with respect to its corresponding pre- and post-conditions. }, keywords = {genetic algorithms, genetic programming}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Teixeira:2008:cec, author = "Flavio Teixeira and Alexandre Romariz", title = "Digital Filter Arbitrary Magnitude and Phase Approximations - Statistical Analysis applied to a Stochastic-Based Optimization Approach", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0868.pdf}, url = {}, size = {}, abstract = {This paper presents a statistical analysis of stochastic-based optimization algorithms applied to a digital filter arbitrary magnitude and phase approximation design problem. Using an already developed rigorous statistical methodology, a completely randomized design is set up and best parameters values are estimated for the adaptive algorithms applied to a specific non-linear approximation problem. After finding the best parameter values, an additional completely randomized design is set up, comparing the performance of the adaptive algorithms with a Quasi-Newton algorithm. Results for the statistical analysis are presented and the performance for different optimization algorithms with the best parameter values are analyzed. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Austermann:2008:cec, author = "Anja Austermann and Seiji Yamada", title = "Learning to Understand Multimodal Rewards for Human-Robot-Interaction using Hidden Markov Models and Classical Conditioning", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0869.pdf}, url = {}, size = {}, abstract = {We are proposing an approach to enable a robot to learn the speech, gesture and touch patterns, that its user employs for giving positive and negative reward. The learning procedure uses a combination of Hidden Markov Models and a mathematical model of classical conditioning. To facilitate learning, the robot and the user go through a training task where the goal is known, so that the robot can anticipate its user's commands and rewards. We outline the experimental framework and the training task and give details on the proposed learning method evaluating the applicability of classical conditioning for the task of learning user rewards given in one or more modalities, such as speech, gesture or physical interaction. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Tantar:2008:cec, author = "Emilia Tantar and Clarisse Dhaenens and Jose Rui Figueira and El-Ghazali Talbi", title = "A priori Landscape Analysis in Guiding Interactive Multi-Objective Metaheuristics", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0871.pdf}, url = {}, size = {}, abstract = {The integration of information provided by an a priori landscape analysis as a guiding tool for interactive EMO methods is proposed. For this purpose, a new type of a priori landscape analysis is introduced, namely ellipse enclosure of the feasible solutions set in the solution space. The interaction takes place in the solution space, the user having as visual guiding tools the computed enclosure as well as the set of solutions found at the previous search phase. Furthermore, reference points are specified by the user thus directing the search. The effectiveness and efficiency of the method are supported through statistical experimentation performed on the bi-objective permutation flow shop problem. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Zhang16:2008:cec, author = "Yan Zhang and Qun Dang and Zhu Jiang and Yong Xuan Huang", title = "A Bi-level Blocked Estimation of Distribution Algorithm with Local Search for Maximum Clique Problems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0872.pdf}, url = {}, size = {}, abstract = {Maximum Clique Problem (MCP) is a complicated deceptive problem for estimation of distribution algorithms (EDAs). The univariate EDAs cannot use the correlations of the variables and the advanced EDAs perform poor due to the expensive computational cost in building the appropriate probability models. In this paper, by using the special structure of MCP, a new Bi-level Blocked Probability model (BBP) is constructed, which achieves the relationships using in a bivariate probability model at the computational cost of univariate probability model. Integrating promising neighborhood search techniques, a new EDA algorithm, called Bi-level Blocked Estimation of Distribution Algorithm (BBEDA) is proposed for MCP. Comparative experiments on extensive DIMACS Benchmark instances show that the proposed BBEDA can be competitive with the evolutionary algorithm with guided mutation (the best evolutionary algorithm reported so far) in terms of solution quality and computational performance. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Jiang4:2008:cec, author = "He Jiang and Zhilei Ren and Yan Hu", title = "A Sampling Based FANT for the 3-Dimensional Assignment Problem", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0873.pdf}, url = {}, size = {}, abstract = {In this paper, we proposed a sampling based FANT (S-FANT) for the 3-Dimensional Assignment Problem (AP3). The AP3 is a well-known NP-hard problem, which aims to choose n disjoint triplets with minimum cost from 3 disjoint sets of size n. Due to its intractability, many heuristics have been proposed to obtain near optimal solutions in reasonable time. Since the solution space size of the AP3 is (n!)2, traditional FANT algorithms can't work well for the AP3. In this paper, we showed that, those triplets frequently contained by local optimal solutions are likely to belong to global optimal solutions. Therefore, those triplets can help the ant to converge faster to global optimal solutions. Upon the observation above, the S-FANT consists of two phases. In the sampling phase, a multi-restart scheme is employed to generate local optimal solutions. After that, the pheromone is initialized according to the frequency of triplets appearing in those local optimal solutions. In the FANT phase, a standard FANT algorithm is conducted to explore for better solutions. Extensive experimental results on the standard AP3 benchmark indicated that the new algorithm outperforms the state-of-the-art heuristics in terms of solution quality. Work of this paper not only provides a new efficient heuristic for the AP3, but shows a promising way to design FANT algorithms for those NP-hard problems with large solution space. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Xu6:2008:cec, author = "Jian Xu and Gilles Gonvalves and Tinte Hsu", title = "Genetic Algorithm for the Vehicle Routing Problem with Time Windows and Fuzzy Demand", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0875.pdf}, url = {}, size = {}, abstract = {This paper considers a VRP with soft time windows and fuzzy demand (VRPTWFD). The objective is to minimize both the total distance covered by all vehicles as well as the sum of lateness at the customer's due to the violation of time windows. This VRPTWFD is formulated as a two stages recourse model in the context of stochastic programming. The goal is then to minimize the expected cost, which includes the initial cost of the solution found in first stage and the additional cost due to the route failure in second stage. The theory of possibility is applied in the capacity constraint. In addition, a route failure estimation method is proposed to evaluate the additional cost as well as the expected cost. A genetic algorithm, in which a simulation phase based on sampling scenarios to evaluate the fitness of chromosome, is specifically designed to solve the two stages recourse model for the VRPTWFD. Finally an experimental evaluation of this developed algorithm is validated on a few VRPTWFD modified from the Solomon benchmarks. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Rachmawati:2008:cec, author = "L. Rachmawati and D. Srinivasan", title = "Multi-Objective Evolutionary Algorithm-Assisted Automated Parallel Parking", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0876.pdf}, url = {}, size = {}, abstract = {The ease with which a human expert driver performs the complex tasks involved in parallel-parking a nonholonomic vehicle motivates the mimicry of an human driving behaviour in automation of the task. This paper presents such an algorithm to achieve automated parallel parking in tight spaces. Unlike other approaches rooted in neural networks and/or fuzzy logic, the proposed algorithm performs maneuvers closely modeled after human driving instructions. Stevens' power law is employed in modeling perceived physical quantities on which the instructions operate while the uncertainty inherent in the natural language formulation is represented by Gaussian distribution. The algorithm consists of five stages: position alignment in preparation for the backward S-turn, the first half of the Sturn, position alignment for the second part of the S-turn, the second part of the S-turn and longitudinal adjustment. Negotiation of available parking space in the second part of the S-turn, arguably the most difficult part, is performed with the help of a rule base documenting the relation between steering angle, vehicle orientation and distance traversed. To achieve parking accuracy and avoid collision in the maneuver, the appropriate steering angle must be employed. This angle is approximated from the most suitable rule, which identification is essentially a multi-objective problem addressed here by a Multi-Objective Evolutionary Algorithm. Computer simulations demonstrate the success of the approach. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Yamazaki:2008:cec, author = "Hirotaka Yamazaki and Ivan Tanev and Tomoyuki Hiroyasu and Katsunori Shimohara", title = "On the Generality of the Evolved Driving Rules of an Agent Operating a Model of a Car", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0877.pdf}, url = {}, size = {}, abstract = {We present an approach for automated evolutionary design of the functionary of driving agent, able to operate a software model of fast running car. The objective of our work is to automatically discover a set of driving rules (if existent) that are general enough to be able to adequately control the car in all sections of predefined circuits. In order to evolve an agent with such capabilities, we propose an indirect, generative representation of the driving rules as algebraic functions of the features of the current surroundings of the car. These functions, when evaluated for the current surrounding of the car yield concrete values of the main attributes of the driving style (e.g., straight line velocity, turning velocity, etc.), applied by the agent in the currently negotiated section of the circuit. Experimental results verify both the very existence of the general driving rules and the ability of the employed genetic programming framework to automatically discover them. The evolved driving rules offer a favourable generality, in that a single rule can be successfully applied (i) not only for all the section of a particular circuit, but also (ii) for the sections in several a priori defined circuits featuring different characteristics. }, keywords = {genetic algorithms, genetic programming}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(García-Sebastian:2008:cec, author = "Maite García-Sebastian and Alex Manhaes Savio and Manuel Graña ", title = "Comments on an Evolutionary Intensity Inhomogeneity Correction Algorithm", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0878.pdf}, url = {}, size = {}, abstract = {We discuss some aspects of a well known algorithm for inhomogeneity intensity correction in Magnetic Resonance Imaging (MRI), the Parametric Bias Correction (PABIC) algorithm. In this approach, the intensity inhomogeneity is modelled by a linear combination of 2D or 3D Legengre polynomials (computed as outer products of 1D polynomials). The model parameter estimation process proposed in the original paper is similar to a (1+1) Evolution Strategy, with some small and subtle differences. In this paper we discuss some features of the algorithm elements, trying to uncover sources of undesired behaviours and the limits to its applicability. We study the energy function proposed in the original paper and its relation to the image formation model. We also discuss the original minimisation algorithm behaviour. We think that this detailed discussion is needed because of the high impact that the original paper had in the literature, leading to an implementation into the well known ITK library, which means that it has become a de facto standard. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Langdon2:2008:cec, author = "W. B. Langdon", title = "Evolving GeneChip Correlation Predictors on Parallel Graphics Hardware", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0881.pdf}, url = {}, size = {}, abstract = {A GPU is used to datamine five million correlations between probes within Affymetrix HG-U133A probesets across 6685 human tissue samples from NCBI's GEO database. These concordances are used as machine learning training data for genetic programming running on a Linux PC with a RapidMind OpenGL GLSL backend. GPGPU is used to identify technological factors influencing High Density Oligonuclotide Arrays (HDONA) performance. GP suggests mismatch (PM/MM) and Adenosine/Guanine ratio influence microarray quality. Initial results hint that Watson-Crick probe self hybridisation or folding is not important. Under GPGPGPU an nVidia GeForce 8800 GTX interprets 300 million GP primitives/second (300 MGPops, approx 8 GFLOPS).}, keywords = {genetic algorithms, genetic programming}, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Jang:2008:cec, author = "Woo Seok Jang and Hwan Il Kang and Byung Hee Lee", title = "Hybrid Simplex-Harmony Search Method for Optimization Problems", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0884.pdf}, url = {}, size = {}, abstract = {This paper proposes the hybrid Simplex Algorithm(SA)-Harmony Search(HS) Method. HS method is, the evolutionary algorithm, conceptualised using the musical process of searching for optimisation problems. SA helps HS find optimisation solution more accurately and quickly. In this paper, the performances of proposed algorithm are compared with the original HS method and other algorithms through unconstrained functions and constrained functions. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Fukui:2008:cec, author = "Shinji Fukui and Yuji Iwahori and Robert J. Woodham", title = "GPU Based Extraction of Moving Objects without Shadows Under Intensity Changes", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0885.pdf}, url = {}, size = {}, abstract = {This paper proposes a GPU based algorithm for extracting moving objects in real time. The whole process of the proposed approach is handled on GPU. GPU is used for acceleration and the proposed approach increases processing speed dramatically. The method uses a* component and b* component of CIELAB colour space without extracting shadow areas as moving objects. It is robust to intensity changes because an estimated background image is generated and moving objects are extracted using background subtraction of the estimated background image and the observed image. The proposed method reduces the times for transferring calculation results from GPU into CPU and the opposite transfer. Reducing the transfer times contributes to speeding up of the proposed method. Results are demonstrated with experiments on real data. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Bhattacharya2:2008:cec, author = "Maumita Bhattacharya ", title = "Counter-Niching for Constructive Population Diversity", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0887.pdf}, url = {}, size = {}, abstract = {Maintaining a desired level of diversity in the Evolutionary Algorithm (EA) population is a requirement to ensure that progress of the EA search is unhindered by premature convergence to suboptimal solutions. Loss of diversity in the EA population pushes the search to a state where the genetic operators can no longer produce superior or even different offspring required to escape the local optimum. Besides diversity's contribution to avoid premature convergence, it is also useful to locate multiple optima where there is more than one solution available. This paper presents a counter-niching technique [8] to introduce and maintain constructive diversity in the EA population. The proposed technique presented here uses informed genetic operations to reach promising, but un-explored or under-explored areas of the search space, while discouraging premature local convergence. Elitism is used at a different level aiming at convergence. The proposed technique's improved performance in terms solution accuracy and computation time is observed through simulation runs on a number of standard benchmark test functions with a genetic algorithm (GA) implementation. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Diaz:2008:cec, author = "R. I. Diaz and R. M. Valdovinos and J. H. Pacheco", title = "Comparative Study of Genetic Algorithms and Resampling Methods for Ensemble Constructing", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0888.pdf}, url = {}, size = {}, abstract = {Diversity and accuracy in the members of the classifier ensemble appear as two of the main issues to take into account for its construction and operation. The resampling method has been the strategy to construct the most used ensembles; however, the subsamples here obtained consider both diversity and high accuracy. In this work two different strategies to construct ensembles with those characteristics are analysed: resampling methods as Bagging and Boosting, and an evolution strategy as Genetic Algorithms. Using a dynamic weighting scheme, the Genetic Algorithm strategy demonstrated its effectiveness in searching the best solution to the problem. In addition, we also introduce other modifications in order to reduce the processing time of the Genetic Algorithm. All of them are studied specifically in the framework of the Nearest Neighbour classification algorithm. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Melo:2008:cec, author = "Vinícius Veloso de Melo and Alexandre Claudio Botazzo Delbem ", title = "On Promising Regions and Optimization Effectiveness of Continuous and Deceptive Functions", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0889.pdf}, url = {}, size = {}, abstract = {This paper evaluates the performance of three evolutionary algorithms to globally optimise complex continuous functions. The performance is evaluated by measuring the algorithms success rate to find the global optimum in several trials. At each set of trials, the search-space is reduced to be closer to the global optimum, so that the starting population is generated in an even more promising region. According to the results, it is possible to can conclude that, in high complexity problems, a good performance of classical evolutionary algorithms can not be expected. The paper also evaluates the performance of an evolutionary algorithm in a deceptive function. In this case, the reduced search-space is the model which generates the deceptive function. The success rates with and without the use of the starting model were compared. In this case, the use of a better starting model substantially increases the performance. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Reynolds:2008:cec, author = "Robert G. Reynolds and Mostafa Z. Ali", title = "Cultural Algorithms: Knowledge-Driven Engineering Optimization via Weaving a Social Fabric as an Enhanced Influence Function", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0893.pdf}, url = {}, size = {}, abstract = {Cultural Algorithms employ a basic set of knowledge sources, each related to knowledge observed in various social species. These knowledge sources are then combined to direct the decisions of the individual agents in solving optimisation problems. While many successful real world applications of Cultural Algorithms have been produced, we are interested in studying the fundamental computational processes involved the use of Cultural Systems as problem solvers. In previous work the influence of the knowledge sources have been on individuals in the population only. In this paper we introduce the notion of a social fabric in which the expression of knowledge sources can be distributed through the population. We apply the social fabric function to the solution of a tension/compression spring design problem. We show that different parameter combinations can affect the rate of solution. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, ) @inproceedings(Reynolds2:2008:cec, author = "Robert G. Reynolds and Mostafa Z. Ali", title = "The Social Fabric Approach as an Approach to Knowledge Integration in Cultural Algorithms", booktitle = "2008 IEEE World Congress on Computational Intelligence", year = 2008, editor = "Jun Wang", pages = {--}, address = "Hong Kong", month = "1-6 June", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN13 = "978-1-4244-1823-7", file = {EC0894.pdf}, url = {}, size = {}, abstract = {Recently there has been increased interest in socially motivated approaches to problem solving. These approaches include particle Swarm Optimisation, Ant Colony Optimisation, and Cultural Algorithms. Each of these approaches is derived from a social system that operates on potentially different scale. In previous work we introduced a toolkit to model Optimization problem solving using Cultural Algorithms. In this paper we extend the influence and integration function in the Cultural Algorithm Toolkit (CAT) by adding a mechanism by which knowledge sources can spread their influence throughout a population. We then compare this enhanced approach with previous approaches using the Cones World Optimization landscape. Dejong and Morrison proposed the Cones World as an alternative to traditional benchmark optimisation problems in the assessment of optimization algorithms. We demonstrate how the social fabric enhances cultural algorithm performance within this environment relative to earlier system. }, notes = {WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.}, )