%Converted with cec2007.awk $Revision: 1.00 $ W.B.Langdon@cs.uc1.ac.uk %substantial additions/corrections R.Poli 14 Nov 2007 @inproceedings(Davarynejad:2007:cec, author={M. Davarynejad and M.-R. Akbarzadeh-T and N. Pariz}, title={A Novel General Framework for Evolutionary Optimization: Adaptive Fuzzy Fitness Granulation}, pages = {951--956}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1004.pdf}, url = {}, size = {}, abstract = {Computational complexity is a major challenge in evolutionary algorithms due to their need for repeated fitness function evaluations. Here, we aim to reduce number of fitness function evaluations by the use of fitness granulation via an adaptive fuzzy similarity analysis. In the proposed algorithm, an individual's fitness is only computed if it has insufficient similarity to a queue of fuzzy granules whose fitness has already been computed. If an individual is sufficiently similar to a known fuzzy granule, then that granule's fitness is used instead as a crude estimate. Otherwise, that individual is added to the queue as a new fuzzy granule. The queue size as well as each granule's radius of influence is adaptive and will grow/shrink depending on the population fitness and the number of dissimilar granules. The proposed technique is applied to a set of 6 traditional optimization benchmarks that are for their various characteristics. In comparison with standard application of evolutionary algorithms, statistical analysis reveals that the proposed method will significantly decrease the number of fitness function evaluations while finding equally good or better solutions.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Feng:2007:cec, author={C. S. Feng and S. Cong and X. Y. Feng}, title={A New Adaptive Inertia Weight Strategy in Particle Swarm Optimization}, pages = {4186--4190}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1015.pdf}, url = {}, size = {}, abstract = {According to the principle of mechanics, a new adaptive inertia weight strategy is proposed. The strategy depends on particle's search states including its location and velocity instead of iteration times. Based on the proposed strategy, an inertia weight function is designed, which is continuous in real domain, thus it's easy to be implemented and the computation cost is low. Experiments on three benchmark functions, comparison between convergence speed, the ability to search the global solution of the linear decreasing strategy (LPOS) and the proposed strategy are done. The experimental results are also analyzed in detail.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Meng:2007:cec, title={Electricity Reference Price Forecasting with Fuzzy C-means and Immune Algorithm}, author={Ke Meng and Rui Xia and Ting Ji and Feng Qian}, pages = {2337--2343}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1017.pdf}, url = {}, size = {}, abstract = {A new hybrid training method for Radial Basis Function (RBF) neural network is presented in this paper. The proposed methodology produces RBF neural network models based on specially designed Fuzzy C-means (FCM) and Fuzzy Immune Algorithm (FIA), which are used to auto-configure the structure of networks and obtain the model parameters. With the proposed method, the number of hidden layer neurons and cluster centers are automatically determined according to the given data; both the output weight values and cluster radii are calculated by fuzzy immune algorithm. Meanwhile, the wavelet de-noising technique is introduced to ensure the neural network performance. This learning approach is proved to be effective by applying the optimized RBF neural network in predicting of Mackey-Glass chaos time series and forecasting of Queensland electricity reference price from Australian National Electricity Market.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Ramanathan:2007:cec, title={Evolutionary Combinatorial Optimization for Recursive Supervised Learning with Clustering}, author={Kiruthika Ramanathan and Sheng Uei Guan}, pages = {1168--1174}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1021.pdf}, url = {}, size = {}, abstract = {The idea of using a team of weak learners to learn a dataset is a successful one in literature. In this paper, we explore a recursive incremental approach to ensemble learning. In this paper, patterns are clustered according to the output space of the problem, i.e., natural clusters are formed based on patterns belonging to each class. A combinatorial optimization problem is therefore formed, which is solved using evolutionary algorithms. The evolutionary algorithms identify the "easy" and the "difficult" clusters in the system. The removal of the easy patterns then gives way to the focused learning of the more complicated patterns. Incrementally, neural networks are added to the ensemble to focus on solving successively difficult examples. The problem therefore becomes recursively simpler. Over fitting is overcome by using a set of validation patterns along with a pattern distributor. An algorithm is also proposed to use the pattern distributor to determine the optimal number of recursions and hence the optimal number of weak learners for the problem. In this paper, we show that the generalization accuracy of the proposed algorithm is always better than that of the underlying weak learner. Empirical studies show generally good performance when compared to other state-of-the- art methods.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Chiang:2007:cec, title={Efficient Trade-Off Algorithm for Hydrothermal Power Systems}, author={Chao-Lung Chiang}, pages = {2325--2330}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1022.pdf}, url = {}, size = {}, abstract = {This study develops an improved genetic algorithm-based multi-objective approach for the optimal economic emission dispatch (EED) of the hydrothermal power system (HPS), considering non-smooth fuel cost and emission level functions. The improved genetic algorithm (IGA) equipped with an improved evolutionary direction operator and a migration operation can efficiently search and actively explore solutions. The multiplier updating (MU) is introduced to handle the equality and inequality constraints of the HPS, and the ε-constraint technique is employed to manage the multi-objective problem. To show the advantages of the proposed algorithm, which is applied to test EED problems of the HPS considering the best compromise. The proposed algorithm integrates the IGA, the MU and the ε-constraint technique, revealing that the proposed approach has the following merits - ease of implementation; applicability to non-smooth fuel cost and emission level functions; better effectiveness than the previous method; better efficiency than genetic algorithm with the MU (GA-MU), and the requirement for only a small population in applying the optimal EED problem of the HPS.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Omran:2007:cec, title={Differential Evolution for Integer Programming Problems}, author={Mahamed G. H. Omran and Andries P. Engelbrecht}, pages = {2237--2242}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1025.pdf}, url = {}, size = {}, abstract = {The performance of two recent variants of Differential Evolution (DE) when applied to Integer Programming problems is investigated. The two DE variants, namely, Self-adaptive DE (SDE) and DE using the ring neighborhood topology (a.k.a. DE/lbest/1) are compared with the standard DE and Particle Swarm Optimization (PSO) methods on several Integer Programming test problems. The results show that the SDE seems to be an efficient alternative for solving Integer Programming problems.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Li:2007:cec, title={Solving SAT Problem with a Multiagent Evolutionary Algorithm}, author={Jinshu Li and Heyong Wang and Jing Liu and Licheng Jiao}, pages = {1416--1422}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1028.pdf}, url = {}, size = {}, abstract = {With the intrinsic properties of satisfiability problem (SAT problem) in mind, we integrate the multiagent systems and evolutionary algorithms to form a new algorithm, Multiagent Evolutionary Algorithm for SAT problem (MAEA-SAT). In MAEA-SAT, all agents live in a latticelike environment. Making use of the designed behaviors, MAEA-SAT realizes the ability of agents to sense and act on the environment in which they live. During the process of interacting with the environment and other agents, each agent increases energy as much as possible, so that MAEA-SAT can find the optima. The benchmarks about SAT problems of different scales in SATLIB are used to test the performance of MAEA-SAT, and we compared MAEA-SAT with standard GA (namely SGA). The experimental results show that the MAEA-SAT obtained an outstanding performance in solving large-scale SAT problems.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Tanimoto:2007:cec, title={Emergence of Cooperation Supported by Communication in a One-Shot 2 $\times$ 2 Game}, author={Jun Tanimoto}, pages = {1374--1381}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1030.pdf}, url = {}, size = {}, abstract = {This paper reports an intelligent agent equipped with 2-layer finite state machines (FSM) that can communicate by turning lighting on and off, leading to social cooperation that solves the dilemma situation, modeled by a one-shot 2 × 2 game. This communication between two gaming agents can be observed in Hero- and Leader-type dilemma games, where alternating reciprocity, repeating cooperation (C)-defeat (D) after D-C, is the equal Pareto Optimum instead of a sequence of mutual cooperation that is the equal Pareto Optimum for a Prisoner's Dilemma (PD) game. This implies that what initially brought about animal communications is not the dilemma situation emulated by PD, but a Hero or Leader type game.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Zhang:2007:cec, title={Immune Genetic Programming Based on Register-Stack Structure}, author={Zeming Zhang and Wenjian Luo and Xufa Wang}, pages = {3751--3758}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1032.pdf}, keywords = {genetic algorithms, genetic programming}, url = {}, size = {}, abstract = {Inspired by biological immune principles, a novel Immune Genetic Programming based on Register-Stack structure (rs-IGP) is proposed in this paper. In rs-IGP, an antigen represents a problem to be solved, and an antibody represents a candidate solution. A flexible and efficient antibody representation based on register-stack structure is designed for rs-IGP. Three populations are adopted in rs-IGP, i.e. the common population, the elitist population and the self set. The immune genetic operators are also developed, including clone operator, recombination operator, mutation operator, hypermutation operator, crossover operator and negative selection operator. The experimental results demonstrate that rs-IGP has better performance.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Xiang:2007:cec, title={An Improved Particle Swarm Optimizer with Momentum}, author={Tao Xiang and JunWang and Xiaofeng Liao}, pages = {3341--3345}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1033.pdf}, url = {}, size = {}, abstract = {In this paper, an improved particle swarm optimization algorithm with momentum (mPSO) is proposed based on inspiration from the back propagation (BP) learning algorithm with momentum in neural networks. The momentum acts as a lowpass filter to relieve excessive oscillation and also extends the PSO velocity updating equation to a second-order difference equation. Experimental results are shown to verify its superiority both in robustness and efficiency.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Hu:2007:cec, title={A Self-Adaptive Genetic Algorithm Based on Fuzzy Mechanism}, author={X. B. Hu and S. F. Wus}, pages = {4646--4652}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1034.pdf}, url = {}, size = {}, abstract = {This paper presents a novel self-adaptive Genetic Algorithm (GA) based on fuzzy mechanism, aiming to improve both the optimizing capability and the convergence speed. Some key factors affecting the performance of GAs are identified and analyzed, and their influences on the optimizing capability and the convergence speed are further elaborated, which are usually difficult to be described with explicit mathematical formulas. Using fuzzy mechanism, a set of fuzzy rules are used to model their complicated relationships, in order to effectively direct the online self-adaptive adjustments, such as changing the crossover and mutation probabilities, and thus to improve the optimizing capability and convergence speed. Numerical simulation tests for a typical optimization problem illustrate the advantages of the new GA against another self-adaptive GA which is based on explicit mathematical modeling of the key factors.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Gonzales:2007:cec, title={Class Association Rule Mining for Large and Dense Databases with Parallel Processing of Genetic Network Programming}, author={Eloy Gonzales and Karla Taboada and Kaoru Shimada and Shingo Mabu and Kotaro Hirasawa and Jinglu Hu}, pages = {4615--4622}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1045.pdf}, keywords = {genetic algorithms, genetic programming}, url = {}, size = {}, abstract = {Among several methods of extracting association rules that have been reported, a new evolutionary computation method named Genetic Network Programming (GNP) has also shown its effectiveness for small datasets that have a relatively small number of attributes.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Ai:2007:cec, title={Dispersion and Velocity Indices for Observing Dynamic Behavior of Particle Swarm Optimization}, author={The Jin Ai and Voratas Kachitvichyanukul}, pages = {3264--3271}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1048.pdf}, url = {}, size = {}, abstract = {A better balance of exploitation and exploration of solution space by the swarm is often mentioned as the key to a good performance of Particle Swarm Optimization (PSO) algorithm. Traditionally, the balance of exploitation and exploration ability of a PSO algorithm is usually shown empirically by the final result of the algorithm over some benchmark functions and not by the dynamic behavior of the swarm during the iteration process. In order to observe the dynamic behavior of the swarm in a PSO algorithm in details, two measurement indices, Dispersion Index and Velocity Index, are proposed. In an empirical study, these indices are embedded in two PSO Algorithms and applied to six benchmark problems. The results of this study indicate that a good balance between exploration and exploitation does lead to a better PSO. This balance could be achieved by allowing enough time or iteration step for both exploration and exploitation processes to take place. Finally, the utilization of these indices to balance strategy for exploitation and exploration on the PSO is discussed. It is also suggested that the velocity index can be used as a basis for controlling the length of iteration step of PSO algorithm.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Huang:2007:cec, title={Feature Selection Using Double Parallel Feedforward Neural Networks and Particle Swarm Optimization}, author={Rui Huang and Mingyi He}, pages = {692--696}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1050.pdf}, url = {}, size = {}, abstract = {In recent years, the Neural Network (NN) based feature selection becomes a promising method for dimensionality reduction. However, Multi-layer Feedforward Neural Network (MFNN) with wide applications has some disadvantages such as local minimal points on the error surface and over-fitting problem. At the same time, the conventional approaches usually fixing the number of hidden nodes and focusing on the input selection hinder further remove of the redundant information and improvement of network generalization performance. To solve these problems, a feature selection algorithm using Double Parallel Feedforward Neural Network (DPFNN) and Particle Swarm Optimization (PSO) is proposed. The algorithm adopts DPFNN with the merits of Single-layer Feedforward Neural Network (SFNN) and MFNN as the criterion function, synchronously performs optimization of structure and selection of inputs based on a new defined fitness function keeping balance between network performance and complexity. Experimental results show that the algorithm can effectively remove the redundant features while improving the generalization ability of network.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Bates:2007:cec, title={On the Robustness Analysis of Nonlinear Systems Subject to Time-Invariant and/or Time-Varying Uncertainty}, author={Declan G. Bates and Sajjad Fekri and Prathyush P. Menon and Ian Postlethwaite}, pages = {3639--3645}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1051.pdf}, url = {}, size = {}, abstract = {We propose a novel approach for computing lower bounds on robust stability or performance for nonlinear systems subject to time-invariant and/or time-varying uncertainty. The approach exploits the binary coding mechanism used in defining chromosomes for evolutionary search algorithms, in order to allow the uncertain parameter space to be searched over time, thus allowing the computation of destabilising time-varying uncertain parameters. The resulting lower bounds can be used to check the conservatism of upper bounds computed using tools such as the Popov Criterion, IQC's and various extensions of the structured singular value theory. The usefulness of the proposed approach is illustrated via an example.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Menon:2007:cec, title={Deterministic versus Evolutionary Optimisation Methods for Nonlinear Robustness Analysis of Flight Control Laws}, author={Prathyush P. Menon and Declan G. Bates and Ian Postlethwaite}, pages = {1910--1917}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1052.pdf}, url = {}, size = {}, abstract = {This paper considers the application of two different global optimisation approaches to the problem of analysing the robustness (flight clearance) of nonlinear flight control systems. The analysis employs a typical nonlinear clearance criterion used by the European aerospace industry together with a detailed simulation model of a high performance aircraft with a full authority control law. The deterministic optimisation algorithm used in the study is DIviding RECTangles (DIRECT), while the evolutionary algorithm is Differential Evolution. Both algorithms are hybridised with local gradientbased optimisation methods to improve convergence rates near the global solution. The reliability, computational complexity and efficiency of the two approaches are compared for this realistic engineering example, and the prospects for application of optimisation-based methods in the industrial flight clearance process are discussed.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(:2007:cec, title={A Hybrid Hopfield Network-Genetic Algorithm Approach for the Lights-up Puzzle Emilio G. Ortiz-Garc\'ia, Sancho Salcedo-Sanz, 'Angel M. P\'erez-Bellido and}, author={Antonio Portilla-Figueras}, pages = {1403--1407}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1056.pdf}, url = {}, size = {}, abstract = {This paper presents a hybrid genetic algorithm for solving a logic-type puzzle known as lights-up puzzle. The algorithm uses a binary Hopfield neural network to solve part of the puzzle constraints. The genetic algorithm manages other set of constraint as it looks for good quality solution in term of the puzzle's objective function. We show the good performance of our approach in a number of lights-up puzzles instances downloaded from the Internet.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Teo:2007:cec, title={Harnessing Mutational Diversity at Multiple Levels for Improving Optimization Accuracy in G3-PCX}, author={Jason Teo and Hanafi A. Hijazi and Zaturrawiah A. Omar and Rafidah Mohamad and Yunus Hamid}, pages = {4502--4507}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1058.pdf}, url = {}, size = {}, abstract = {The objective of this paper is to implement a multipronged strategy for generating diversity using non-adaptive, adaptive as well as self-adaptive methods for controlling mutation operations in a real-coded genetic algorithm (RCGA). Currently, one of the state-of-the-art RCGAs for function optimization is called the G3-PCX algorithm. However, its performance for solving multimodal problems is known to be poor compared with its performance for unimodal problems. In G3-PCX, the main problem primarily stems from premature convergence to local rather than global optima due to lack of explorative capabilities of the algorithm. As the G3-PCX algorithm relies completely on crossover for promoting diversity, this paper proposes a multilevel mutation operator to augment the algorithm's capability of escaping local optima. The proposed algorithm is called G3M2 (G3-PCX with Multilevel Mutation) and empirical tests on four benchmark multimodal test functions have shown highly competitive performance. The objective of this paper is to investigate whether the proposed multilevel mutation is able to improve the precision accuracy of G3-PCX in solving multimodal function optimization problems. In three of the four problems, G3M2 outperformed the standard G3-PCX algorithm in terms of solution quality. Thus, the multilevel combination of non-adaptive, adaptive and self-adaptive parameter control strategies into a single paradigm is empirically shown to have beneficial effects for enhancing the effectiveness of the G3-PCX algorithm for solving multimodal optimization problems in terms of solution quality.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Greenwood:2007:cec, title={A New Local Search Algorithm for Continuous Spaces Based on Army Ant Swarm Raids}, author={Garrison W. Greenwood and Hussein Abbass}, pages = {1097--1102}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1062.pdf}, url = {}, size = {}, abstract = {It is well known that evolutionary algorithms often perform much better when augmented with a local search mechanism. While many local search methods exist for combinatorial optimization problems, there are relatively few methods designed to work over continuous fitness landscapes. This paper describes a novel continuous space local search algorithm for evolutionary algorithms that emulates army ant swarm raids. Our preliminary results show the method is remarkably effective.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Dong:2007:cec, title={Covariance Matrix Repairing in Gaussian Based EDAs}, author={Weishan Dong and Xin Yao }, pages = {415--422}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1065.pdf}, url = {}, size = {}, abstract = {Gaussian models are widely adopted in continuous Estimation of Distribution Algorithms (EDAs). In this paper, we analyze continuous EDAs and show that they don't always work because of computation error: covariance matrix of Gaussian model can be ill-posed and Gaussian based EDAs using full covariance matrix will fail under specific conditions. It is a universal problem that all existing Gaussian based EDAs using full covariance matrix suffer from. Through theoretical analysis with examples of simulated data and experiments, we show that the ill-posed covariance matrix strongly affects those EDAs. This paper proposes a Covariance Matrix Repairing (CMR) method to fix ill-posed covariance matrix. CMR significantly improves the robustness of EDAs. Even some EDA's performance that was previously thought inefficient can be improved surprisingly with the help of CMR. CMR can also guarantee those EDAs to be used with small scale of population (but still should be large enough to find the global optimum) to accelerate the convergence rate while maintaining the quality of solutions.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Ma:2007:cec, title={Measurement-based Load Modeling Using Genetic Algorithms}, author={Jin Ma and Zhao-yang Dong and Ren-mu He and David J. Hill}, pages = {2909--2916}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1067.pdf}, url = {}, size = {}, abstract = {Load modeling is very important to power system operation and control. Measurement-based load modeling has been widely practiced in recent years. Mathematically, measurement-based load modeling problem are closely related to the parameter identification area. Consequently, an efficient optimization method is needed to derive the load model parameters based on the feedback of estimation errors between the measurements and model outputs. This paper reports our work on applying genetic algorithms on measurement-based load modeling research. Due to its robustness to the initial guesses on the load model parameters, genetic algorithms are very suitable for load model parameter identification. Two cases including both the real measurement in a power station and the digital simulation are studied in the paper. For comparison purpose, the classical nonlinear least square estimation method is also applied to find the load model parameters. The simulated outputs from the load model confirm the efficiency of genetic algorithms in measurement-based load modeling analysis. Future work will focus on fastening the converging speed of the genetic algorithms, and/or utilizing more efficient evolutionary computation methods.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Chen:2007:cec, title={On the Analysis of Average Time Complexity of Estimation of Distribution Algorithms}, author={Tianshi Chen and Ke Tang and Guoliang Chen and Xin Yao }, pages = {453--460}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1068.pdf}, url = {}, size = {}, abstract = {Estimation of Distribution Algorithm (EDA) is a well-known stochastic optimization technique. The average time complexity is a crucial criterion that measures the performance of the stochastic algorithms. In the past few years, various kinds of EDAs have been proposed, but the related theoretical study on the time complexity of these algorithms is relatively few. This paper analyzed the time complexity of two early versions of EDA, the Univariate Marginal Distribution Algorithm (UMDA) and the Incremental UMDA (IUMDA). We generalize the concept of convergence to convergence time, and manage to estimate the upper bound of the mean First Hitting Times (FHTs) of UMDA (IUMDA) on a well-known pseudo-modular function, which is frequently studied in the field of genetic algorithms. Our analysis shows that UMDA (IUMDA) has O(n) behaviors on the pseudo-modular function. In addition, we analyze the mean FHT of IUMDA on a hard problem. Our result shows that IUMDA may spend exponential generations to find the global optimum. This is the first time that the mean first hitting times of UMDA (IUMDA) are theoretically studied.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Zhao:2007:cec, title={An Evolutionary Modular Neural Network for Unbalanced Pattern Classifications}, author={Zhong-Qiu Zhao and De-Shuang Huang}, pages = {1662--1669}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1071.pdf}, url = {}, size = {}, abstract = {In this paper, an evolutionary modular neural network is proposed to solve multi-class problems with unbalanced training sets. The proposed model can transform an unbalanced classification problem into a set of symmetrical two-class problems, each of which can be solved by a single simple neural network. The experimental results show that the proposed method reduces time consumption for training and improves the classification performance.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Graaff:2007:cec, title={A Local Network Neighbourhood Artificial Immune System for Data Clustering}, author={A. J. Graaff and A. P. Engelbrecht}, pages = {260--267}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1072.pdf}, url = {}, size = {}, abstract = {The artificial immune system (AIS) is inspired by the functioning of the natural immune system. There are different theories with regards to the organisational behaviour of the natural immune system. One of these theories is the network theory. In this paper a novel network based AIS model is proposed. The proposed Local Network Neighbourhood Artificial Immune System (LNNAIS) is inspired by the network topology of lymphocytes to learn the antigen structure from one another. LNNAIS has a different interpretation of the network theory compared to existing network based AIS models. LNNAIS uses a concept of an artificial lymphocyte (ALC) neighbourhood to determine the network links between the ALCs. The purpose of this paper is to provide a proof of concept that an artificial lymphocyte (ALC) neighbourhood can cluster data in a dynamic environment. LNNAIS only requires one pass through the training data of antigen patterns for clustering.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Knight:2007:cec, title={Comparison of a Multi-Layered Artificial Immune System with a Kohonen Network}, author={T. Knight and J. Timmis}, pages = {3735--3742}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1076.pdf}, url = {}, size = {}, abstract = {We present a novel multi-layered unsupervised learning artifical immune system (MARIA). We have employed vector quantisation to augment MARIA (and Kohonen Networks) to allow for a comparison of performance between the two systems. Analysis shows that MARIA is competitive with Kohonen Networks on some clustering tasks.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Joslin:2007:cec, title={Greedy Transformation of Evolutionary Algorithm Search Spaces for Scheduling Problems}, author={David Joslin and Justin Collins }, pages = {407--414}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1078.pdf}, url = {}, size = {}, abstract = {Many scheduling algorithms search the space of possible solutions (schedules), but some instead search the space of permutations of the set of jobs, employing a greedy algorithm to map any such permutation to a schedule that can be evaluated by the fitness function. The search algorithm is thus simplified because knowledge about problem domain details is encapsulated in the greedy algorithm that constructs schedules, and the fitness function that evaluates them. The variety of types of algorithms for which this sort of "greedy transformation" has proven effective, and the range of successful applications, prompts us to look more closely at how such transformations may also make good solutions easier to find. In this paper we experimentally evaluate some characteristics of search spaces under greedy transformations as a first step toward understanding why this technique is effective.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Yu:2007:cec, title={Double-Deck Elevator Group Supervisory Control System Using Genetic Network Programming with Ant Colony Optimization}, author={Lu Yu and Jin Zhou and Shingo Mabu and Kotaro Hirasawa and Jinglu Hu and Sandor Markon}, pages = {1015--1022}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1081.pdf}, keywords = {genetic algorithms, genetic programming}, url = {}, size = {}, abstract = {Recently, Artificial Intelligence (AI) technology has been applied to many applications. As an extension of Genetic Algorithm (GA) and Genetic Programming (GP), Genetic Network Programming (GNP) has been proposed, whose gene is constructed by directed graphs. GNP can perform a global searching, but its evolving speed is not so high and its optimal solution is hard to obtain in some cases because of the lack of the exploitation ability of it. To alleviate this difficulty, we developed a hybrid algorithm that combines Genetic Network Programming (GNP) with Ant Colony Optimization (ACO). Our goal is to introduce more exploitation mechanism into GNP. In this paper, we applied the proposed hybrid algorithm to a complicated real world problem, that is, Elevator Group Supervisory Control System (EGSCS). The simulation results showed the effectiveness of the proposed algorithm.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Hanne:2007:cec, title={A Primal-Dual Multiobjective Evolutionary Algorithm for Approximating the Efficient Set}, author={Thomas Hanne}, pages = {3127--3134}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1084.pdf}, url = {}, size = {}, abstract = {In this article, we present a novel evolutionary algorithm for approximating the efficient set of a multiobjective optimization problem (MOP) with continuous variables. The algorithm is based on populations of variable size and exploits new rules for selecting alternatives generated by mutation and recombination. A special feature of the algorithm is that it solves at the same time the original problem and a dual problem such that solutions converge towards the efficient border from two "sides", the feasible set and a subset of the infeasible set. Together with additional assumptions on the considered MOP and further specifications on the algorithm, theoretical results on the approximation quality and the convergence of both subpopulations, the feasible and the infeasible one, are derived.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Bugeda:2007:cec, title={Influence of the Finite Element Discretization Error Over the Convergence of Structural Shape Optimization Algorithms}, author={Gabriel Bugeda and Juan J. R\'odenas and Jos\'e Albelda and Eugenio O\~uate}, pages = {3909--3916}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1085.pdf}, url = {}, size = {}, abstract = {This work analyzes the influence of the discretization error contained in the Finite Element (FE) analyses of each design configuration proposed by structural shape optimization algorithms over the behavior of the algorithm. If the FE analyses are not accurate enough, the final solution will neither be optimal nor satisfy the constraints. The need for the use of adaptive FE analysis techniques in shape optimum design will be shown. The paper also proposes the use of the algorithm described in [1] in order to reduce the computational cost associated to the adaptive FE analysis of each geometrical configuration when evolutive optimization algorithms are used.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Sato:2007:cec, title={Applying GA to Self-allotment of Rewards in Event-driven Hybrid Learning Classifier Systems}, author={Yuji Sato and Yuhki Inoue and Yosuke Akatsuka}, pages = {1800--1807}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1089.pdf}, url = {}, size = {}, abstract = {This paper describes our study into the concept of using rewards in a classifier system applied to the acquisition of decision-making algorithms 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. This paper proposes applying genetic algorithms to the search for rewards in reinforcement learning where designers have hitherto used empirical trial-and-error methods. By pitting this new technique against an existing soccer game with algorithms designed by humans, we demonstrate the possibility of using genetic algorithms to automate the setting of rewards from the viewpoint of achieving a greater success rate and faster convergence than in cases where the success rewards for each play are set by the designers based on trial and error.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Chan:2007:cec, title={Lot Splitting Under Different Job Shop Conditions}, author={Felix T. S. Chan and T. C. Wong and L. Y. Chan}, pages = {4722--4728}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1090.pdf}, url = {}, size = {}, abstract = {Lot splitting is defined as the process of splitting lots into smaller sub-lots such that successive operations of the same lot can be overlapped on distinct machines. Hence, the lead time of the lot can be possibly shortened. In this paper, a genetic algorithm-based approach is proposed to examine the lot splitting effect under different job shop conditions as defined by three parameters: processing time range, setup time and system congestion index. The experimental results suggest that lot splitting technique has a significant impact on job shop system with longer processing time and less due date tightness.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Wanner:2007:cec, title={Local Search with Quadratic Approximation in Genetic Algorithms for Expensive Optimization Problems}, author={Elizabeth F. Wanner and Frederico G. Guimaraes and Ricardo H. C. Takahashi and Peter J. Fleming}, pages = {677--683}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1093.pdf}, url = {}, size = {}, abstract = {In this paper, we propose a local search methodology to be coupled with a Genetic Algorithm to solve optimization problems with non-linear constraints. This methodology uses quadratic approximations for both objective function and constraints. In the local search phase, these quadratic approximations define an associated problem that is solved using a linear matrix inequality (LMI) formulation. The number of function evaluations needed for finding the point of optimum is significantly reduced with this procedure, what makes the proposed methodology suitable for dealing with costly blackbox optimization problems. A case study is presented: the wellknown TEAM 22 benchmark problem, an expensive problem of electromagnetic design. The results show that the hybrid algorithm has a better performance when compared to the same Genetic Algorithm without the proposed local search operator.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Ibrahim:2007:cec, title={An Improved Readout Method of Molecular Computation Based on Real-Time PCR Implemented on DNA Engine Opticon 2 System}, author={Zuwairie Ibrahim and Muhammad Faiz Mohamed Saaid and Adi S. Paramita and Akira Suyama and John A. Rose}, pages = {1829--1834}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1095.pdf}, url = {}, size = {}, abstract = {A readout approach for the Hamiltonian Path Problem (HPP) in DNA computing based on the real-time polymerase chain reaction (PCR) is re-implemented on DNA Engine Opticon 2 System. Several types of fluorescent probes and detection mechanisms are currently employed in real-time PCR, including SYBR Green, molecular beacons, and hybridization probes. Based on the new approach, real-time amplification performed using the TaqMan probes is adopted, as the TaqMan detection mechanism can be exploited for the design and development of the proposed readout approach. In this study, double-stranded DNA molecules of length 140 basepairs are selected as the input molecules, which represent the solving path for an HPP instance. These input molecules are prepared via the self-assembly of 20-mer and 30-mer singlestranded DNAs, by parallel overlap assembly. The proposed readout approach consists of two steps: real-time amplification in vitro using TaqMan-based real-time PCR, followed by information processing in silico to assess the results of real-time amplification, which in turn, enables extraction of the Hamiltonian path. The experimental result is compared with that of previously implementation on Roche LightCycler System. Experimental results establish an easier method to interpret the output of real-time PCR for the subsequent in silico information processing.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Drozda:2007:cec, title={AIS for Misbehavior Detection in Wireless Sensor Networks: Performance and Design Principles}, author={Martin Drozda and Sven Schaust and Helena Szczerbicka}, pages = {3719--3726}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1096.pdf}, url = {}, size = {}, abstract = {A sensor network is a collection of wireless devices that are able to monitor physical or environmental conditions. These devices are expected to operate autonomously, be battery powered and have very limited computational capabilities. This makes the task of protecting a sensor network against misbehavior or possible malfunction a challenging problem. In this document we discuss performance of Artificial immune systems (AIS) when used as the mechanism for detecting misbehavior. We concentrate on performance of respective genes; genes are necessary to measure a network's performance from a sensor's viewpoint. We conclude that the choice of genes has a profound influence on the performance of the AIS. We identified a specific MAC layer based gene that showed to be especially useful for detection. We also discuss implementation details of AIS when used with sensor networks.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Gómez:2007:cec, title={Evolutionary Synthesis of Grasping Through Self-Exploratory Movements of a Robotic Hand}, author={Gabriel G\'omez and Peter Eggenberger Hotz}, pages = {3418--3425}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1098.pdf}, url = {}, size = {}, abstract = {This paper explores an evolutionary approach extended by developmental processes ("embryogenic evolution") to evolve adaptive neural controllers for different robotic platforms. These controllers are able to grow, learn, and adapt to different tasks. We use a PC cluster and a physically realistic simulator of a robotic hand to synthesize grasping from random movements. We present the "ligand-receptor" concept that can be used by artificial evolution to explore (a) the growth of a neural network, (b) value systems, and (c) learning mechanisms for a given task (grasping). Different objects require different grasps, when we pick up a glass, manipulate a screwdriver, or turn the pages of a book, our fingers move very differently. The position of the hand also varies. That is a fundamental problem for a robot, because it either needs to be pre-programmed to handle every object it might encounter in the future and its possible orientations, or it must be able to learn to adjust its grasp according to what it sees and feels. Thus why a neural controller should be capable to explore its own movement capabilities, reconfigure itself to cope with environmental and morphological changes, and coherently adapt its behavior to new situations. The results show that this self exploratory activity can make the robot more robust and adaptive, and that grasping can be produced from totally random and independent movements of the fingers generated intrinsically by the neural controller.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Sauter:2007:cec, title={Final Design Interpretation of the Complex-Shaped Beam Optimizations for Compliant Mechanisms}, author={M. Sauter and T. Kern and P. Ermanni}, pages = {3930--3937}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1107.pdf}, url = {}, size = {}, abstract = {Compliant mechanisms are one-piece devices that combine the features of both structures and mechanisms. Compliant mechanism design solutions were identified by using evolutionary topology optimization, a complex-shaped beam element formulation in conjunction with the ground structure approach, and a parametrization concept based on graphtheory. This work addresses the final design interpretation of the optimization results. Three different methods are presented and the parallel gripper problem is used to address the question of how our contribution affects the final design and the design interpretation efficiency.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Alfonseca:2007:cec, title={A Simple Genetic Algorithm for Music Generation by Means of Algorithmic Information Theory}, author={Manuel Alfonseca and Manuel Cebri\'an and Alfonso Ortega}, pages = {3035--3042}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1108.pdf}, url = {}, size = {}, abstract = {Recent large scale experiments have shown that the Normalized Information Distance, an algorithmic information measure, is among the best similarity metrics for melody classification. This paper proposes the use of this distance as a fitness function which may be used by genetic algorithms to automatically generate music in a given pre-defined style. The minimization of this distance of the generated music to a set of musical guides makes it possible to obtain computer-generated music which recalls the style of a certain human author. The recombination operator plays an important role in this problem and thus several variations are tested to fine tune the genetic algorithm for this application. The superiority of the relative pitch envelope over other music parameters, such as the lengths of the notes, brought us to develop a simplified algorithm that nevertheless obtains interesting results.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Hutsell:2007:cec, title={Applying Evolutionary Techniques to Quantum Computing Problems}, author={Steven R. Hutsell and Garrison W. Greenwood}, pages = {4081--4085}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1110.pdf}, url = {}, size = {}, abstract = {A new approach based on Evolution Strategies (ES) is proposed to evolve quantum unitary operators which represent the computational algorithm a quantum computer would perform to solve an arbitrary problem. This approach has superior attributes over previous endeavors by using real valued vectors and not limited to a predefined set of quantum gates. We show this method can be used to solve a variety of key problems from evolving simple quantum gates, quantum oracles, to generalized problem instances. This method is highly extensible and useful for propagating future research of quantum unitary operator development.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(To:2007:cec, title={Binary Classification Using Parallel Genetic Algorithm}, author={Cuong To and Jiri Vohradsky}, pages = {1281--1287}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1113.pdf}, url = {}, size = {}, abstract = {Binary classification is one of the most popular methods in supervised pattern classification. In this paper, we would like to propose an algorithm based on genetic algorithm for binary classification. Binary classification here is presented in a nonlinear programming form. Genetic algorithm is then used to search solutions of nonlinear programming. Four databases (one transcriptomics, one proteomics, and two breast cancers) were used to test the algorithm and six other well-known methods. Parallel computing based on island model was also experimented. The results show that the algorithm could identify most similar patterns in the database with high precision. Island model not only increases computational speed but also gives high quality result.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Liu:2007:cec, title={A Memetic Co-Evolutionary Differential Evolution Algorithm for Constrained Optimization}, author={Bo Liu and Hannan Ma and Xuejun Zhang and Bo Liu and Yan Zhou}, pages = {2996--3002}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1115.pdf}, url = {}, size = {}, abstract = {In this paper, a memetic co-evolutionary differential evolution algorithm (MCODE) for constrained optimization is proposed. Two cooperative populations are constructed and evolved by independent differential evolution (DE) algorithm. The purpose of the first population is to minimize the objective function regardless of constraints, and that of the second population is to minimize the violation of constraints regardless of the objective function. Interaction and migration happens between the two populations when separate evolutions go on for several iterations, by migrating feasible solutions into the first group, and infeasible ones into the second group. Then, a Gaussian mutation is applied to the individuals when the best solution keep unchanged for several generations. The algorithm is tested by five famous benchmark problems, and is compared with methods based on penalty functions, co-evolutionary genetic algorithm (COGA), and co-evolutionary differential evolution algorithm (CODE). The results proved the proposed cooperative MCODE is very effective and efficient.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Zhi:2007:cec, title={Comparison of Discriminatory Pricing and Uniform Pricing Rules in Electricity Markets Using an Agent Model with Risk Consideration}, author={G. Zhi and S. Watanabe}, pages = {2546--2553}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1118.pdf}, url = {}, size = {}, abstract = {Agent-based simulation is widely applied in modeling generators' bid behavior and analyzing market dynamics in electricity markets. A generator agent's profit depends on market price and scheduled dispatch quantity, both of which are uncertain due to competition among generators and demand fluctuation. An agent model with risk consideration is proposed where risk with respect to a bid action is measured as standard deviation or VaR of the profit obtained after performing the action. Instead of solely expected return, the weighted sum of expected return and risk is thereby defined as a bid action's value based on which actions are evaluated and selected. The model is applied to market simulation with discriminatory pricing and uniform pricing rules. Our experiment demonstrated that uniform pricing leads to higher variance in market price than discriminatory pricing when demand is high and uncertain.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Hong:2007:cec, title={A Comprehensive Comparison Between Real Population Based Tournament Selection and Virtual Population Based Tournament Selection}, author={Yi Hong and Sam Kwong and Qingsheng Ren and Xiong Wang }, pages = {445--452}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1119.pdf}, url = {}, size = {}, abstract = {This paper gives a comprehensive comparison between real population based tournament selection and virtual population based tournament selection both in theory and in experiment. We claim that virtual population based EDA is able to obtain a comparative or even better solution when compared with the one captured by real population based EDA. EDA using virtual population based tournament selection does not store the whole population in the memory. Therefore, less memory is required when compared with the one using real population based tournament selection. Another advantage of EDA using virtual population based tournament selection is higher selection intensity can be achieved, therefore less fitness evaluations are needed to converge.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Hong:2007:cec, title={Over-Selection: An Attempt to Boost EDA Under Small Population Size}, author={Yi Hong and Sam Kwong and Qingsheng Ren and Xiong Wang}, pages = {1075--1082}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1120.pdf}, url = {}, size = {}, abstract = {Estimation of distribution algorithm (EDA) is a new class of evolutionary algorithms with a wide range of real-world applications. However, it has been well known that the performance of EDA is not satisfactory enough if its population size is small. But to simply increase its population size may result in slow convergence. To the best knowledge of the authors', very few work has been done on improving the performance of EDA under small population size. This paper illustrates why EDA does not work well under small population size and proposes a novel approach termed as Over-Selection to boost EDA under small population size. Experimental results on several benchmark problems demonstrate that Over-Selection based EDA is often able to achieve a better solution without significantly increasing its time consumption when compared with the original version of EDA.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Ding:2007:cec, title={Reducing Computational Complexity of Estimating Multivariate Histogram-Based Probabilistic Model}, author={Nan Ding and Ji Xu and Shude Zhou and Zengqi Sun}, pages = {111--118}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1124.pdf}, url = {}, size = {}, abstract = {In continuous domain, how to efficiently learn the complex probabilistic graphical model is a bottleneck problem for estimation of distribution algorithms (EDAs). The predominant researches focus on Gaussian probabilistic model instead of histogram distribution model because of its comparative superiority in the computational complexity. In this paper, however, we find that using the histogram model does not necessarily bring into exponential computational complexity. Based on the fact many bins are zero-height, we propose a novel method that can learn the multivariatedependency histogram based probabilistic graphical model with acceptable polynomial computational complexity. Several strategies previously used in the HEDA are combined into the new algorithm to improve the convergence and diversity. Experiments showed the superior performance of the new algorithm on several continuous problems compared with UMDA_c, IDEA-G and sur-shr-HEDA.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Xu:2007:cec, title={A Compatible Energy-Saving Control Algorithm for a Class of Conflicted Multi-Objective Control Problem}, author={Lihong Xu and Qingsong Hu and Erik D. Goodman}, pages = {4446--4453}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1125.pdf}, url = {}, size = {}, abstract = {A new two-layer multi-objective compatible control algorithm is proposed for a class of control problems with two conflicting control objectives, control error and energy consumption. The first layer is devoted to obtaining a user's desired controlled objectives region, assured to be not only achievable but also Pareto-optimal. The second layer is devoted to designing an effective controller by optimizing the most important controlled objective (such as the energy consumption), subject to system constraints from the controlled objectives region in the first layer. This control algorithm provides an effective robust controller design method for multiobjective control problems with precise models and uncertain initial conditions. Simulations illustrate that the two-layer multi-objective compatible control (MOCC) algorithm has some advantages over traditional multi-objective control methods.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Sanders:2007:cec, title={Optimal Offline Path Planning of a FixedWing Unmanned Aerial Vehicle (UAV) Using an Evolutionary Algorithm}, author={Glenn Sanders and Tapabrata Ray}, pages = {4410--4416}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1127.pdf}, url = {}, size = {}, abstract = {Path Planning is the process of generating a path between an initial location and a target location that has optimal performance against specific criteria. This paper addresses the problem of offline path planning as applied to autonomous miniature fixed wing unmanned aerial vehicles (mini-UAVs). The path representation takes into account aircraft dynamics by incorporating the turn rates and velocities of the UAV and follows a waypoint guidance method that is adopted in commercial aviation industry. The aircraft dynamics model allows the computation of fuel use, throttle, and velocity at different time instants throughout the path. A rigorous model validation is carried out prior to using the model for optimal path identification. An evolutionary algorithm is used to optimize the path distance and threat exposure encountered by the UAV for a mission. The optimization algorithm is a stochastic, zero order, elitist method similar in many respects to Nondominated Sorting Genetic Algorithm (NSGA-II) but includes explicit diversity maintaining mechanism in both the objective and variable space. A number of case studies are included to highlight the benefits offered by our approach.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Eto:2007:cec, title={Genetic Network Programming with Control Nodes}, author={Shinji Eto and Shingo Mabu and Kotaro Hirasawa and Takayuki Huruzuki}, pages = {1023--1028}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1128.pdf}, keywords = {genetic algorithms, genetic programming}, url = {}, size = {}, abstract = {Many methods of generating behavior sequences of agents by evolution have been reported. A new evolutionary computation method named Genetic Network Programming (GNP) has been also developed recently along with these trends. GNP has a directed graph structure and the search for obtaining optimal GNP becomes difficult when the scale of GNP is large. The aim of this paper is to find a well structured GNP considering Breadth and Depth of GNP searching. It has been shown that the proposed method is efficient compared with conventional GNPs from simulations using a garbage collector problem.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Deep:2007:cec, title={A New Hybrid Self Organizing Migrating Genetic Algorithm for Function Optimization}, author={Kusum Deep and Dipti}, pages = {2796--2803}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1129.pdf}, url = {}, size = {}, abstract = {This paper presents a new Self Organizing Migrating Genetic Algorithm (SOMGA) for function optimization, which is inspired by the features of Self Organizing Migrating Algorithm (SOMA). The uniqueness of this algorithm is that it is hybridization of binary coded GA and real coded SOMA. We compare its performance to Simple Genetic Algorithm (GA) and SOMA on 25 test functions. This algorithm is shown to be far more robust than GA and SOMA, providing fast convergence across a broad range of parameter settings.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Omran:2007:cec, title={Self-Adaptive Barebones Differential Evolution}, author={Mahamed G. H. Omran and Andries P. Engelbrecht and Ayed Salman}, pages = {2858--2865}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1130.pdf}, url = {}, size = {}, abstract = {Differential Evolution (DE) is generally considered as a reliable, accurate, robust and fast optimization technique. DE has been successfully applied to solve a wide range of numerical optimization problems. However, the user is required to set the values of the control parameters of DE for each problem. Such parameter tuning is a time consuming task. In this paper, a new version of DE which eliminates the need for manual parameter tuning is proposed. The performance of the proposed approach is investigated and compared with other well-known approaches. The results show that the new algorithm provides good performance when applied to multimodal problems with the added advantage that no parameter tuning is needed.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Ji:2007:cec, title={A Novel Intelligent Particle Optimizer for Global Optimization of Multimodal Functions}, author={Zhen Ji and Huilian Liao and Yiwei Wang and Q. H. Wu}, pages = {3272--3275}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1131.pdf}, url = {}, size = {}, abstract = {A novel intelligent particle optimizer based on subvectors (IPO) is proposed in this paper, which is inspired by conventional particle swarm optimization (PSO). IPO uses only one particle instead of a particle swarm. The position vector of this particle is partitioned into a certain number of subvectors, and the updating process is based on subvectors and evolved to subvectors updating process, in which the particle adjusts the velocity intelligently by introducing a new learning factor. This learning factor utilizes the information contained in the previous updating process. The particle is capable of increasing its velocity towards the global optimum in lower dimensional subspaces and not being trapped in local optima. Experimental results have demonstrated that IPO has impressive ability to find global optimum. IPO performs better than recently developed PSO-based algorithms in solving some complicated multimodal functions.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Matsumura:2007:cec, title={Grid Task Scheduling Algorithm R3Q for Evolution Strategies Yoshiyuki Matsumura, Kazuhiro Ohkura, Yoshiki Matsuura, Masashi Oiso, Noriyuki Fujimoto and Kenichi}, author={Hagihara}, pages = {1756--1763}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1134.pdf}, url = {}, size = {}, abstract = {A computational method for implementation of evolution strategies (ES) in grid computing environments is discussed. In this paper, list scheduling with round-robin order replication (RR) is adopted to reduce waiting times due to synchronization in ES. However, RR is suitable for coarsegrained tasks. For ES as medium-grained tasks, we propose a new technique to reduce the communication overhead, called the remote work queue (RWQ) method. We then define roundrobin replication remote work queue (R3Q) as RWQ with RR. Our results show that R3Q can reduce both the synchronous waiting time and communication time, and provides efficient forced termination of tasks compared to other methods.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Kishor:2007:cec, title={Genetic Programming Approach for Model Structure Determination of Hydro Turbine in Closed Loop Operation}, author={Nand Kishor and Madhusudan Singh and A. S. Raghuvanshi}, pages = {2751--2757}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1135.pdf}, url = {}, size = {}, abstract = {This paper addresses the appropriate structure selection of linear-in parameter model for speed identification of hydro turbine. The genetic programming (GP) approach with parameters determined by orthogonal least square (OLS) is adopted in the study. The simulation of second order H infinity turbine penstock dynamic transfer function in closed loop with random load variation is performed to generate data for model structure determination. The data generated from among the available PID variants, forward rectangular controller in conjunction with online identification algorithm, LSMADF provided the best model structure.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Silva:2007:cec, title={A New Performance Metric for Multiobjective Optimization: The Integrated Sphere Counting}, author={Vin\'icius L. S. Silva and Elizabeth F. Wanner and S\'ergio A. A. G. Cerqueira and Ricardo H. C. Takahashi}, pages = {3625--3630}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1137.pdf}, url = {}, size = {}, abstract = {A large number of evolutionary algorithms for solving multiobjective optimization problems has been already developed. Several merit factors for comparing the outcomes of these algorithms have also been proposed. However, evaluating Pareto-surface sample sets is still considered an open problem, since the result of a multiobjective evolutionary algorithm is a collection of vectors forming a nondominated set, that can be viewed under rather different merit criteria. In this paper, we present a new performance metric: the Integrated Sphere Counting. This metric is motivated on two reasoning principles: (i) the Pareto-surface is an object that is to be described via sample sets, in a sense that is similar to the sampled function description in signal processing; and (ii) the resolution that is to be employed in the Pareto-surface sample set depends on the decision-making procedure resolution, instead of the surface structure itself. We test this metric with two benchmark problems: the 0/1 Knapsack Problem and ZDT number 6 test suite.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Gu:2007:cec, title={Immunity Diversity Based Multi-Agent Intrusion Detection}, author={Yu Gu and Jiashu Zhao and Dong Liang and Zongben Xu}, pages = {3404--3409}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1138.pdf}, url = {}, size = {}, abstract = {In this paper, we propose a new method combining artificial immune with support vector machine for intrusion detection, where SVM is used as a core classification algorithm for detector. We introduce immunity diversity concept and we utilize immunity approach to create diversity detectors. We embed detector in Agents in use of the communication mechanism between the Agents, integrate each detection Agent's result to get the judgment of intrusion detection. This distributing character makes a more robust system. Experiments show that this approach has higher detection accuracy than single SVM and Bagging.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Chu:2007:cec, title={Evolving Genetic Regulatory Networks for Systems Biology}, author={Dominique Chu}, pages = {875--882}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1139.pdf}, url = {}, size = {}, abstract = {Recently there has been significant interest in evolving genetic regulatory networks with a user-determined behaviour. It is unclear whether or not artificial evolution of biochemical networks can be of direct benefit for or biological relevance to Systems Biology. This article highlights some pitfalls when concluding from artificially evolved genetic regulatory networks to real networks. This article also gives a (brief) review of some previous attempts to evolve genetic regulatory networks with oscillatory behaviour; it also describes a new system to evolve networks and describes the networks that have been evolved. These networks seem to be very diverse sharing no apparent common motifs either with one another or with their real-life counterparts.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Maturana:2007:cec, title={Towards a Generic Control Strategy for Evolutionary Algorithms: An Adaptive Fuzzy-Learning Approach}, author={Jorge Maturana and Fr\'ed\'eric Saubion}, pages = {4546--4553}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1140.pdf}, url = {}, size = {}, abstract = {This paper presents a new method to generalize strategies in order to control parameters of Evolutionary Algorithms (EAs). A learning process establishes the relationship between optimal quality parameters and diversity, and simplifies control to just one variable, highly correlated with Exploration/ Exploitation Balance, in such way that strategies can be defined in more abstract terms. The acquired knowledge is expressed in a simple fashion that helps the user to understand internal mechanics of EA. The model is built after a careful example gathering and encoded in Fuzzy Logic Controllers.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Lau:2007:cec, title={Fuzzy Logic Guided Genetic Algorithms for the Location Assignment of Items}, author={H. C. W. Lau and T. M. Chan and W. T. Tsui}, pages = {4281--4288}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1143.pdf}, url = {}, size = {}, abstract = {In today's logistics environment, large-scale combinatorial problems will inevitably be met during industrial operations. This paper deals with a novel real-world optimization problem, called the 'Item-location assignment problem', faced by a logistics company in Shenzhen, China. The objective of the company in this particular operation is to assign items to suitable locations such that the sum of the total traveling time of the workers required for all orders is minimized. We propose to use a stochastic search technique called fuzzy logic guided genetic algorithms (FLGA) to solve this operational problem. In GA, a specially designed crossover operation, called a shift and uniform based multi-point (SUMP) crossover, and swap mutation are adopted. Furthermore, the role of fuzzy logic is to dynamically adjust the crossover and mutation rates after each ten consecutive generations. In order to demonstrate the effectiveness of the FLGA and make a comparison with the FLGA through simulations, several search methods, branch and bound, standard GA (i.e. without the guide of fuzzy logic), simulated annealing, and tabu search, are adopted. Results show that the FLGA outperforms the other search methods in the considered scenario.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Chan:2007:cec, title={A GA-Based Data Mining Approach to Process Improvement of Fluid Dispensing for Electronic Packaging}, author={K. Y. Chan and S. H. Ling and H. H. C. Iu and C. K. Kwong}, pages = {4350--4357}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1145.pdf}, url = {}, size = {}, abstract = {Determination of the initial process parameters for fluid dispensing process is a highly skilled task and is usually based on skilled engineers' intuitive sense acquired through long-term experience rather than on a knowledge-based approach. In the face of global competition, the current trial-and -error practice is inadequate. In this paper, a rule-based system is developed to aid the determination of initial process parameters for fluid dispensing process by the genetic algorithm. Based on the rule based system, a set of ranges of process parameters can be recommended with a pre-defined quality requirement of microchip encapsulation. The preliminary validation test of the rule-based system has indicated that it can determine a set of ranges of initial process parameters for fluid dispensing process effectively, from which quality requirement can be achieved without totally relying on engineers' experience.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Lam:2007:cec, title={Control of Nonlinear Systems with a Linear State-Feedback Controller and a Modified Neural Network Tuned by Genetic Algorithm}, author={H. K. Lam and S. H. Ling and H. H. C. Iu and C. W. Yeung and F. H. F. Leung}, pages = {1614--1619}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1146.pdf}, url = {}, size = {}, abstract = {This paper presents the control of nonlinear systems with a neural network. In the proposed neural network, the neuron has two activation functions and exhibits a node-to-node relationship in the hidden layer. By using a genetic algorithm with arithmetic crossover and non-uniform mutation, the parameters of the proposed neural network can be tuned. Application examples are given to illustrate the merits of the proposed neural network.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Matsui:2007:cec, title={An Empirical Performance Evaluation of a Parameter-Free Genetic Algorithm for Job-Shop Scheduling Problem}, author={Shouichi Matsui and Seiji Yamada}, pages = {3796--3803}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1148.pdf}, url = {}, size = {}, abstract = {The Job-Shop Scheduling Problem (JSSP) is well known as one of the most difficult NP-hard combinatorial optimization problems. Several GA-based approaches have been reported for the JSSP. Among them, there is a parameter-free genetic algorithm (PfGA) for JSSP proposed by Matsui et al., based on an extended version of PfGA, which uses random keys for representing permutation of operations in jobs, and uses a hybrid scheduling for decoding a permutation into a schedule. They reported that their algorithm performs well for typical benchmark problems, but the experiments were limited to a small number of problem instances. This paper shows the results of an empirical performance evaluation of the GA for a wider range of problem instances. The results show that the GA performs well for many problem instances, and the performance can be improved greatly by increasing the number of subpopulations in the parallel distributed version.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Chan:2007:cec, title={Solving Multi-Contingency Transient Stability Constrained Optimal Power Flow Problems with an Improved GA}, author={K. Y. Chan and S. H. Ling and K. W. Chan and H. H. C. Iu and G. T. Y. Pong}, pages = {2901--2908}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1149.pdf}, url = {}, size = {}, abstract = {In this paper, an improved genetic algorithm has been proposed for solving multi-contingency transient stability constrained optimal power flow (MC-TSCOPF) problems. The MC-TSCOPF problem is formulated as an extended optimal power flow (OPF) with additional generator rotor angle constraints and is converted into an unconstrained optimization problem, which is suitable for genetic algorithms to deal with, using a penalty function. The improved genetic algorithm is proposed by incorporating an orthogonal design in exploring solution spaces. A case study indicates that the improved genetic algorithm outperforms the existing genetic algorithm-based method in terms of robustness of solutions and the convergence speed while the solution quality can be kept.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Boumaza:2007:cec, title={Convergence and Rate of Convergence of a Foraging Ant Model}, author={Amine Boumaza and Bruno Scherrer }, pages = {469--476}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1150.pdf}, url = {}, size = {}, abstract = {We present an ant model that solves a discrete foraging problem. We describe simulations and provide a complete convergence analysis: we show that the ant population computes the solution of some optimal control problem and converges in some well defined sense. We discuss the rate of convergence with respect to the number of ants: we give experimental and theoretical arguments that suggest that this convergence rate can be superlinear with respect to the number of agents. Furthermore, we explain how this model can be extended in order to solve optimal control problems in general and argue that such an approach can be applied to any problem that involves the computation of the fixed point of a contraction mapping. This allows to design a large class of formally well understood ant like algorithms for problem solving.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Stibor:2007:cec, title={Comments on Real-Valued Negative Selection vs. Real-Valued Positive Selection and One-Class SVM}, author={Thomas Stibor and Jonathan Timmis}, pages = {3727--3734}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1154.pdf}, url = {}, size = {}, abstract = {Real-valued negative selection (RVNS) is an immune-inspired technique for anomaly detection problems. It has been claimed that this technique is a competitive approach, comparable to statistical anomaly detection approaches such as one-class Support Vector Machine. Moreover, it has been claimed that the complementary approach to RVNS, termed real-valued positive selection, is not a realistic solution. We investigate these claims and show that these claims can not be sufficiently supported.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Parkin:2007:cec, title={Evolutionary Optimization of Parameters for Distributed Virtual Environments}, author={Simon E. Parkin and Peter Andras and Graham Morgan}, pages = {1390--1397}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1159.pdf}, url = {}, size = {}, abstract = {In a distributed virtual environment interest management schemes attempt to ensure virtual world objects exchange messages only if they are interacting. This provides an opportunity to allow such systems to scale to support many hundreds, even thousands, of virtual world objects by reducing the need to send and process unnecessary messages. However, arriving at the optimum configuration for the interest management scheme is a challenging and complex problem. The customary approach is to configure parameters in an adhoc manner (supported by experience). However, such an approach is unlikely to yield optimum system performance. Here we propose a means of finding optimal parameter values by way of a simulation tool equipped with evolutionary optimization capabilities. We provide a series of experiments that indicate that our simulator can aid in configuring interest management schemes to gain better system performance.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Liu:2007:cec, title={Using Two-Stage Genetic Algorithms to Solve the Nonlinear Time Series Models for Ten-Day Streamflow Forecasting}, author={Chin-Hui Liu and Chang-Shian Chen}, pages = {4365--4371}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1161.pdf}, url = {}, size = {}, abstract = {Streamflow forecasting is of utmost importance for the management of water resources. A higher accuracy in flow prediction can lead to a more effective and comprehensive application of water resources. The characteristics of hydrological data can be classified as non-steady and nonlinear. This study used two-stage genetic algorithms to solve complex nonlinear time series models. Ten-day streamflows of the Wu-shi river in Taiwan were taken as an example. Compared with the traditional linear time series, the analysis verified that nonlinear time series models by two-stage genetic algorithms are superior.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Taboada:2007:cec, title={Mining Association Rules from Databases with Continuous Attributes using Genetic Network Programming}, author={Karla Taboada and Eloy Gonzales and Kaoru Shimada and Shingo Mabu and Kotaro Hirasawa and Jinglu Hu}, pages = {1311--1317}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1162.pdf}, keywords = {genetic algorithms, genetic programming}, url = {}, size = {}, abstract = {Most association rule mining algorithms make use of discretization algorithms for handling continuous attributes. Discretization is a process of transforming a continuous attribute value into a finite number of intervals and assigning each interval to a discrete numerical value. However, by means of methods of discretization, it is difficult to get highest attribute interdependency and at the same time to get lowest number of intervals. In this paper we present an association rule mining algorithm that is suited for continuous valued attributes commonly found in scientific and statistical databases. We propose a method using a new graph-based evolutionary algorithm named "Genetic Network Programming (GNP)" that can deal with continues values directly, that is, without using any discretization method as a preprocessing step. GNP represents its individuals using graph structures and evolve them in order to find a solution; this feature contributes to creating quite compact programs and implicitly memorizing past action sequences. In the proposed method using GNP, the significance of the extracted association rule is measured by the use of the chi-squared test and only important association rules are stored in a pool all together through generations. Results of experiments conducted on a real life database suggest that the proposed method provides an effective technique for handling continuous attributes.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Abadeh:2007:cec, title={Intrusion Detection Using a Hybridization of Evolutionary Fuzzy Systems and Artificial Immune Systems}, author={M. Saniee Abadeh and J. Habibi and M. Daneshi and M. Jalali and M. Khezrzadeh}, pages = {3547--3553}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1165.pdf}, url = {}, size = {}, abstract = {This paper presents a novel hybrid approach for intrusion detection in computer networks. The proposed approach combines an evolutionary based fuzzy system with an artificial immune system to generate high quality fuzzy classification rules. The performance of final fuzzy classification system has been investigated using the KDD-Cup99 benchmark dataset. The results indicate that in comparison to several traditional techniques, such as C4.5, Naïve Bayes, k-NN and SVM, the proposed hybrid approach achieves better classification accuracies for most of the classes of the intrusion detection classification problem. Therefore, the resulted fuzzy classification rules can be used to produce a reliable intrusion detection system.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Keller:2007:cec, title={Linear Genetic Programming of Parsimonious Metaheuristics}, author={R. E. Keller and R. Poli}, pages = {4508--4515}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1166.pdf}, keywords = {genetic algorithms, genetic programming}, url = {}, size = {}, abstract = {We use a form of grammar-based linear Genetic Programming (GP) as a hyperheuristic, i.e., a search heuristic on the space of heuristics. This technique is guided by domainspecific languages that one designs taking inspiration from elementary components of specialised heuristics and metaheuristics for a domain. We demonstrate this approach for travelingsalesperson problems for which we test different languages, including one containing a looping construct. Experimentation with benchmark instances from the TSPLIB shows that the GP hyperheuristic routinely and rapidly produces parsimonious metaheuristics that find tours whose lengths are highly competitive with the best real-valued lengths from literature.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Pugh:2007:cec, title={Parallel Learning in Heterogeneous Multi-Robot Swarms}, author={Jim Pugh and Alcherio Martinoli}, pages = {3839--3846}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1169.pdf}, url = {}, size = {}, abstract = {Designing effective behavioral controllers for mobile robots can be difficult and tedious; this process can be circumvented by using unsupervised learning techniques which allow robots to evolve their own controllers in an automated fashion. In multi-robot systems, robots learning in parallel can share information to dramatically increase the evolutionary rate. However, manufacturing variations in robotic sensors may result in perceptual differences between robots, which could impact the learning process. In this paper, we explore how varying sensor offsets and scaling factors affects parallel swarm-robotic learning of obstacle avoidance behavior using both Genetic Algorithms and Particle Swarm Optimization. We also observe the diversity of robotic controllers throughout the learning process in an attempt to better understand the evolutionary process.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Basseur:2007:cec, title={Indicator-Based Multi-Objective Local Search}, author={M. Basseur and E. K. Burke}, pages = {3100--3107}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1171.pdf}, url = {}, size = {}, abstract = {This paper presents a simple and generic indicator-based multi-objective local search. This algorithm is a direct extension of the IBEA algorithm, an indicator-based evolutionary algorithm proposed in 2004 by Zitzler and Kuenzli, where the optimization goal is defined in terms of a binary indicator defining the selection operator. The methodology proposed in this paper has been defined in order to be easily adaptable and to be as parameter-independent as possible. We carry out a range of experiments on different binary indicators: Those used in IBEA experiments, and also the indicators derived from classical Pareto ranking methods taken from well-known multi-objective evolutionary algorithms of the literature. Experiments show that the best results are obtained using selection indicators which are not only based on Pareto dominance relation. Moreover, the generic local search algorithm presented in this paper and the proposed indicators obtain promising results which lead to a number of future research directions.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Babu:2007:cec, title={Multi-Objective Differential Evolution (MODE) for Optimization of Supply Chain Planning and Management}, author={B. V. Babu and Ashish M. Gujarathi}, pages = {2732--2739}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1173.pdf}, url = {}, size = {}, abstract = {Many problems in the engineering domain involve more than one objective to be optimized simultaneously. The optimal solution to a multi-objective function results in a set of equally good solutions (Pareto optimal set), rather than a unique solution. Several entities are present in a typical supply chain problem. Each of these entities has its individual objectives. When all the objectives of supply chain are combined they work towards a common goal of increasing the profitability of an organization. The supply chain model is thus multi-objective in nature which involves several conflicting objectives. A three-stage supply chain problem (involving a network of supplier, plant and customer zones) is solved using Multi-Objective Differential Evolution (MODE) algorithm in this work. Three cases of objective functions are considered in this study. Pareto optimal solutions are obtained for each case. The results are compared with those reported using NSGA-II in the literature.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Turner:2007:cec, title={An Experimental Evaluation of Feedback Loops in a Business Process Mining Genetic Algorithm}, author={C. J. Turner and A. Tiwari}, pages = {2679--2686}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1174.pdf}, url = {}, size = {}, abstract = {This paper experiments with the use of feedback loops in a genetic business process mining algorithm. The use of genetic algorithms for process mining is explained along with a description of the research background to process mining. Of particular interest in this paper is the crossover operator. Experiments are described where problems encountered in mining processes are fed back into the crossover operator and used in the selection of crossover points. Both roulette wheel and tournament methods are used in the process of selecting crossover points. The paper concludes that the use of such problem feedback loops can be beneficial in the mining of simple business processes. However the paper makes clear that feedback loops are best employed as part of an 'intelligent' mining technique.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Cruz:2007:cec, title={A Multiobjective Non-Linear Dynamic Programming Approach for Optimal Biological Control in Soy Farming via NSGA-II}, author={Andr\'e R. da Cruz and Rodrigo T. N. Cardoso and Elizabeth F. Wanner and Ricardo H. C. Takahashi}, pages = {3093--3099}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1175.pdf}, url = {}, size = {}, abstract = {The biological control of plagues in agriculture, a practice that has been growing around the world, is performed by leaving a suitable quantity of natural enemies of the plague in the farm during the finite time horizon of the farming cycle. This work proposes a multi-objective mathematical solution for the problem of optimal biological plague control for soy farmings, considering the control cost and the cost of farming damage due to plague. The system model is non-linear with impulsive control dynamics, in order to cope with the realproblem feature of control action, that should be performed in a finite number of discrete time instants. The dynamic optimization problem is solved using the NSGA-II, a fast and elitist multiobjective genetic algorithm. The results suggest a dual plague control policy, in which the relative price of control action versus the associated additional harvesting determine the usage of either a low control action or a higher well-defined one.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(He:2007:cec, title={Hybridisation of Evolutionary Programming and Machine Learning with k-Nearest Neighbor Estimation}, author={Jingsong He and Zhenyu Yang and Xin Yao}, pages = {1693--1700}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1176.pdf}, url = {}, size = {}, abstract = {Evolutionary programming(EP) focus on the search step size which decides the ability of escaping local minima, however does not touch the issue of search in promising region. Estimation of Distribution Algorithms(EDAs) focus on where the promising region is, however have less consideration about behavior of each individual in solution search algorithms. Since the basic ideas of EP and EDAs are quite different, it is possible to make them reinforce each other. In this paper, we present a hybrid evolutionary framework to make use of both the ideas of EP and EDAs through introducing a mini estimation operator into EP's search cycle. Unlike previous EDAs that use probability density function(PDF), the estimation mechanism used in the proposed framework is the k-nearest neighbor estimation which can perform better with relative small amount of training samples. Our experimental results have shown that the incorporation of machine learning techniques, such as knearest neighbor estimation, can improve the performance of evolutionary optimisation algorithms for a large number of benchmark functions.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Smith:2007:cec, title={Evolutionary Data Mining of Digital Logic and the Effects of Uncertainty}, author={James F. Smith}, pages = {39--46}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1179.pdf}, url = {}, size = {}, abstract = {A data mining based procedure for automated reverse engineering has been developed. The data mining algorithm for reverse engineering uses a genetic program (GP) as a data mining function. A genetic program is an algorithm based on the theory of evolution that automatically evolves populations of computer programs or mathematical expressions, eventually selecting one that is optimal in the sense it maximizes a measure of effectiveness, referred to as a fitness function. The system to be reverse engineered is typically a sensor. Design documents for the sensor are not available and conditions prevent the sensor from being taken apart. The sensor is used to create a database of input signals and output measurements. Rules about the likely design properties of the sensor are collected from experts. The rules are used to create a fitness function for the genetic program. Genetic program based data mining is then conducted. This procedure incorporates not only the experts' rules into the fitness function, but also the information in the database. The information extracted through this process is the internal design specifications of the sensor. Significant experimental and theoretical results related to GP based data mining for reverse engineering and the related uncertainties will be provided.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Djerou:2007:cec, title={Towards the Best Points of Interpolation Using Particles Swarm Optimisation Approach}, author={L. Djerou and M. Batouche and N. Khelil and A. Zerarka}, pages = {3211--3214}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1180.pdf}, url = {}, size = {}, abstract = {Many studies, mostly empirical, have been devoted to finding optimal choice nodes for interpolating. When exploring the underlying factors that determine what is a good such choice, we are led to consider the Runge phenomenon (RP; best known in case of high order polynomial interpolation) as a key error mechanism. However, we shall see by using PSO that interpolation with polynomials of high degree can be very useful for the obtaining of the estimates of functions. This paper presents the advantage of using the Particle Swarm Optimization (PSO) interpolation nodes. PSO is a technique based on the cooperation between particles. The exchange of information between these particles allows to resolve difficult problems.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Mammen:2007:cec, title={Genetic Swarm Grammar Programming: Ecological Breeding Like a Gardener}, author={Sebastian von Mammen and Christian Jacob}, pages = {851--858}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1181.pdf}, keywords = {genetic algorithms, genetic programming}, url = {}, size = {}, abstract = {We recently introduced swarm grammars as an extension of Lindenmayer systems to model dynamic growth processes in 3D space through a large number of interacting (swarm) agents. Grammatical rewrite rules define different types of agents and their evolution over time. Sets of parameters determine specific interaction behaviors among the generated swarms.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Guo:2007:cec, title={Hybrid Quantum Probabilistic Coding Genetic Algorithm for Large Scale Hardware-Software Co-Synthesis of Embedded Systems}, author={Ronghua Guo and Bin Li and Yi Zou and Zhenquan Zhuang}, pages = {3454--3458}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1182.pdf}, url = {}, size = {}, abstract = {Hardware-software co-synthesis is a key step of future design of embedded systems. It involves three interdependent subproblems: allocation of resources, assignment of tasks to resources, and scheduling the execution of tasks. Both assignment and scheduling are known to be NP-complete. So it is a really hard and challenging task to optimization algorithms. Both heuristic and evolutionary algorithms are commonly used in real world. Heuristic algorithms converge rapidly but often be trapped in local minima and evolutionary algorithms own high exploration capacity but become time-consuming when handling large-scale systems. In this paper, a new hybrid evolutionary algorithm, called Hybrid Quantum probabilistic coding Genetic Algorithm, is proposed to implement the co-synthesis of large scale multiprocessor embedded systems, in which a heuristic algorithm is combined with the Quantum probabilistic coding Genetic Algorithm to enhance the performance on the hard task. The experimental results show that HQGA has better performance than both HA and QGA on large scale HW/SW co-synthesis problems.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Wang:2007:cec, title={Hybrid Particle Swarm Optimizer with Tabu Strategy for Global Numerical Optimization}, author={Yu-XuanWang and Zhen-Dong Zhao and Ran Ren}, pages = {2310--2316}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1184.pdf}, url = {}, size = {}, abstract = {Particle swarm optimizer (PSO) is a populationbased evolutionary algorithm which is widely adopted due to its simple implementation and fast convergence. But, when optimizing complex problems, PSO may lead to premature convergence. In this paper, inspired by the core idea of the tabu search algorithm, we incorporate the tabu strategy and propose a revised PSO with a view to increase population diversity and to reduce the repeated attractions by local minima. The two-stage searching strategy offers a good trade-off between exploration and exploitation and meanwhile, experimental results show significant performance improvements on seven benchmark functions.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Isaacs:2007:cec, title={A Hybrid Evolutionary Algorithm with Simplex Local Search}, author={A. Isaacs and T. Ray and W. Smith}, pages = {1701--1708}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1185.pdf}, url = {}, size = {}, abstract = {Presented in this paper is a hybrid algorithm simplex search enabled evolutionary algorithm (SSEA) which is fundamentally an evolutionary algorithm (EA) embedded with a local simplex search for unconstrained optimization problems. Evolutionary algorithms have been quite successful in solving a wide class of intractable problems and the Non-dominated Sorting Genetic Algorithm (NSGA-II) is a popular choice. However, like any other evolutionary algorithms, the rate of convergence of NSGA-II slows down with generations and often there is no improvement in the best candidate solution over a number of generations. The simplex search component comes into effect once the basic evolutionary algorithm encounters a slow rate of convergence. To allow exploitation around multiple promising regions, the simplex search is invoked from multiple promising regions of the variable space identified using hierarchical agglomerative clustering. In this paper, results are presented for a series of unconstrained optimization test problems that cover problems with a single minimum, a few minima and a large number of minima. Provided is a comparison of results with NSGA-II, Fast Evolutionary Strategy (FES), Fast Evolutionary Programming (FEP) and Improved Fast Evolutionary Programming (IFEP) where it's clear that SSEA outperforms all other algorithms for unimodal problems. On the suite of problems with large number of minima, SSEA performs better on some of them. For problems with fewer minima, SSEA performs better than FES, FEP and IFEP while demonstrating comparable performance to NSGA-II.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Xiao:2007:cec, title={A Genetic Algorithm for Solving Multi-Constrained Function Optimization Problems Based on KS Function}, author={Jianhua Xiao and Jin Xu and Zehui Shao and Congfeng Jiang and Linqiang Pan}, pages = {4497--4501}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1186.pdf}, url = {}, size = {}, abstract = {In this paper, a new genetic algorithm for solving multi-constrained optimization problems based on KS function is proposed. Firstly, utilizing the agglomeration features of KS function, all constraints of optimization problems are agglomerated to only one constraint. Then, we use genetic algorithm to solve the optimization problem after the compression of constraints. Finally, the simulation results on benchmark functions show the efficiency of our algorithm.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Wang:2007:cec, title={A Game Theory and BCC Based Flexible QoS Unicast Routing Scheme}, author={Xingwei Wang and Lin Han and Min Huang}, pages = {2479--2484}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1189.pdf}, url = {}, size = {}, abstract = {QoS (Quality of Service) routing is essential in NGI (Next Generation Internet). Due to difficulty on the exact expression of the user QoS requirements, the flexible QoS should be supported. In addition, with gradual commercialization of network operation, the benefit conflicts between the network provider and the user ask the so called win-win to be supported. In this paper, the knowledge of the fuzzy mathematics, game theory and artificial life computing method is introduced to design a flexible QoS unicast routing scheme. Based on BCC (Bacteria Colony Chemotaxis), it searches for a QoS unicast path with Pareto optimum under Nash equilibrium between the network provider utility and the user utility achieved or approached. Simulation results have shown that the proposed scheme is both feasible and effective with better performance.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Hong:2007:cec, title={Composite of Support Vector Regression and Evolutionary Algorithms in Car-Rental Revenue Forecasting}, author={Wei-Chiang Hong and Young-Jou Lai and Ping-Feng Pai and Shao-Lun Lee and Shun-Lin Yang}, pages = {2872--2878}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1190.pdf}, url = {}, size = {}, abstract = {In the car-rental industry, it is generally assumed that a 1% increase in price contributes an 8% profit increment. Therefore, pricing is of priority concern in revenue management. Accurately forecasting monthly revenue per unit (RPU) (per car) has received increasing attention owing to its ability to provide a benchmarking performance index for annual pricing. However, RPU in the car-rental industry suffers serious nonlinearity. Recently, support vector regression (SVR) has been successfully applied to solve nonlinear regression and time series problems. This study elucidates the feasibility of using SVR to forecast RPU. Moreover, the parameters of a SVR model are derived using the immune algorithm (IA). Subsequently, RPU data from DTAG (Dollar Thrifty Automotive Group, USA) are used to illustrate the proposed SVRIA (support vector regression with immune algorithm) model. The empirical results reveal that the proposed model outperforms the other two models, namely the Holt-Winters (HW) model and the seasonal HW (SHW) model. Consequently, the SVRIA model provides a promising alternative method of forecasting RPU.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) abstract = {This paper proposes a novel hybrid GA/SVM method that can predict the interactions between proteins intermediated by the protein-domain relations. Firstly, we represented a protein by the domains contained inside, which can consider the effects of Domain duplication. To simulate the combination of different domains, a transformation of the domain composition was taken subsequently. Further, a genetic algorithm was used to seek the optimized transformation, which had been adopted as the input vector of a predictor constructed using support vector machines method. Finally, experiment results validated the effectiveness and efficiency of our proposed approach by a better performance.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Zhao:2007:cec, title={Design for Self-Organizing Fuzzy Neural Networks Using a Novel Hybrid Learning Algorithm}, author={Liang Zhao and Fei-YueWang}, pages = {2972--2979}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1193.pdf}, url = {}, size = {}, abstract = {A novel hybrid learning algorithm to implement automatically structure identification and parameter optimization for designing a self-organizing TSK-Type fuzzy neural network (FNN) is proposed in this paper. It includes mean shift clustering algorithm (MSC) and mean firing strength method (MFS) which are employed to identify the network structure of fuzzy neural network (FNN) and the particle swarm optimization enhancing genetic algorithm (PSO-EGA) and the modified back-propagation algorithm (MBP) which are applied to learn the free parameters of it. That is, the MSC is used to partition the input vector space to generate initial network structure. Then the MFS is used to prune the least important rule neurons of initial structure and generate optimal network structure. After the structure identification is completed, the PSO-EGA is adopted to perform a global search in free parameter space of the FNN and seek a near-optimal initial free parameters point for the next stage. Then, it is considered as the initial weights of the FNN and the MBP is used to perform the learning process until a terminal condition is satisfied. The simulation experiment has verified that the proposed hybrid learning algorithm achieves superior performance in learning accuracy than those of some traditional methods.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Wang:2007:cec, title={Opposition-based Particle Swarm Algorithm with Cauchy Mutation}, author={Hui Wang and Yong Liu and Sanyou Zeng and Hui Li and Changhe Li}, pages = {4750--4756}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1196.pdf}, url = {}, size = {}, abstract = {Particle Swarm Optimization (PSO) has shown its fast search speed in many complicated optimization and search problems. However, PSO could often easily fall into local optima. This paper presents an Opposition-based PSO (OPSO) to accelerate the convergence of PSO and avoid premature convergence. The proposed method employs opposition-based learning for each particle and applies a dynamic Cauchy mutation on the best particle. Experimental results on many wellknown benchmark optimization problems have shown that OPSO could successfully deal with those difficult multimodal functions while maintaining fast search speed on those simple unimodal functions in the function optimization.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(:2007:cec, title={Direct Search forWave Operator by a Genetic Algorithm (GA): Route to Few Eigenvalues of a Hamiltonian}, author={Rahul Sharma and S. P. Bhattacharyya}, pages = {3812--3817}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1198.pdf}, url = {}, size = {}, abstract = {A Genetic Algorithm is invoked to search out the wave operator leading to the determination of a few eigenvalues and eigenvectors of a specially designed real symmetric matrix (Durand matrix) that simulates a Hamiltonian supporting bound states coupled to continuum. The performance is compared with that of a standard iterative method for different partition sizes, and parallelizability of the GA-based approach is tested. In many cases the GA-based approach smoothly converges while the standard iterative schemes diverge.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Judy:2007:cec, title={A Solution to Protein Folding Problem Using a Genetic Algorithm with Modified Keep Best Reproduction Strategy}, author={M. V. Judy and K. S. Ravichandran}, pages = {4776--4780}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1200.pdf}, url = {}, size = {}, abstract = {Genetic algorithms have proved to be a successful method for predicting the protein structure. In this paper, we propose a new intermediate selection strategy for genetic algorithms and we implement it for protein folding problem. In a standard genetic algorithm the children replace their parents. The idea behind this is that both parents pass on their good genetic material to their children. In practice however, children can have worse fitness than their parents. We therefore propose another intermediate selection step, which we call as modified keep-best reproduction (MKBR) that ensures that new genetic information is entered into the gene pool, as well as good previous genetic material is being preserved. We have demonstrated the superiority of modified keep-best reproduction on several instances of the proteinfolding problem, which not only finds the optimum solution, but also finds them faster than the standard generational replacement schemes.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Ashlock:2007:cec, title={Fingerprint Analysis of the Noisy Prisoner's Dilemma }, author={Daniel Ashlock and Eun-Youn Kim and Wendy Ashlock}, pages = {4073--4080}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1202.pdf}, url = {}, size = {}, abstract = {Fingerprinting is a technique that permits the identification of strategies for playing a game without doing detailed hand analysis. In this study the evolution of strategies for playing the iterated prisoner's dilemma in the presence of noise was analyzed using fingerprinting and other techniques. Agents were evolved for 6400 generations taking samples at eight exponentially-spaced epochs with noise levels of 0, 1, and 5 percent. Populations were tested for probability of cooperative play, for competitive ability against agents evolved with different noise levels, for competitive ability against agents from other epochs, and for their distribution of strategy types. The ability of agents in noisy environments to cooperate was significantly enhanced over evolutionary time with substantial gains in cooperation made after the 3000th generation. Also, evolution in the presence of noise was found to significantly improve an agent's competitive ability. Agents evolved for a longer time tended to beat agents evolved for a shorter time, though there were some intriguing exceptions. And, populations evolved in the presence of noise had significantly different strategy distributions than populations evolved without noise.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Hiwa:2007:cec, title={Hybrid Optimization Using DIRECT, GA, and SQP for Global Exploration}, author={Satoru Hiwa and Tomoyuki Hiroyasu and Mitsunori Miki}, pages = {1709--1716}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1203.pdf}, url = {}, size = {}, abstract = {This paper presents a new hybrid optimization approach, which combines multiple optimization algorithms. To develop an efficient hybrid optimization algorithm, it is necessary to determine how the optimization process is performed. This paper focuses on the balance between local and broad searches, and multiple optimization methods are controlled to derive both the optimum point and the information of the landscape. By this approach, we can describe the global landscape after derivation of optimization. To achieve the proposed optimization strategy, three distinguished optimization algorithms are introduced: DIRECT (DIviding RECTangles), GAs (Genetic Algorithms), and SQP (Sequential Quadratic Programming). To integrate these three algorithms, each algorithm, especially DIRECT, was modified and developed. The performance of the proposed hybrid algorithm was examined through numerical experiments. From these experiments, not only the optimum point but also the information of the landscape was determined. The information of the landscape verified the reliability of optimization results.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Won:2007:cec, title={A Genetic Algorithm with Cycle Representation and Contraction Digraph Model for Guideway Network Design of Personal Rapid Transit}, author={Jin-MyungWon and Fakhreddine Karray}, pages = {2405--2412}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1204.pdf}, url = {}, size = {}, abstract = {In this paper, we propose a steady-state genetic algorithm (GA) with cycle-based representation and a contraction digraph model to deal with the guideway network design problem of personal rapid transit (PRT). PRT is a novel transportation paradigm, where many computer-controlled vehicles running on an elevated guideway network (GN). A GN may contain hundreds of guideway links and how to design the minimum-cost feasible GN is a challenging problem. Given a set of stations, the proposed GA models a candidate GN as a union of one or more simple directed cycles visiting two or more stations. This cycle representation not only provides high solution locality but allows us to establish a contraction digraph model, where its feasibility can be efficiently evaluated. We also develop special genetic operators well suited for the cycle representation. Numerical experiments conducted for various problem instances show the proposed GA outperforms the conventional ones once the solution is represented by a moderate number of cycles.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Yuen:2007:cec, title={A Non-Revisiting Genetic Algorithm}, author={Shiu Yin Yuen and Chi Kin Chow}, pages = {4583--4590}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1205.pdf}, url = {}, size = {}, abstract = {Genetic Algorithm (GA) is a revisiting stochastic algorithm. In other words, a solution that has been visited before may be revisited. The fitness of the solution has to be evaluated each time. Since fitness evaluation is the most computationally intensive process in the execution of the GA, revisits should be minimized or eliminated. In this paper, a novel dynamic binary partitioning tree archive is proposed to eliminate all revisits. It works as follows: When the GA generates a solution, the tree is accessed. A leaf node is appended to the tree if the solution has not been visited before and so has no record in the tree. Otherwise, a search is initiated from the leaf node that is the duplicate to the solution to find the nearest neighbor solution in the search space that is not visited. During this process, whole sub-trees may be pruned if all the leaf nodes it contains are visited. The search naturally implements a self adaptive mutation mechanism. Hence the GA requires no other mutation parameter or mutation scheme. Experimental results reveal that this new GA is superior in performance compared with the standard GA with revisits, and the tree archive is not memory intensive.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Katayama:2007:cec, title={Variable Depth Search and Iterated Local Search for the Node Placement Problem in Multihop WDM Lightwave Networks}, author={Kengo Katayama and Hiroshi Yamashita and Hiroyuki Narihisa}, pages = {3508--3515}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1207.pdf}, url = {}, size = {}, abstract = {We address a problem of finding an optimal node placement that minimizes the amount of traffics by reducing the weighted hop distances in multihop lightwave networks. The problem is called Node Placement Problem (NPP). NPP is known to be NP-hard and one of the most important problems in wavelength division multiplexing (WDM) based networks. In this paper we propose a new local search algorithm for the NPP based on variable depth search, and show its extension to an iterated local search algorithm. To evaluate the performance of the proposed methods, we provided the benchmark instances with known optimal solutions, and performed extensive experiments on the instances. The computational results showed that our iterated local search outperformed multistart local search methods and the best available metaheuristic for the problem.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Yang:2007:cec, title={Pareto Archived PSO Optimization for Time-Cost Tradeoff Analysis}, author={I-Tung Yang}, pages = {3329--3334}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1208.pdf}, url = {}, size = {}, abstract = {The present study proposes a bi-objective particle swarm optimization algorithm to find the cost profile over the set of feasible project durations, i.e., the time-cost tradeoff analysis. The proposed algorithm aims to achieve two goals: (1) to obtain the entire Pareto front in a single run, and (2) to treat various types of activity time-cost functions, such as linear, nonlinear, discrete, discontinuous, and a hybrid of the above. The proposed algorithm adopts an elite archiving scheme to store nondominated solutions and to direct further search. Through a fast food outlet example, the proposed algorithm is shown effective and efficient in conducting bi-objective time-cost analysis. The proposed algorithm has also been shown competitive with SPEA2 by achieving better solution diversity.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Iijima:2007:cec, title={Proposal of Transmission Line Modeling Using Multi-Objective Optimization Techniques}, author={Yosuke Iijima and Masahiro Murakawa and Yuji Kasai and Eiichi Takahashi and Tetsuya Higuchi}, pages = {2094--2100}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1209.pdf}, url = {}, size = {}, abstract = {This paper presents the first successful application of multi-objective genetic algorithms (MOGA) to transmission line modeling. Conventionally, it is difficult to simultaneously realize high-accuracy transmission line simulations for both the frequency and time-domains. In order to overcome the problem, this paper proposes the application of MOGA to transmission line modeling. The proposed modeling method has two distinctive features: (1) Simultaneous modeling of both frequency and time-domain characteristics. (2) Selection of conventionalmodel from among set of Pareto optimization solutions based on simulation objectives. The results of an experiment with a micro-strip line demonstrate that the proposed method accomplished a transmission line simulation up to 2.54 times more accurate than conventional methods.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Abdullah:2007:cec, title={A Hybrid Evolutionary Approach to the University Course Timetabling Problem}, author={Salwani Abdullah and Edmund K. Burke and and Barry McCollum}, pages = {1764--1768}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1211.pdf}, url = {}, size = {}, abstract = {Combinations of evolutionary based approaches with local search have provided very good results for a variety of scheduling problems. This paper describes the development of such an algorithm for university course timetabling. This problem is concerned with the assignment of lectures to specific timeslots and rooms. For a solution to be feasible, a number of hard constraints must be satisfied. The quality of the solution is measured in terms of a penalty value which represents the degree to which various soft constraints are satisfied. This hybrid evolutionary approach is tested over established datasets and compared against state-of-the-art techniques from the literature. The results obtained confirm that the approach is able to produce solutions to the course timetabling problem which exhibit some of the lowest penalty values in the literature on these benchmark problems. It is therefore concluded that the hybrid evolutionary approach represents a particularly effective methodology for producing high quality solutions to the university course timetabling problem.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Lee:2007:cec, title={Using Genetic Algorithms to Solve Luggage Typesetting Problem}, author={Shao-Lun Lee and Wei-Chiang Hong}, pages = {2886--2892}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1213.pdf}, url = {}, size = {}, abstract = {The Bin Packing Problems play an important role in plans of production and saving cost in factories. This paper is to develop a set of Intellectual Automatic Typesetting System (IATS) for luggage factories through Bin Packing Algorithm and Genetic Algorithm. Firstly, we attain the cost of raw materials by Bill of Material (BOM) from orders. Secondly, the producing procedure of the luggage is to use One-Dimensional Typesetting Algorithm to solve the problem of fabric cutting. Finally, we combine the IATS with mobile phone to offer an effective Quick Response/Efficient Consumer Response (QR/ECR). Hence, users can look up the minimal cost of raw materials and received the quote rapidly. The result shows that the overall average utility rate of fabric typesetting was 95.32% in IATS. It is not only to enhance the efficiency of utility rate for luggage material but also to save plenty of human resources for luggage factories.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Korosec:2007:cec, title={The Differential Ant-Stigmergy Algorithm: An Experimental Evaluation and a Real-World Application}, author={Peter Koro\v{s}ec and Jurij \v{S}ilc and Klemen Oblak and Franc Kosel}, pages = {157--164}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1214.pdf}, url = {}, size = {}, abstract = {This paper describes the so-called Differential Ant-Stigmergy Algorithm (DASA), which is an extension of the Ant-Colony Optimization for a continuous domain. An experimental evaluation of the DASA on a benchmark suite from CEC 2005 is presented. The DASA is compared with a number of evolutionary optimization algorithms, including the covariance matrix adaptation evolutionary strategy, the differential evolution, the real-coded memetic algorithm, and the continuous estimation of distribution algorithm. The DASA is also compared to some other ant methods for continuous optimization. The experimental results demonstrate the promising performance of the new approach. Besides this experimental work, the DASA was applied to a real-world problem, where the efficiency of the radial impeller of a vacuum cleaner was optimized. As a result the aerodynamic power was increased by twenty per cent.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Gu:2007:cec, title={A Novel Niche Genetic Algorithm with Local Search Ability}, author={Jun-Hua Gu and Na-Na Li and Qing Tan and Wei Wei}, pages = {4606--4609}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1216.pdf}, url = {}, size = {}, abstract = {The insufficiency of local search and slow convergence in later generations are two main disadvantages of niche genetic algorithm (NGA). In this paper, we propose an improved novel niche genetic algorithm with local search ability. Depending on the number of iteration, the new algorithm adopts the mechanism of crossover operator and mutation operator in niche population instead of between different niches to make the searching more effective. This new method is used in Shubert function optimization and experimental results show its superiority compared with GA and NGA.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Tsutsui:2007:cec, title={Solving Quadratic Assignment Problems with the Cunning Ant System}, author={Shigeyoshi Tsutsui and Lichi Liu}, pages = {173--179}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1219.pdf}, url = {}, size = {}, abstract = {In a previous paper, we proposed a variant of the ACO algorithm called the cunning Ant System (cAS) and evaluated it using TSP instances in the TSPLIB. The results showed that it could be one of the most promising ACO algorithms. In this paper, we applied cAS to solving the QAP and showed cAS has promising performance on the QAP as well. We introduced the entropy measure to evaluate the diversity of pheromone density and to analyze the convergence process of cAS. The results clearly showed evidence that the cunning scheme in cAS is effective in maintaining diversity of pheromone density and leads to successful search. The effectiveness of cAS was also confirmed when it was combined with the taboo local search.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Zhang:2007:cec, title={Cooperative Ant Colony Optimization for Multisatellite Resource Scheduling Problem}, author={Na Zhang and Zuren Feng}, pages = {2822--2828}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1222.pdf}, url = {}, size = {}, abstract = {Multisatellite resource scheduling problem is complicated and difficult to be solved because of the limited resources available. This paper develops a construction graph model of satellite overpasses, which involves several directed subgraphs. A cooperative ant colony optimization is employed to this model and to construct proper solutions, the ants in the same colony search in different subgraphs and communicate with each other by a cooperative strategy. Experimental results demonstrate that this algorithm is superior to the existing scheduling method with better qualities.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Sakprasat:2007:cec, title={Classification Rule Mining for Automatic Credit Approval Using Genetic Programming}, author={Sum Sakprasat and Mark C. Sinclair }, pages = {548--555}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1223.pdf}, url = {}, size = {}, abstract = {Automatic credit approval is important for the efficient processing of credit applications. Eight different genetic programming (GP) approaches for the classification rule mining of a credit card application dataset are investigated, using both a Booleanizing technique and strongly-typed GP. In addition, the use of GP for missing value handling is evaluated. Overall, on the Australian Credit Approval dataset, those GP approaches that had poorer classification correctness on the training data often proved better at generalizing for the test set.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Goh:2007:cec, title={Locating Voltage Collapse Points Using Evolutionary Computation Techniques}, author={S. H. Goh and Z. Y. Dong and T. K. Saha}, pages = {2923--2930}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1225.pdf}, url = {}, size = {}, abstract = {In recent years, Evolutionary Computation (EC) techniques have proven to be an useful alternative approach for solving many highly nonlinear power system planning and operation problems. The objective of this paper is to investigate mathematically-complex voltage collapse problems using EC techniques, in particular the Particle Swarm Optimization (PSO) and Differential Evolution (DE) algorithms. It demonstrates the exceptional searching capabilities of both the PSO and DE algorithms to locate voltage collapse point solutions (also widely known as nose points or critical points), which are at least comparable to those obtained using the well-known Continuation Power Flow (CPF) technique. The feasibility and practicality of this approach has been tested on a 3-machine 9-bus, the IEEE 118-bus and the IEEE 300-bus power systems.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Hu:2007:cec, title={An Efficient Genetic Algorithm with Uniform Crossover for the Multi-Objective Airport Gate Assignment Problem}, author={X. B. Hu and E. Di Paolo}, pages = {55--62}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1229.pdf}, url = {}, size = {}, abstract = {Genetic Algorithms (GAs) have a good potential of solving the Gate Assignment Problem (GAP) at airport terminals, and the design of feasible and efficient evolutionary operators, particularly, the crossover operator, is crucial to successful implementations. This paper reports an application of GAs to the multi-objective GAP. The relative positions between aircraft rather than their absolute positions in the queues to gates is used to construct chromosomes in a novel encoding scheme, and a new uniform crossover operator, free of feasibility problems, is then proposed, which is effective and efficient to identify, inherit and protect useful common sub-queues to gates during evolution. Extensive simulation studies illustrate the advantages of the proposed GA scheme with uniform crossover operator.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Pinaud:2007:cec, title={On the Influence of the Instance Structure on Metaheuristic Performances . Application to a Graph Drawing Problem}, author={Bruno Pinaud and Pascale Kuntz}, pages = {4684--4690}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1230.pdf}, url = {}, size = {}, abstract = {Metaheuristics are now so common that for some classical hard combinatorial problems, there exist more than ten variants. Thus, the issue of comparing optimization methods is crucial. In this paper, we focus on one aspect of this question: the impact of the choice of the test instances on the metaheuristic performances and the possible link with the fitness landscape structure. We base our experimental framework on the arc crossing minimization problem for layered digraphs. We compare a hybridized genetic algorithm and a multistart descent which are among the best approaches to this problem. We worked on two instance families with various sizes and structural complexities: small graphs which are easy to draw on a standard size support, and large graphs specifically built for our experiments. We show that, for the smallest instances, there is no significant difference between methods whereas for graphs similar to those classically used nowadays in applications the genetic algorithm is better, and for the largest graphs (with a scaling factor up to 10300), the multistart descent is the best method. These results suggest that for "structured" fitness landscapes associated with real-life instances the GA exploits its implicit learning. On the other hand for very large landscapes with probably numerous local optima, only one exploration on a larger scale can be provided by local searches from a random starting point, cheap in computing effort.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Pattananupong:2007:cec, title={Genetic Programming and Neural Networks as Interpreters for a Distributive Tactile Sensing System}, author={U. Pattananupong and P. Tongpadungrod and N. Chaiyaratana}, pages = {4027--4034}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1231.pdf}, keywords = {genetic algorithms, genetic programming}, url = {}, size = {}, abstract = {This paper describes performance of a neural network and genetic programming (GP) in identifying the state of contact in a distributive tactile sensing system. The chosen architecture for the neural network is a multilayer perceptron while that for the genetic programming is a structured representation on genetic algorithms for non-linear function fitting (STROGANOFF). The tactile system comprises a small matrix of sensors for detecting deformation of a tactile surface. The determination of contact state is completed using both simulated and experimental inputs. Because the system relies on few sensing positions hence a robust interpreting algorithm plays a vital role. The study involves the identification of the position of a pointed load for a range between 200-600 g which can be applied across the surface. The performance in determining the position is described in the form of absolute deviation from the actual applied position. The simulation result indicates that the multilayer perceptron is the best inference technique while the GP-based mapping model produces a better result in an experiment with a high load. The difference between the simulation and the experiment is the result of an inability of the simulation model at capturing true plate deflection characteristics.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Sugawara:2007:cec, title={Performance Variation Due to Interference Among a Large Number of Self-Interested Agents}, author={Toshiharu Sugawara and Toshio Hirotsu and Satoshi Kurihara and Kensuke Fukuda}, pages = {766--773}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1232.pdf}, url = {}, size = {}, abstract = {The performance features of a massively multiagent system (MMAS) when applying the contract net protocol (CNP) are examined. The recent growth in the volume of e-commerce on the Internet is increasing the opportunities for coordinated transactions by agents, concurrently occurring everywhere. Because of limited CPU and network resources, running many interactive tasks among agents can lower the quality or efficiency of MMASs. Although CNP is a widely used negotiation protocol that can allocate tasks and resources to appropriate agents, it is unclear how effectively CNP works in an MMAS where thousands of agents work together and interfere with each other. The performance of CNP in such an MMAS, especially the overall efficiency and the reliability of promised completion times, is investigated by using an MAS simulation environment. The results show that only managerside control of CNP can improve performance in an MMAS.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Benkhider:2007:cec, title={A New Generationless Parallel Evolutionary Algorithm for Combinatorial Optimization}, author={S. Benkhider and A. R. Baba-Ali and H. Drias}, pages = {4691--4697}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1237.pdf}, url = {}, size = {}, abstract = {This paper presents a new parallel evolutionary approach where the concept of generation has been removed and replaced by the cycle one. Indeed, the classical genetic algorithms (GAs) deals with operations on the whole population through all generations. These operations are performed during the evolution towards the best individual or solution of the considered combinatorial problem. In our approach, each individual participates to the evolutionary process uniquely during some iterations. There is no generation where all individuals are created at the same time and disappear at the same time at the end of the evolutionary process genation. In our approach, each individual owns one lifespan represented by a number of cycles which are affected to it randomly at its birth and at the end of which it disappears from the population. Consequently, only certain individuals of the population are evaluated within each iteration of the algorithm and not all the population. This causes the substantial reduction of the total running time of the algorithm since the evaluations of all individuals of each generation necessitates more than 80% of the total running time of a classical GA. This approach has been developed with the goal to present a new and efficient parallel scheme of the classical GA with better performances in terms of running time. In this paper, we will present a new asynchronous parallel Master/Slave scheme of the GA and will show the power of our approach with the classification extraction rules problem.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Praditwong:2007:cec, title={How Well Do Multi-Objective Evolutionary Algorithms Scale to Large Problems}, author={Kata Praditwong and Xin Yao}, pages = {3959--3966}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1238.pdf}, url = {}, size = {}, abstract = {In spite of large amount of research work in multiobjective evolutionary algorithms, most have evaluated their algorithms on problems with only two to four objectives. Little has been done to understand the performance of the multi-objective evolutionary algorithms on problems with a larger number of objectives. It is unclear whether the conclusions drawn from the experiments on problems with a small number of objectives could be generalised to those with a large number of objectives. In fact, some of our preliminary work [1] has indicated that such generalisation may not be possible. This paper first presents a comprehensive set of experimental studies, which show that the performance of multi-objective evolutionary algorithms, such as NSGA-II and SPEA2, deteriorates substantially as the number of objectives increases. NSGA-II, for example, did not even converge for problems with six or more objectives. This paper analyses why this happens and proposes several new methods to improve the convergence of NSGA-II for problems with a large number of objectives. The proposed methods categorise members of an archive into small groups (non-dominated solutions with or without domination), using dominance relationship between the new and existing members in the archive. New removal strategies are introduced. Our experimental results show that the proposed methods clearly outperform NSGA-II in terms of convergence.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Colombo:2007:cec, title={Problem Decomposition for Minimum Interference Frequency Assignment}, author={G. Colombo and S. M. Allen}, pages = {3492--3499}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1239.pdf}, url = {}, size = {}, abstract = {This paper applies a problem decomposition approach in order to solve hard Frequency Assignment Problem instances with standard meta-heuristics. The proposed technique aims to divide the initial problem into a number of easier subproblems, which can then be solved either independently or in sequence respecting the constraints between them. Finally, partial subproblems solutions are recomposed into a solution of the original problem. Our results focus on the COST-259 MI-FAP instances, for which some good assignments produced by local search meta-heuristics are widely available. However, standard implementations do not usually produce the best performance and, in particular, no good results have been previously obtained using evolutionary techniques. We show that problem decomposition can improve standard heuristics, both in terms of solution quality and runtime. Furthermore, genetic algorithms seem to benefit more from this approach, showing a higher percentage improvement, therefore reducing the gap with other local search methods.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Souza:2007:cec, title={The Boosting Technique Using Correlation Coefficient to Improve Time Series Forecasting Accuracy}, author={Luzia Vidal de Souza and Aurora T. R. Pozo and Joel M. C. da Rosa and Anselmo Chaves Neto}, pages = {1288--1295}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1242.pdf}, keywords = {genetic algorithms, genetic programming}, url = {}, size = {}, abstract = {Time series forecasting has been considered an important tool to support decisions in different domains. A highly accurate prediction is essential to ensure the quality of these decisions. Time series forecasting is based on historical data and the predictions are usually made using statistical methods. These characteristics make the forecasting problem an interesting application of Machine learning techniques, especially for Boosting techniques and Genetic Programming. Boosting techniques currently receive a lot of attention; they combine predictions from different forecasting methods as a procedure to improve the accuracy. This paper explores Genetic Programming (GP) and Boosting technique to obtain an ensemble of regressors and proposes a new formula for the updating of the weights and for the final hypothesis. This new formula is based on the correlation coefficient instead of the loss function used by traditional boosting algorithms, this new algorithm is called Boosting using Correlation Coefficient (BCC). To validate this method, experiments were accomplished using real, financial and artificial series generated by Monte Carlo Simulation. The results obtained by using this new methodology were compared with the results obtained from GP, GPBoost and the traditional statistical methodology (ARMA). The results show advantages in the use of the proposed approach.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Poli:2007:cec, title={Theoretical Derivation, Analysis and Empirical Evaluation of a Simpler Particle Swarm Optimiser}, author={Riccardo Poli and Dan Bratton and Tim Blackwell and Jim Kennedy}, pages = {1955--1962}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1243.pdf}, url = {}, size = {}, abstract = {In this paper we derive a simpler form of Particle Swarm Optimiser (PSO) which still retains the key properties of the original model. We do so by progressively altering the original model via mathematical transformations which have clear and understandable probabilistic effects. Then we study this new PSO mathematically and compare it to the original PSO. In particular we analyse the stability and the sampling distribution for the new PSO during stagnation. Finally, we test it on 14 standard benchmark problems and with two different communication topologies, with very encouraging results.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Amos:2007:cec, title={An Ant-Based Algorithm for Annular Sorting}, author={Martyn Amos and Oliver Don}, pages = {142--148}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1245.pdf}, url = {}, size = {}, abstract = {We present an ant-based algorithm for spatially sorting objects into an annular structure. The model is minimal, as it requires only stochastic ant behaviour coupled with a pheromone-inspired "attraction-repulsion" mechanism. This is the first annular sorting algorithm to consider the problem of objects with non-uniform size, as well as the situation where objects are pre-sorted. The algorithm consistently generates high-quality annular structures, and is particularly powerful in situations where the initial configuration of objects is similar to those observed in nature.We suggest that this model provides a powerful algorithmic framework, as well as a contribution to the study of "attraction-repulsion" models of animal behaviour.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(:2007:cec, title={Multiagent Evolutionary Algorithm for Floorplanning Using Moving Block Sequence}, author={Heyong Wang and Kang Hu and Jing Liu and Licheng Jiao}, pages = {4372--4377}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1246.pdf}, url = {}, size = {}, abstract = {Focusing on the floorplan problem, a non-slicing representation, moving block sequence(MBS), is introduced and suitable for evolutionary computation since no extra constraints are exerted on the solution space. Then we design a multiagent evolutionary algorithm with the intrinsic properties of MBS in mind, which is named as MAEA-MBS. In experiments, 17 benchmarks from MCNC and GSRC are used to test the performance of MAEA-MBS. The experimental results are compared with other 9 existing well-designed algorithms and demonstrate that the algorithm is quite promising.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(He:2007:cec, title={A Novel Binary Differential Evolution Algorithm Based on Artificial Immune System}, author={Xingshi He and Lin Han}, pages = {2267--2272}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1248.pdf}, url = {}, size = {}, abstract = {Differential evolution, a heuristic optimization algorithm, has been successful in solving a wide range of real-value optimization problems. However, it is of low efficiency in dealing with the discrete problems. In this paper, a new binary differential evolution algorithm based on the theory of immunity in biology is proposed. In spite of the simplicity of the technique, simulation results show the improvement of the searching ability and increment in the convergence speed in comparison with the other binary particle swarm optimization.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Vahdat:2007:cec, title={Mobile Robot Global Localization Using Differential Evolution and Particle Swarm Optimization}, author={Ali R. Vahdat and Naser NourAshrafoddin and Saeed Shiry Ghidary}, pages = {1527--1534}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1249.pdf}, url = {}, size = {}, abstract = {For a mobile robot to move in a known environment and operate successfully, first it needs to robustly determine its initial position and orientation relative to the map, and then update its position while moving in the environment. Thus determining robot's position is one of the most important tasks in mobile robotics. This task consists of "global localization" and "robot's pose tracking". In this paper two recent sample-based evolutionary methods for globally localizing the position of a mobile robot are proposed. The first method is a modified version of genetic algorithm called Differential Evolution (DE) which is based on natural selection. The second one is Particle Swarm Optimization (PSO) which is based on bird flocking. DE evaluates initial population using the probabilistic motion and observation models and the evolution of the individuals is performed by evolutionary operators. PSO adjusts the velocity and location of particles towards target (robot's pose) through a problem space on the basis of information about each particle's previous best location and the best previous location of its neighbors. Our results illustrate the excellence of these two methods over standard Monte Carlo localization algorithm with regard to convergence rate, speed and computational cost.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Kelly:2007:cec, title={Decentralised Car Traffic Control using Message Propagation Optimized with a Genetic Algorithm}, author={Martin Kelly and Giovanna Di Marzo Serugendo}, pages = {744--750}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1250.pdf}, url = {}, size = {}, abstract = {This paper describes a decentralised car traffic control simulation with re-routing and propagation of messages among traffic nodes (roads intersections and traffic lights). The values of the parameters governing the simulations are identified through the use of a genetic algorithm. This paper reports as well on results obtained regarding the convergence of the genetic algorithm towards fittest solutions.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Eberbach:2007:cec, title={Evolution of Evolution: Self-Constructing Evolutionary Turing Machine Case Study}, author={Eugene Eberbach and Mark Burgin}, pages = {4599--4605}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1251.pdf}, url = {}, size = {}, abstract = {The goal of this paper is to study the process of evolution of evolution. In other words, we study evolution that adapts evolutionary algorithms in parallel with their solutions. For this purpose, we define and investigate several extensions of Evolutionary Turing Machine model: selfconstructing evolutionary Turing machines (SETM), selfconstructing evolutionary Turing machines with a basic constructor (SBETM), self-constructing evolutionary Turing machines with a basic constructor and control information (CSBETM), and self-constructing evolutionary Turing machines with evolvable control information (CSETM). Such properties as expressiveness and complexity of different types of self-constructing evolutionary Turing machines are studied. It is demonstrated how self-constructive abilities allow one to essentially increase efficiency of evolutionary processes in general and evolutionary computations, in particular. We also investigate computation and construction universatility in the context of these new models.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Chen:2007:cec, title={IPGA Based Multi-Objective Compatible Control Algorithm and Its Application in Oversaturated Adjacent Intersection Control}, author={Juan Chen and Lihong Xu and Changliang Yuan}, pages = {3187--3194}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1253.pdf}, url = {}, size = {}, abstract = {This paper propose an IPGA based multi-objective compatible control algorithm to control oversaturated adjacent intersections. The concept of feeding delay and non-feeding delay is introduced; A BPNN method is used to set up a MIMO delay model based on the simulated data got from cell transmission model. Then, the control problem is formulated as an conflicted multi-objective control problem, and the IPGA based multi-objective compatible control algorithm is proposed to solve the control problem. Results show that the proposed algorithm is robust and capable of deal with real-time oversaturated adjacent intersections control problem. The algorithm is tested in a network consisting of a core area of 11 oversaturated intersections. It can be concluded that the proposed method is much more effective in relieving oversaturation in a network than the isolated intersection control strategy.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Koh:2007:cec, title={Solving Transportation Bi-Level Programs with Differential Evolution}, author={Andrew Koh}, pages = {2243--2250}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1254.pdf}, url = {}, size = {}, abstract = {Bi-level programming problems arise in situations when the decision maker has to take into account the responses of the users to his decisions. These problems are recognized as one of the most difficult and challenging problems in transportation systems management. Several problems within the transportation literature can be cast in the bi-level programming framework. At the same time, significant advances have been made in the deployment of stochastic heuristics for function optimization. This paper reports on the use of Differential Evolution (DE) for solving bi-level programming problems with applications in the field of transportation planning. After illustrating our solution algorithm with some mathematical functions, we then apply this method to two control problems facing the transportation network manager. DE is integrated with conventional traffic assignment techniques to solve the resulting bi-level program. Numerical computations of this DE based algorithm (known as DEBLP) are presented and compared with existing results. Our numerical results augment the view that DE is a suitable contender for solving these types of problems.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Dräger:2007:cec, title={Comparing Various Evolutionary Algorithms on the Parameter Optimization of the Valine and Leucine Biosynthesis in Corynebacterium glutamicum}, author={Andreas Dr\"ager and Jochen Supper and Hannes Planatscher and Jørgen B. Magnus and Marco Oldiges and Andreas Zell}, pages = {620--627}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1256.pdf}, url = {}, size = {}, abstract = {Parameter estimation for biochemical model systems has become an important problem in systems biology. Here we focus on the metabolic subnetwork of the valine and leucine biosynthesis in C. glutamicum. Due to the lack of indisputable information regarding reversibility of the reactions in the pathway we derived two alternative ordinary differential equation models based on the formalisms of the generalized mass-action rate law. We introduced two alternative modeling approaches for feedback inhibition and evaluated the applicability of six optimization procedures (multi start Hill Climber, binary and real valued Genetic Algorithm, standard and covariance matrix adaption Evolution Strategy as well as Simulated Annealing) to the problem of parameter fitting. The model considering irreversible reactions performed worse and was therefore rejected from further analysis. We benchmarked the impact of different mutation and crossover operators as well as the influence of the population size on the remaining system and the two best optimization procedures namely binary Genetic Algorithm and the Evolution Strategy. The GA performed best on average and found the best total result based on the relative squared error.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(:2007:cec, title={Adaptive Control of Acceleration Coefficients for Particle Swarm Optimization Based on Clustering Analysis}, author={Zhi-hui Zhan and Jing Xiao and Jun Zhang and Wei-Neng Chen}, pages = {3276--3282}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1257.pdf}, url = {}, size = {}, abstract = {Research into setting the values of the acceleration coefficients c1 and c2 in Particle Swarm Optimization (PSO) is one of the most significant and promising areas in evolutionary computation. Parameters c1 and c2 in PSO indicate the "self-cognitive" and "social-influence" components which are important for the ability to explore and converge respectively. Instead of using fixed value of c1 and c2 with 2.0, this paper presents the use of clustering analysis to adaptively adjust the value of these two parameters in PSO. By applying the K-means algorithm, distribution of the population in the search space is clustered in each generation. An adaptive system which is based on considering the relative size of the cluster containing the best particle and the one containing the worst particle is used to adjust the values of c1 and c2. The proposed method has been applied to optimize multidimensional mathematical functions, and the simulation results demonstrate that the proposed method performs with a faster convergence rate and better solutions when compared with the methods with fixed values of c1 and c2.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(:2007:cec, title={A New Pheromone Design in ACS for Solving JSP}, author={Xiao-Lan Zhuo and Jun Zhang and Wei-neng Cheng}, pages = {1963--1969}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1258.pdf}, url = {}, size = {}, abstract = {Job Shop Scheduling Problem (JSP) is one of the most difficult NP-hard combinatorial optimization problems due to the "Combination Explosion" effect. This paper presents the implementation of Ant Colony System on JSP by proposing a novel combination of path-construction and pheromone-representation. Based on the simple traditional path-construction, a kind of more effective pheromone is employed to improve the optimization performance. Numerical experiment is executed on several benchmark JSP cases, and yields favorable results compared with results obtained by traditional implementation of ACS for JSP.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Au:2007:cec, title={Biasing Mutations in Cooperative Coevolution}, author={Chun-Kit Au and Ho-Fung Leung}, pages = {828--835}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1259.pdf}, url = {}, size = {}, abstract = {In coevolution, species are coevolving in a way that the genetic changes of one species in response to another species are reciprocal. One class of coevolution is cooperative coevolution in which species collaborate to solve the problems. The fitness of an individual in a species is assigned based on how well its collaboration with other individuals of another species can perform. As an extension of evolutionary algorithms (EAs), cooperative coevolutionary algorithms (CCEAs) operate similar to EAs, except during fitness evaluations. In this paper, we focus on genetic variation operations of a CCEA: mutations. We present how to bias mutations in cooperative coevolution and compare the performance of a CCEA adopting biasing mutations (CCEA-BM) and a conventional CCEA in which all individuals are encoded in binary representations. Our experimental study shows that biasing mutations can improve the performance of a CCEA on function optimization, in particular when high orders of binary representations are used.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Sentinella:2007:cec, title={Comparison and Integrated Use of Differential Evolution and Genetic Algorithms for Space Trajectory Optimisation}, author={Matteo Rosa Sentinella}, pages = {973--978}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1261.pdf}, url = {}, size = {}, abstract = {An accurate analysis of the behaviour of a differential evolution (DE) algorithm and a genetic algorithm (GA) when dealing with two problems concerning space trajectory optimisation is presented. Results show that, depending on the features of the problem, the comparison may produce different results, as GA can be better than DE in terms of efficiency, i.e., capability of finding the global optimum, and number of function evaluations, or vice versa. An integrated use of DE and GA in a multi-population optimisation procedure is then performed, showing improvements in both efficiency and number of function evaluations.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(:2007:cec, title={A Novel Discrete Particle Swarm Optimization to Solve Traveling Salesman Problem}, author={Wen-Liang Zhong and Jun Zhang and Wei-Neng Chen}, pages = {3283--3287}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1262.pdf}, url = {}, size = {}, abstract = {Particle Swarm Optimization (PSO), which simulates the unpredictable flight of a bird flock, is one of the intelligent computation algorithms. PSO is well-known to solve the continuous problems, yet by proper modification, it can also be applied to discrete problems, such as the classical test model: Traveling Salesman Problem (TSP). In this paper, a novel discrete PSO call C3DPSO for TSP, with modified update formulas and a new parameter c3 (called mutation factor, to help to keep the balance between exploitation and exploration), is proposed. In the new algorithm, the particle is not a permutation of numbers but a set of edges, which is different from most other algorithms for TSP. However, it still keeps the most important characteristics of PSO that the whole swarm is guided by pbest and gbest. According to some benchmarks in TSP lib, it is proved that the proposed PSO works well even with 200 cities.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(:2007:cec, title={Workflow Scheduling in Grids: An Ant Colony Optimization Approach}, author={Wei-Neng Chen and Jun Zhang and Yang Yu}, pages = {3308--3315}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1263.pdf}, url = {}, size = {}, abstract = {Grid applications in virtue of open service grid architecture (OGSA) are promising next-generation computation techniques. One of the most important and challenging problems about grid application is the workflow scheduling problem to achieve the users' QoS (quality of service) requirements as well as to minimize the cost. This paper proposes an ant colony optimization (ACO) algorithm to tackle this problem. Several new features are introduced to the algorithm. First, we define two kinds of pheromone and three kinds of heuristic information to guide the search direction of ants for this bi-criteria problem. Each ant uses either one from these heuristic types and pheromone types in each iteration based on the probabilities controlled by two parameters. These two parameters are adaptively adjusted in the process of the algorithm. Second, we use the information of partial solutions to modify the bias of ants so that inferior choices will be ignored. Moreover, the experimental results in 3 workflow applications under different deadline constraints show that the performance of our algorithm is very promising, for it outperforms the Deadline-MDP algorithm in most cases.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Garrett:2007:cec, title={NEMO: Neural Enhancement for Multiobjective Optimization}, author={Aaron Garrett and Gerry Dozier and Kalyanmoy Deb}, pages = {3108--3113}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1265.pdf}, url = {}, size = {}, abstract = {In this paper, a neural network approach is presented to expand the Pareto-optimal front for multiobjective optimization problems. The network is trained using results obtained from the nondominated sorting genetic algorithm (NSGA-II) on a set of well-known benchmark multiobjective problems. Its performance is evaluated against NSGA-II, and the neural network is shown to perform extremely well. Using the same number of function evaluations, the neural network produces many times more non-dominated solutions than NSGA-II.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Tasgetiren:2007:cec, title={A Genetic Algorithm for the Generalized Traveling Salesman Problem}, author={M. Fatih Tasgetiren and P. N. Suganthan and Quan-Ke Pan and Yun-Chia Liang}, pages = {2382--2389}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1266.pdf}, url = {}, size = {}, abstract = {In a traveling salesman problem, if the set of nodes is divided into clusters so that a single node from each cluster can be visited, then the problem is known as the generalized traveling salesman problem where the objective is to find a tour with minimum cost passing through only a single node from each cluster. In this paper, a genetic algorithm is presented to solve the problem on a set of benchmark instances. The genetic algorithm is hybridized with an iterated local search to further improve the solution quality. Some speed-up methods are presented to accelerate the greedy node insertions. The genetic algorithm is tested on a set of benchmark instances with symmetric distances ranging from 51 to 442 nodes from the literature. Computational results show that the proposed genetic algorithm is the best performing algorithm so far in the literature in terms of solution quality.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Carnelli:2007:cec, title={Optimizing Low-Thrust Gravity Assist Interplanetary Trajectories Using Evolutionary Neurocontrollers}, author={I. Carnelli and B. Dachwald and M. Vasile}, pages = {965--972}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1268.pdf}, url = {}, size = {}, abstract = {The combination of low-thrust propulsion and gravity assists allows designing high-energy missions. However the optimization of such trajectories is no trivial task. In this paper, we present a novel method that is based on evolutionary neurocontrollers. The main advantage of using a neurocontroller is the generation of a control law with a limited number of decision variables. On the other hand the evolutionary algorithm allows to look for globally optimal solutions more efficiently than a systematic search. In addition, a steepest ascent algorithm is introduced that acts as a navigator during the planetary encounter, providing the neurocontroller with the optimal insertion parameters. Results are presented for a Mercury rendezvous with a Venus gravity assist and for a Pluto flyby with a Jupiter gravity assist.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Catala:2007:cec, title={Strategies for Accelerating Ant Colony Optimization Algorithms on Graphical Processing Units}, author={Alejandro Catala and Javier Jaen and Jose A. Mocholi }, pages = {492--500}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1269.pdf}, url = {}, size = {}, abstract = {Ant Colony Optimization (ACO) is being used to solve many combinatorial problems. However, existing implementations fail to solve large instances of problems effectively. In this paper we propose two ACO implementations that use Graphical Processing Units to support the needed computation. We also provide experimental results by solving several instances of the well-known Orienteering Problem to show their features, emphasizing the good properties that make these implementations extremely competitive versus parallel approaches.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Woldesenbet:2007:cec, title={Constraint Handling in Multi-Objective Evolutionary Optimization}, author={Yonas G. Woldesenbet and Biruk G. Tessema and Gary G. Yen}, pages = {3077--3084}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1270.pdf}, url = {}, size = {}, abstract = {This paper introduces a new constraint handling technique for multi-objective evolutionary algorithms based on adaptive penalty functions and distance measures of an individual. These two values are used to modify the objective space. The modified objective functions are used in the non-dominance sorting so that the algorithm evolves feasible optimal solutions not only from the feasible space but also from the infeasible space. The search in the infeasible space is designed to encourage those individuals with better objective value and low constraint violation. The number of feasible individuals in the population is used to guide the search process either toward finding more feasible solutions or toward finding optimum solutions. The proposed method is simple to implement and does not need any parameter tuning. The constraint handling technique was tested on several constrained multi-objective problems and has shown superior results.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Woldesenbet:2007:cec, title={Dynamic Evolutionary Algorithm with Variable Relocation Vectors}, author={Yonas G. Woldesenbet and Gary G. Yen}, pages = {1748--1755}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1271.pdf}, url = {}, size = {}, abstract = {Many real-world optimization problems have to be performed under the presence of various uncertainties. A significant number of these uncertainty problems fall into the dynamic optimization category. For this class of problems, an evolutionary algorithm is expected to perform well under different levels and frequencies of change in the landscape. In addition, the dynamic evolutionary algorithm should warrant an acceptable performance improvement to justify the additional computational cost. Effective reuse of previous evolutionary information is a must as it facilitates a faster convergence after a change has occurred. This paper introduces a new dynamic evolutionary algorithm that uses variable relocation vectors to adapt already converged or currently evolving individuals to the new environmental condition. The proposed algorithm relocates those individuals based on their change in functional value due to the change in the environment and the average sensitivities of their decision variables to the corresponding change in the objective space. The relocated population is shown to be better fit to the new environment than the original or any other randomly generated population. The algorithm has been tested for several dynamic benchmark problems and has shown better results.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Xu:2007:cec, title={A Novel Genetic Algorithm for the Layout Optimization Problem}, author={Yi-Chun Xu and Ren-Bin Xiao and Martyn Amos}, pages = {3938--3943}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1272.pdf}, url = {}, size = {}, abstract = {In this paper we present a new algorithm for the Layout Optimization Problem: this concerns the placement of circular, weighted objects inside a circular container, the two objectives being to minimize imbalance of mass and to minimize the radius of the container. This problem carries real practical significance in industrial applications (such as the design of satellites), as well as being of significant theoretical interest. We present a genetic algorithm solution and compare it with two existing nature-inspired methods, one of which is the best published algorithm for this problem. Experimental results show that our approach out-performs these existing methods in terms of both solution quality and execution time.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Machwe:2007:cec, title={Enabling Generative Behavior within an Interactive Evolutionary Design System Using a Component-Based Representation}, author={Azahar T. Machwe and Ian C. Parmee}, pages = {2964--2971}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1274.pdf}, url = {}, size = {}, abstract = {The paper describes further research relating to an Interactive Evolutionary Design system (IEDS) with emphasis upon extending it to support free-form design. A generative feedback loop is also introduced which, when using a component based representation, extracts user preferences regarding the various components at run-time. This information is fed back into the system to enhance the quality of the generated solutions. The background work leading to the freeform version of the IEDS for urban furniture design is introduced followed by a description of the component preferences extracted from user evaluated solutions. Finally, results of a multi-user comparative study between randomly generated solutions and solutions generated using preference data is presented.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Leong:2007:cec, title={Dynamic Swarms in PSO-Based Multiobjective Optimization}, author={Wen-Fung Leong and Gary G. Yen}, pages = {3172--3179}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1276.pdf}, url = {}, size = {}, abstract = {In this paper, a multiple swarms MOPSO (called DSMOPSO) in which the number of swarms is dynamically adjusted is proposed to solve for multiobjective optimization. Three novel ideas are introduced to DSMOPSO: the dynamic swarm strategy to allocate an appropriate number of swarms as needed and justified, the modified PSO update mechanism to better manage the convergence and communication among and within swarms, and objective space compression and expansion strategy to progressively exploit the objective space during different stages of search process. Compared with some state-of- the-art designs, the proposed algorithm shows competitive results in producing well extended and near optimum Pareto fronts.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Sharma:2007:cec, title={Fuzzy Logic for Priority Based Genetic Search in Evolving a Neural Network Architecture}, author={S. K. Sharma and G. W. Irwin and R. Sutton}, pages = {1648--1653}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1277.pdf}, url = {}, size = {}, abstract = {In neural network optimization, multiple goals and constraints cannot be handled independently of the underlying optimizer. While "better" solutions should be rated higher than "worse" ones, the resulting cost landscapes must also comply with requirements such as continuity and differentiability of the cost surface. The genetic algorithm (GA), which has found application in many areas not amenable to optimization by other methods, is a random search technique which requires the assignment of a scalar measure of quality, or fitness, to candidate solutions. This paper proposes that the fitness assignment be interpreted as, or at least related to, a multicriterion decision process. A suitable decision-making framework, based on goals and priority, is subsequently formulated in term of fuzzy reasoning and shown to encompass a number of simpler decision strategies. Since the GA is a random search process and therefore takes more time to find a solution in the problem domain, a proper search direction is required in order to produce an optimum result. Fuzzy logic cannot provide an exact solution but can be used as a useful tool for reasoning. In this paper, the reasoning capability of fuzzy logic is used to provide a proper direction for genetic search in a problem domain and thus to achieve faster convergence in the GA. The effectiveness of this is shown in neural network optimization applied to dynamic modelling of an experimental flexible manipulator. The results show that the new fuzzy logic approach is superior to conventional exploration of the genetic search region.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Rohlfshagen:2007:cec, title={Implicit Alternative Splicing for Genetic Algorithms}, author={Philipp Rohlfshagen and John A. Bullinaria}, pages = {47--54}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1278.pdf}, url = {}, size = {}, abstract = {In this paper we present a new nature-inspired variation operator for binary encodings in genetic algorithms (GAs). Our method, called implicit alternative splicing (iAS), is repeatedly applied to the individual encodings in the algorithm's population and inverts randomly chosen segments of decreasing size in a systematic fashion. Its goal is to determine the largest possible segment the inversion of which yields no loss in the encoding's quality. The application of iAS potentially produces a new encoding of equal or greater quality that is maximum possible Hamming distance from its source. This allows iAS to uphold the diversity of the population even if a minimal population size is chosen. This significantly improves the performance of an otherwise standard GA on a representative set of three different optimisation problems. Empirical results are compared and analysed and future work prospects are considered.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Newth:2007:cec, title={Asynchronous Spatial Evolutionary Games: Spatial Patterns, Diversity and Chaos}, author={D. Newth and D. Cornforth}, pages = {2463--2470}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1279.pdf}, url = {}, size = {}, abstract = {Over the past 50 years, much attention has been given to the Prisoner's Dilemma as a metaphor for problems surrounding the evolution and maintenance of cooperative and altruistic behavior. The bulk of this work has dealt with the successfulness and robustness of various strategies. Nowak and May [1], considered an alternative approach to studying evolutionary games. They assumed that players were distributed across a two-dimensional lattice, and interactions between players occurred locally, rather than at long range, as in the well mixed situation. The resulting spatial evolutionary games display dynamics not seen in there well-mixed counterparts. An assumption underlying much of the work on spatial evolutionary games is that the state of all players is updated in unison or in synchrony. Using the framework outline in [1], we examine the effect of various asynchronous updating schemes on the dynamics of spatial evolutionary games. There are potential implications for the dynamics of a wide variety of spatially extended systems in physics, biology and chemistry.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Mazurowski:2007:cec, title={Solving Decentralized Multi-Agent Control Problems with Genetic Algorithms}, author={Maciej A. Mazurowski and Jacek M. Zurada}, pages = {1029--1034}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1280.pdf}, url = {}, size = {}, abstract = {In decentralized control of multi-agent systems each agent is making a decision regarding its action autonomously, based on its own observations. In the light of the formal models of decentralized environments presented in the last decade, finding an optimal solution to a decentralized control problem is computationally prohibitive, even for moderately complicated environments. The problem, however, is of great significance since many of the real world systems can be treated as multi-agent systems with decentralized control. In this article, the authors propose an approximate algorithm for the problem based on a genetic algorithm. First, the problem is formalized using Decentralized Partially Observable Markov Decision Processes. Then a way of representing a solution (joint policy) in a chromosome is introduced and a genetic algorithm is proposed as a search mechanism. Finally, a multi-agent tiger problem is used as an experimental framework to show the effectiveness of the algorithm.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Mazurowski:2007:cec, title={Case-Base Reduction for a Computer Assisted Breast Cancer Detection System Using Genetic Algorithms}, author={Maciej A. Mazurowski and Piotr A. Habas and Jacek M. Zurada and Georgia D. Tourassi }, pages = {600--605}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1283.pdf}, url = {}, size = {}, abstract = {A knowledge-based computer assisted decision (KB-CAD) system is a case-based reasoning system previously proposed for breast cancer detection. Although it was demonstrated to be very effective for the diagnostic problem, it was also shown to be computationally expensive due to the use of mutual information between images as a similarity measure. Here, the authors propose to alleviate this drawback by reducing the case-base size. The problem is formalized and a genetic algorithm is utilized as an optimization tool. Appropriate for the problem representation and operators are presented and discussed. A clinically relevant index of the area under the receiver operator characteristic curve is used as a measure of the system performance during the optimization and testing stages. Experimental results show that application of the proposed method can significantly reduce the case-base size while the classification performance of the KB-CAD, in fact, increases.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Boric:2007:cec, title={Genetic Programming-Based Clustering Using an Information Theoretic Fitness Measure}, author={Neven Boric and Pablo A. Est\'evez}, pages = {31--38}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1285.pdf}, keywords = {genetic algorithms, genetic programming}, url = {}, size = {}, abstract = {A clustering method based on multitree genetic programming and an information theoretic fitness is proposed. A probabilistic interpretation is given to the output of trees that does not require a conflict resolution phase. The method can cluster data with irregular shapes, estimate the underlying models of the data for each class and use those models to classify unseen patterns. The proposed scheme is tested on several real and artificial data sets, outperforming k-means algorithm in all of them.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(:2007:cec, title={Particle Swarm Optimization with Varying Bounds}, author={Mohammed El-Abd and Mohamed S. Kamel}, pages = {4757--4761}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1287.pdf}, url = {}, size = {}, abstract = {Particle Swarm Optimization (PSO) is a stochastic approach that was originally developed to simulate the behavior of birds and was successfully applied to many applications. In the field of evolutionary algorithms, researchers attempted many techniques in order to build probabilistic models that capture the search space properties and use these models to generate new individuals. Two approaches have been recently introduced to incorporate building a probabilistic model of the promising regions in the search space into PSO. This work proposes a new method for building this model into PSO, which borrows concepts from population-based incremental learning (PBIL). The proposed method is implemented and compared to existing approaches using a suite of well-known benchmark optimization functions.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Sagara:2007:cec, title={A Study on Social Diffusive Impacts of a Novel Car-Navigation-System Sharing Individual Information in Urban Traffic Systems}, author={Hiroki Sagara and Jun Tanimoto}, pages = {836--842}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1288.pdf}, url = {}, size = {}, abstract = {As one of the authors' trials to establish a model for human-environment-social Systems, a multi-agent simulation model to deal with urban traffic congestion problems involving automobiles embedded with several strategies of car navigation systems (CNS), has been presented. A shortest time route with route information sharing strategy (ST-RIS) is believed to be one of the solutions for a novel CNS based on bilateral information shared among automobile agents. The question of which strategy is most appropriate for solving urban traffic congestion can be seen as a social dilemma, since the social holistic utility is opposite to each agent's individual utility. The presented model shows that this social dilemma is observed as a typical Chicken type dilemma, or as a typical Minority Game, where an agent who has adopted a minority strategy can earn more utility than other strategies. Consequently, the model has illustrated that ST-pRIS, which is a further advanced strategic form of ST-RIS, where only partial information is shared among agents, has a moderate potential to be diffused in a society from the viewpoint of the evolutionary game theory.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Sumanbabu:2007:cec, title={Robust Tuning of Modern Power System Stabilizers Using Bacterial Foraging Algorithm}, author={B. Sumanbabu and S. Mishra and B. K. Panigrahi and G. K. Venayagamoorthy}, pages = {2317--2324}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1289.pdf}, url = {}, size = {}, abstract = {IEEE Std 421.5, revised by the IEEE excitation system subcommittee introduced a new type of power system stabilizer model, the multiband power system stabilizers (IEEE PSS4B). Although it requires two input signals, like the widely used IEEE PSS2B, the underlying principle of the new IEEE PSS4B makes it sharply different. This paper presents a method based on Bacterial Foraging Algorithm (BFA) to simultaneously tune these modern power system stabilizers (PSSs) in multimachine power system. Simulation results of multi-machine power system validate the efficiency of this approach. The proposed method is effective for the tuning of multi-controllers in large power systems.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Shingyochi:2007:cec, title={Comparative Study with Several GAs for Optimal Component Arrangement in a Circular Consecutive-k-out-of-n: F System}, author={K. Shingyochi and H. Yamamoto and Y. Tsujimura and Y. Kambayashi}, pages = {1149--1155}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1290.pdf}, url = {}, size = {}, abstract = {A circular consecutive-k-out-of-n: F system consists of n components arranged along a circular path. This system fails if no less than k consecutive components fail. One of the most important problems about this system is to obtain the optimal component arrangement that maximizes the system reliability. In order to obtain the exact solution for this problem, one needs to calculate n! system reliabilities. As n increases, however, the amount of calculation would be too large to obtain the solution within a reasonable computing time. In this paper, we propose four kinds of genetic algorithms to obtain quasi optimal solutions. They employ the ordinal representation scheme we have developed on the basis of ideas that eliminates arrangements with the same system reliabilities produced by rotation and/or reversal of certain arrangements, and allocates components with low failure probabilities at equal intervals. We have compared their performances and investigated how many components with low failure probabilities should be allocated at equal intervals to obtain good solution through the numerical experiments.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Moser:2007:cec, title={A Simple and Efficient Multi-Component Algorithm for Solving Dynamic Function Optimisation Problems}, author={I. Moser and T. Hendtlass}, pages = {252--259}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1292.pdf}, url = {}, size = {}, abstract = {A new multi-phase multi-individual version of the Extremal Optimisation algorithm was devised for dynamic function optimisation. The algorithm was tested on the three standardised benchmark scenarios of the publicly available Moving Peaks (MP) problem and observed to outperform all numerical results of other algorithmic approaches currently available in the literature. Parts of the algorithm were subsequently tested on variations of the scenarios to establish the role of each algorithm component in solving the problem as well as its contribution to the overall result. The reasons for the algorithm's impressive performance on the particular problem instance are discussed and possible limitations to its wider applicability are identified.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Lacevic:2007:cec, title={Population Diversity Measure Based on Singular Values of the Distance Matrix}, author={Bakir Lacevic and Samim Konjicija and Zikrija Avdagic}, pages = {1863--1869}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1293.pdf}, url = {}, size = {}, abstract = {This paper deals with both introducing novel technique of calculating population diversity and analyzing the existing ones. This motivation to investigate new methods of determining population diversity lies in significant disadvantages of commonly used techniques, particularly the ones that operate in a parameter space. The problem with these methods is that they can produce inexact information about population state, e.g. indicate high diversity when it is far from being high. For the purpose of eliminating these problems, new diversity mechanisms are investigated. The main idea was to use the information that is contained in the matrix with all mutual distances between individuals. New mechanism can be employed within a standard paralell search algorithms (wheter as analyzing or guiding mechanism), or in general, as a mechanism for determining how well does the finite set of points sample a compact region of space.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Skinner:2007:cec, title={Distributed Classifier Migration in XCS for Classification of Electroencephalographic Signals}, author={B. T. Skinner and H. T. Nguyen and D. K. Liu}, pages = {2829--2836}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1294.pdf}, url = {}, size = {}, abstract = {This paper presents an investigation into combining migration strategies inspired by multi-deme Parallel Genetic Algorithms with the XCS Learning Classifier System to provide parallel and distributed classifier migration. Migrations occur between distributed XCS classifier sub-populations using classifiers ranked according to numerosity, fitness or randomly selected. The influence of the degree-of-connectivity introduced by Fully-Connected, Bi-directional Ring and Uni-directional Ring topologies is examined. Results indicate that classifier migration is an effective method for improving classification accuracy, improving learning speed and reducing final classifier population size, in the single-step classification of noisy, artefactinclusive human electroencephalographic signals. The experimental results will be used as part of our larger research effort investigating the feasibility of using EEG signals as an interface to allow paralysed persons to control a powered wheelchair or other devices.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Newth:2007:cec, title={On the Evolution of Altruistic Behavior in Asynchronous Environments}, author={D. Newth}, pages = {3679--3684}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1295.pdf}, url = {}, size = {}, abstract = {The Prisoner's Dilemma is widely accepted as a standard model for studying the emergence of mutual cooperation, within populations of selfish individuals. Simulation studies of the Prisoner's Dilemma, where players make probabilistic choices based on previous actions, find that strategies such as Win-Stay-Lose-Shift, Tit-For-Tat, and Firm-But-Fair come to dominate the long term-behavior of the population. Often these models assume that decisions are made in synchrony. In many biological contexts this is an unrealistic assumption, as individuals change their behavior on different and uncorrelated time scales. Here we develop a model where both, one or neither players can update their behavior. We demonstrate that as the assumption of synchrony is relaxed, less reactive and more generous strategies such as Firm-But-Fair come to dominate the long term population dynamics.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Vergidis:2007:cec, title={Composite Business Processes: An Evolutionary Multi-objective Optimization Approach}, author={Kostas Vergidis and Ashutosh Tiwari and Basim Majeed}, pages = {2672--2678}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1297.pdf}, url = {}, size = {}, abstract = {Business process optimization has received little coverage compared to business process modeling and analysis techniques. This paper introduces composite business process models, i.e. conceptual business processes with tasks that each has its own library of alternatives. This paper formulates an optimization problem based on this concept. A series of experiments is designed to address processes of various sizes in terms of participating tasks and libraries of alternatives. Evolutionary algorithms such as NSGA2, SPEA2 and MOPSO attempt to generate optimum solutions to a bi-objective and triobjective problem formulation. The results show that SPEA2 performs better in the bi-objective problem, while NSGA2 has a clear advantage in the tri-objective problem, although both provide good solutions in all instances. This paper attempts to establish a viewpoint regarding business processes that will provoke and encourage further optimization attempts in this area.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Shimoyama:2007:cec, title={An Approach for Multi-Objective Robust Optimization Assisted by Response Surface Approximation and Visual Data-Mining}, author={Koji Shimoyama and Jin Ne Lim and Shinkyu Jeong and Shigeru Obayashi and Masataka Koishi}, pages = {2413--2420}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1298.pdf}, url = {}, size = {}, abstract = {A new approach for multi-objective robust design optimization has been proposed and applied to a real-world design problem with a large number of objective functions. The present approach is assisted by response surface approximation and visual data-mining, which results in two major gains regarding computational time and data interpretation. The Kriging model for response surface approximation can realize accurate predictions of robustness measures, and dramatically reduces the computational time for objective function evaluation. In addition, the use of self-organizing maps as a datamining technique allows visualization of complicated design information between optimality and robustness of design in a comprehensible two-dimensional form. Therefore, the extraction and interpretation of trade-off relations between optimality and robustness of design, and also the location of sweet-spots in the design space, can be performed in a comprehensive manner.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Alves:2007:cec, title={Integrating Planning and Scheduling Based on Genetic Algorithms to an Workflow System}, author={Fabiano S. R. Alves and Kairon F. Guimar.aes and M\'arcia A. Fernandes}, pages = {3766--3775}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1300.pdf}, url = {}, size = {}, abstract = {Workflow systems have been widely employed; however, the automatic generation of process models, specially applied in the Web Service domains is still an area to be explored. The Web services composition in Workflow is an emerging paradigm that allows the interaction both of internal applications and of applications that go beyond organizational boundaries. In this context, this paper presents an architecture based on the use of a genetic planner in order to obtain the automatic generation of models for Web Service Workflow. A simulation environment is also proposed by using scheduling techniques based on the use of genetic algorithms to select the most suitable process model.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Qing:2007:cec, title={A Parametric Study on Differential Evolution Based on Benchmark Electromagnetic Inverse Scattering Problem}, author={Anyong Qing}, pages = {1904--1909}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1302.pdf}, url = {}, size = {}, abstract = {Electromagnetic inverse scattering problem is of great scientific and engineering importance. Differential evolution has been successfully applied to solve electromagnetic inverse scattering problem. However, it is noted that the performance of differential evolution strongly depends on its intrinsic control parameters, namely, population size, mutation intensity, and crossover probability. A parametric study on differential evolution based on benchmark electromagnetic inverse scattering problem is therefore carried out. The findings presented here should be highly beneficial to future applicants of differential evolution in the community of electromagnetic inverse scattering problems. Such findings may be directly or indirectly applicable to researchers in other areas.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Rahnamayan:2007:cec, title={Quasi-Oppositional Differential Evolution}, author={Shahryar Rahnamayan and Hamid R. Tizhoosh and Magdy M. A. Salama}, pages = {2229--2236}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1303.pdf}, url = {}, size = {}, abstract = {In this paper, an enhanced version of the Opposition-Based Differential Evolution (ODE) is proposed. ODE utilizes opposite numbers in the population initialization and generation jumping to accelerate Differential Evolution (DE). Instead of opposite numbers, in this work, quasi opposite points are used. So, we call the new extension Quasi- Oppositional DE (QODE). The proposed mathematical proof shows that in a black-box optimization problem quasi- opposite points have a higher chance to be closer to the solution than opposite points. A test suite with 15 benchmark functions has been employed to compare performance of DE, ODE, and QODE experimentally. Results confirm that QODE performs better than ODE and DE in overall. Details for the proposed approach and the conducted experiments are provided.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Jayaswal:2007:cec, title={Single- and Multi-Objective Phylogenetic Analysis of Primate Evolution Using a Genetic Algorithm}, author={V. Jayaswal and L. Poladian and L. S. Jermiin}, pages = {4146--4153}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1304.pdf}, url = {}, size = {}, abstract = {Starting with the protein-coding mitochondrial DNA sequences of 20 different species, we reconstruct the primate evolutionary tree using maximum likelihood fitness functions based on a general Markov model of evolution. There is evidence that first and second codon sites in this DNA evolve under different conditions. Thus, we used a combination of a genetic algorithm (GA) and both single and multi-objective optimisation (MOO) to search tree-space for optimal solutions. Various genetic operators were used to search the combinatorial space of evolutionary trees, and a Pareto set was obtained. The implications of the common evolutionary subtrees to all trees found on the Pareto set are that the first codon sites play a far more important role in determining the optimal tree for these data. In the present case, the evolutionary relationship among the simian and other primates considered here remains in question.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Hendtlass:2007:cec, title={Fitness Estimation and the Particle Swarm Optimisation Algorithm}, author={Tim Hendtlass}, pages = {4266--4272}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1305.pdf}, url = {}, size = {}, abstract = {The time taken performing fitness calculations can dominate the total computational time when applying Particle Swarm Optimisation (PSO) to complex real life problems. This paper describes a method of estimating fitness, and the reliability of that estimation, that can be used as an alternative to performing some true fitness calculations. The fitness estimation is always made, but, should the reliability of this fitness estimation drop below a user specified threshold, the estimate is discarded and a true fitness evaluation performed. Results are presented for three problems that show that the number of true fitness evaluations can be significantly reduced by this method without degrading the performance of PSO. Further the value used for the threshold, the only new parameter introduced, is shown not to be sensitive, at least on these test problems. Provided that the time to perform a true fitness evaluation is far longer than the time for the fitness and reliability calculations, a substantial amount of computing time can be saved while still achieving the same end result.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Naznin:2007:cec, title={An Evolutionary Progressive Multiple Sequence Alignment}, author={Farhana Naznin and Morikazu Nakamura and Takeo Okazaki and Yumiko Nakajima}, pages = {3886--3893}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1306.pdf}, url = {}, size = {}, abstract = {This paper proposes an evolutionary tree-base (progressive multiple sequence alignment) method using a genetic algorithm (GA) for solving multiple sequence alignment problems. In our evolutionary tree-base method, chromosomes are represented as guide trees. Two kinds of crossover are proposed for chromosomes of tree structure; subtree selection crossover and tree uniform order crossover. They can generate new chromosomes with inheriting tree structure of parents. The indirect representation of multiple alignments, namely, the guide tree representation of chromosomes, and the proper genetic operations make searching drastically efficient. Experimental results for benchmark problems from BAliBASE and the NCBI database show that the proposed method is superior to SAGA (a well-known GA-base approach, 1996), TCoffee (sensitive progressive method, 2000), MUSCLE (progressive/iterative method, 2004), MAFFT (progressive/iterative method, 2005), and ProbCons (probabilistic/consistency method, 2005) with regard to quality of solutions.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Sing:2007:cec, title={Empirical Testing on 3-Parents Differential Evolution (3PDE) for Unconstrained Function Optimization}, author={Teng Nga Sing and Jason Teo and Mohd. Hanafi Ahmad Hijazi}, pages = {2259--2266}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1308.pdf}, url = {}, size = {}, abstract = {The objective of this paper is to investigate whether the performance of the self-adaptive the parameters in 3PDE can improve the performance for function optimization. In this paper, we firstly propose three new algorithms (3PDESACr, 3PDE-SAF and 3PDE-SACrF). The preliminary testing is carried out to compare their performance with 3PDE to determine the best algorithm for the next step to self-adapt the population size. Here, the best algorithm from the preliminary testing will be chosen for the testing on self-adapting the population size in absolute and relative encodings. The preliminary testing showed that 3PDE-SAF performed the best for the first three proposed algorithms. So, 3PDE-SAF is chosen for the self-adaptive population size to test in absolute (3PDE-SAF-Abs) and relative (3PDE-SAF-Rel) encodings and the final result showed that 3PDE-SAF-Rel performed slightly better than all the proposed algorithms in terms of its average performance and its stability.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Isaacs:2007:cec, title={Novel Evolutionary Algorithm with Set Representation Scheme for Truss Design}, author={A. Isaacs and T. Ray and W. Smith}, pages = {3902--3908}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1309.pdf}, url = {}, size = {}, abstract = {Presented in this paper is a novel scheme of representation for truss geometry. Trusses are represented as a set of elements having a collection of properties (e.g. cross-sectional area, type of material). These sets can be of varying cardinality representing truss structures with different numbers of elements and hence distinctly different topologies. A recombination operator to handle a set representation that can generate offspring topologies that can be different from the parents is also proposed. Depending on the physical problem being solved, one can introduce specific operators which will aid the optimization process. One such mutation operator is used for the truss design to reduce the number of elements, hence finding the smallest feasible topology for the truss structure. Another mutation operator perturbs the properties of the elements using the Gaussian mutation}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(:2007:cec, title={Analysis ofWeight Changes of Prematurely Born Babies with Neuromolecular Evolvable Hardware}, author={Tzu-Lan Lin and Yo-Hsien Lin and Jong-Chen Chen}, pages = {4386--4391}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1313.pdf}, url = {}, size = {}, abstract = {Prematurely born babies usually suffer from the treat of a nutrition deficiency problem. Total parental nutrition (TPN) has been one of the major treatments commonly used by clinicians to improve their nutritional needs. This paper described the application of an artificial neuromolecluar (ANM) chip to a database of prematurely born babies who received TPN. The objective was to investigate the weight changes of these babies from a number of factors that might affect their weight. Experimental results showed that the ANM chip achieved satisfactory results, outperforming the back-propagation neural networks.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Zhang:2007:cec, title={Integrated-Optimal Design of Airplane and Flight Control Using Genetic Algorithms}, author={Yifeng Zhang and Kai-Yew Lum}, pages = {2980--2987}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1314.pdf}, url = {}, size = {}, abstract = {Genetic algorithms (GA) are applied to a problem of conceptual airplane design. This paper discusses an integrated approach of designing the airplane geometry and flight control by means of GAs, where the two designs are conducted concurrently. The aim is to redesign the airplane's physical configuration in order to offer better closed-loop performance will less control effort. The overall design objective comprises control performances based on the linear quadratic regulator (LQR), and a cost function in terms of sizes of airplane components. Both single-objective and multi-objective GAs are explored. The flexibility offered by the proposed integrated-design approach is demonstrated in numerical examples.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Tripathi:2007:cec, title={Adaptive Multi-objective Particle Swarm Optimization Algorithm}, author={P. K. Tripathi and Sanghamitra Bandyopadhyay and S. K. Pal}, pages = {2281--2288}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1315.pdf}, url = {}, size = {}, abstract = {In this article we describe a novel Particle Swarm Optimization (PSO) approach to Multi-objective Optimization (MOO) called Adaptive Multi-objective Particle Swarm Optimization (AMOPSO). AMOPSO algorithm's novelty lies in its adaptive nature, that is attained by incorporating inertia and the acceleration coefficient as control variables with usual optimization variables, and evolving these through the swarming procedure. A new diversity parameter has been used to ensure sufficient diversity amongst the solutions of the non dominated front. AMOPSO has been compared with some recently developed multi-objective PSO techniques and evolutionary algorithms for nine function optimization problems, using different performance measures.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Yin:2007:cec, title={Scatter PSO. A More Effective Form of Particle Swarm Optimization}, author={Peng-Yeng Yin and Fred Glover and Manuel Laguna and Jia-Xian Zhu}, pages = {2289--2296}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1316.pdf}, url = {}, size = {}, abstract = {A fertile complementarity exists between scatter search (SS) and particle swarm optimization (PSO). Shared and contrasting principles underlying these methods provide a fertile basis for combining them to create a hybrid method. We identify a specific hybrid, Scatter PSO, giving rise to two variants that prove more effective than the constriction factor model of PSO. Applied to finding global minima for continuous nonlinear functions, Scatter PSO not only is able to obtain better solutions to a widely used set of benchmark functions, but also proves more robust under a variety of experimental conditions.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Jiang:2007:cec, title={A Foundational Study on the Applicability of Genetic Algorithm to Software Engineering Problems}, author={Hsinyi Jiang and Carl K. Chang and Dan Zhu and Shuxing Cheng}, pages = {2210--2219}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1317.pdf}, url = {}, size = {}, abstract = {Many problems in software engineering (SE) can be formulated as optimization problems. Genetic algorithm (GA) is one of the more effective tools for solving such optimization problems and has attracted the attention of SE researchers in recent years. However, there is a general lack of sound support theory to help SE researchers investigate the applicability of GA to certain classes of SE problems. Without such a theory, numerous attempts to conduct a wide spectrum of experiments for solution validation appear to be ad hoc and the results are often difficult to generalize. This paper reports a foundational study to develop such a support theory. Some preliminary results are also given.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Illich:2007:cec, title={Multi-Objective Strip Packing Using an Evolutionary Algorithm}, author={Simon Illich and Lyndon and Luigi Barone}, pages = {4207--4214}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1318.pdf}, url = {}, size = {}, abstract = {Good algorithms exist for solving the strip packing problem when the objective is to minimise the amount of wasted material. We describe a multi-objective evolutionary algorithm for strip packing (MOSP) that optimises not only for wastage, but also for the operating speed of the cutting equipment, by minimising the number of independent cuts required by a packing. We show that MOSP returns a set of packings offering a range of trade-offs between the two objectives, and also that, by using heuristics that consider cuts, it derives packings with wastage levels that are better than most previously-published algorithms that optimise for wastage alone.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Wu:2007:cec, title={Research on the Applications of the Genetic Algorithm to Sensor Registration}, author={Ling Wu and FaXing Lu and PeiFa Jia}, pages = {4405--4409}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1319.pdf}, url = {}, size = {}, abstract = {Sensor registration is a basis for well-organized sensor network, and a precondition for data fusion. In cases of constant registration errors, batch processing methods are always applied, where the registration is actually viewed as an optimization problem. Such methods are fast convergent, but sometimes they are not flexible in different cases and the optimal techniques used in batch processing methods may provide the suboptimum as solution. What's more, when dealing with a large number of sensors, the batch processing methods may come across numeric problems. To address the registration problem in some practical cases, the evolutionary algorithm based method can be explored. A method based on genetic algorithm, as well as the least squares method, are developed for sensor registration in different simulation scenarios and compared. Simulation results are analyzed to make clear the advantages and disadvantages of the methods.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(XiuLing:2007:cec, title={Generalization of HSO Algorithm for Computing Hypervolume for Multiobjective Optimization Problems}, author={Zhou XiuLing and Sun ChengYi and Mao Ning and Li WenJuan}, pages = {3114--3118}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1320.pdf}, url = {}, size = {}, abstract = {A frame of GHSO (Generalization of HSO) algorithm is proposed in this paper. One case of the GHSO is HSO (Hypervolume by Slicing Objectives) for computing hypervolume. Another two new cases are CHSO (Contribution of a Point to the Hypervolum by Slicing Objective) and DHSO (Contribution of a Point to the Hypervolume of Deleted Set by Slicing Objective), which are for computing the contribution of a point to the whole hypervolume under different conditions. Compared with the performance of LAHC (Lebesgue Archiving Hillcimber), the CHSO is improved significantly. Thus the CHSO will enable the use of hypervolume as a diversity mechanism with larger population in more objectives.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Orito:2007:cec, title={Index Fund Optimization Using a Genetic Algorithm and a Heuristic Local Search Algorithm on Scatter Diagrams}, author={Y. Orito and H. Yamamoto}, pages = {2562--2568}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1321.pdf}, url = {}, size = {}, abstract = {It is well known that index funds are popular passively managed portfolios and have been used very extensively for investment. Index funds consist of a certain number of stocks of listed companies on a stock market such that the fund's return rates follow a similar path to the changing rates of the market indices. However it is hard to make a perfect index fund consisting of all companies included in the market. Thus, the index fund optimization can be viewed as a combinatorial optimization for portfolio managements. In this paper, we propose a method that consists of a genetic algorithm and a heuristic local search algorithm to maximize the correlation between the fund's return rates and the changing rates of the market index. We then apply the method to the Tokyo Stock Exchange and compare it with a GA method and a hybrid GA method. The results show that our proposed method is effective for the index fund optimization.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Kwok:2007:cec, title={Efficient Particle Swarm Optimization: A Termination Condition Based on the Decision-Making Approach}, author={N. M. Kwok and Q. P. Ha and D. K. Liu and G. Fang and K. C. Tan}, pages = {3353--3360}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1322.pdf}, url = {}, size = {}, abstract = {Evolutionary computation algorithms, such as the particle swarm optimization (PSO), have been widely applied in numerical optimizations and real-world product design, not only for their satisfactory performances but also in their relaxing the need for detailed mathematical modelling of complex systems. However, as iterative heuristic searching methods, they often suffer from difficulties in obtaining high quality solutions in an efficient manner. Since unnecessary resources used in computation iterations should be avoided, the determination of a proper termination condition for the algorithms is desirable. In this work, termination is cast as a decision-making process to end the algorithm. Specifically, the non-parametric signtest is incorporated as a hypothetical test method such that a quantifiable termination in regard to specifiable decision-errors can be assured. Benchmark optimization problems are tackled using the PSO as an illustrative optimizer to demonstrate the effectiveness of the proposed termination condition.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Poladian:2007:cec, title={Excluding the Best and Worst Individuals from Parent Selection}, author={L. Poladian }, pages = {400--406}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1324.pdf}, url = {}, size = {}, abstract = {Any crossover operator has both beneficial and detrimental effects: it can bring building blocks together or it can tear them apart. In this paper, we provide evidence that the recombination can be biased towards its more beneficial aspects by modifying the parent selection process. A crude motivation is that the worst individuals do not have valuable building blocks to contribute, and it is too risky to subject the best individuals to crossover and have their building blocks separated. This idea is tested on both the hierarchical if and only if (HIFF) and the one dimensional Ising spin glass (SPIN) fitness functions. Four different parent selection schemes are compared and simulations are shown for both generational and steady state implementations. The results indicate this is a promising direction for future research.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Zhang:2007:cec, title={An Effective Method for Total Transfer Capability Calculation}, author={Qiang Zhang and Xueshan Han and MingqiangWang}, pages = {2931--2935}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1326.pdf}, url = {}, size = {}, abstract = {Under normal conditions, total transfer capability (TTC) mainly depends on voltage phase change, whereas power loss depends on reactive power distribution. The minimization of power loss is consistent with maximization of TTC. Based on this idea, TTC calculation can be divided into two sub-problems, namely (A) prediction of incremental TTC, which can be calculated by means of simplified linear lossless equivalent circuit and (B) with incremental TTC changes, a nonlinear optimization model minimizing power loss under certain operating mode is to be built to correct the influence of reactive power distribution on TTC. Thereby, an algorithm by which the two sub-problems are alternatively calculated is formed and the effect which is as same as that by complete optimization, i.e., the optimal power flow, is finally realized. Analysis results from several IEEE test systems and a large, realistic test system show that the proposed method is advisable.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Majhi:2007:cec, title={Stock Market Prediction of S&P 500 and DJIA using Bacterial Foraging Optimization Technique}, author={Ritanjali Majhi and G. Panda and G. Sahoo and P. K. Dash and D. P. Das}, pages = {2569--2575}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1327.pdf}, url = {}, size = {}, abstract = {The present paper introduces the Bacterial Foraging Optimization (BFO) 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 BFO 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 = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Wai:2007:cec, title={Total Sliding-Mode-Based Particle Swarm Optimization Control Design for Linear Induction Motor}, author={Rong-Jong Wai and Kun-Lun Chuang and Jeng-Dao Lee}, pages = {4729--4734}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1329.pdf}, url = {}, size = {}, abstract = {In this study, a total sliding-mode-based particle swarm optimization control (TSPSOC) scheme is designed for the periodic motion control of an indirect field-oriented linear induction motor (LIM) drive. First, an indirect field-oriented mechanism for a LIM drive is introduced to preserve the decoupling control characteristic. Then, the concept of total sliding-mode control (TSC) is incorporated into particle swarm optimization (PSO) to form an on-line TSPSOC framework for preserving the robust control characteristics and reducing the chattering control phenomena of TSC. Moreover, an adaptive inertial weight is devised to accelerate the searching speed effectively. In this control scheme, a PSO control system is utilized to be the major controller, and the stability can be indirectly ensured by the concept of TSC without strict constraint and detailed system knowledge. Numerical simulations are given to verify the effectiveness of the proposed control scheme for the tracking of periodic reference trajectories. In addition, the superiority of the proposed TSPSOC scheme is indicated in comparison with the TSC, Petri fuzzy-neural-network control (PFNNC), and traditional fuzzy-neural -network control (TFNNC) systems.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Majhi:2007:cec, title={Bacterial Foraging based Identification of Nonlinear Dynamic System}, author={Babita Majhi and G. Panda}, pages = {1636--1641}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1330.pdf}, url = {}, size = {}, abstract = {Identification of nonlinear dynamic system plays an important role in many applications such as control engineering, telecommunication and intelligent instrumentation. The present paper investigates on the use of Bacterial Foraging in identification of nonlinear dynamic systems employing an efficient Functional link artificial neural network (FLANN) model. The BFO is a derivative free optimization tool and hence does not permit the solution of connecting weights to fall in local minima. This potential tool is employed in the paper to update the weights of the FLANN model. To assess the performance of the new model simulation studies of both the BFO-FLANN and multilayered ANN (MLANN) identification models are carried out. These experiments reveal that the two models exhibit identical identification performance. But, the proposed model offers low computational complexity and achieves faster convergence compared to its MLANN counterpart.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Iwase:2007:cec, title={Evolutionary Search for Cellular Automata that Exhibit Self-Organizing Properties Induced by External Perturbations}, author={Yusuke Iwase and Reiji Suzuki and Takaya Arita}, pages = {759--765}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1331.pdf}, url = {}, size = {}, abstract = {Cellular Automata (CAs) have been investigated extensively as abstract models of the distributed systems composed of autonomous entities characterized by local interaction. However, how CAs can interact with their external environment still needs discussion. This paper aims at understanding emergent properties of CAs induced by external perturbations. We assumed a task in which a CA has to change its global state distinguished by the distribution ratio of cell states after every occurrence of a perturbation period. In the perturbation period, each cell state is modified by using an external rule with a small probability. By conducting evolutionary searches for rules of CAs that can solve this task, we obtained interesting behaviors of CAs in which their global state cyclically transited among different stable states in either ascending or descending order. Detailed analyses showed that such behaviors are due to their self-organizing properties that a drastic change in their global state occurs every when the number of the subsequent dominant cell state goes beyond a certain threshold through occurrences of perturbation periods.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Gong:2007:cec, title={A Simulative Study of the Roles of Cultural Transmission in Language Evolution}, author={Tao Gong and James W. Minett and William S.-Y.Wang}, pages = {843--850}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1332.pdf}, url = {}, size = {}, abstract = {A multi-agent computational model is proposed to simulate language evolution in an acquisition framework. This framework involves many major forms of cultural transmission, and the simulation results of the model systematically examine the role of cultural transmission in language emergence and maintenance. In addition, this study discusses the effects of conventionalization during horizontal transmission on diffusing linguistic innovations, maintaining high levels of linguistic understandability, and triggering inevitable changes in the communal languages across generations. All these reflect that conventionalization could be a self-organizing property of the human communication system that drives language evolution.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Monteiro:2007:cec, title={A Particle Swarm Optimization-Based Approach for Hyperspectral Band Selection}, author={Sildomar Takahashi Monteiro and Yukio Kosugi}, pages = {3335--3340}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1333.pdf}, url = {}, size = {}, abstract = {In this paper, a feature selection algorithm based on particle swarm optimization for processing remotely acquired hyperspectral data is presented. Since particle swarm optimization was originally developed to search only continuous spaces, it could not deal with the problem of spectral band selection directly. We propose a method utilizing two swarms of particles in order to optimize simultaneously a desired performance criterion and the number of selected features. The candidate feature sets were evaluated on a regression problem using artificial neural networks to construct nonlinear models of chemical concentration of glucose in soybean crops. Experimental results attesting the viability of the method utilizing real-world hyperspectral data are presented. The particle swarm optimization-based approach presented superior performance in comparison with a conventional feature extraction method.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Ling:2007:cec, title={A New Hybrid Particle Swarm Optimization with Wavelet Theory Based Mutation Operation}, author={S. H. Ling and C. W. Yeung and K. Y. Chan and H. H. C. Iu and F. H. F. Leung}, pages = {1977--1984}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1334.pdf}, url = {}, size = {}, abstract = {An improved hybrid particle swarm optimization (PSO) that incorporates a wavelet-based mutation operation is proposed. It applies wavelet theory to enhance PSO in exploring solution spaces more effectively for better solutions. A suite of benchmark test functions and an application example on tuning an associative-memory neural network are employed to evaluate the performance of the proposed method. It is shown empirically that the proposed method outperforms significantly the existing methods in terms of convergence speed, solution quality and solution stability.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Zhou:2007:cec, title={Double-Deck Elevator Systems Using Genetic Network Programming with Reinforcement Learning}, author={Jin Zhou and Lu Yu and Shingo Mabu and Kotaro Hirasawa and Jinglu Hu and Sandor Markon}, pages = {2025--2031}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1339.pdf}, url = {}, size = {}, abstract = {In order to increase the transportation capability of elevator group systems in high-rise buildings without adding elevator installation space, double-deck elevator system (DDES) is developed as one of the next generation elevator group systems. Artificial intelligence (AI) technologies have been employed to find some efficient solutions in the elevator group control systems during the late 20th century. Genetic Network Programming (GNP), a new evolutionary computation method, is reported to be employed as the elevator group system controller in some studies of recent years. Moreover, reinforcement learning (RL) is also verified to be useful for more improvements of elevator group performances when it is combined with GNP. In this paper, we proposed a new approach of DDES using GNP with RL, and did some experiments on a simulated elevator group system of a typical office building to check its efficiency. Simulation results show that the DDES using GNP with RL performs better than the one without RL in regular and down-peak time, while both of them outperforms a conventional approach and a heuristic approach in all three traffic patterns.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Worasucheep:2007:cec, title={A New Self Adaptive Differential Evolution: Its Application in Forecasting the Index of Stock Exchange of Thailand}, author={Chukiat Worasucheep}, pages = {1918--1925}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1340.pdf}, url = {}, size = {}, abstract = {This paper proposes wDE, a new differential evolution with self-adaptive CR and F, and adaptive NP. Its performance is evaluated on the 25 benchmark problems in CEC2005. The results indicated that wDE is comparative to SaDE in most benchmark problems and has ability of escaping from local optima in some complex problems. Its adaptation of NP helps accelerate the convergence. In a real-world application, wDE is applied to forecast the market index of Stock Exchange of Thailand during a period of January 2003 to September 2006. The forecasting model provides a more accurate prediction than the multiple linear regression over the same data set with statistical significance. In addition, the model does not require fine-tuning of essential parameters in which it takes time and experience.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Baghshah:2007:cec, title={Evolving Fuzzy Classifiers Using a Symbiotic Approach}, author={M. Soleymani Baghshah and S. Bagheri Shouraki and R. Halavati and C. Lucas}, pages = {1601--1607}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1345.pdf}, url = {}, size = {}, abstract = {Fuzzy rule-based classifiers are one of the famous forms of the classification systems particularly in the data mining field. Genetic algorithm is a useful technique for discovering this kind of classifiers and it has been used for this purpose in some studies. In this paper, we propose a new symbiotic evolutionary approach to find desired fuzzy rulebased classifiers. For this purpose, a symbiotic combination operator has been designed as an alternative to the recombination operator (crossover) in the genetic algorithms. In the proposed approach, the evolution starts from simple chromosomes and the structure of chromosomes gets complex gradually during the evolutionary process. Experimental results on some standard data sets show the high performance of the proposed approach compared to the other existing approaches.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Saha:2007:cec, title={On Some Symmetry Based Validity Indices}, author={Sriparna Saha and Sanghamitra Bandyopadhyay}, pages = {697--704}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1346.pdf}, url = {}, size = {}, abstract = {Identification of the correct number of clusters and the corresponding partitioning are two important considerations in clustering. In this paper, a newly developed point symmetry based distance is used to propose symmetry based versions of six cluster validity indices namely, DB-index, Dunn-index, Generalized Dunn-index, PS-index, I-index and XB-index. These indices provide measures of "symmetricity" of the different partitionings of a data set. A Kd-tree-based data structure is used to reduce the complexity of computing the symmetry distance. A newly developed genetic point symmetry based clustering technique, GAPS-clustering is used as the underlying partitioning algorithm. The number of clusters are varied from 2 to √n where n is the total number of data points present in the data set and the values of all the validity indices are noted down. The optimum value of a validity index over these √n-1 partitions corresponds to the appropriate partitioning and the number of partitions as indicated by the validity index. Results on five artificially generated and four real-life data sets show that symmetry distance based I-index performs the best compared to all the other five indices.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Avery:2007:cec, title={Static Experts and Dynamic Enemies in Coevolutionary Games}, author={Phillipa M. Avery and Zbigniew Michalewicz}, pages = {4035--4042}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1347.pdf}, url = {}, size = {}, abstract = {The usage of memory in coevolutionary systems offers a wide range of research possibilities, especially when evolving computationally intelligent computer players for games. The research discussed here continues from previous work done to include memory with coevolution for the game of TEMPO. The strategy of inserting a simple human derived rule base to kick start the evolutionary process with memory is investigated further, with tests done on the effectiveness of the expert as a participant in the evolutionary process. There is also further research presented on reproducing the human long term memory mechanism in the coevolutionary process, with a process used to mimic the way humans recall information relevant to the current scenario. This creates a memory that changes as the environmental situation changes, and results in a dynamic opposition to coevolve against.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Luerssen:2007:cec, title={Graph Design by Graph Grammar Evolution}, author={Martin H. Luerssen and David M. W. Powers }, pages = {386--393}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1348.pdf}, keywords = {genetic algorithms, genetic programming}, url = {}, size = {}, abstract = {Determining the optimal topology of a graph is pertinent to many domains, as graphs can be used to model a variety of systems. Evolutionary algorithms constitute a popular optimization method, but scalability is a concern with larger graph designs. Generative representation schemes, often inspired by biological development, seek to address this by facilitating the discovery and reuse of design dependencies and allowing for adaptable exploration strategies. We present a novel developmental method for optimizing graphs that is based on the notion of directly evolving a hypergraph grammar from which a population of graphs can be derived. A multi-objective design system is established and evaluated on problems from three domains: symbolic regression, circuit design, and neural control. The observed performance compares favorably with existing methods, and extensive reuse of subgraphs contributes to the efficient representation of solutions. Constraints can also be placed on the type of explored graph spaces, ranging from tree to pseudograph. We show that more compact solutions are attainable in less constrained spaces, although convergence typically improves with more constrained designs.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C is it a GP? Evolution of executable grammar?}, ) @inproceedings(Yang:2007:cec, title={Differential Evolution for High-Dimensional Function Optimization}, author={Zhenyu Yang and Ke Tang and Xin Yao}, pages = {3523--3530}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1351.pdf}, url = {}, size = {}, abstract = {Most reported studies on differential evolution (DE) are obtained using low-dimensional problems, e.g., smaller than 100, which are relatively small for many real-world problems. In this paper we propose two new efficient DE variants, named DECC-I and DECC-II, for high-dimensional optimization (up to 1000 dimensions). The two algorithms are based on a cooperative coevolution framework incorporated with several novel strategies. The new strategies are mainly focus on problem decomposition and subcomponents cooperation. Experimental results have shown that these algorithms have superior performance on a set of widely used benchmark functions.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Kulvanit:2007:cec, title={Biped Fast Walking Gait Shaping via Evolutionary Multi-Objective Optimization}, author={P. Kulvanit and N. Chaiyaratana and D. Laowattana}, pages = {4019--4026}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1352.pdf}, url = {}, size = {}, abstract = {The multi-objective optimization of the fast walking gait using a Multi-Objective Genetic Algorithm (MOGA) is applied to the real biped robot to get the optimal set of walking parameters based on the desired walking performances such as walking speed, swaying in the saggittal plane, and power level during the walk. The robot, which walks in a dynamically stable manner based on the inverted pendulum model, is used as an objective function evaluator in the MOGA process. The method can be used as a biped gait shaping process or a recipe to extract the best performance of the existing robot.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Ghandar:2007:cec, title={A Computational Intelligence Portfolio Construction System for Equity Market Trading}, author={Adam Ghandar and Zbigniew Michalewicz and Martin Schmidt and Thuy-Duong T.o and Ralf Zurbruegg}, pages = {798--805}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1354.pdf}, url = {}, size = {}, abstract = {This paper describes an adaptive computational intelligence system for learning trading rules used in equity market trading. The rules are represented using fuzzy logic, an evolutionary process facilitates the learning process. By controlling the evolutionary process and through selection of training data the trading rules are adapted to market conditions. Results of the systems performance are obtained using historical data from the Australian Stock Exchange (ASX).}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Zhang:2007:cec, title={Learning Based Gaits Evolution for an AIBO Dog}, author={Jiaqi Zhang and Qijun Chen}, pages = {1523--1526}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1356.pdf}, url = {}, size = {}, abstract = {Developing fast gaits for legged robots is a difficult task that requires optimizing parameters in a multidimensional space. In most previous works, it was done by hand-tuning the parameters related to walking, using evolutionary algorithm or reinforcement learning to optimize these parameters. As we know, the approach combining evolution and learning would have some special characters compared to any solo one. But few papers contributed on this direction. In this paper, we combined evolution and learning and produced a fast forward gait for an AIBO dog. On considering the whole time to train the robot, we took an analogy steepest descent method as the learning method. Although it's a rather simple learning method, the final results showed it improved the performance not only in the walking speed but also in the evolution efficiency.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Kornienko:2007:cec, title={From Real Robot Swarm to Evolutionary Multi-Robot Organism}, author={S. Kornienko and O. Kornienko and A. Nagarathinam and P. Levi}, pages = {1483--1490}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1359.pdf}, keywords = {genetic algorithms, genetic programming}, url = {}, size = {}, abstract = {Collective working allows microrobots to achieve more functionality, better performance and higher reliability on the macroscopic level. In this paper we demonstrate the on-going work in developing novel collective systems, where swarm robots work not only collectively, but are also capable of autonomous aggregation and disaggregation into a higher multi-robot organism. The main issues of such an organism, as well as its genome-based control, are discussed. We show the developed docking approach and investigate topological transformations in a prototype of self-assembling robots.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C Is it a GP? Execeutable content?}, ) @inproceedings(Mukhopadhyay:2007:cec, title={Multiobjective Approach to Categorical Data Clustering}, author={Anirban Mukhopadhyay and Ujjwal Maulik}, pages = {1296--1303}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1361.pdf}, url = {}, size = {}, abstract = {Categorical data clustering has been gaining significant attention from researchers since the last few years, because most of the real life data sets are categorical in nature. In contrast to numerical domain, no natural ordering can be found among the elements of a categorical domain. Hence no inherent distance measure, like the Euclidean distance, would work to compute the distance between two categorical objects. Most of the clustering algorithms designed for categorical data are based on optimizing a single objective function. However, a single objective function is often not applicable for different kinds of categorical data sets. Motivated by this fact, in this article, the categorical data clustering problem has been modeled as a multiobjective optimization problem. A popular multiobjective genetic algorithm has been used in this regard to optimize two objectives simultaneously, thus generating a set of non-dominated solutions. The performance of the proposed algorithm has been compared with that of different well known categorical data clustering algorithms and demonstrated for a variety of synthetic and real life categorical data sets. Also a statistical significance test has been performed to establish the superiority of the proposed algorithm.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Raghuwanshi:2007:cec, title={Study of Operator's Adaptability and Scale-Up Study for RAGA}, author={M. M. Raghuwanshi and O. G. Kakde}, pages = {2866--2871}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1362.pdf}, url = {}, size = {}, abstract = {The studies have shown that a class of recombination operators is more suitable to tackle certain problems than others. It is observed that the multi-parent recombination operator with polynomial distribution (MPX) is exploitative and the multi-parent recombination operator with lognormal distribution (MLX) is explorative, in nature. Use of productive operators is necessary for a genetic algorithm to uncover new fitter points in the search space to improve its overall performance. Real-coded self-Adaptive GA (RAGA) uses two multi-parent recombination operators (MPX and MLX). The use of particular operator to generate offspring during evolution process depends on its ability to produce good offspring. This paper presents the effect of operator's adaptability in solving test problems. Also scale-up study analyses the performance of RAGA with increasing number of control variables.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Tsuji:2007:cec, title={A Network Design Problem by a GA with Linkage Identification and Recombination for Overlapping Building Blocks}, author={Miwako Tsuji and Masaharu Munetomo and Kiyoshi Akama}, pages = {349--356}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1363.pdf}, url = {}, size = {}, abstract = {Efficient mixing of building blocks is important for genetic algorithms and linkage identification that identify variables tightly linked to form a building block have been proposed. In this paper, we apply D5-GA with CDC - a genetic algorithm incorporating a linkage identification method called D5 and a crossover method called CDC - to a network design problem to verify its performance and examine the applicability of the linkage identification genetic algorithms.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Luerssen:2007:cec, title={Evolvability and Redundancy in Shared Grammar Evolution}, author={Martin H. Luerssen and David M. W. Powers }, pages = {370--377}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1364.pdf}, keywords = {genetic algorithms, genetic programming}, url = {}, size = {}, abstract = {Shared grammar evolution (SGE) is a novel scheme for representing and evolving a population of variablelength programs as a shared set of grammatical productions. Productions that fail to contribute to selected solutions can be retained for several generations beyond their last use. The ensuing redundancy and its effects are assessed in this paper on two circuit design tasks associated with random number generation: finding a recurrent circuit with maximum period, and reproducing a De Bruijn counter from a set of seed/output pairs. In both instances, increasing redundancy leads to significantly higher success rates, outperforming comparable increases in population size. The results support previous studies that have shown that representational redundancy can be beneficial to evolutionary search. However, redundancy promotes an increase in further redundancy by encouraging the creation of large offspring, the evaluation of which is computationally costly. This observation should generalize to any unconstrained variablelength representation and therefore represents a notable drawback of redundancy in evolution.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Breaban:2007:cec, title={A New PSO Approach to Constraint Satisfaction}, author={Mihaela Breaban and Madalina Ionita and Cornelius Croitoru}, pages = {1948--1954}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1367.pdf}, url = {}, size = {}, abstract = {Constraint satisfaction arises in many domains in different forms. Search and inference compete for solving constraint satisfaction problems (CSPs) but the most successful approaches are those which benefit from both techniques. Based on this idea, this article introduces a new scheme for solving the general Max-CSP problem. The new approach exploits the simplicity and efficiency of a modified Particle Swarm Optimization and the advantage of adaptable inference levels offered by the Mini-Bucket Elimination algorithm. Experiments conducted on binary CSPs using different levels of inference are illustrative for the inference/search trade-off. Comparative studies highlight the differences between our stochastic population-based method and the systematic search performed by a Branch and Bound algorithm.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Pérez-Pérez:2007:cec, title={Multiobjective Algorithms to Optimize Broadcasting Parameters in Mobile Ad-hoc Networks}, author={R. P\'erez-P\'erez and C. Luque and A. Cervantes and P. Isasi}, pages = {1948--1955}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1369.pdf}, url = {}, size = {}, abstract = {A mobile adhoc network (MANETs) is a selfconfiguring network of mobile routers (and associated hosts). The routers tend to move randomly and organize themselves arbitrarily; thus, the network's wireless topology may change fast and unpredictably. Nowadays, these networks are having a great influence due to the fact that they can create networks without a specific infrastructure. In MANETs message broadcasting is critical to network existence and organization. The broadcasting strategy in MANETs can be optimized by defining a multiobjective problem whose inputs are the broadcasting algorithm's parameters and whose objectives are: reaching as many stations as possible, minimizing the network utilization, and reducing the makespan. The network can be simulated to obtain the expected response to a given set of parameters. In this paper, we face this multiobjective problem with two algorithms: Multiobjective Particle Swarm Optimization and ESN (Evolution Strategy with NSGAII). Both algorithms are able to find an accurate approximation to the Paretooptimal front that is the solution of the problem. ESN improves the results of MOPSO in terms of the set coverage and hypervolume metrics used for comparison.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Atyabi:2007:cec, title={Particle Swarm Optimization with Area Extension (AEPSO)}, author={A. Atyabi and S. Phon-Amnuaisuk}, pages = {1970--1976}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1370.pdf}, url = {}, size = {}, abstract = {Particle Swarm Optimization (PSO) is one of the evolutionary algorithms which proved to be useful in solving multi-robots tasks. PSO outperforms other evolutionary algorithms, such as GA, in this area. In this paper we introduce a new modified version of PSO called Area Extension PSO (AEPSO). Information about the environment in extended area together with various heuristics improves the performance of each robot and the group. We believe this AEPSO is suitable to solve problems in environments with large area which have more similarity to real world robotic problems. The result of this study shows a magnificent improvement and the potential of AEPSO, especially in dynamic environments.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Kumar:2007:cec, title={Evolving Virtual Reconfigurable Circuit for a Fault Tolerant System}, author={P. Nirmal Kumar and S. Anandhi and J. Raja Paul Perinbam}, pages = {1555--1561}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1375.pdf}, url = {}, size = {}, abstract = {This paper describes about the design and implementation of a virtual reconfigurable circuit (VRC) for a fault tolerant system which averages the (three) sensor inputs. It deals with evolving the desired circuit by decoding the configuration bit streams uploaded into its SRAM. The circuits that are successfully evolved in this system is, corresponds to different situation such as (i) all three sensors are faultless (ii) one of the input sensor fails as open. The objective of this work is to decode the configuration bit streams and thereby evolving the desired optimal circuit in the VRC and also to obtain the power consumed by the VRC.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(:2007:cec, title={A Polygon Description Based Similarity Measurement of Stock Market Behavior}, author={Por-Shen Lai and Hsin-Chia Fu}, pages = {806--812}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1376.pdf}, url = {}, size = {}, abstract = {This paper proposes (1) a polygon distribution descriptor and (2) an EC-based similarity measurement for stock market behavior analysis. After learning stock market historical data, a polygon descriptor can capture the dependencies among stock market quantities, such as stock prices, volumes, EPS (earn per share) and so on. By applying the EC-based similarity measurement on polygon descriptors which were trained by stock market data during different periods, the similarity of corresponding stock market behavior can be analyzed. To demonstrate the representation capabilities of the proposed polygon descriptor, Taiwan stock market data from 1986 to 2006 are used. Experimental results show that the polygon descriptor captures the dependencies of stock market quantities, and the similarity measurement shows that the proposed methods capture the changes of market behavior as expected.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Takahama:2007:cec, title={Structural Learning of Neural Networks by Differential Evolution with Degeneration Using Mappings}, author={Tetsuyuki Takahama and Setsuko Sakai and Akira Hara and Noriyuki Iwane}, pages = {3434--3441}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1378.pdf}, url = {}, size = {}, abstract = {Structural learning, in which the structure of estimation systems are optimized, has been actively studied in researches on supervised learning of neural networks and fuzzy rules. The GAd(Genetic Algorithm with Degeneration) is one of the structural learning methods, which is modeled on genetic damage and degeneration. In GAd, a gene is defined by a pair of a normal value and a damaged rate that shows how much the gene is damaged. Simple one-point crossover and Gaussian mutation are adopted to deal with the pair. It was very difficult to incorporate newly proposed genetic operations such as blend crossover in GA or operations in differential evolution (DE). In this study, we propose a new idea to incorporate such operations by unifying the values according to a mapping, applying operations and separating the values according to the inverse mapping. This idea is applied to differential evolution, which is known to be an efficient and robust algorithm and DEd(Differential Evolution with Degeneration) is proposed. To show the advantage of DEd, it is applied to the structural learning of a simple test function and neural networks. It is shown that DEd is more robust to high degeneration pressure and can find better estimation models faster, which have less number of parameters and less estimation errors, than GAd.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Khan:2007:cec, title={A Simulated Evolution-Tabu Search Hybrid Metaheuristic for Routing in Computer Networks}, author={Salman A. Khan and Zubair A. Baig}, pages = {3818--3823}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1379.pdf}, url = {}, size = {}, abstract = {Routing in computer networks is a nonlinear combinatorial optimization problem with numerous constraints and is classified as an NP-complete problem. There are certain important QoS metrics which affect the performance of a network. One of these metrics is the average network delay, which should be minimized. In this paper, a routing strategy based on Simulated Evolution algorithm to find suboptimal routing solution for computer networks while optimizing the above metric is presented. To intensify the search, a hybrid variant of the proposed algorithm has also been implemented. This variant incorporates Tabu Search characteristics into the Simulated Evolution algorithm. Performance evaluation of the two approaches is done via simulation. Empirical results suggest that the hybrid variant performs better than the original Simulated Evolution algorithm.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Korenaga:2007:cec, title={Performance Improvement of Particle Swarm Optimization for High-Dimensional Function Optimization}, author={Takeshi Korenaga and Toshiharu Hatanaka and Katsuji Uosaki}, pages = {3288--3293}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1380.pdf}, url = {}, size = {}, abstract = {Particle swarm optimization (PSO) is a kind of population-based search methods, that is inspired by social behavior observed in nature, such as flocks of irds and schools of fish. PSO has been receiving attentions, since it has a powerful search ability in function optimization problems, and several improvement has been studied to apply PSO to the multimodal function optimization and optimization in the dynamic environments. The purpose of this paper is to improve PSO performance deteriorated by the degeneracy of particle velocities, in case of high-dimensional optimization problems. We propose a novel PSO model, called the Rotated Particle Swarm (RPS), by introducing the coordinate conversion. The numerical simulation results show that the proposed RPS is effective in optimizing high-dimensional functions.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Tavakoli:2007:cec, title={Multi-Objective Optimization Approach to the PI Tuning Problem}, author={Saeed Tavakoli and Ian Griffin and Peter J. Fleming}, pages = {3165--3171}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1381.pdf}, url = {}, size = {}, abstract = {This paper presents a simple PI control design approach for first order plus dead time processes. The design method aims to obtain good responses to setpoint and load disturbance signals, good robustness to model errors and small variation of the control signal, simultaneously. The design problem is formulated as a multi-objective optimization problem. Using multi-objective genetic algorithms, the optimization problem is solved and optimal PI tuning formulae are developed. Simulation results demonstrate the effectiveness of the proposed method in coping with conflicting design objectives.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Millard:2007:cec, title={Evolutionary Motion Inverse Kinematics}, author={John Millard}, pages = {3671--3678}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1385.pdf}, url = {}, size = {}, abstract = {In disciplines, such as robotics and computer graphics (CG) or industries such as, game development, various tasks involve the manipulation of articulated structures in interesting and complex ways. This may involve controlling robotic manipulators or perhaps posing the skeleton of a virtual human. When performed manually, the task of rotating each individual joint in order to produce a desired pose can be tedious and time consuming. The process of rotating each joint independently is known as forward kinematics (FK). One way to simplify this process is to automatically calculate the angles required for a given joint (known as the end-effector) to reach a goal position - this process is known as inverse kinematics (IK). In this paper an evolutionary algorithm (EA) is proposed as a solution to inverse kinematics. Several extensions are also proposed to allow extra constraints to be included, such as obstacle avoidance and angular joint limits. This approach is called Evolutionary Motion IK (E-MOTION IK).}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Dornberger:2007:cec, title={Use of a Genetic Algorithm in a Distributed Resource Management Software}, author={Rolf Dornberger and Lukas Frey and Claudia Schmid}, pages = {2428--2434}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1386.pdf}, url = {}, size = {}, abstract = {Resource management and planning in railway operating companies is a highly complex task. Based on simulation, service planning, vehicle rotation planning, personnel rotation planning and operations management, the resources (e.g. people, vehicles, and infrastructure) must be combined to timetables and rosters. In this work, the development of an optimization agent, using a genetic algorithm, for an existing distributed software product for resource planning (RailOpt) is treated. Main aspects are the description of an underlying real world problem, the definition of a fitness function in order to solve a sub-problem, the particular implementation of the GA and its configuration.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Lee:2007:cec, title={Evolutionary Multi-Objective Optimization for Generating Artificial Creature's Personality}, author={Chi-Ho Lee and Kang-Hee Lee and Jong-Hwan Kim}, pages = {2450--2455}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1388.pdf}, url = {}, size = {}, abstract = {This paper proposes the evolutionary generation of an artificial creature's personality by using the concept of multi-objective optimization. The artificial creature has its own genome and in which each chromosome consists of many genes that contribute to defining its personality. The large number of genes allows for a highly complex system, however it becomes increasingly difficult and time-consuming to ensure reliability, variability and consistency for the artificial creature's personality while manually assigning gene values for the individual genome. Moreover, there needs user's preference to obtain artificial creature's personality by using evolutionary generation. Preference is strongly depend on each user and most of them would have difficulty to define their preference as a fitness function. To solve this problem, this paper proposes multi-objective generating process of an artificial creature's personality. Genome set is evolved by applying strength Pareto evolutionary algorithm (SPEA). To facilitate the individuality of generated artificial creature, complement of (1-k) dominance and pruning method considering deviation are proposed. Obtained genomes are tested by using an artificial creature, Rity in the virtual 3D world created in a PC.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Hiroyasu:2007:cec, title={Flexibility of Design Variables to Pareto-Optimal Solutions in Multi Objective Optimization Problems}, author={Tomoyuki Hiroyasu and Shinpei Chino and Mitsunori Miki}, pages = {4462--4468}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1391.pdf}, url = {}, size = {}, abstract = {In this paper, we propose the concept of the flexibility of design variables to Pareto-optimal solutions in Multi-Objective Optimization problems. In addition, we introduce a method for measurement of the flexibility of design variables to Pareto-optimal solutions. Increases in the number of design variables usually result in a wide variety of optimum solutions. However, when the flexibilities of some design variables are small, the contributions of these design variables are also very small. This means that the same Pareto-optimal solutions can be derived without these parameters. Therefore, it is very important to find the flexibility of the design variables to the Pareto-optimal solutions. To find the flexibility, the values of one of the design variables are changed, while those of the remaining parameters are fixed. In this procedure, it is very important to determine the fixed values. We describe these procedures to determine the flexibility of the design variable to the Pareto-optimal solutions. Finally, we illustrate using the diesel engine fuel emission scheduling problem that the Paretooptimal solutions can be derived with only the design variables whose flexibilities are high.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Liu:2007:cec, title={A New Constrained Optimization Evolutionary Algorithm by Using Good Point Set}, author={Hui Liu and Zixing Cai and Yong Wang}, pages = {1247--1254}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1394.pdf}, url = {}, size = {}, abstract = {Solving constrained optimization problems (COPs) via evolutionary algorithms (EAs) has attracted much attention recently. A new constrained optimization evolutionary algorithm by using good point set (COEAGP) is presented in this paper. In the process of population evolution, multi-objective optimization techniques and good point set in number theory are integrated into our algorithm. The approach transforms COP into a bi-objective optimization problem firstly. Then the crossover operator is designed by using the principle of good point set. The purpose of the new crossover is to enrich the exploration and exploitation abilities of the approach proposed. The new crossover operator can produce a small but representative set of points as the potential offspring. After that the BGA mutation operator is applied to potential offspring for enhancing the diversity of the potential offspring population. Furthermore, the update operator incorporates Pareto dominance and the tournament selection operator to choose the best individuals in the current offspring for the next generation. The new approach is tested on 8 well-known benchmark functions, and the empirical evidence suggests that it is robust and efficient when handling linear/nonlinear equality/inequality constraints and that COEAGP outperforms or performs similarly to the other techniques referred in this paper in terms of the quality of the resulting solutions.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Park:2007:cec, title={A Dual Population Genetic Algorithm with Evolving Diversity}, author={Taejin Park and Kwang Ryel Ryu}, pages = {3516--3522}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1395.pdf}, url = {}, size = {}, abstract = {We propose a dual population genetic algorithm inspired by the complementary and dominance mechanism prevalent in nature. The proposed algorithm has two distinct populations: a main population and a reserve population. The main population is similar to that of an ordinary genetic algorithm and evolves to find good solutions. The reserve population evolves to maintain and offer diversity to the main population. While most multi-population genetic algorithms use migration as a means of information ex-change between different populations, our algorithm uses crossbreeding and survival selection because the two populations have different evolutionary objectives. The experimental results on various multimodal optimization problems show that the proposed algorithm is better than not only ordinary genetic algorithms but also than the other algorithms based on similar idea.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Hu:2007:cec, title={Traffic Signal Control using Fuzzy Logic and Evolutionary Algorithms}, author={Yi Hu and Peter Thomas and Russel J. Stonier}, pages = {1785--1792}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1396.pdf}, url = {}, size = {}, abstract = {This paper presents a fuzzy control system to regulate the traffic flow approaching a single real intersection which consists of multiple lanes with turns, by adjusting time parameters and phases of traffic signals. The lanes are cataloged into several groups controlled by individual traffic lights. These lights are further arranged into several light phases. A fuzzy controller was developed to control the time length of each light phase. Evolutionary algorithms were employed to generate the fuzzy logic rule base, using real statistical traffic data for the intersection. To simulate real car flows, new acceleration and deceleration movement models were developed to ensure safe driving by avoiding possible collision. A new fitness function that comprehensively characterizes car flow delay induced from signals was constructed to evaluate the performance of the fuzzy logic controller. Key performance criteria obtained using the fuzzy logic controller were compared with those obtained by the controller used by the Department of Main Roads, Queensland at the intersection.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Saha:2007:cec, title={MRI Brain Image Segmentation by Fuzzy Symmetry Based Genetic Clustering Technique}, author={Sriparna Saha and Sanghamitra Bandyopadhyay}, pages = {4417--4424}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1397.pdf}, url = {}, size = {}, abstract = {In this paper, an automatic segmentation technique of multispectral magnetic resonance image of the brain using a new fuzzy point symmetry based genetic clustering technique is proposed. The proposed real-coded variable string length genetic fuzzy clustering technique (Fuzzy-VGAPS) is able to evolve the number of clusters present in the data set automatically. Here assignment of points to different clusters are made based on the point symmetry based distance rather than the Euclidean distance. The cluster centers are encoded in the chromosomes, whose value may vary. A newly developed fuzzy point symmetry based cluster validity index, FSym-index, is used as a measure of 'goodness' of the corresponding partition. This validity index is able to correctly indicate presence of clusters of different sizes as long as they are internally symmetrical. A Kd-tree based data structure is used to reduce the complexity of computing the symmetry distance. The proposed method is applied on several simulated T1-weighted, T2-weighted and proton density normal and MS lesion magnetic resonance brain images. Superiority of the proposed method over Fuzzy C-means, Expectation Maximization, Fuzzy Variable String Length Genetic Algorithm (Fuzzy-VGA) clustering algorithms are demonstrated quantitatively. The automatic segmentation obtained by Fuzzy-VGAPS clustering technique is also compared with the available ground truth information.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Chen:2007:cec, title={A Modified Approach to Speed up Genetic-Fuzzy Data Mining with Divide-and-Conquer Strategy}, author={Chun-Hao Chen and Tzung-Pei Hong and Vincent S. Tseng}, pages = {1--6}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1399.pdf}, url = {}, size = {}, abstract = {In the past, we proposed a fuzzy data-mining algorithm for extracting both association rules and membership functions from quantitative transactions based on the divide-and-conquer strategy. In this paper, an enhanced approach, called the cluster-based genetic-fuzzy mining algorithm, is thus proposed to speed up the evaluation process and keep nearly the same quality of solutions as the previous one. It first divides the chromosomes in a population into k clusters by the k-means clustering approach and evaluates each individual according to its own information and the information of the cluster it belongs to. The final best sets of membership functions in all the populations are then gathered together for mining fuzzy association rules. Experimental results also show the effectiveness and efficiency of the proposed approach.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Godley:2007:cec, title={Directed Intervention Crossover Applied to Bio-Control Scheduling}, author={Paul M. Godley and David E. Cairns and Julie Cowie}, pages = {638--645}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1400.pdf}, url = {}, size = {}, abstract = {This paper describes two directed intervention crossover approaches that are applied to a bio-control dynamic system. Unlike traditional uniform crossover, both the Calculated Expanding Bin (CalEB) method and Targeted Intervention with Stochastic Selection (TInSSel) approach actively choose an intervention level and spread based on the fitness of the parents selected for crossover. Results indicate that these approaches lead to significant improvements over Uniform Crossover (UC) when a penalty is introduced for each intervention point used by the crossover algorithm.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Chen:2007:cec, title={Construction of Surrogate Model Ensembles with Sparse Data}, author={Dingding Chen and Allan Zhong and John Gano and Syed Hamid and Orlando De Jesus and Stan Stephenson}, pages = {244--251}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1401.pdf}, url = {}, size = {}, abstract = {Construction of neural network ensembles (NNE) with sparse data requires comprehensive performance measure, multi-stage validation and usually a large member size. This paper presents a hybrid method which takes a selective optimization approach and is characterized with several novel features. First, candidate ensembles are widely explored using a multi-objective genetic algorithm. Secondly, the best local ensembles registered with each distinct objective weighting are determined based on the multi-stage validation results. Finally, a large global ensemble is formed by combining several local ensembles and virtually evaluated in the voids of possible parameter space. The demonstration of the proposed method is presented in a case study in which sparse data from FEA simulations are used to construct NNE for expandable pipe design, a novel application in oil and gas industry.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Bhattacharya:2007:cec, title={Surrogate Based EA for Expensive Optimization Problems}, author={Maumita Bhattacharya}, pages = {3847--3854}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1403.pdf}, url = {}, size = {}, abstract = {Real life optimization problems often require finding optimal solution to complex high dimensional, multimodal problems involving computationally very expensive fitness function evaluations. Use of a population based iterative technique like evolutionary algorithms in such problem domains is thus practically prohibitive. An attractive alternative is to build surrogates or use an approximation of the actual fitness functions to be evaluated. Many regression and interpolation tools are available to build such meta models. This paper briefly discusses the architectures and use of such meta-modeling tools in an evolutionary optimization context. We further present an evolutionary algorithm framework which involves use of surrogate models for fitness function evaluation. The original framework namely, the Dynamic Approximate Fitness based Hybrid EA (DAFHEA) model [14] reduces computation time by controlled use of meta-models (in this case approximate model generated by Support Vector Machine regression) to partially replace the actual function evaluation by approximate function evaluation. However, the underlying assumption in DAFHEA is that the training samples for the meta-model are generated from a single uniform model. This does not take into account uncertain scenarios involving noisy fitness functions. The enhanced model, DAFHEA-II, incorporates a multiple-model based learning approach for the support vector machine approximator to counter effects of noise [15]. Empirical results obtained by evaluating the frameworks using several benchmark functions (both nonnoisy and noisy versions) are presented.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Chen:2007:cec, title={Performance Tuning of Genetic Algorithms with Reserve Selection}, author={Yang Chen and Jinglu Hu and Kotaro Hirasawa and Songnian Yu}, pages = {2202--2209}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1405.pdf}, url = {}, size = {}, abstract = {This paper provides a deep insight into the performance of genetic algorithms with reserve selection (GARS), and investigates how parameters can be regulated to solve optimization problems more efficiently. First of all, we briefly present GARS, an improved genetic algorithm with a reserve selection mechanism which helps to avoid premature convergence. The comparable results to state-of-the-art techniques such as fitness scaling and sharing demonstrate both the effectiveness and the robustness of GARS in global optimization. Next, two strategies named Static RS and Dynamic RS are proposed for tuning the parameter reserve size to optimize the performance of GARS. Empirical studies conducted in several cases indicate that the optimal reserve size is problem dependent.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(:2007:cec, title={Cross-Searching Strategy for Multi-Objective Particle Swarm Optimization}, author={Shih-Yuan Chiu and Tsung-Ying Sun and Sheng-Ta Hsieh and Cheng-Wei Lin}, pages = {3135--3141}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1406.pdf}, url = {}, size = {}, abstract = {The main difference between an original PSO (single-objective) with a multi-objective PSO (MOPSO) is the local guide (global best solution) distribution must be redefined in order to obtain a set of non-dominated solutions (Pareto front). In MOPSO, the selection of local guide for particles will direct affect the performance of finding Pareto optimum. This paper presents a local guide assignment strategy for MOPSO called cross-searching strategy (CSS) which will distribute suitable local guides for particles to lead them toward to Pareto front and also keeping diversity of solutions. Experiments were conducted on several test functions and metrics from the standard literature on evolutionary multi-objective optimization. The results demonstrate good performance of the CSS for MOPSO in solving multi-objective problems when compare with recent approaches of multi-objective optimizer.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Pathak:2007:cec, title={MOGA-Based Time-Cost Tradeoffs: Responsiveness for Project Uncertainties}, author={Bhupendra Kumar Pathak and Sanjay Srivastava}, pages = {3085--3092}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1407.pdf}, url = {}, size = {}, abstract = {Existing methods for analyzing the responsiveness of time-cost tradeoff (TCT) profiles with regard to project uncertainties ignore the cost parameter of project activities. To comprise this problem a novel method is developed in this work—it examines the effects of project uncertainties on both, the duration as well as the cost of the activities. The method integrates the two important paradigms of Soft Computing—Multiobjective Genetic Algorithm (MOGA), and Fuzzy Logic—the method is referred to as the integrated Fuzzy-GA. A rule based fuzzy logic framework is developed which brings up the changes in the duration and the cost of each activity for the inputted uncertainties. The framework is integrated with MOGA, which would search for Pareto-optimal front (a TCT profile) for a given set of time-cost pair of each project activity. Two standard test problems from the literature are successfully attempted using MOGA. A test case of TCT problem is solved using the integrated Fuzzy-GA. The method provides an efficient tool to project manager to carry out a sensitivity analysis of time-cost tradeoff profiles under varying conditions of existing uncertainties normally encountered in realistic projects.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Liu:2007:cec, title={Extraction of Hybrid Trace Features with Evolutionary Computation for Face Recognition}, author={Nan Liu and Han Wang}, pages = {2493--2500}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1408.pdf}, url = {}, size = {}, abstract = {The Hybrid Trace Features (HTF), a new face representation, is proposed for face authentication system. Trace transforms of multiple Trace functionals are used to construct the HTF, and Genetic Algorithms is implemented as the data fusion tool. In addition, rotation based Hybrid Trace Features (r-HTF) is also introduced as facial features. The systemic evaluations on Cambridge ORL face database reveal that HTF and r-HTF present high discriminatory power and outperform the features extracted by Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Kernel PCA in the task of classification.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Yang:2007:cec, title={Mining Equalized Association Rules from Multi Concept Layers of Ontology Using Genetic Network Programming}, author={Guangfei Yang and Kaoru Shimada and Shingo Mabu and Kotaro Hirasawa and Jinglu Hu}, pages = {705--712}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1409.pdf}, url = {}, size = {}, abstract = {In this paper, we propose a Genetic Network Programming based method to mine equalized association rules in multi concept layers of ontology. We first introduce ontology to facilitate building the multi concept layers and propose Dynamic Threshold Approach (DTA) to equalize the different layers. We make use of an evolutionary computation method called Genetic Network Programming (GNP) to mine the rules and develop a new genetic operator to speed up searching the rule space. The simulation results show that our method could efficiently find some rules even in the early generations.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Secker:2007:cec, title={WAIRS: Improving Classification Accuracy byWeighting Attributes in the AIRS Classifier}, author={Andrew Secker and Alex A. Freitas}, pages = {3759--3765}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1410.pdf}, url = {}, size = {}, abstract = {AIRS (Artificial Immune Recognition System) has shown itself to be a competitive classifier. It has also proved to be the most popular immune inspired classifier. However, rather than AIRS being a classifier in its own right as previously described, we see AIRS more as a pre-processor to a KNN classifier. It is our view that by not explicitly classing it as such development of this algorithm has been rather held back. Seeing it as a pre-processor allows inspiration to be taken from the machine learning literature where such pre-processors are not uncommon. With this in mind, this paper takes a core feature of many such pre-processors, that of attribute weighting, and applies it to AIRS. The resultant algorithm called WAIRS (Weighted AIRS) uses a weighted distance function during all affinity evaluations. WAIRS is tested on 9 benchmark datasets and is found to outperform AIRS in the majority of cases.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Halavati:2007:cec, title={Symbiotic Evolutionary Algorithm, A General Purpose Optimization Approach}, author={Ramin Halavati and Saeed Bagheri Shouraki and Mojdeh Jalali Heravi and Bahareh Jafari Jashmi}, pages = {4538--4545}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1411.pdf}, url = {}, size = {}, abstract = {Recombination in the Genetic Algorithm (GA) is supposed to extract the component characteristics from two parents and reassemble them in different combinations - hopefully producing an offspring that has the good characteristics of both parents and this requires explicit chromosome and recombination operator design. This paper presents a novel evolutionary approach based on symbiogenesis which uses symbiotic combination instead of sexual recombination and using this operator, it requires no domain knowledge for chromosome or combination operator design. The algorithm is benchmarked on three problem sets, combinatorial optimization, deceptive, and fully deceptive, and is compared with standard genetic algorithm and symbiotic evolutionary adaptation model, showing higher success rates and faster results in compare with both cited algorithms.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Zeng:2007:cec, title={A Lower-Dimensional-Search Evolutionary Algorithm and Its Application in Constrained Optimization Problems}, author={Sanyou Zeng and Hui Shi and Hui Li and Guang Chen and Lixin Ding and Lishan Kang}, pages = {1255--1260}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1413.pdf}, url = {}, size = {}, abstract = {This paper proposes a new evolutionary algorithm, called lower-dimensional-search evolutionary algorithm (LDSEA). 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 therefore the new algorithm converges fast, especially for high-dimensional constrained optimization problems. The niche-impaction operator and the mutation operator preserve the diversity of the population to make the LDSEA algorithm not to be trapped in local optima as much as possible. What's more is that the LDSEA algorithm is simple and easy to be implemented. We have used the 24 constrained benchmark problems [18] to test the LDSEA algorithm. The experimental results show it works better than or competitive to a known effective algorithm [7] for higher-dimensional constrained optimization problems.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Wang:2007:cec, title={A Time-Fuel Optimal Algorithm for Spacecraft Formation Reconfiguration}, author={Shuyan Wang and Changwen Zheng and YuxinWang}, pages = {994--998}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1415.pdf}, url = {}, size = {}, abstract = {In this paper, trajectory planning for spacecraft formation reconfiguration is modeled as a MOP and a niched evolutionary algorithm is proposed. With a problem specific real-valued representation of candidate solutions and evolutionary operators, the algorithm could find the Time-Fuel front of this problem and generate multiple solutions simultaneously. Higher selected probability of better individuals and the diversity of the front are guaranteed by equivalence class sharing. The algorithm is immune to issues of local minima and solutions are global optimal or near global optimal.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Dengiz:2007:cec, title={A Hybrid Ant Colony Optimization Approach for the Design of Reliable Networks}, author={B. Dengiz and F. Altiparmak and O. Belgin}, pages = {1118--1125}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1416.pdf}, url = {}, size = {}, abstract = {This paper presents a new solution approach, which is a hybridization of ant colony optimization (ACO) and simulated annealing (SA), called (h_ACO) to design of communication networks. The design problem is to find the optimal network topology where total cost is minimum and allterminal reliability is not less-than a given level of reliability. The effectiveness of the h_ACO is investigated comparing its results with those obtained by SA and ACO, which are basic forms of the h_ACO, and also GAs given in the literature for the design problem. Computational results show that the h_ACO is an effective heuristic approach to design of reliable networks.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Goschin:2007:cec, title={Combine and Compare Evolutionary Robotics and Reinforcement Learning as Methods of Designing Autonomous Robots}, author={Sergiu Goschin and Eduard Franti and Monica Dascalu and Sanda Osiceanu}, pages = {1511--1516}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1418.pdf}, url = {}, size = {}, abstract = {The purpose of this paper is to present a comparison between two methods of building adaptive controllers for robots. In spite of the wide range of techniques which are used for defining autonomous robot architectures, few attempts have been made in comparing their performance under similar circumstances. This comparison is particularly important in establishing benchmarks and in determining the best approach methods. The robotic tasks in our research concern mainly the convergence of behaviors like obstacle avoidance, hitting targets and shortest path finding using various methods of synthesizing control architectures' parameters. The first approach that has been used combines Neural Networks and Genetic Algorithms in a simple yet robust controller using an Evolutionary Robotics technique. The second one introduces a manner of using Reinforcement Learning with a Neural Network based architecture. The experiments take place in a simulated 3D environment, which was designed to allow the development, testing and comparison of various controllers in terms of advantages and disadvantages in order to establish a benchmark for autonomous robots.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Xiao:2007:cec, title={A Good Nodes Set Evolution Strategy for Constrained Optimization}, author={Chixin Xiao and Zixing Cai and Yong Wang}, pages = {943--950}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1420.pdf}, url = {}, size = {}, abstract = {Good Nodes Set (GNS) is a concept in number theory. To overcome the deficiency of orthogonal design to handle constrained optimization problems (COPs), this paper presents a method that incorporate GNS principle to enhance the crossover operator of the evolution strategy (ES) can make the resulting evolutionary algorithm more robust and statically sound. In order to gain the rapid and stable rate of converging to the feasible region, traditional crossover operator is split into two steps. GNS initialization methods is applied to ensure the initial population span evenly in relatively large search space and reliably locate the good points for further exploration in subsequent iterations. The proposed method achieves the same sound results just as the orthogonal method does, but its precision is not confined by the dimension of the space. The simplex selected and diversity mechanism similar to Carlos's SMES is used to enrich the exploration and exploitation abilities of the approach proposed. Experiment results on a set of benchmark problems show the efficiency of our methods.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Bhattacharya:2007:cec, title={Optimization of Continuous Casting Mould Oscillation Parameters in Steel Manufacturing Process Using Genetic Algorithms}, author={Arya K. Bhattacharya and S. Debjani and Abhik Roychowdhury and Jadav Das}, pages = {3998--4004}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1421.pdf}, url = {}, size = {}, abstract = {This paper describes a new approach towards optimum selection of the different parameters of the mould oscillation system in the continuous casting process of steel manufacturing. The objective of optimization is to enhance lubrication within the mould, especially at higher casting speeds, and reduce the intensity of oscillation marks. The need to improve lubrication conditions is primarily addressed by making the mould oscillate on its longitudinal axis. It is known that non-sinusoidal oscillation, where the time for upward motion of the mould is longer than that of downward motion in an oscillation cycle, reduces depth of oscillation marks while providing better lubrication. In the present work, a Genetic Algorithm is applied to optimize the amplitude, frequency, and waveform of the oscillation of the continuous casting mould based on objective functions that maximize the lubrication, and minimize the depth of oscillation marks and the cycle peak friction. Optimization is performed within constraints imposed by machine limits. The objective function and constraints are extracted from an analysis of the physics of oscillation, lubrication and heat transfer within the continuous casting process. The application of the Genetic Algorithm within a unified framework encompassing all oscillation performance metrics and constraints is seen to generate an optimal parameter set that provides better performance than existing oscillation parameters supplied by Original Equipment Manufacturers.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Steinzen:2007:cec, title={A Hybrid Evolutionary Algorithm for the Vehicle and Crew Scheduling Problem in Public Transit}, author={Ingmar Steinzen and Matthias Becker and Leena Suhl}, pages = {3784--3789}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1422.pdf}, url = {}, size = {}, abstract = {The vehicle and crew scheduling problem in public transit aims at finding minimum cost bus and crew schedules such that all trips of a given timetable are operated respecting all operational constraints. In this paper we present a novel hybrid evolutionary algorithm for the multiple-depot integrated vehicle and crew scheduling problem that combines mathematical programming techniques with an evolutionary algorithm. Computational results on randomly generated benchmark instances demonstrate that our approach outperforms the traditional sequential treatment of vehicle and crew scheduling. Furthermore, it is competitive with solution approaches from literature that fully integrate both planning problems.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Altiparmak:2007:cec, title={A Genetic ant Colony Optimization Approach for Concave Cost Transportation Problems}, author={F. Altiparmak and I. Karaoglan}, pages = {1685--1692}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1423.pdf}, url = {}, size = {}, abstract = {The concave cost transportation problem (ccTP) is one of the practical distribution and logistics problems. The ccTP arises when the unit cost for transporting products decreases as the amount of products increases. Generally, these costs are modeled as nonlinear, especially concave. Since the ccTP is NP-hard, solving large-scale problems is timeconsuming. In this paper, we propose a hybrid search algorithm based on genetic algorithms (GA) and ant colony optimization (ACO) to solve the ccTP. This algorithm, called h_GACO, is a GA supplemented with ACO in where ACO is implemented to exploit information stored in pheromone trails during genetic operations, i.e. crossover and mutation. The effectiveness of h_GACO is investigated comparing its results with those obtained by five different metaheuristic approaches given in the literature for the ccTP.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Panda:2007:cec, title={Identification of Nonlinear Systems using Particle Swarm Optimization Technique}, author={G. Panda and D. Mohanty and Babita Majhi and G. Sahoo}, pages = {3253--3257}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1426.pdf}, url = {}, size = {}, abstract = {System identification in noisy environment has been a matter of concern for researchers in many disciplines of science and engineering. In the recent past the Least Mean Square Algorithm (LMS), Genetic Algorithm (GA) etc. have been employed for developing a parallel model. During training by LMS algorithm the weights rattle around and does not converge to optimal solution. This gives rise to poor performance of the model. Although GA always ensures the convergence of the weights to the global optimum but it suffers from slower convergence rate. To alleviate the problem we propose a novel Particle Swarm Optimization (PSO) technique for identifying nonlinear systems. The PSO is also a population based derivative free optimization technique like GA, and hence ascertains the convergence of the model parameters to the global optimum, there by yielding the same performance as provided by GA but with a faster speed. Comprehensive computer simulations validate that the PSO based identification is a better candidate even under noisy condition both in terms of convergence speed as well as number of input samples used.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Polat:2007:cec, title={Cooperative Coevolutionary Algorithms for Fuzzy Vehicular Routing Problem: An Analysis of Efficiency vs. Geographical Distribution}, author={Latif Polat and Adnan Acan and Ahmet Unveren}, pages = {1126--1132}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1427.pdf}, url = {}, size = {}, abstract = {In this article, a cooperative coevolutionary genetic algorithm for the solution of fuzzy vehicular routing problem (FVRP) is presented. FVRP is a variant of VRP with time windows; based on additional use of fuzzy due-times for customers' preferences. The objectives of FVRP are the minimization of total number of vehicles in service, total travel distance, and the total waiting time over all vehicles; while maximizing the average grade of customer satisfaction. Benchmark problem instances classified by geographical distribution of customers are used in experimental studies and results of cooperative coevolutionary GAs are compared with those of panmictic GAs. It has been observed that cooperative coevolutionary approach generates significantly improved solutions for multi-cluster tightly grouped problem instances, while it also performs equally well for other problem classes.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Senkerik:2007:cec, title={Comparison of Evolutionary Algorithms in the Task of Chaos Control Optimization}, author={Roman Senkerik and Ivan Zelinka and Donald Davendra}, pages = {3952--3958}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1428.pdf}, url = {}, size = {}, abstract = {This work deals with optimization of the control of chaos by means of the evolutionary algorithms. The main aim of this work is to show that evolutionary algorithms are capable of optimization of chaos control leading to satisfactory results. As a model of deterministic chaotic system, the two dimensional Henon map was used. The optimization was realized in several ways, each one for another evolutionary algorithm or another desired periodic orbit and behavior of system. The evolutionary algorithms, SOMA (Self-Organizing Migrating Algorithm) and DE (Differential Evolution) were used in several versions. For each version, simulations were repeated 50 times to show and check robustness of the used method and constructed cost function. At the end of this work the results of optimized chaos control are compared with classical control technique - OGY.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Acan:2007:cec, title={A Shared-Memory ACO+GA Hybrid for Combinatorial Optimization}, author={Adnan Acan and Ahmet Unveren}, pages = {2078--2085}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1429.pdf}, url = {}, size = {}, abstract = {A novel hybrid algorithm combining the search capabilities of evolutionary genetic and artificial ant colony optimization algorithms through a common library of partial permutations is introduced. The two algorithms work independently in parallel to construct two different populations of individuals representing potential solutions. A shared memory containing variable size and partially incomplete permutations from above-average individuals of the two populations is used as the medium for information exchange between the two algorithms. The aim is to support the solution construction procedures of the two algorithms by knowledge incorporation through a shared external memory that contains experienced based knowledge gained through two different solution methods. Constructed solutions are also used to update the memory. The proposed approach is used for the solution of TSP and QAP for which the obtained results demonstrate that both the speed and solution quality are improved compared to the two individual component algorithms.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Oliveto:2007:cec, title={Evolutionary Algorithms and the Vertex Cover Problem}, author={P. S. Oliveto and J. He and X. Yao}, pages = {1870--1877}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1430.pdf}, url = {}, size = {}, abstract = {Experimental results have suggested that evolutionary algorithms may produce higher quality solutions for instances of Vertex Cover than a very well known approximation algorithm for this NP-Complete problem. A theoretical analysis of the expected runtime of the (1+1)-EA on a well studied instance class confirms such a conjecture for the considered class. Furthermore, a class for which the (1+1)-EA takes exponential optimization time is examined. Nevertheless, given polynomial time, the evolutionary algorithm still produces a better solution than the approximation algorithm. Recently, the existence of an instance class has been proved for which the (1+1)-EA produces poor approximate solutions, given polynomial time. Here it is pointed out that, by using multiple runs, the (1+1)-EA finds the optimal cover of each instance of the considered graph class in polynomial time.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Auvinen:2007:cec, title={Topology Management in Unstructured P2P Networks Using Neural Networks}, author={Annemari Auvinen and Teemu Keltanen and Mikko Vapa}, pages = {2358--2365}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1431.pdf}, url = {}, size = {}, abstract = {Resource discovery is an essential problem in peer-to-peer networks since there is no centralized index in which to look for information about resources. In a pure P2P network peers act as servers and clients at the same time and in the Gnutella network for example, peers know only their neighbors. In addition to developing different kinds of resource discovery algorithms, one approach is to study the different topologies or structures of the P2P network. In many cases topology management is based on either technical characteristics of the peers or their interests based on the previous resource queries. In this paper, we propose a topology management algorithm which does not predetermine favorable values of the characteristics of the peers. The decision whether to connect to a certain peer is done by a neural network, which is trained with an evolutionary algorithm. Characteristics, which are to be taken into account, can be determined by the inputs of the neural network.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Rahhal:2007:cec, title={Viterbi Decoder Algorithm using Quantum Computing}, author={Jamal S. Rahhal and Dia I. Abu-Al-Nadi and Mohammed Hawa}, pages = {4094--4099}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1432.pdf}, url = {}, size = {}, abstract = {Quantum Computing promises a leap in performance over classical computing. Its computational power was revealed after the introduction of some quantum algorithms such as Grover's search algorithm. Many classical algorithms especially that deal with Error Correcting Codes are introduced to solve an exhaustive search problem. The quantum techniques promise an optimal search speed that is of special importance when the size of the searched domain is quite large. In this paper we devise the use of quantum search algorithm introduced by Grover to implement the Viterbi Algorithm (VA). Due to lack of resources and the nature of implementing quantum algorithms, only theoretical results are obtained. The use of quantum search algorithm showed that we can reduce the number of search computations exponentially. For example if the classical VA requires 262144 searching operations for a signalling length of 1024 intervals, the quantum algorithm will need only 512 searching operations when Single Search Quantum Viterbi Algorithm (SSQVA) is used. And for the Multi Search Quantum Viterbi Algorithm (MSQVA) it needs 16384 searching operations for the same signalling length.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Munawar:2007:cec, title={Optimization Problem Solving Framework Employing GAs with Linkage Identification Over a Grid Environment}, author={Asim Munawar and Masaharu Munetomo and Kiyoshi Akama}, pages = {1191--1198}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1433.pdf}, url = {}, size = {}, abstract = {This paper is a step towards a general purpose optimization problem-solving framework that can solve a large number of global optimization problems on its own with a minimal input from the user. It relies on competent GAs (Genetic Algorithms) as the solver and depends on Grid computing for the required computational resources. In this paper we will discuss the architecture of the framework in detail. In the results section we will discuss the speedups obtained by using parallel GAs over a Grid computing environment and the effects of Grid overheads on the speedup. Even though there are various advantages of using Grids but in the results section we will focus on the reduction in total execution time due to parallelism.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Singh:2007:cec, title={Migration Based Parallel Differential Evolution Learning in Asymmetric Subsethood Product Fuzzy Neural Inference System : A Simulation Study}, author={Lotika Singh and Satish Kumar}, pages = {1608--1613}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1434.pdf}, url = {}, size = {}, abstract = {This paper presents a detailed study on the various parameters of an island model based differential evolution learning scheme in Asymmetric Subsethood Product Fuzzy Neural Inference System (ASuPFuNIS). The systematic experimental study of the migration size, migration interval coupled with an in depth view of the diversity on each island leads to a better understanding of the algorithm. In the course of studying the effects of parameters some significant performance changes were observed on a standard benchmark function approximation problem.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Li:2007:cec, title={Real-Coded ECGA for Solving Decomposable Real-Valued Optimization Problems}, author={Minqiang Li and David E. Goldberg and Kumara Sastry and Tian-Li Yu}, pages = {2194--2201}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1435.pdf}, url = {}, size = {}, abstract = {This paper presents the real-coded extended compact genetic algorithms (rECGA) for decomposable real-valued optimization problems. Mutual information among real-valued variables is employed to measure variables interaction or dependency, and the variables clustering and aggregation algorithms are proposed to identify the substructures of a problem through partitioning variables. Then, mixture Gaussian probability density function is estimated to model the promising individuals for each substructure, and the sampling of multivariate Gaussian probability density function is done by adopting Cholesky decomposition. Finally, experiments on decomposable test functions are conducted. The results show that the rECGA is able to correctly identify the substructure of decomposable problems with linear or nonlinear correlations, and achieves a good scalability.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Lones:2007:cec, title={A Co-Evolutionary Framework for Regulatory Motif Discovery}, author={Michael A. Lones and Andy M. Tyrrell}, pages = {3894--3901}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1436.pdf}, url = {}, size = {}, abstract = {In previous work, we have shown how an evolutionary algorithm with a clustered population can be used to concurrently discover multiple regulatory motifs present within the promoter sequences of co-expressed genes. In this paper, we extend the algorithm by co-evolving a population of Boolean classification rules in parallel with the motif population. Results using synthetic data suggest that this approach allows poorly conserved motifs to be identified in promoter sequences an order of magnitude longer than using population clustering alone, whilst results using muscle-specific promoter data show the algorithm is able to evolve meaningful sequence classifiers in parallel with motifs—suggesting that co-evolution provides a suitable framework for composite motif discovery within eukaryotic sequences.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Rabil:2007:cec, title={Task Allocation Using Inherited Area Density Multiobjective Particle Swarm Optimization}, author={Bassem S. Rabil and Mona A. Fahmy and Gamal M. Aly}, pages = {3300--3307}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1438.pdf}, url = {}, size = {}, abstract = {In this paper, we present a new approach that helps managers to optimize task allocation and work load distribution using Multiobjective Particle Swarm Optimization(MOPSO). A new algorithm has been introduced to increase number of nondominated solutions(Pareto Front size), by using inheritance of nondominated solutions density estimators and modifying density estimation algorithm. The performance of the new algorithm is evaluated on test functions and metrics from literature. The results show that the proposed algorithm is competitive in converging towards the Pareto front and generates a well distributed set of nondominated solutions. The new approach helps managers to avoid juggling many objectives to develop a project plan. These include minimizing cost, defects, and completion time; and maximizing worker utilization and customer satisfaction. Many of these objectives are conflicting. For example, a demand to decrease completion time clashes with a goal to minimize defects.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Jr:2007:cec, title={Diversity-Based Model Reference for Genetic Algorithms in Dynamic Environment}, author={Maury M. Gouv.ea Jr and Aluizio F. R. Ara\'ujo}, pages = {4639--4645}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1439.pdf}, url = {}, size = {}, abstract = {Preservation of diversity in the evolutionary process is crucial to solve problems considering dynamic environments. This work proposes an adaptive evolutionary algorithm to control the population diversity based on a diversity function. The evolutionary process searches for the optimum while the diversity is controlled to track the diversity function. To control the population diversity, the proposed method creates a selection mechanism to adjust the fitnesses of a part of the population based on a fitness penalty. The proposed adaptive method uses the model-reference adaptive system as the control strategy to adjust the fitness penalty parameter. The proposed method is called diversity-reference adaptive control (DRAC). The performance of DRAC method was evaluated for multimodal and dynamic test functions. The results show that DRAC method often reached the optimum area, following environment changes, faster than SGA.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Mitchell:2007:cec, title={A Cost Benefit Operator for Efficient Multi Level Genetic Algorithm Searches}, author={George G. Mitchell and Barry McMullin and James Decraene}, pages = {1344--1350}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1440.pdf}, url = {}, size = {}, abstract = {In this paper we present a novel cost benefit operator that assists multi level genetic algorithm searches. Through the use of the cost benefit operator, it is possible to dynamically constrain the search of the base level genetic algorithm, to suit the user's requirements. Initially we review meta-evolutionary (multi-level genetic algorithm) approaches. We note that the current literature has abundant studies on meta-evolutionary GAs. However these approaches have not identified an efficient approach to termination of base GA search or a means to balance practical consideration such as quality of solution and the expense of computation. Our Quality time tradeoff operator (QTT) is user defined, and acts as a base level termination operator and also provides a fitness value for the meta-level GA. In this manner the amount of computation time spent on less encouraging configurations can be specified by the user. Our approach has been applied to a computationally intensive test problem which evaluates a large set of configuration settings for the base GAs. This approach should be applicable across a wide range of practical problems (e.g. routing, logistic and biomedical applications).}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(:2007:cec, title={Simultaneous Gradient and Evolutionary Neural NetworkWeights Adaptation Methods}, author={Pavol Mali\v{n}\'ak and Rudolf Jak\v{s}a}, pages = {2665--2671}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1442.pdf}, url = {}, size = {}, abstract = {In this paper, two novel methods, BP/ES and ES/LMS, for simultaneous gradient and evolutionary adaptation of weights of neural network are proposed. In the BP/ES, an evolution strategy is used to optimize the last layer of the multilayer perceptron type of neural network, and backpropagation algorithm trains the rest of the network. The main idea of ES/LMS is to employ the least mean square algorithm to adapt the last layer of network and evolution strategy to optimize the rest of the network. Hybrid approaches to neural network learning, based on gradient and evolutionary techniques combinations, are aimed to raise the advantages of both approaches mentioned above – reliable computational requirements of gradient techniques and global search capabilities of evolutionary approaches. In general, neural network hybrid learning approaches are usually "sequential", rather than simultaneous. In the first step, the evolutionary technique is used to locate a promising region in the search space, and then the gradient technique is employed for fine tuning of network parameters in this region. The proposed BP/ES and ES/LMS methods investigate different approach. They perform "spatial" synthesis of gradient and evolutionary techniques, in which the neural network is partitioned into two parts – output layer versus the other layers – which are adapted simultaneously, however using these two different methods. Experimental results with error back-propagation algorithm, evolution strategies with and without covariances, BP/ES and ES/LMS method on the benchmark "XOR" and "circle in square" data are provided.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Seebach:2007:cec, title={Design and Construction of Organic Computing Systems}, author={Hella Seebach and Frank Ortmeier and Wolfgang Reif}, pages = {4215--4221}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1443.pdf}, url = {}, size = {}, abstract = {The next generation of embedded computing systems will have to meet new challenges. The systems are expected to act mainly autonomously, to dynamically adapt to changing environments and to interact with one another if necessary. Such systems are called organic. Organic Computing systems are similar to Autonomic Computing systems. In addition Organic Computing systems often behave life-like and are inspired by nature/biological phenomena. Design and construction of such systems brings new challenges for the software engineering process. In this paper we present a framework for design, construction and analysis of Organic Computing systems. It can facilitate design and construction as well as it can be used to (semi-)formally define organic properties like self-configuration or self-adaptation. We illustrate the framework on a real-world case study from production automation.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Hancher:2007:cec, title={Evolving Quadruped Gaits with a Heterogeneous Modular Robotic System}, author={Matthew D. Hancher and Gregory S. Hornby}, pages = {3631--3638}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1444.pdf}, url = {}, size = {}, abstract = {There has been much research into the development of robotic controllers in educational, industrial and government research labs, but limited hardware budgets constrain the types of morphologies in which researchers can experiment with. One option is to use LEGO components or other toy-grade kits, but these have well-known limitations. Here we present a heterogeneous modular robotic platform that can be reconfigured to a wide variety of robot morphologies, such as legged robots and manipulator arms. In addition, we have developed a simulation environment for our modules allowing for the artificial evolution, or learning, of behaviors to occur in simulation for transfer to reality. We demonstrate the effectiveness of our system by evolving a quadruped gait in simulation which successfully transfers to a hardware version of this robot.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Mantere:2007:cec, title={Solving, Rating and Generating Sudoku Puzzles with GA}, author={Timo Mantere and Janne Koljonen}, pages = {1382--1389}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1445.pdf}, url = {}, size = {}, abstract = {This paper studies the problems involved in solving, rating and generating Sudoku puzzles with genetic algorithms (GA). Sudoku is a number puzzle that has recently become a worldwide phenomenon. Sudoku can be regarded as a constraint satisfaction problem. When solved with genetic algorithms it can be handled as a multi-objective optimization problem. The three objectives of this study was: 1) to test if genetic algorithm optimization is an efficient method for solving Sudoku puzzles, 2) can GA be used to generate new puzzles efficiently, and 3) can GA be used as a rating machine that evaluates the difficulty of a given Sudoku puzzle. The last of these objectives is approached by testing if puzzles that are considered difficult for a human solver are also difficult for the genetic algorithm. The results presented in this paper seem to support the conclusion that these objectives are reasonably well met with genetic algorithm optimization.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Arcuri:2007:cec, title={A Memetic Algorithm for Test Data Generation of Object-Oriented Software}, author={Andrea Arcuri and Xin Yao}, pages = {2048--2055}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1447.pdf}, url = {}, size = {}, abstract = {Generating test data for Object-Oriented (OO) software is a hard task. Little work has been done on the subject, and a lot of open problems still need to be investigated. In this paper we focus on container classes. They are used in almost every type of software, hence their reliability is of utmost importance. We present novel techniques to generate test data for container classes in an automatic way. A new representation with novel search operators is described and tested. A way to reduce the search space for OO software is presented. This is achieved by dynamically eliminating the functions that cannot give any further help from the search. Besides, the problem of applying the branch distances of disjunctions and conjunctions to OO software is solved. Finally, Hill Climbing, Genetic Algorithms and Memetic Algorithms are used and compared. Our empirical case study shows that our Memetic Algorithm outperforms the other algorithms.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Landgraaf:2007:cec, title={Parameter Calibration Using Meta-Algorithms}, author={W. A. de Landgraaf and A. E. Eiben and V. Nannen}, pages = {71--78}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1449.pdf}, url = {}, size = {}, abstract = {Calibrating an evolutionary algorithm (EA) means finding the right values of algorithm parameters for a given problem. This issue is highly relevant, because it has a high impact (the performance of EAs does depend on appropriate parameter values), and it occurs frequently (parameter values must be set before all EA runs). This issue is also highly challenging, because finding good parameter values is a difficult task. In this paper we propose an algorithmic approach to EA calibration by describing a method, called REVAC, that can determine good parameter values in an automated manner on any given problem instance. We validate this method by comparing it with the conventional hand-based calibration and another algorithmic approach based on the classical meta-GA. Comparative experiments on a set of randomly generated problem instances with various levels of multi-modality show that GAs calibrated with REVAC can outperform those calibrated by hand and by the meta-GA.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Geis:2007:cec, title={An Ant Colony Optimizer for Melody Creation with Baroque Harmony}, author={Michael Geis and Martin Middendorf }, pages = {461--468}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1450.pdf}, url = {}, size = {}, abstract = {We propose an algorithm that is based on the Ant Colony Optimization (ACO) metaheuristic for producing harmonized melodies. The algorithm works in two stages. In the first stage it creates a melody. This melody is then harmonized according to the rules of Baroque harmony in the second stage. This is the first ACO algorithm to create music that uses domain knowledge and the first employed for harmonization of a melody.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Torácio:2007:cec, title={Multiple Objective Particle Swarm for Classification-Rule Discovery}, author={Augusto de Almeida Prado G. Torīacio and Aurora Trinidad Ramirez Pozo}, pages = {684--691}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1451.pdf}, url = {}, size = {}, abstract = {This paper presents a method of classificationrule discovery based on multiple objective particle swarm technique. The rules are selected at the creation rule process following Pareto dominance concepts and forming unordered classifiers. Initial executions, compared with other algorithms of the literature, show that this approach can be competitive and gives more liberty to choose rules.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Saxena:2007:cec, title={Design for Manufacture of Optimal Compliant Topologies with Honeycomb Continuum Representation}, author={Anupam Saxena and Nilesh D. Mankame}, pages = {2956--2963}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1452.pdf}, url = {}, size = {}, abstract = {We present and compare two approaches to obtain optimal binary designs of compliant mechanisms that can be manufactured as is. Honeycomb representation is used to avoid singularities due to checkerboard and point flexure pathologies that appear in square cell based discretization of the domain. A Hex cell accurately models displacement and stress fields especially when approaching its non-existing state which is contrary to its square cell counterpart. In the first approach, sigmoid material interpolation function is employed with a gradient based strategy that poses topology design as a limiting case of size optimization. Though this technique shows promise in obtaining binary designs, the latter cannot always be guaranteed. The other approach based on stochastic search decouples topology optimization from size optimization and uses a novel material mask overlay strategy that ensures optimal binary design and can be manufactured without any post processing.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Ikeda:2007:cec, title={State Evaluation Strategy for Exemplar-Based Policy Optimization of Dynamic Decision Problems}, author={Kokolo Ikeda and Hajime Kita}, pages = {3685--3691}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1453.pdf}, url = {}, size = {}, abstract = {Direct policy search (DPS) that optimizes the parameters of a decision making model, combined with evolutionary algorithms which enable robust optimization, is a promising approach to dynamic decision problems. Exemplar-based policy (EBP) optimization is a novel framework for DPS in which the policy is composed of a set of exemplars and a case-based action selector, with the set of exemplars being refined and evolved using a GA. In this paper, state evaluation type EBP representations are proposed for the problem class whose state transition can be predicted. For example, the vector-real representation defines pairs of feature vector and its desirability as exemplars, and evaluate the predicted next states using the exemplars. The state evaluation type EBP-based optimization procedures are shown to be superior to conventional state-action type EBP optimization through application to the Tetris game.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Tanev:2007:cec, title={Interactive Learning of Consensus Sequences in Genetic Programming for Evolution of Snake-like Robot}, author={Ivan T. Tanev and Katsunori Shimohara}, pages = {3662--3670}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1454.pdf}, keywords = {genetic algorithms, genetic programming}, url = {}, size = {}, abstract = {We discuss an approach of incorporating interactively learned consensus sequences (ILCS) in genetic programming (GP) for efficient evolution of simulated Snakebot situated in a challenging environment. ILCS introduce a biased mutation in GP via probabilistic context sensitive grammar, in which the probabilities of applying the production rules with multiple right-hand side alternatives depend on the grammatical context. The distribution of these probabilities is learned interactively from the syntax of the Snakebots, exhibiting behavioral traits that according to the human observer are relevant for the emergence of ability to overcome obstacles. Because at the earlier stages of evolution these behavioral traits are not necessarily pertinent to the best performing (i.e. fastest) Snakebots, the user feedback provides the evolution with an additional insight about the promising areas in the fitness landscape. Empirical results verify that employing ILCS improves the efficiency of GP in that the evolved Snakebots are faster than those obtained via canonical GP.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(:2007:cec, title={A Learning Classifier System with Mutual-Information-Based Fitness}, author={R. E. Smith and Max Kun Jiang}, pages = {2173--2180}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1455.pdf}, url = {}, size = {}, abstract = {This paper introduces a new variety of learning classifier system (LCS), called MILCS, which utilizes mutual information as fitness feedback. Unlike most LCSs, MILCS is specifically designed for supervised learning. We present preliminary results, and contrast them to results from XCS. We discuss the explanatory power of the resulting rule sets, and introduce a new technique for visualizing explanatory power. Final comments include future directions for this research, including investigations in neural networks and other systems.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Jeganathan:2007:cec, title={Self-Assembly of Graphs}, author={L. Jeganathan and K. Krithivasan and R. Rama}, pages = {1849--1855}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1456.pdf}, url = {}, size = {}, abstract = {Self-assembly is a process in which simple objects autonomously combine themselves into larger objects. It is considered as a promising technique in nano-technology. In this paper, we propose a graph-based self-assembly model. Two simple graphs G1and G2with a vertex of common degree overlap and a new self-assembled graph is formed. Besides studying the properties of these self assembled graphs, we answer the question: Can a given set of graphs be generated through the process of self-assembly? If so, how to find the generator that could generate the given set of graphs by the process of self-assembly. The question of the existence of the minimal generator is also discussed. The necessary and sufficient condition for a graph H to be obtained by the iterated self-assembly of the graph G is also answered. We also conclude that the problem of finding the generator is decidable.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Alecu:2007:cec, title={A Genetic Algorithm for Computing the k-Error Linear Complexity of Cryptographic Sequences}, author={A. Alecu and A. M. \V{S}al\v{a}gean}, pages = {3569--3576}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1459.pdf}, url = {}, size = {}, abstract = {Some cryptographical applications use pseudorandom sequences and require that the sequences are secure in the sense that they cannot be recovered by only knowing a small amount of consecutive terms. Such sequences should therefore have a large linear complexity and also a large k-error linear complexity. Efficient algorithms for computing the k- error linear complexity of a sequence over a finite field only exist for sequences of period equal to a power of the characteristic of the field. It is therefore useful to find a general and efficient algorithm to compute a good approximation of the k-error linear complexity. In this paper we investigate the design of a genetic algorithm to approximate the k-error linear complexity of a sequence. Our preliminary experiments show that the genetic algorithm approach is suitable to the problem and that a good scheme would use a medium sized population, an elitist type of selection, a special design of the two point random crossover and a standard random mutation. The algorithm outputs an approximative value of the k-error linear complexity which is on average only 19.5% higher than the exact value. This paper intends to be a proof of concept that the genetic algorithm technique is suitable for the problem in hand and future research will further refine the choice of parameters. }, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Ketcham:2007:cec, title={An Algorithm for Intelligent AudioWatermarking Using Genetic Algorithm}, author={Mahasak Ketcham and Sartid Vongprahip}, pages = {4454--4461}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1460.pdf}, url = {}, size = {}, abstract = {In this paper, we present an innovative watermarking scheme based on genetic algorithms (GA) in the discrete wavelet transforms, we search for the optimal intensity of watermark by using the adaptive tabu seacrh (ATS) is proposed. It is robust against watermarking attacks, which are commonly employed in literature. In addition, the watermarked image quality is also considered. We employ GA and ATS for the optimal localization and intensity of watermark. The watermark detection process can be performed without using the original audio signal. The experimental results are presented to demonstrate that watermark is inaudible and robust to many digital signal processing, such as cropping, low pass filter, additive noise.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Araújo:2007:cec, title={An Evolutionary Morphological-Rank-Linear Approach for Time Series Prediction}, author={Ricardo de A. Ara\'ujo and Germano C. Vasconcelos and Tiago A. E. Ferreira}, pages = {4321--4328}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1461.pdf}, url = {}, size = {}, abstract = {In this paper, a hybrid evolutionary Morphological-Rank-Linear (MRL) approach is proposed for time series forecasting. The proposed method consists of an Intelligent Hybrid Evolutionary MRL (IHEMRL) model composed of an MRL filter and a modified Genetic Algorithm (GA) that employs optimal genetic operators that accelerate its search convergence. The modified GA 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 (mixing parameter (λ), rank (r), linear Finite Impulse Response (FIR) filter (a) and the Morphological-Rank (MR) filter (b) coefficients). Thus, each individual of the GA population is trained by the averaged Least Mean Squares (LMS) algorithm to further improve the MRL filter parameters supplied by the GA. Experiments are conducted with the proposed approach using three real world time series according to a group of relevant performance metrics and the results are compared both to ARIMA models and MultiLayer Perceptrons (MLP).}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Ferentinos:2007:cec, title={A Memetic Algorithm for Dynamic Design ofWireless Sensor Networks}, author={Konstantinos P. Ferentinos and Theodore A. Tsiligiridis}, pages = {2774--2781}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1463.pdf}, url = {}, size = {}, abstract = {We present a memetic algorithm that dynamically optimizes the design of a wireless sensor network towards energy conservation and extension of the life span of the network, taking into consideration application-specific requirements, communication constraints and energy consumption of operation and communication tasks of the sensors. The memetic algorithm modifies an already successful genetic algorithm design system and manages to improve its performance. The obtained optimal sensor network designs satisfy all application-specific requirements, fulfill the existing connectivity constraints and incorporate energy conservation characteristics stronger than those of the original genetic algorithm system. Energy management is optimized to guarantee maximum life span of the network without lack of the network characteristics that are required by the specific sensing application.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Jang:2007:cec, title={Optimized Fuzzy Clustering by Predator Prey Particle Swarm Optimization}, author={Woo-seok Jang and Hwan-il Kang and Byung-hee Lee and Kab il Kim and Dong-il Shin and Seung-chul Kim}, pages = {3232--3238}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1465.pdf}, url = {}, size = {}, abstract = {In this paper, we focus on the optimization of fuzzy clustering. Particle Swarm Optimizations (PSO) is used for optimizing the algorithms. PSO is an algorithm which takes a cue from nature's bird flock or fish school and is known to have superior ability in search and fast convergence. But it might be difficult to find global optimal solution of the fuzzy clustering when it comes to complex higher dimensions. So we optimize the fuzzy clustering using Predator Prey Particle Swarm Optimizations (PPPSO). The concept of PPPSO is that predators chase the center of prey's swarm, and preys escape from predators, in order to avoid local optimal solutions and find global optimal solution efficiently. The performance of fuzzy c-means (FCM), particle swarm fuzzy clustering (PSFC) and predator prey particle swarm fuzzy clustering (PPPSFC) are compared. Through experiments, we show that the proposed algorithm has the best performance among them.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Tinós:2007:cec, title={Continuous Dynamic Problem Generators for Evolutionary Algorithms}, author={Renato Tin\'os and Shengxiang Yang}, pages = {236--243}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1467.pdf}, url = {}, size = {}, abstract = {Addressing dynamic optimization problems has attracted a growing interest from the evolutionary algorithm community in recent years due to its importance in the applications of evolutionary algorithms in real world problems. In order to study evolutionary algorithms in dynamic environments, one important work is to develop benchmark dynamic environments. This paper proposes two continuous dynamic problem generators. Both generators use linear transformation to move individuals, which preserves the distance among individuals. In the first generator, the linear transformation of individuals is equivalent to change the direction of some axes of the search space while in the second one it is obtained by successive rotations in different planes. Preliminary experiments were carried out to study the performance of some standard genetic algorithms in continuous dynamic environments created by the proposed generators.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Lung:2007:cec, title={A Collaborative Model for Tracking Optima in Dynamic Environments}, author={Rodica Ioana Lung and D. Dumitrescu }, pages = {564--567}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1468.pdf}, url = {}, size = {}, abstract = {A new hybrid approach to optimization in dynamic environments called Collaborative Evolutionary-Swarm Optimization (CESO) is presented. CESO is a simple method for tracking moving optima in a dynamic environment by combining the search abilities of an evolutionary algorithm for multimodal optimization and a particle swarm optimization algorithm. A collaborative mechanism is designed for the two methods. Numerical experiments indicate CESO to be an efficient method for the selected test problems compared with other evolutionary approaches.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Ballester:2007:cec, title={Model Calibration of a Real Petroleum Reservoir using a Parallel Real-Coded Genetic Algorithm}, author={Pedro J. Ballester and Jonathan N. Carter}, pages = {4313--4320}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1469.pdf}, url = {}, size = {}, abstract = {An application of a Real-coded Genetic Algorithm (GA) to the model calibration of a real petroleum reservoir is presented. In order to shorten the computation time, the possible solutions generated by the GA are evaluated in parallel on a group of computers. This required the GA to be adapted to a multi-processor structure, so that the scalability of the computation is maximised. The best solutions of each run enter the ensemble of calibrated models, which is finally analysed using a clustering algorithm. The aim is to identify the optimal regions contained in the ensemble and thus to reveal the distinct types of reservoir models consistent with the historic production data, as a way to assess the uncertainty in the Reservoir Characterisation due to the limited reliability of optimisation algorithms. The developed methodology is applied to the char- acterisation of a real petroleum reservoir. Results show a large improvement with respect to previous studies on that reservoir in terms of the quality and diversity of the obtained calibrated models. Our main conclusion is that, even with regularisation, many distinct calibrated models are possible, which highlights the importance of applying optimisation methods capable of identifying all such solutions.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Casadei:2007:cec, title={Self-Organized Over-Clustering Avoidance in Tuple-Space System}, author={Matteo Casadei and Ronaldo Menezes and Mirko Viroli and Robert Tolksdorf}, pages = {1408--1415}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1470.pdf}, url = {}, size = {}, abstract = {When it comes to communication performance, open distributed tuple-space systems depend heavily on the proximity of tuples to processes. Researchers have proposed many approaches for storing tuples in a way that processes benefit from the organization of tuples. Although some progress has been made, most of the proposed solutions fail to address the reverse problem: if most tuples are kept near the processes, the system's robustness is affected; the over-clustering of tuples in particular nodes creates a dependence to that particular node. Hence, we have a dichotomy where it is important to organize tuples in clusters near the processes, but it is equally important to avoid over-clustering. The ideal is to have a balance where tuples are clustered but not totally concentrated in very few tuple spaces (eg. one or two). This paper presents a self-organized solution to the tuple distribution problem, in which the possibility of over-clustering is considered.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Uy:2007:cec, title={Initialising PSO with Randomised Low-Discrepancy Sequences: The Comparative Results}, author={Nguyen Quang Uy and Nguyen Xuan Hoai and RI McKay and Pham Minh Tuan}, pages = {1985--1992}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1471.pdf}, url = {}, size = {}, abstract = {In this paper, we investigate the use of some wel- known randomised low-discrepancy sequences (Halton, Sobol, and Faure sequences) for initialising particle swarms. We experimented with the standard global-best particle swarm algorithm for function optimization on some benchmark problems, using randomised low-discrepancy sequences for initialisation, and the results were compared with the same particle swarm algorithm using uniform initialisation with a pseudo-random generator. The results show that, the former initialisation method could help the particle swarm algorithm improve its performance over the latter on the problems tried. Furthermore the comparisons also indicate that the use of different randomised low-discrepancy sequences in the initialisation phase could bring different effects on the performance of PSO.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Qualls:2007:cec, title={Neural-Based Agents Cooperate to Survive in the Defend and Gather Computer Game}, author={Joseph Qualls and Max Garzon and David J. Russomanno}, pages = {1398--1402}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1472.pdf}, url = {}, size = {}, abstract = {The computer game Defend and Gather was created to evaluate two neural-based agents' ability to learn how to play and win the game. The agents navigate an environment to find resources and defeat enemies. Traditional game agents are often neither challenging enough to human opponents over time, nor scalable to environments not anticipated at the time the agents were originally programmed. We show that neural- based agents have the ability to learn from their human counterparts or from the environment, thus remaining competitive over time. The neural-based agents developed for Defend and Gather have the ability to formulate tactics within increasingly difficult environments involving more sophisticated enemies and can win the game over seventy-five percent of the time.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Ünveren:2007:cec, title={Multi-Objective Optimization with Cross Entropy Method: Stochastic Learning with Clustered Pareto Fronts}, author={Ahmet U\" nveren and Adnan Acan}, pages = {3065--3071}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1476.pdf}, url = {}, size = {}, abstract = {This paper presents a novel multiobjective optimization strategy based on the cross entropy method (MOCE). The cross-entropy method (CE) is a stochastic learning algorithm inspired from rare event simulations and proved to be successful in the solution of difficult single objective real-valued optimization problems. The presented work extends the use of cross-entropy method to real-valued multiobjective optimization. For this purpose, parameters of CE search are adapted using the information collected from clustered nondominated solutions on the Pareto front. Comparison with well known multiobjective optimization algorithms on the solution of provably difficult benchmark problem instances demonstrated that CEMO performs at least as good as its competitors.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Shir:2007:cec, title={The Application of Evolutionary Multi-Criteria Optimization to Dynamic Molecular Alignment}, author={Ofer M. Shir and Michael Emmerich and Thomas B\"ack and Marc J. J. Vrakking}, pages = {4108--4115}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1480.pdf}, url = {}, size = {}, abstract = {This study introduces the multi-criteria approach to the optimization of dynamic molecular alignment by shaped femtosecond laser pulses, which has been considered so far only as a single-criterion problem. The paper applies advanced Pareto front approximation algorithms to this challenging real-world, high-dimensional, and computationally expensive problem, working with low-dimensional parameterizations of the electric field. Standard approaches (NSGA-II) and their metamodel-assisted extensions based on Kriging, are applied to this optimization task and compared among each other. The study confirms the conflicting nature of the objectives. Interesting features of the problem domain, such as the geometry of the Pareto front are revealed. Furthermore, metamodel-assistance, in particular pre-screening with the Kriging-based expected improvement criterion, proves to be a valuable ingredient for improving the numerical results.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Banerjee:2007:cec, title={A Recursive Clustering Methodology using a Genetic Algorithm}, author={Amit Banerjee and Sushil J. Louis}, pages = {2165--2172}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1482.pdf}, url = {}, size = {}, abstract = {This paper presents a recursive clustering scheme that uses a genetic algorithm-based search in a dichotomous partition space. The proposed algorithm makes no assumption on the number of clusters present in the dataset; instead it recursively uncovers subsets in the data until all isolated and separated regions have been classified as clusters. A test of spatial randomness serves as a termination criteria for the recursive process. Within each recursive step, a genetic algorithm searches the partition space for an optimal dichotomy of the dataset. A simple binary representation is used for the genetic algorithm, along with classical selection, crossover and mutation operators. Results of clustering on test cases, ranging from simple datasets in 2-D to large multidimensional datasets compare favorably with state of the art approaches in genetic algorithm-driven clustering.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Terrazas:2007:cec, title={Evolving Tiles for Automated Self-Assembly Design}, author={Germ\'an Terrazas and Marian Gheorghe and Graham Kendall and Natalio Krasnogor}, pages = {2001--2008}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1483.pdf}, url = {}, size = {}, abstract = {Self-assembly is a distributed, asynchronous mechanism that is pervasive across natural systems where hierarchical complex structures are built from the bottom-up. The lack of a centralised master plan, no external intervention, and pre- programmed interactions among entities are within its most relevant and technologically appealing properties. This paper tackles the self-assembly Wang tiles designability problem by means of artificial evolution. This research is centred in the use of tiles that are extended with rotation and probabilistic motion, and an evolutionary algorithm using the Morphological Image Analyses method as a fitness function. The obtained results support this approach as a successful engineering mechanism for the computer-aided design of self-assembled patterns.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Smorodkina:2007:cec, title={Toward Automating EA Configuration: The Parent Selection Stage}, author={Ekaterina Smorodkina and Daniel Tauritz}, pages = {63--70}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1485.pdf}, keywords = {genetic algorithms, genetic programming}, url = {}, size = {}, abstract = { One of the obstacles to Evolutionary Algorithms (EAs) fulfilling their promise as easy to use general-purpose problem solvers, is the difficulty of correctly configuring them for specific problems such as to obtain satisfactory performance. Having a mechanism for automatically configuring parameters and operators of every stage of the evolutionary life-cycle would give EAs a more widely spread popularity in the non-expert community. This paper investigates automatic configuration of one of the stages of the evolutionary life-cycle, the parent selection, via a new concept of semi-autonomous parent selection, where mate selection operators are encoded and evolved as in Genetic Programming. We compare the performance of the EA with semi-autonomous parent selection to that of a manually configured EA on three common test problems to determine the "price" we pay for user-friendliness.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Mukherjee:2007:cec, title={Emergent (Re)Optimization for Stream Queries in Grids}, author={Saikat Mukherjee and Srinath Srinivasa and Sanket Patil}, pages = {729--735}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1487.pdf}, url = {}, size = {}, abstract = {Query optimization in sensor grids have two major challenges: (a) optimizing in a multi-query environment, and (b) continuous re-optimization occurring due to new query registrations and de-queries, i.e. queries being stopped unexpect- edly. Addressing this problem continuously on a system-wide basis is an infeasible option. In this work called EstuaryDB, we propose a notion of emergent optimization, where globally optimal configurations emerge as a result of a number of local autonomous decisions carried out in self-interest. Grid nodes act as self-interested autonomous agents that continuously seek to maximize their "wealth." The agents are unaware of system- wide issues such as when do queries arrive, what are they asking for, or when are they revoked. Every query brings with it a certain amount of wealth, and each agent continuously tries to save as much of the wealth as possible. The amount of latent wealth in the system at any time gives a quantitative measure of the efficiency achieved over naïve stream retrieval.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Peconick:2007:cec, title={Projection-Based Local Search Operator for Multiple Equality Constraints within Genetic Algorithms}, author={Gustavo Peconick and Elizabeth F.Wanner and Ricardo H. C. Takahashi}, pages = {3043--3049}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1488.pdf}, url = {}, size = {}, abstract = {This paper presents a new operator for genetic algorithms that enhances convergence in the case of multiple nonlinear equality constraints. The proposed operator, named CQA-MEC (Constraint Quadratic Approximation for Multiple Equality Constraints), performs the steps: (i) the approximation of the non-linear constraints via quadratic functions; (ii) the determination of exact equality-constrained projections of some points onto the approximated constraint surface, via an iterative projection algorithm; and (iii) the re-insertion of the constraint- satisfying points in the genetic algorithm population. This operator can be interpreted both as a local search engine (that employs local approximations of constraint functions for correcting the feasibility) and a kind of elitism operator for equality constrained problems that plays the role of "fixing" the best estimates of the feasible set. The proposed operator has the advantage of not requiring any additional function evaluation per algorithm iteration, solely making usage of the information that is already obtained in the course of the usual genetic algorithm iterations. The test cases that were performed suggest that the new operator can enhance both the convergence speed (in terms of the number of function evaluations) and the accuracy of the final result.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Bianco:2007:cec, title={Psim: A Simulator for Biomolecular Dynamics Based on P Systems}, author={Luca Bianco and Vincenzo Manca and Luca Marchetti and Michele Petterlini}, pages = {883--887}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1489.pdf}, url = {}, size = {}, abstract = {Metabolic P systems, shortly MP systems, are a special class of P systems, introduced for expressing biological metabolism. Their dynamics is computed by metabolic algorithms which transform populations of objects according to a mass partition principle, based on suitable generalizations of chemical laws. The basic principles of MP systems are discussed and Psim, a simulation tool we developed in this context to discretely compute systems dynamics, is highlighted in its basic features. A concrete example is reported as well including a real simulation experiment by means of Psim.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Buchsbaum:2007:cec, title={Toward a Winning GP Strategy for Continuous Nonlinear Dynamical System Identification}, author={Thomas Buchsbaum}, pages = {1269--1275}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1490.pdf}, keywords = {genetic algorithms, genetic programming}, url = {}, size = {}, abstract = {System identification is the scientific art of building models from data. Good models are of essential importance in many areas of science and industry. Models are used to analyze, simulate, and predict systems and their states. Model structure selection and estimation of the model parameters with respect to a chosen criterion of fit are essential parts of the identification process. In this article, we investigate the suitability of genetic programming for creating continuous nonlinear state-space models from noisy time series data. We introduce methodologies from the field of chaotic time series estimation and present concepts for integrating them into a genetic programming system. We show that even small changes of the fitness evaluation approach may lead to a significantly improved performance. In combination with multiobjective optimization, a multiple shooting approach is able to create powerful models from noisy data.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(:2007:cec, title={A Genetic Approach to Search for Glider Guns in Cellular Automata}, author={E. Sapin and L. Bull and A. Adamatzky}, pages = {2456--2462}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1491.pdf}, url = {}, size = {}, abstract = {We aim to search for cellular automata candidate to an automatic system for the demonstration of collision-based universality and that can be able to simulate Turing machines in their space-time dynamics using gliders and glider guns. In this paper, we demonstrate a variety of novel glider guns discovered by genetic algorithms.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Graham:2007:cec, title={A Multiple-Function Toy Model of Exaptation in a Genetic Algorithm}, author={Lee Graham and Franz Oppacher}, pages = {4591--4598}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1492.pdf}, url = {}, size = {}, abstract = {In this paper we present a simple genetic algorithm consisting of a number of small niches, each with a different fitness function. The niches share a common genetic encoding and genotype-phenotype mapping, allowing for interniche migration of individuals. A notion of viability is introduced whereby population initialization produces viable individuals in one niche and is extremely unlikely to do so in all other niches. The niche fitness functions have been devised so as to demonstrate the gradual evolution of a population via multiple exaptation events where migrants seed, at each step, a new niche, adapt, and then spread to another in a predictable sequence. Such exaptation events take advantage of "hidden" relationships between fitness functions and allow evolving populations to explore regions of phenotype space that are otherwise inaccessible.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Pitangui:2007:cec, title={Improved Natural Crossover Operators in GBML}, author={Cristiano Pitangui and Gerson Zaverucha}, pages = {2157--2164}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1495.pdf}, url = {}, size = {}, abstract = {Aguilar-Ruiz et al proposed crossover operators, both discrete and continuous, for the natural representation (henceforth called NCO). NCO showed advantages in accuracy and in efficiency compared to the binary ones. However, they do not explore the search space like the two points crossover when the binary coding is used. In order to do so, in our previous work we proposed a new natural discrete crossover operator, which gave very good results compared to C4.5 in several UCI databases. Nonetheless, it was not experimentally compared to NCO. So, in this work, we perform this comparison in the same datasets and define a new natural continuous crossover operator, which is also compared to the continuous NCO operator. The experimental results showed the advantages of both new natural operators: our discrete operator achieves better accuracy and simpler concepts using less time, whereas our continuous operator is also able to explore the search space in a more efficient way, leading to better results in less time.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Omran:2007:cec, title={Barebones Particle Swarm Methods for Unsupervised Image Classification}, author={M. Omran and S. Al-Sharhan}, pages = {3247--3252}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1496.pdf}, url = {}, size = {}, abstract = {A clustering method that is based on barebones Particle Swarm (BB) is developed in this paper. BB is a variant of Particle Swarm Optimization (PSO) where parameter tuning is not required. The proposed algorithm finds the centroids of a user specified number of clusters, where each cluster groups together similar patterns. The application of the proposed clustering algorithm to the problem of unsupervised classification and segmentation of images is investigated. To illustrate its wide applicability, the proposed algorithms are then applied to synthetic, MRI and satellite images. Experimental results show that the BB-based clustering algorithm performs very well compared to other state-of-the-art clustering algorithms in all measured criteria.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Santos:2007:cec, title={Conditional Independence Based Learning of Bayesian Classifiers Guided by a Variable Ordering Genetic Search}, author={Edimilson Batista dos Santos and Estevam Rafael Hruschka Junior and Maria do Carmo Nicoletti}, pages = {1444--1449}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1497.pdf}, url = {}, size = {}, abstract = {This work proposes a genetic strategy for learning a Bayesian classifier using an algorithm based on conditional independence and the information given by a variable ordering. The strategy has been implemented as the system VOGAC-PC. The paper presents and analyses the results of experiments in various domains using VOGAC-PC as well as a previous system, named VOGA-K2, based on algorithm K2.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Gog:2007:cec, title={New Selection Operators based on Genetic Relatedness for Evolutionary Algorithms}, author={Anca Gog and D. Dumitrescu and B\'eat Hirsbrunner}, pages = {4610--4614}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1498.pdf}, url = {}, size = {}, abstract = {One of the most important decisions that influence the performance of evolutionary algorithms is the way individuals are selected for recombination. Two new selection operators that explore more promising regions of the search space are proposed in order to avoid the search becoming trapped into a local optimum. The first operator is a variant of the proportional selection and the second a variant of the tournament selection - both of them using information about the best ancestor of each individual within the population. In order to prove the efficiency of the proposed operators, several instances of the Travelling Salesman Problem are considered. Experimental results show an acceleration of the search process when using the proposed selection schemes, compared to the most popular existing selection operators. While the first operator performs better only in the first stages of the algorithm, the second outperforms the other selection operators in all its stages.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Shir:2007:cec, title={Self-Adaptive Niching CMA-ES with Mahalanobis Metric}, author={Ofer M. Shir and Michael Emmerich and Thomas B\"ack}, pages = {820--827}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1499.pdf}, url = {}, size = {}, abstract = {Existing niching techniques commonly use the Euclidean distance metric in the decision space for the classification of feasible solutions to the niches under formation. This approach is likely to encounter problems in high-dimensional landscapes with non-isotropic basins of attraction. Here we consider niching with the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), and introduce the Mahalanobis distance metric into the niching mechanism, aiming to allow a more accurate spatial classification, based on the ellipsoids of the distribution, rather than hyper-spheres of the Euclidean metric. This is tested with the CMA-(+) routines, and compared to two niching frameworks - fixed niche radius as well as selfadaptive niche radius, which is based on the coupling to the step-size. The performance of the different variants is evaluated on a suite of theoretical test-functions. We thus present here the Mahalanobis-assisted CMA-niching as a state-of-the-art niching technique within Evolution Strategies (ES), and propose it as a solution to the so-called niche radius problem.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(:2007:cec, title={Job Shop Optimization Through Multiple Independent Particle Swarms}, author={Brian Ivers and Gary G. Yen}, pages = {3361--3368}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1500.pdf}, url = {}, size = {}, abstract = {This study examines the optimization of the Job Shop Scheduling Problem (JSP) by a search space division scheme and use of the meta-heuristic method of Particle Swarm Optimization (PSO) to solve it. The Job Shop Scheduling Problem (JSP) is a well known huge combinatorial problem from the field of Deterministic Scheduling. It is considered the one of the hardest in the class of NP-Hard problems. The objective is to optimally schedule a finite number of operations to a finite number of resources while complying with ordering constraints. The Particle Swarm Optimization Algorithm (PSO) is a new meta-heuristic optimization method modeled after the behavior of a flock of birds in flight. "Particles" are initialized in the search space of a particular problem by assigning them a position, which represents a solution to the objective function, and a velocity. They "fly" through the search space with out direct control, but are given both a cognitive personal component and a global or social component of the best positions (thereby solutions) in space. The PSO algorithm is considered a very fast algorithm and is emerging as a widely studied widely used algorithm for optimization problems. The proposed method uses this meta-heuristic to solve the JSP by assigning each machine in a JSP an independent swarm of particles.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Yates:2007:cec, title={Non-separable Fitness Functions for Evolutionary Shape Optimization Benchmarking}, author={Tim A. Yates and Thorsten Schnier}, pages = {2893--2900}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1501.pdf}, url = {}, size = {}, abstract = {Target shape matching can be used as a quick and easy surrogate task when evaluating optimization algorithms intended for computationally expensive tasks, such as turbine blade design using computational fluid dynamics. Many reasonable shape representations render the shape matching fitness landscape linearly separable, unlike that of the turbine design task. Optimization algorithms may exploit this property, so evaluations based on shape matching may be inappropriate for turbine design. To address this disparity, a method is proposed to generate a linearly non-separable shape matching fitness landscape: the test shape is compared to one or more distracter shapes as well as the target, and the individual fitness measures are combined non-linearly. A demonstration is given, using a simple polygon representation and exhaustively varying two parameters, to show that this method does indeed generate a fitness landscape that cannot be linearly decomposed.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Shah_Hosseini:2007:cec, title={Problem Solving by Intelligent Water Drops}, author={Hamed Shah Hosseini}, pages = {3226--3231}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1503.pdf}, url = {}, size = {}, abstract = {In this paper, we propose a new problem solving algorithm called "Intelligent Water Drops" or IWD algorithm which is based on the processes that happen in the natural river systems and the actions and reactions that take place between water drops in the river and the changes that happen in the environment that river is flowing. It is observed that a river often chooses an optimum path regarding the conditions of its surroundings to get to its ultimate goal which is often a lake or sea. These ideas are embedded into the proposed algorithm for solving the Traveling Salesman Problem or the TSP. The IWD algorithm is tested with artificial and standard TSP problems and the experimental results demonstrate that it is a very promising problem solving algorithm and deserves more research to improve it and/or to adapt it to other engineering problems.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Rodríguez:2007:cec, title={An Investigation of Hyper-Heuristic Search Spaces}, author={Jos\'e Antonio V\'azquez Rodr\'iguez and Sanja Petrovic and Abdellah Salhi}, pages = {3776--3783}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1505.pdf}, url = {}, size = {}, abstract = {Hyper-heuristics or "heuristics that coordinate heuristics" are fastly becoming popular for solving combinatorial optimisation problems. These methods do not search directly the solution space; they do it indirectly through the exploration of the space of heuristics and/or their combinations. This space is named the associated space. The task of finding the best element in the associated space is referred to as the associated problem. This paper presents a formal definition of the associated problem, and investigates several of its properties. An empirical study of the associated problem is performed using a production scheduling problem as experimental environment. Obtained results allow us to partly explain what are the advantages of hyper-heuristic solution representations over other traditional ones, and, to give recommendations on hyperheuristic design.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Higo:2007:cec, title={Hierarchical Importance Sampling Instead of Annealing}, author={Takayuki Higo and Keiki Takadama}, pages = {134--141}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1506.pdf}, url = {}, size = {}, abstract = {This paper proposes a novel method, Hierarchical Importance Sampling (HIS), which can be used instead of converging the population for Evolutionary Algorithms based on Probabilistic Models (EAPM). In HIS, multiple populations are simulated simultaneously so that they have different diversities. This mechanism allows HIS to obtain promising solutions with various diversities. Experimental comparisons between HIS and the annealing (i.e., general EAPM) have revealed that HIS outperforms the annealing when applying to a problem of a 2D Ising model, which have many local optima. Advantages of HIS can be summarized as follows: (1) Since populations do not need to converge and do not change rapidly, HIS can build probability models with stability; (2) Since samples with better cost function values can be used for building probability models in HIS, HIS can obtain better probability models; (3)HIS can reuse historical results, which are normally discarded in the anealing.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Brancovici:2007:cec, title={Towards Trustworthy Intelligence on the Road: A Flexible Architecture for Safe, Adaptive, Autonomous Applications}, author={George Brancovici}, pages = {4230--4237}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1508.pdf}, url = {}, size = {}, abstract = {We present our results in developing trustworthy autonomous software to assist the user at solving complex tasks in very dynamic scenarios. We describe a generic architecture to facilitate the systematic design of autonomous, adaptive and safe applications. Generic modules including one for trustworthiness enforcement dedicated to ensure the system functional stability are proposed. Modularization based on the cognitive function of the components is encouraged. A key premise is that domain knowledge is explicitly specified as a parameter of each application, with the side effect of enabling seamless integration with other remote autonomous or infrastructure applications. Our design choices are exemplified on a demonstrative travel management application.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Uddin:2007:cec, title={Cryptanalysis of Pointcheval's Identification Scheme Using Ant Colony Optimization}, author={Mohammad Faisal Uddin and Amr M. Youssef}, pages = {2942--2947}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1509.pdf}, url = {}, size = {}, abstract = {We investigate the use of a binary version of ant colony optimization for the cryptanalysis of an identification scheme based on the permuted perceptron problem (PPP) proposed by Pointcheval. Based on our experimental results, ACO-based attacks proved to be very effective on recovering the secret key of this scheme for various sets of PPP parameters.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Kashiwaya:2007:cec, title={Chemical Reaction Rate Parameter Estimation by MAP Particle Filter Algorithm}, author={Shigeru Kashiwaya}, pages = {4489--4496}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1511.pdf}, url = {}, size = {}, abstract = {A fast search algorithm for estimation of chemical reaction kinetics parameters is investigated. A novel parameter estimation technique using maximum a posteriori (MAP) particle filter is presented. Low discrepancy sequences (LDS) are applied to generate a population of particles that are regarded as a set of samples drawn from a uniformly distributed probability density in a high dimensional parameter space. The notion of simulated annealing (SA) is adopted to "rejuvenate" a system of particles around an MAP estimate that is obtained through evolution of such filter particles.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Zahid:2007:cec, title={A Comprehensive Formal Framework for Analyzing the Behavior of Nature-Inspired Routing Protocols}, author={Saira Zahid and Muhammad Shahzad and Syed Usman Ali and Muddassar Farooq}, pages = {180--187}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1512.pdf}, url = {}, size = {}, abstract = {Nature-inspired routing protocols are becoming an active area of research. Researchers in the community follow a well known engineering philosophy: inspire, abstract, design, develop and validate. As a consequence, the protocols are designed on the basis of heuristics and then their performance is evaluated in a network simulator. To the best of our knowledge, virtually no attention has been paid in developing a formal framework that provides an analytical insight into the behavior and performance of such algorithms. The lack of formal treatment of Nature-inspired routing protocols is often criticized in the networking community. In this paper we propose a formal framework that helps in analyzing a Nature-inspired routing protocol, BeeHive.We have verified the correctness of our model by comparing its estimated values with the results obtained from extensive network simulations. An important outcome of the work is that the estimated and measured values only differ by a small deviation. We believe that this work will be instrumental for Nature-inspired Telecommunications.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Sekaj:2007:cec, title={Some Aspects of Parallel Genetic Algorithms with Population Re-Initialization}, author={I. Sekaj and J. Perkacz}, pages = {1333--1338}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1513.pdf}, url = {}, size = {}, abstract = {In case of highly non-smooth search/optimization problems it is not easy to avoid the premature convergence of the genetic algorithm. For that reason it is important to provide for a high measure of population diversity of the GA. In such a case, an effective means is the population re-initialization. In this paper the influence of population re-initialization on the parallel genetic algorithm (PGA) performance is experimentally analyzed. In various PGA architectures three types of re-initialization are described. Next the following factors are studied: re-initialization period and the number of re-initialized nodes. The results are demonstrated on the minimization of real number test functions.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Sekaj:2007:cec, title={Genetic Programming - based Controller Design}, author={I. Sekaj and J. Perkacz}, pages = {1339--1343}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1515.pdf}, url = {}, size = {}, abstract = {Two genetic programming-based approaches are proposed for continuous-time process control design. The first approach uses a network of interconnected continuous-time elementary dynamic building blocs with a specific table-based representation of individuals. In the second approach the control algorithm is represented as a recurrent function of discrete-time variables with the tree representation of individuals. Both approaches are demonstrated on controller design examples, which are compared with the genetic algorithm-based PID controller design.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Isaacs:2007:cec, title={Enhanced Prediction of Protein Cellular Localization Sites with Genetic Algorithm Optimal Kernel Projection Analysis}, author={Jason C. Isaacs and Simon Foo and Anke Meyer-Baese}, pages = {1429--1435}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1516.pdf}, url = {}, size = {}, abstract = {The localization of proteins can help us to better understand their functions. Currently a number of localization machine learning algorithms have been employed on this problem, including SVM and K-NN. However, in terms of performance there is little success. In this paper, we apply a Genetic Algorithm to optimize a kernel component analysis solution, an algorithm that we believe will have better performance than standalone Kernel PCA. We will experiment using the protein location data from the Horton and Nakai yeast and E.Coli databases. We will then compare the performance of our optimized system with previous methods. Results show that GA enhanced Kernel Component Analysis can improve classification.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Wallin:2007:cec, title={On the Diversity of Diversity}, author={David Wallin and Conor Ryan}, pages = {95--102}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1517.pdf}, url = {}, size = {}, abstract = {Estimation of Distribution Algorithms (EDA) is an active area of research within the field of Evolutionary Algorithms. While EDAs have shown great promise on difficult problems with strong epistasis between genes, such as hierarchical and deceptive problems, they have not been a choice for non-stationary problems where the target solution changes over time. This work aims to explore the diversity within the population of an EDA using a supervised classifier.We introduce a technique, Sampling-Mutation, that can help increase the useful diversity within the population. We show that Sampling-Mutation increases the performance of an EDA on a non-stationary problem and a hierarchical problem.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Hornby:2007:cec, title={Measuring Complexity by Measuring Structure and Organization}, author={Gregory S. Hornby}, pages = {2017--2024}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1518.pdf}, keywords = {genetic algorithms, genetic programming}, url = {}, size = {}, abstract = {Necessary for furthering the development of more powerful evolutionary design systems, capable of scaling to evolving more sophisticated and complex artifacts, is the ability to meaningfully and objectively compare these systems by applying complexity measures to the artifacts they evolve. Previously we have proposed measures of modularity, reuse and hierarchy (MR&H), here we compare these measures to ones from the fields of Complexity, Systems Engineering and Computer Programming. In addition, we propose several ways of combining the MR&H measures into a single measure of structure and organization. We compare all of these measures empirically as well as on three sample objects and find that the best measures of complexity are two of the proposed measures of structure and organization.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Tang:2007:cec, title={A Bacterial Swarming Algorithm for Global Optimization}, author={W. J. Tang and Q. H. Wu and J. R. Saunders}, pages = {1207--1212}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1522.pdf}, url = {}, size = {}, abstract = {This paper presents a novel Bacterial Swarming Algorithm (BSA) for global optimization. This algorithm is inspired by swarming behaviors of bacteria, in particular, focusing on the study of tumble and run actions which are the major part of the chemotactic process. Adaptive tumble and run operators are developed to improve the global and local search capability of the BSA, based on the existing bacterial foraging algorithm (BFA). Simplified quorum-sensing mechanism is also incorporated to enhance the performance of this algorithm. BSA has been evaluated, in comparison with existing Evolutionary Algorithms (EAs), such as Fast Evolutionary Programming (FEP) and Particle Swarm Optimizer (PSO), on a number of mathematical benchmark functions. The simulation studies have been undertaken and the results show that the BSA can provide superior performance than FEP and PSO in optimizing these benchmark functions, particularly, in terms of its convergence rates and robustness.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Ono:2007:cec, title={Multiple Solution Search Based on Hybridization of Real-Coded Evolutionary Algorithm and Quasi-Newton Method}, author={Satoshi Ono and Yusuke Hirotani and Shigeru Nakayama}, pages = {1133--1140}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1523.pdf}, url = {}, size = {}, abstract = {In recent years, many evolutionary computation methods have been proposed and applied to real-world problems; however, gradient methods are regarded as promising for their capacity to solve problems involving real-coded parameters. Addressing real-world problems should not only involve the search for a single optimal solution, but also a set of several quasi-optimal solutions. Although some methods aiming the search for multiple solutions have been proposed (e.g. Genetic Algorithm with Sharing and Immune Algorithm), these could not render highly optimized solutions to real-coded problems. This paper proposes hybrid algorithms combining real-coded evolutionary computation algorithms and gradient search methods for multiple-solution search in multimodal optimization problems. Furthermore, a new evaluation function of solution candidates with gradient is presented and discussed in order to find quasi-optimal solutions. Two hybrid algorithms are proposed - a hybridization between Immune Algorithm and Quasi-Newton method (IA+QN) and a hybridization between Genetic Algorithm with Sharing and Quasi-Newton method (GAS+QN). Experimental results have shown that the proposed methods can find optimal and quasi-optimal solutions with high accuracy and efficiency even in high-dimensional multimodal benchmark functions. The results have also shown that GAS+QN has better performance and higher robustness in terms of parameter configuration than IA+QN.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Yu:2007:cec, title={TS-Based Generation and Transmission Maintenance Scheduling}, author={Dayang Yu and Qiang Zhang and Xueshan Han and Jianguo Zhao}, pages = {2936--2941}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1525.pdf}, url = {}, size = {}, abstract = {Measuring the value of reliability with the load interruption cost, this paper proposes a single objective model, which provides a quantitative coordination between the economy and reliability. To this non-convex master-slave optimal decision problem with both discrete and continuous variables, the tabu search is used to solve the master problem of schedules, and the linear programming is applied to solve the slave problem of stochastic N-1 economic dispatch at each time interval. According to the difference between units and transmission lines, this paper gives four searching strategies and compares their efficiency with IEEE 30 bus system. Numerical results shows the model and algorithm are effective.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Lohn:2007:cec, title={Automated Design of a MEMS Resonator}, author={Jason D. Lohn and William F. Kraus and Gregory S. Hornby}, pages = {3486--3491}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1527.pdf}, url = {}, size = {}, abstract = {As the application and complexity of microelectromechanical (MEMS) devices increases, there is a corresponding need for automated design and optimization tools to augment engineers' design skills. Evolutionary computation provides a set of tools that may prove very effective in this application domain. Here we present a novel evolutionary computation encoding scheme for specifying MEMS designs, and use this scheme to evolve a MEMS meandering resonator. Using fewer evaluations, we are able to evolve designs that perform as well as those previous reported.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Gandelli:2007:cec, title={Development and Validation of Different Hybridization Strategies between GA and PSO}, author={A. Gandelli and F. Grimaccia and M. Mussetta and P. Pirinoli and R. E. Zich}, pages = {2782--2787}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1528.pdf}, url = {}, size = {}, abstract = {In this paper a new class of hybridization strategies between GA and PSO is presented and validated. The Genetical Swarm Optimization (GSO) approach is presented here with respect with different test cases to prove its effectiveness. GSO is a hybrid evolutionary technique developed in order to exploit in the most effective way the uniqueness and peculiarities of two classical optimization approaches, the Particle Swarm Optimization (PSO) and Genetic Algorithms (GA). This algorithm is essentially, as PSO and GA, a population-based heuristic search technique, which can be used to solve combinatorial optimization problems, modeled on the concepts of natural selection and evolution (GA), but also based on cultural and social rules derived from the analysis of the swarm intelligence and from the interaction among particles (PSO). The here proposed class of hybrid algorithms is tested for various benchmark problems, analyzing different computational costs, and finally reporting some numerical results.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Shafiq:2007:cec, title={Extended Thymus Action for Improving Response of AIS Based NID System Against Malicious Traffic}, author={M. Zubair Shafiq and Mehrin Kiani and Bisma Hashmi and Muddassar Farooq}, pages = {3369--3376}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1530.pdf}, url = {}, size = {}, abstract = {Artificial Immune Systems (AISs) are being increasingly utilized to develop Network Intrusion Detection (NID) systems. The fundamental reason for their success in NID is their ability to learn normal behavior of a network system and then differentiate it from an anomalous behavior. As a result, they can detect a majority of innovative attacks. In comparison, classical signature based systems fail to detect innovative attacks. Light Weight Intrusion Detection System (LISYS) provides the basic framework for AIS based NID systems. This framework has been improved incrementally, including incorporation of thymus action, since it was first developed. In this paper, we have extended the basic thymus action model, which provides immature detectors with multiple chances to develop tolerization to normal. However, AIS is prone to successful attacks by malicious traffic which appears similar to the normal traffic. This results in high number of false positives. In this paper, we present a mathematical model of malicious traffic for TCP-SYN flood based Distributed Denial of Services (DDoS) attacks. This model is used to generate different sets of malicious traffic. These sets are used for performance comparison of the proposed extended thymus action with the simple thymus action model. The results of our experiments demonstrate that the extended model has significantly reduced the number of false positives.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(:2007:cec, title={Random Search Can Outperform Mutation}, author={Cameron Skinner and Patricia J. Riddle}, pages = {2584--2590}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1533.pdf}, url = {}, size = {}, abstract = {Efficient discovery of lowest level building blocks is a fundamental requirement for a successful genetic algorithm. Although considerable effort has been directed at techniques for combining existing building blocks there has been little emphasis placed on discovering those blocks in the first place. This paper describes an analysis of the canonical genetic algorithm that demonstrates a significant weakness in the algorithm and suggests that careful use of random search will lead to better performance than the use of mutation. Experimental results show that this can result in significant performance improvements over the canonical genetic algorithm.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Lima:2007:cec, title={Evolutionary Algorithm to ab initio Protein Structure Prediction with Hydrophobic Interactions}, author={T. W. de Lima and P. H. R. Gabriel and A. C. B. Delbem and R. A. Faccioli and I. N. da Silva }, pages = {612--619}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1534.pdf}, url = {}, size = {}, abstract = {Proteins are polymers whose chains are composed of 20 different monomers, called amino acids. The problem of Protein Structure Prediction (PSP) is the determination of protein 3D conformation from its amino acid sequence. Two main strategies are usually employed to work with PSP: homology and Ab initio approaches. This paper presents an Evolutionary Algorithm to PSP using an Ab initio approach (ProtPred). The predictions are evaluated using fitness functions based on potential energies (electrostatic and van der Waals) and hydrophobic interactions. The proposed approach uses dihedral angles and main angles of the lateral chains to model a protein structure. ProtPred is evaluated using relatively complex cases for an Ab initio approach. Results have shown that ProtPred is a consistent approach.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Valdés:2007:cec, title={Visualizing High Dimensional Objective Spaces for Multi-objective Optimization: A Virtual Reality Approach}, author={J. J. Vald\'es and A. J. Barton}, pages = {4199--4206}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1535.pdf}, url = {}, size = {}, abstract = {This paper presents an approach for constructing visual representations of high dimensional objective spaces using virtual reality. These spaces arise from the solution of multi-objective optimization problems with more than 3 objective functions which lead to high dimensional Pareto fronts which are difficult to use. This approach is preliminarily investigated using both theoretically derived high dimensional Pareto fronts for a test problem (DTLZ2) and practically obtained objective spaces for the 4 dimensional knapsack problem via multi-objective evolutionary algorithms like HLGA, NSGA, and VEGA. The expected characteristics of the high dimensional fronts in terms of relative sizes, sequencing, embedding and asymmetry were systematically observed in the constructed virtual reality spaces.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Hoque:2007:cec, title={Protein Folding Prediction in 3D FCC HP Lattice Model Using Genetic Algorithm}, author={Md Tamjidul Hoque and Madhu Chetty and Abdul Sattar}, pages = {4138--4145}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1536.pdf}, url = {}, size = {}, abstract = {In most of the successful real protein structure prediction (PSP) problem, lattice models have been essentially utilized to have the folding backbone sampling at the top of the hierarchical approach. A three dimensional face-centred-cube (FCC), with the provision for providing the most compact core, can map closest to the folded protein in reality. Hence, our successful hybrid Genetic Algorithms (HGA) proposed earlier for a square and cube lattice model is being extended in this paper for a 3D FCC model. Furthermore, twins (conformations having similarity with each other), in GA population have also been considered for removal from the search space for improving the effectiveness of GA. The HGA combined with the twin removal (TR) strategy showed best performance when compared with the Simple GA (SGA), SGA with TR, and HGA only versions. Experiments were carried out on the publicly available benchmark HP sequences and results are expressed based on the fitness of the corresponding applied lattice model, which will help any future novel approach to be compared.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Neruda:2007:cec, title={Evolving Neural Network Which Control a Robotic Agent}, author={Roman Neruda}, pages = {1517--1522}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1537.pdf}, url = {}, size = {}, abstract = {Intelligent embodied agents should be able to adopt to changes of the environment and to modify their behavior according to acquired knowledge. The goal of this work is the study of emergence of intelligent behavior within a simple intelligent agent. Cognitive agent functions will be realized by mechanisms based on neural networks of the perceptron type. The adaptation mechanism is realized by the evolutionary algorithms which is responsible for setting the weights of a neural network in a simulated environment. Several tasks including obstacle avoidance and efficient maze exploration are presented in the experimental section. The behaviors developed during the adaptation process compare favorably with hard coded strategies.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Shibasaka:2007:cec, title={Species-Based Differential Evolution with Switching Search Strategies for Multimodal Function Optimization}, author={Miyuki Shibasaka and Akira Hara and Takumi Ichimura and Tetsuyuki Takahama}, pages = {1183--1190}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1539.pdf}, url = {}, size = {}, abstract = {In multimodal optimization problems, the objective is not only to find one global optimal solution, but also to find various global optimal solutions. For this purpose, the Species-based Differential Evolution (SDE) has been proposed previously. In this method, the population is divided into multiple subpopulations by using speciation, and each species focuses its search for one optimal solution. By this way, multiple optimal solutions can be discovered simultaneously. However, this algorithm takes a long time for complicated problems to acquire global optima. In this paper, we propose SDE with switching search strategies, which selects global search by the population or local search in each species according to the search situation. The comparison of the conventional SDE and our proposed method is performed on five test functions. The experimental results show that the SDE with switching search strategies outperforms the conventional SDE in a complicated function problem.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Cottam:2007:cec, title={Anticipation: Breaking the Evolutionary Time Barrier}, author={Ron Cottam and Willy Ranson and Roger Vounckx}, pages = {4653--4660}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1543.pdf}, url = {}, size = {}, abstract = {Evolutionary Computation (EC) has developed over the past few decades to successfully address problems across a wide range of domains. Its capabilities make EC a prime candidate for the development of anticipatory computation, but its applicability is inhibited by reliance on the formal logic of closed systems. Living organisms have evolved from stimulatory-reaction to intelligent-anticipation as part of an open ecosystem, and such a relationship is prerequisite for the evolutionary development of anticipatory computation. Effective anticipation depends on the accurate correlation of historical multiscalar data, and ultimately it must be realized as a computer, rather than on a computer. This paper presents an early 'roadmap' for the implementation of evolutionary anticipatory 'machines', through the critical concepts of ecosystemic rationality and hyperscale to the first construction of multiscalar simulators.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Neto:2007:cec, title={LS-DRAUGHTS . A Draughts Learning System based on Genetic Algorithms, Neural Network and Temporal Differences}, author={Henrique Castro Neto and Rita Maria Silva Julia}, pages = {2523--2529}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1544.pdf}, url = {}, size = {}, abstract = {The objective of this paper is the proposal of a Learning System - the LS-Draughts - which aims at using Genetic Algorithms (GAs) to automatically generate a concise and efficient set of features which are relevant in representing the game board states and for optimizing the training of a draught player agent. This agent consists of an Artificial Neural Network whose weights are updated by the Temporal Differences (TD) Reinforcement Learning methods. The NET-FEATUREMAP mapping is used to represent a game board state in the Network input. The Network output corresponds to a real number (prediction) that indicates to what extent the input state is favorable to the agent. The agent is trained by self-play coupled with a cloning technique. The Minimax Algorithm is used to choose the best action to be executed considering the current game board state. Such a learning process is close to that proposed by Mark Lynch (NeuroDraughts). However, the LS-Draughts expands the NeuroDraughts as it automatically generates an effective and concise set of features to be used in the NET-FEATUREMAP mapping, whereas the latter uses a fixed and manually defined set of features. A tournament was promoted between the best player obtained by the LS-Draughts and the best available player of the NeuroDraughts. The tournament was won by the player of the LS-Draughts, which confirms that the GAs can be an important tool for improving the general performance of automatic players.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Skolicki:2007:cec, title={The Importance of a Two-level Perspective for Island Model Design}, author={Z. Skolicki and K. De Jong}, pages = {4623--4630}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1546.pdf}, url = {}, size = {}, abstract = {Our theoretical understanding of island models (IMs) is much worse than of single-population evolutionary algorithms (EAs). As a consequence there is relatively little guidance available to a practitioner for even the most basic aspects of IM design such as choosing the size and number of the islands. In this paper we improve on this situation by showing how a particular two-level perspective can in fact provide guidance for IM design.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Nannen:2007:cec, title={Efficient Relevance Estimation and Value Calibration of Evolutionary Algorithm Parameters}, author={Volker Nannen and A. E. Eiben}, pages = {103--110}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1547.pdf}, url = {}, size = {}, abstract = {Calibrating the parameters of an evolutionary algorithm (EA) is a laborious task. The highly stochastic nature of an EA typically leads to a high variance of the measurements. The standard statistical method to reduce variance is measurement replication, i.e., averaging over several test runs with identical parameter settings. The computational cost of measurement replication scales with the variance and is often too high to allow for results of statistical significance. In this paper we study an alternative: the REVAC method for Relevance Estimation and Value Calibration, and we investigate how different levels of measurement replication influence the cost and quality of its calibration results. Two sets of experiments are reported: calibrating a genetic algorithm on standard benchmark problems, and calibrating a complex simulation in evolutionary agent-based economics. We find that measurement replication is not essential to REVAC, which emerges as a strong and efficient alternative to existing statistical methods.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Zhang:2007:cec, title={DE-AEC: A Differential Evolution Algorithm Based on Adaptive Evolution Control}, author={Jingqiao Zhang and Arthur C. Sanderson}, pages = {3824--3830}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1548.pdf}, url = {}, size = {}, abstract = {A new differential evolution algorithm, DE-AEC, is proposed based on adaptive evolution control utilizing the information provided by a surrogate model. The algorithm is useful for optimization problems with expensive function evaluations, because it can significantly reduce the number of true function evaluations. Specifically, DE-AEC generates multiple offspring for each parent and chooses the promising one based on the accuracy and the predicted function value of the current surrogate model. The model's accuracy is also used as an indicator of potential false convergence and special measures are taken to improve the convergence reliability. Simulation results on a set of fifteen test functions show that, compared to an already improved DE algorithm, DE-AEC reduces the number of true function evaluations by 30% - 80% for fourteen functions in the achievement of either low-level (10-2) or high-level (10-8) accuracy.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Togelius:2007:cec, title={Multi-Population Competitive Co-Evolution of Car Racing Controllers}, author={Julian Togelius and Peter Burrow and Simon M. Lucas}, pages = {4043--4050}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1549.pdf}, keywords = {genetic algorithms, genetic programming}, url = {}, size = {}, abstract = {Multi-population competitive co-evolution is explored as a way of developing controllers for a simple (but definitely not trivial) car racing game. The three main uses we see for this method are to evolve more complex general intelligence than would be possible with other methods, to compare different evolvable architectures for controllers, and to develop behaviourally diverse populations of agents for computer games. Nine-population co-evolution is compared with single-population co-evolution and standard evolution strategies, steady-state and generational versions of the algorithm are compared, and a number of different controller architectures are compared with each other.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Sirisanyalak:2007:cec, title={An Artificial Immunity-Based Spam Detection System B. Sirisanyalak and O. Sornil}, author={aStamford Ballroom and Swiss.otel The Stamford (Conference Hotel) Pre-dinner Cocktail (soft drinks and nuts) will start at hrs}, pages = {3392--3398}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1550.pdf}, url = {}, size = {}, abstract = {Spam is considered a significant security problem for computer users everywhere. Spammers exploit a variety of tricks to conceal parts of messages that can be used to identify spam. A number of different spam detection techniques have been proposed using a large number of message features, heuristic rules, or evidences from other detectors. This paper presents an email feature extraction technique for spam detection based on artificial immune systems. The proposed method extracts a set of four features that can be used as inputs to a spam detection model. The performance evaluation against a standard spam collection and reference systems shows that the proposed spam detection system performs well compared to other systems with large sets of features, rules, or external evidences. The detection performance of the best system in this study is 0.91% and 1.95% of false positive and false negative rates, respectively.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Graham:2007:cec, title={Irreducible Complexity in a Genetic Algorithm}, author={Lee Graham and Franz Oppacher and Steffen Christensen}, pages = {3692--3697}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1552.pdf}, url = {}, size = {}, abstract = {There has been much news about the ability of evolution to evolve systems where function ceases with the removal of any of its multiple parts. In particular, a challenge has been lofted to explain how evolution can evolve such a system, given its gradualistic nature. We present an example utilizing a dynamic fitness function and parsimony pressure. Given unambiguous definitions of system, function, and part, the genetic algorithm presented here readily produces such complex systems.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Privošnik:2007:cec, title={Scalability in Evolved Multi-Agent Systems}, author={Marko Privo\v{s}nik}, pages = {1091--1096}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1553.pdf}, url = {}, size = {}, abstract = {The scalability of an evolved multi-agent system is an important characteristic of the system. The multi-agent system is normally evolved in a particular configuration of system parameters. However, an optimized solution using one set of system parameters does not necessarily means a good solution using a different set of system parameters. The research presented in this paper studies the performance scalability of evolved solutions in reactive multi-agent systems for the heap formation task. The scalability variable considered in the research was the cardinality of the agents group, whereas keeping the rest of the system and collective resources invariable. In the first phase of the experiments, best solutions for different system configurations were found using genetic algorithm. In the second phase, the evolved solutions were cross-tested in all system configurations. The research showed that evolved solutions performed well if the agents/objects ratio of the multi-agent system used in testing was similar to the agents/objects ratio of the multi-agent system used in evolution. Additionally, evolved solutions scaled well only on limited intervals that did not span over the critical point, which corresponded to the condition where the number of agents is equal to the number of objects. Typically, solutions that performed well on one side of the critical point performed badly on the other side, suggesting the solutions performances were significantly dependant on agents/objects ratio.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Bird:2007:cec, title={Using Regression to Improve Local Convergence}, author={Stefan Bird and Xiaodong Li }, pages = {592--599}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1555.pdf}, url = {}, size = {}, abstract = {Traditionally Evolutionary Algorithms (EAs) choose candidate solutions based on their individual fitnesses, usually without directly looking for patterns in the fitness landscape discovered. These patterns often contain useful information that could be used to guide the EA to the optimum. While an EA is able to quickly locate the general area of a peak, it can take a considerable amount of time to refine the solution to accurately reflect its true location. We present a new technique that can be used with most EAs. A surface is fitted to the previously-found points using a least squares regression. By calculating the highest point of this surface we can guide the EA to the likely location of the optimum, vastly improving the convergence speed. This technique is tested on Moving Peaks, a commonly used dynamic test function generator. It was able to significantly outperform the current state of the art algorithm.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Kamalian:2007:cec, title={Use of Interactive Evolutionary Computation with Simplified Modeling for Computationally Expensive Layout Design Optimization}, author={Raffi R. Kamalian and Alice M. Agogino and Hideyuki Takagi}, pages = {4124--4129}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1556.pdf}, url = {}, size = {}, abstract = {This paper presents the use of Interactive Evolutionary Computation (IEC) as a method to allow a human user to embed their expert domain knowledge and experience to overcome the deficiencies of Modified Nodal Analysis (MNA)-based EC in a fraction of the time associated with computationally expensive Finite Element Modeler (FEM)-based EC. From our comparison tests for the design of a MEMS resonating mass, results show that MNA-based IEC performance was within 10% of FEM-based EC, yet it performed in only 1/24th of the computation time.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Chuang:2007:cec, title={Integrated Radiation Optimization: Inspired by the Gravitational Radiation in the Curvature of Space-Time}, author={Cheng-Long Chuang and Joe-Air Jiang}, pages = {3157--3164}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1558.pdf}, url = {}, size = {}, abstract = {A novel method for evolutionary optimization, called integrated radiation optimization (IRO), is proposed for solving nonlinear multidimensional optimization problems. Many modern optimization techniques explore the search space by sharing information they have found. In this study, the concept of gravitational radiation in Einstein's theory of general relativity is utilized as a fundamental theory for searching optimal solution in the search space. The idea of developing the algorithm and its detailed procedures are introduced. This work applied the proposed IRO to find the minimum value of a static polynomial function, and some applications that are known to be difficult. The preliminary experimental results show that the performance of the proposed IRO is promising, and IRO shows great performance in solving other NP-hard search and optimization problems.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(:2007:cec, title={On the Role of an Evolutionary Solution for the Brachistochrone-Problem}, author={Markus Borschbach and Winfried Dreckmann}, pages = {2188--2193}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1562.pdf}, url = {}, size = {}, abstract = {An evolutionary solution for the approximation of the Brachistochrone problem is presented by a specific configuration and operator setup of an evolutionary strategy (ES). To evaluate and compare the quality of the solution, an approximation of the theoretical cycloid-solution is determined by an approximation of n points with similar distance. The outperforming quality of the evolutionary determined shape of the approximated curve between a given starting- and end-point is verified by the time an idealized particle needs. All empirical results are carried out in a Monte Carlo simulation study. The dependency of the quality enhancement of the evolutionary solution on the number of approximating points is analyzed. Further the role of the evolutionary approach is discussed to encourage the usage of evolutionary computation for linear approximating polygons of well solved analytical problems.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Feng:2007:cec, title={A Parallel Evolutionary Approach to Multi-objective Optimization}, author={Xiang Feng and Francis C. M. Lau}, pages = {1199--1206}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1563.pdf}, url = {}, size = {}, abstract = {Evolutionary algorithms have been used since the mid-eighties to solve complex single and multi-objective optimization problems. More recently the swarm intelligent approaches such as particle swarm optimization and ant colony optimization have been successfully used for multiobjective optimization. This paper proposes a new approach based on the generic generalized particle model (GE-GPM) for computing in parallel approximate efficient solutions for the distribution problem with multiple objectives. Unlike the swarm optimization approaches, GE-GPM is inspired by physical models of particle dynamics. We use mathematical formulations to describe or predict the properties and evolution of different states of the particles. In particular, according to "differential equation theory", we develop efficient optimization techniques for multi-objective problems. We also adopt methods of classical mechanics to tackle the problem of modeling the interaction among the particles. We show that GE-GPM, being inspired by classical mechanics, enables feasible multi-objective optimization in very large scales. The GE-GPM approach has a low computational complexity, which is crucial for the functioning of large-scale distribution problems.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Ramos:2007:cec, title={Computational Chemotaxis in Ants and Bacteria over Dynamic Environments}, author={Vitorino Ramos and Carlos Fernandes and Agostinho C. Rosa and Ajith Abraham}, pages = {1109--1117}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1565.pdf}, url = {}, size = {}, abstract = {Chemotaxis can be defined as an innate behavioural response by an organism to a directional stimulus, in which bacteria, and other single-cell or multicellular organisms direct their movements according to certain chemicals in their environment. This is important for bacteria to find food (e.g., glucose) by swimming towards the highest concentration of food molecules, or to flee from poisons. Based on self-organized computational approaches and similar stigmergic concepts we derive a novel swarm intelligent algorithm. What strikes from these observations is that both eusocial insects as ant colonies and bacteria have similar natural mechanisms based on stigmergy in order to emerge coherent and sophisticated patterns of global collective behaviour. Keeping in mind the above characteristics we will present a simple model to tackle the collective adaptation of a social swarm based on real ant colony behaviors (SSA algorithm) for tracking extrema in dynamic environments and highly multimodal complex functions described in the well-know DeJong test suite. Then, for the purpose of comparison, a recent model of artificial bacterial foraging (BFOA algorithm) based on similar stigmergic features is described and analyzed. Final results indicate that the SSA collective intelligence is able to cope and quickly adapt to unforeseen situations even when over the same cooperative foraging period, the community is requested to deal with two different and contradictory purposes, while outperforming BFOA in adaptive speed. Results indicate that the present approach deals well in severe Dynamic Optimization problems.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Chuang:2007:cec, title={A Neuro-Fuzzy Inference System to Infer Gene-Gene Interactions Based on Recognition of Microarray Gene Expression Patterns}, author={Cheng-Long Chuang and Chung-Ming Chen and Grace S. Shieh and Joe-Air Jiang}, pages = {904--910}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1568.pdf}, url = {}, size = {}, abstract = {A neuro-fuzzy inference system that recognizes the expression patterns of genes in microarray gene expression (MGE) data, called GeneCFE-ANFIS, is proposed to infer gene interactions. In this study, three primary features are utilized to extract genes' expression patterns and used as inputs to neuro-fuzzy inference system. The proposed algorithm learns expression patterns from the known genetic interactions, such as the interactions confirmed by qRT-PCR experiments or collected through text-mining technique by surveying previously published literatures, and then predicts other gene interactions according to the learned patterns. The proposed neuro-fuzzy inference system was applied to a public yeast MGE data set. Two simulations were conducted and checked against 112 pairs of qRT-PCR confirmed gene interactions and 77 TFs pairs collected from literature respectively to evaluate the performance of the proposed algorithm.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Mittal:2007:cec, title={Three-Dimensional Offline Path Planning for UAVs Using Multiobjective Evolutionary Algorithms}, author={Shashi Mittal and Kalyanmoy Deb}, pages = {3195--3202}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1570.pdf}, url = {}, size = {}, abstract = {In this paper, we present 3D offline path planner for Unmanned Aerial Vehicles (UAVs) using Multiobjective Evolutionary Algorithms for finding solutions corresponding to conflicting goals of minimizing length of path and maximizing margin of safety. In particular, we have chosen the commonly-used NSGA-II algorithm for this purpose. The algorithm generates a curved path which is represented using B-Spline curves. The control points of the B-Spline curve are the decision variables in the genetic algorithm. In particular, we solve two problems, assuming the normal flight envelope restriction: i) Path planning for UAV when no other constraint is assumed to be present and ii) Path planning for UAV if the vehicle has to necessarily pass through a particular point in the space. The use of a multiobjective evolutionary algorithm helps in generating a number of feasible paths with different trade-offs between the objective functions. The availability of a number of trade-off solutions allows the user to choose a path according to his/her needs easily, thereby making the approach more pragmatic. Although an automated decision-making aid is the next immediate need of research, we defer it for another study.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Hu:2007:cec, title={Identification of Structural Parameters Based on PZT Impedance Using Genetic Algorithms}, author={Y. H. Hu and Y. W. Yang and L. Zhang and Y. Lu}, pages = {4170--4177}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1575.pdf}, url = {}, size = {}, abstract = {Electromechanical (EM) impedance method for structural health monitoring (SHM) is based on detecting the changes of the measured signatures of the Lead Zirconate Titanate (PZT) EM admittance (the inverse of the impedance). Although this method has been successfully applied for various engineering structures for damage detection, it is unable to specify the effect of damage on structural properties. The direct indicator of the structural properties is the structural mechanical impedance which can be extracted from the PZT EM admittance signatures. To model the structural impedance, this paper presents a multiple-degrees-of-freedom system consisting of a number of one-degree-of-freedom elements with mass, spring and damper components. Genetic algorithms (GAs) are employed to search for the optimal solution of the unknown dynamic system parameters by minimizing an objective function. Experiment has been carried on a two-storey concrete frame subjected to base vibrations that simulate earthquake. A number of PZT transducers are regularly arrayed and bonded to the frame structure to acquire PZT EM admittance signatures. The changes of the structural parameters in the model system are quantified using GAs. The relation between the distance of the PZT transducer away from the damage and the changes of the structural parameters identified by the PZT transducer is studied. Finally, the sensitivity of the PZT transducers is discussed.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Jie:2007:cec, title={Adaptive Particle Swarm Optimization with PD Controller}, author={Jing Jie and Jianchao Zeng and Chongzhao Han and Youzhi Ren}, pages = {4762--4767}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1576.pdf}, url = {}, size = {}, abstract = {The paper develops an adaptive particle swarm optimization(PSO) based on its simplification. With the hope to prompt its global optimization performance, the improved menthod introduces a PD controller into the architecture of the standard PSO(SPSO-PD). The PD controller can control the particle dynamics, and prompt the particles to respond to the change of their exemplars correctly and rapidly, further to surpass the limit of their exemplars with more chances, which greatly contributes to a successful global search. The proposed SPSO-PD was applied to some well-known benchmarks and compared with the standard PSO. The relative experimental results show SPSO-PD performs better than SPSO on the complex optimization functions.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Tapiador:2007:cec, title={Heuristic Search for Non-Linear Cryptanalytic Approximations}, author={Juan M. E. Tapiador and Julio C. Hernandez-Castro and John A. Clark}, pages = {3561--3568}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1577.pdf}, url = {}, size = {}, abstract = {In this work, we show that heuristic techniques (particularly Simulated Annealing) can be successfully applied in the search of good non-linear approximations of cryptographic primitives. We also provide some experimental results, including two excellent non-linear approximations for the output of the Salsa20 stream cipher with 2 and 4 rounds. From these two approximations, very efficient distinguishers for Salsa20 could easily be obtained, leading to a much more practical attack that any other published so far against this cipher.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Pawar:2007:cec, title={Context-Aware Task Assignment in Ubiquitous Computing Environment - A Genetic Algorithm Based Approach}, author={Pravin Pawar and Hailiang Mei and Ing Widya and Bert-Jan van Beijnum and Aart van Halteren}, pages = {2695--2702}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1578.pdf}, url = {}, size = {}, abstract = {With the advent of ubiquitous computing, a user is surrounded by a variety of devices including tiny sensor nodes, handheld mobile devices and powerful computers as well as diverse communication networks. In this networked society, the role of a human being is evolving from the data consumer to the data producer. In these changing circumstances, pipelined processing finds applications where the data obtained from the human producer needs to be processed and interpreted in real-time. For example, in an MHealth system, the vital signs acquired from the patient are processed in the pipelined fashion. This paper proposes a genetic algorithm (GA) based approach for the optimal assignment of pipelined processing tasks onto a chain of networked devices that minimizes total end-to-end processing delay considering knowledge about the communication and computation resources as the context information. Although some existing graph-based algorithms can solve this problem in polynomial time, we expect that GA can take less computational time and requires less memory while providing a reasonably good assignment. We compare the performance of GA approach with the graph-based approaches. It is observed that when the number of devices and the number of processing tasks are large, the GA approach performs better in terms of the satisfactory quality of the obtained sub-optimal solution considering the advantage of reduced computational time.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Neri:2007:cec, title={Fitness Diversity Based Adaptation in Multimeme Algorithms: A Comparative Study}, author={Ferrante Neri and Ville Tirronen and T. K\"arkk\"ainen and T. Rossi}, pages = {2374--2381}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1582.pdf}, url = {}, size = {}, abstract = {This paper compares three different fitness diversity adaptations in Multimeme Algorithms (MmAs). These diversity indexes have been integrated within a MmA present in literature, namely Fast Adaptive Memetic Algorithm. Numerical results show that it is not possible to establish a superiority of one of these adaptive schemes over the others and choice of a proper adaptation must be made by considering features of the problem under study. More specifically, one of these adaptations outperforms the others in the presence of plateaus or limited range of variability in fitness values, another adaptation is more proper for landscapes having distant and strong basins of attraction, the third one, in spite of its mediocre average performance can occasionally lead to excellent results.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Sohn:2007:cec, title={Training of Multi-Branch Neural Networks using RasID-GA}, author={Dongkyu Sohn and Shingo Mabu and Kaoru Shimada and Kotaro Hirasawa and Jinglu Hu}, pages = {2064--2070}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1584.pdf}, url = {}, size = {}, abstract = {This paper applies a Adaptive Random search with Intensification and Diversification combined with Genetic Algorithm (RasID-GA) to neural network training. In the previous work, we proposed RasID-GA which combines the best properties of RasID and Genetic Algorithm for optimization. Neural networks are widely used in pattern recognition, system modeling, prediction and other areas. Although most neural network training uses gradient based schemes such as wellknown back-propagation (BP), but sometimes BP is easily dropped into local minima. In this paper, we train multi-branch neural networks using RasID-GA with constraint coefficient C by which the feasible solution space is controlled. In addition, we use Mackey-Glass time prediction to test a generalization ability of the proposed method.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Goh:2007:cec, title={Noise-Induced Features in Robust Multi-Objective Optimization Problems}, author={C. K. Goh and K. C. Tan and C. Y. Cheong and Y. S. Ong }, pages = {568--575}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1585.pdf}, url = {}, size = {}, abstract = {Apart from the need to satisfy several competing objectives, many real-world applications are also sensitive to decision or environmental parameter variation which results in large or unacceptable performance variation. While evolutionary optimization techniques have several advantages over operational research methods for robust optimization, it is rarely studied by the evolutionary multi-objective (MO) optimization community. This paper addresses the issue of robust MO optimization by presenting a robust continuous MO test suite with features of noise-induced solution space, fitness landscape and decision space variation. The work presented in this paper should encourage further studies and the development of more effective algorithms for robust MO optimization.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Ramirez:2007:cec, title={An Evolutionary Computation Approach to Cognitive States Classification}, author={Rafael Ramirez and Montserrat Puiggros}, pages = {1793--1799}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1588.pdf}, keywords = {genetic algorithms, genetic programming}, url = {}, size = {}, abstract = {The study of human brain functions has dramatically increased in recent years greatly due to the advent of Functional Magnetic Resonance Imaging. This paper presents a genetic programming approach to the problem of classifying the instantaneous cognitive state of a person based on his/her functional Magnetic Resonance Imaging data. The problem provides a very interesting case study of training classifiers with extremely high dimensional, sparse and noisy data. We apply genetic programming for both feature selection and classifier training. We present a successful case study of induced classifiers which accurately discriminate between cognitive states produced by listening to different auditory stimuli.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Stoean:2007:cec, title={Concerning the Potential of Evolutionary Support Vector Machines}, author={Ruxandra Stoean and Mike Preuss and Catalin Stoean and D. Dumitrescu}, pages = {1436--1443}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1590.pdf}, url = {}, size = {}, abstract = {Within the present paper, we put forward a novel hybridization between support vector machines and evolutionary algorithms. Evolutionary support vector machines consider the classification task as in support vector machines but use an evolutionary algorithm to solve the optimization problem of determining the decision function. They can explicitly acquire the coefficients of the separating hyperplane, which is often not possible within the classical technique. More important, evolutionary support vector machines obtain the coefficients directly from the evolutionary algorithm and can refer them at any point during a run. In addition, they do not require properties of positive (semi-)definition for kernels within nonlinear learning. The concept can be furthermore extended to handle large amounts of data, a problem frequently occurring e.g. in spam mail detection, one of our test cases. An adapted chunking technique is therefore alternatively used. In addition to two different representations, a crowding variant of the evolutionary algorithm is tested in order to investigate whether the performance of the algorithm is maintained; its global search capabilities would be important for the prospected coevolution of non-standard kernels. Evolutionary support vector machines are validated on four real-world classification tasks; obtained results show the promise of this new approach.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Nanayakkara:2007:cec, title={Genetic Algorithm Based Route Planner for Large Urban Street Networks Suranga Chandima Nanayakkara, Dipti Srinivasan, Lai Wei Lup, Xavier German, Elizabeth Taylor}, author={and S. H. Ong}, pages = {4469--4474}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1592.pdf}, url = {}, size = {}, abstract = {Finding the shortest path from a given source to a given destination is a well known and widely applicable problem. Most of the work done in the area have used static route planning algorithms such as A*, Dijkstra's, Bellman-Ford algorithm etc. Although these algorithms are said to be optimum, they are not capable of dealing with certain real life scenarios. For example, most of these single objective optimizations fails to find the equally good solutions when there is more than one optimum (shortest distance path, least congested path). We believe that the Genetic Algorithm (GA) based route planning algorithm proposed in this paper has the ability to tackle the above problems. In this paper, the proposed GA based route planning algorithm is successfully tested on the entire Singapore map with more than 10,000 nodes. Performance of the proposed GA is compared with an ant based path planning algorithm. Simulation results demonstrate the effectiveness of the proposed algorithm over ant based algorithm. Moreover, the proposed GA may be used as a basis for developing an intelligent route planning system.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Avigad:2007:cec, title={A Simultaneous EMO for the Solution of the Multi-Multi-Objective Optimization Problem}, author={Gideon Avigad}, pages = {2117--2124}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1593.pdf}, url = {}, size = {}, abstract = {In this paper the recently introduced multi-Multi- Objective Optimization Problem (m-MOOP) is solved using a novel 'Simultaneous' approach. This is in contrast to both the 'Sequential' approach, which has been introduced previously and to a straightforward solution of the m-MOOP by posing it as a MOOP. The 'Simultaneous' approach is motivated by the need to overcome the apparent deficiencies of the other approaches. The simultaneous EMO algorithm, which is introduced in order to solve the m-MOOP, possesses several new EC related algorithmic features, including a multiproblem individual and a multi-problem sorting procedure. Formerly presented measures together with a newly introduced one, serve for a comparison between the introduced simultaneous approach with both the sequential approach and with a straightforward implementation of an EMO to an m-MOOP, which is posed as a MOOP. The comparison between the different approaches is practiced by using both academic examples as well as an engineering design example.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Ramkumar:2007:cec, title={An Evolutionary Search Heuristic for Solving QAP Formulation in Facility Layout Design}, author={A. S. Ramkumar and S. G. Ponnambalam and N. Jawahar}, pages = {4005--4011}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1594.pdf}, url = {}, size = {}, abstract = {The quadratic assignment problem (QAP) is one of the most challenging combinatorial optimization problems in existence and is known for its diverse applications. In this paper, we propose an evolutionary search heuristic (ESH) with population size equal to two, for solving QAPs and reported its performance on solution quality. The ideas we incorporate in the ESH is iterated self-improvement with evolutionary based perturbation tool, which includes (i) recombination crossover as perturbation tool and (ii) self improvement in mutation operation followed by a local search. Three schemes of ESH are proposed and the obtained solution qualities by the three schemes are compared. We test our algorithm on the benchmark instances of QAPLIB, a well-known library of QAP instances. The performance of proposed recombination crossover with sliding mutation (RCSM) scheme of ESH is well superior to the other two schemes of ESH.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Wang:2007:cec, title={An Experimental Study of the Emergent Behaviors of Self-Organized Agent Communities}, author={Shufeng Wang and Hong Zhu}, pages = {3239--3246}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1596.pdf}, url = {}, size = {}, abstract = {Emergent behavior is an essential feature in multi-agent systems and plays a significant role in the applications of agent technology. Because of the huge gap between individual agents' behaviors and those of the whole system, specifying and reasoning about emergent behaviors are notoriously difficult. Simulation has been the essential method to study emergent behaviors in multi-agent systems. In this paper, we report an experimental study of the emergent behaviors of self-organized agent communities, in which emergent behaviors play a crucial role. The experiments confirmed the results of a theoretical analysis of agent communities using a formal theory called Scenario Calculus. It further provided insight into the dynamic features of the system that were very difficult to obtain by using formal logic, such as the speed of convergence to the emergent states and the relationships between the convergence time and various parameters of self-organized agent communities systems.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Poli:2007:cec, title={A Histogram-Matching Approach to the Evolution of Bin-Packing Strategies}, author={Riccardo Poli and John Woodward and Edmund K. Burke}, pages = {3500--3507}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1597.pdf}, keywords = {genetic algorithms, genetic programming}, url = {}, size = {}, abstract = {We present a novel algorithm for the one-dimension offline bin packing problem with discrete item sizes based on the notion of matching the item-size histogram with the bin-gap histogram. The approach is controlled by a constructive heuristic function which decides how to prioritise items in order to minimise the difference between histograms. We evolve such a function using a form of linear register-based genetic programming system. We test our evolved heuristics and compare them with hand-designed ones, including the wellknown best fit decreasing heuristic. The evolved heuristics are human-competitive, generally being able to outperform highperformance human-designed heuristics.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Sinha:2007:cec, title={Hybrid PSO/Self-Adaptive Improved EP for Economic Dispatch with Nonsmooth Cost Function}, author={Nidul Sinha and Bipul Syam Purkayastha and Biswajit Purkayastha}, pages = {2917--2922}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1598.pdf}, url = {}, size = {}, abstract = {This paper investigates the performance of a hybrid algorithm developed by hybridization between Particle Swarm Optimization (PSO) and self-adaptive improved fast evolutionary programming (IFEP) techniques for solving economic load dispatch (ELD) problem with non-smooth cost curves where conventional gradient based methods are inapplicable. The reported excellent performance of both IFEP and PSO techniques in solving ELD problems has encouraged us in hybridizing them in the effort of enhancing the convergence capability of IFEP with PSO intelligence. The performance of the hybrid algorithm is demonstrated on a power system with 15 units and comparison is drawn in between CEP-PSO' and IFEPPSO' in terms of the solution quality and computational efficiency. The simulation results show that IFEP-PSO' method is more efficient in finding higher quality solutions in nonconvex ELD problems.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Tinós:2007:cec, title={Self-Adaptation of Mutation Distribution in Evolutionary Algorithms}, author={Renato Tin\'os and Shengxiang Yang}, pages = {1390--1397}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1599.pdf}, url = {}, size = {}, abstract = {This paper proposes a self-adaptation method to control not only the mutation strength parameter, but also the mutation distribution for evolutionary algorithms. For this purpose, the isotropic q-Gaussian distribution is employed in the mutation operator. The q-Gaussian distribution allows to control the shape of the distribution by setting a real parameter q and can reproduce either finite second moment distributions or infinite second moment distributions. In the proposed method, the real parameter q of the q-Gaussian distribution is encoded in the chromosome of an individual and is allowed to evolve. An evolutionary programming algorithm with the proposed idea is presented. Experiments were carried out to study the performance of the proposed algorithm.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Davendra:2007:cec, title={Enhanced Differential Evolution Hybrid Scatter Search for Discrete Optimization}, author={Donald Davendra and Godfrey Onwubolu}, pages = {1156--1162}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1600.pdf}, url = {}, size = {}, abstract = {A hybrid approach of the enhanced differential evolution (EDE) and scatter search (SS), termed HEDE-SS, is presented in order to solve discrete domain optimization problems. This approach is envisioned in order to capture the randomization properties of EDE and the memory adaptation programming (MAP) properties of SS. Two highly demanding problems of quadratic assignment problem (QAP) and traveling salesman problem (TSP) are optimized with this new heuristic approach. The hybrid obtains the optimal results for almost all of the QAP instances, compares very well for symmetric TSP by getting results around 98 per cent to the optimal.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Deb:2007:cec, title={Finding Trade-off Solutions Close to KKT Points Using Evolutionary Multi-Objective Optimization}, author={Kalyanmoy Deb and Rahul Tewari and Mayur Dixit and Joydeep Dutta}, pages = {2109--2116}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1601.pdf}, url = {}, size = {}, abstract = {Despite having a wide-spread applicability of evolutionary optimization procedures over the past few decades, EA researchers still face criticism about the theoretical optimality of obtained solutions. In this paper, we address this issue for problems for which gradients of objectives and constraints can be computed either exactly, or numerically or through subdifferentials. We suggest a systematic procedure of analyzing a representative set of Pareto-optimal solutions for their closeness to satisfying Karush-Kuhn-Tucker (KKT) points, which every Pareto-optimal solution must also satisfy. The procedure involves either a least-square solution or an optimum solution to a set of linear system of equations involving Lagrange multipliers. The procedure is applied to a number of differentiable and non-differentiable test problems and to a highly nonlinear engineering design problem. The results clearly show that EAs are capable of finding solutions close to theoretically optimal solutions in various problems. As a by-product, the error metric suggested in this paper can also be used as a termination condition for an EA application. Hopefully, this study will bring EAs and its research closer to classical optimization studies.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Liu:2007:cec, title={Improving the Performance of ICA Based Microarray Data Prediction Models with Genetic Algorithm}, author={Kun-Hong Liu and De-Shuang Huang and Bo Li }, pages = {606--611}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1604.pdf}, url = {}, size = {}, abstract = {It is a challenging task to diagnose tumor type precisely based on microarray data because the number of variables p (genes) is far larger than that of samples, n. Many independent component analysis (ICA) based models had been proposed to tackle the microarray data classification problem with great success. Although it was pointed out that different independent components (ICs) are of different biological significance, up to now, it is still far from well explored for the problem that how to select proper IC subsets to predict new samples best. We try to improve the performance of ICA based classification models by using proper IC subsets instead of all the ICs. A genetic algorithm (GA) based selection process is proposed in this paper, and the selected IC subset is evaluated by the leave-one-out cross validation (LOOCV) technique. The experimental results demonstrate that our GA based IC selection method can further improve the classification accuracy of the ICA based prediction models.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Tsang:2007:cec, title={The Significance of Thermodynamic Models in the Accuracy Improvement of RNA Secondary Structure Prediction Using Permutation-Based Simulated Annealing}, author={Herbert H. Tsang and Kay C. Wiese}, pages = {3879--3885}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1605.pdf}, url = {}, size = {}, abstract = {Ribonucleic acid, a single stranded linear molecule, is essential to all biological systems. Different regions of the same RNA strand will fold together via base pair interactions to make intricate secondary and tertiary structures that guide crucial homeostatic processes in living organisms. Since the structure of RNA molecules is key to their function, algorithms for the prediction of RNA structure are of great value. This paper discusses significant improvements made to SARNAPredict, an RNA secondary structure prediction algorithm based on Simulated Annealing (SA). One major improvement is the incorporation of a sophisticated thermodynamic model (efn2). This model is used by mfold to rank sub-optimal structures, but cannot be used directly by mfold during the structure prediction. Experiments on eight individual known structures from four RNA classes (5S rRNA, Group I intron 23S rRNA, Group I intron 16S rRNA and 16S rRNA) were performed. The data demonstrate the robustness and the effectiveness of our improved prediction algorithm. The new algorithm shows results which surpass the dynamic programming algorithm mfold in terms of prediction accuracy on all tested structures.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Veenhuis:2007:cec, title={Evolutionary Multi-Objective Optimization of Particle Swarm Optimizers}, author={Christian Veenhuis and Mario K\"oppen and Raul Vicente-Garcia}, pages = {2273--2280}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1608.pdf}, url = {}, size = {}, abstract = {One issue in applying Particle Swarm Optimization (PSO) is to find a good working set of parameters. The standard settings often work sufficiently but don't exhaust the possibilities of PSO. Furthermore, a trade-off between accuracy and computation time is of interest for complex evaluation functions. This paper presents results for using an EMO approach to optimize PSO parameters as well as to find a set of trade-offs between mean fitness and swarm size. It is applied to four typical benchmark functions known from literature. The results indicate that using an EMO approach simplifies the decision process of choosing a parameter set for a given problem.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Liu:2007:cec, title={Microarray Data Prediction by Evolutionary Classifier Ensemble System}, author={Kun-Hong Liu and De-Shuang Huang and Jun Zhang}, pages = {634--637}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1610.pdf}, url = {}, size = {}, abstract = {Microarray data prediction is a hard task due to the small sample and high dimension property. This paper proposes a classifier fusion approach to solve this problem based on genetic algorithm (GA). In this fusion strategy, GA is applied to select proper feature subsets and weight value for the fusion of classifiers. The experimental results show that the proposed scheme can improve the prediction accuracy.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Wang:2007:cec, title={Reliability Evaluation of Power-Generating Systems Including Time-Dependent Sources Based on Binary Particle Swarm Optimization}, author={Lingfeng Wang and Chanan Singh and Kay Chen Tan}, pages = {3346--3352}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1613.pdf}, url = {}, size = {}, abstract = {Reliability evaluation of power generation systems using probabilistic methods has drawn much attention due to their capacity to account for system uncertainties. However, because of the large number of possible failure states involved in the power system, it is normally not viable to exhaustively enumerate and evaluate all the states which may contribute to system failure. Meanwhile, time-dependent sources such as wind turbine generators are being more significantly integrated into the traditional power grid for cleaner power generation. The intermittency of wind power sources further complicates the reliability evaluation process. In this paper, a binary particle swarm optimization (BPSO) is adopted to derive a set of meaningful system states, which significantly affects the adequacy indices of generation system including loss of load expectation (LOLE), loss of load frequency (LOLF), and expected energy not supplied (EENS). A numerical example is used to verify the applicability and validity of the proposed population-based intelligent search (PIS) based evaluation procedure. Especially, a comparative study in relation to the exact method and Monte Carlo simulation (MCS) is carried out.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Tan:2007:cec, title={A Cooperative Coevolutionary Algorithm for Multiobjective Particle Swarm Optimization}, author={C. H. Tan and C. K. Goh and K. C. Tan and A. Tay}, pages = {3180--3186}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1616.pdf}, url = {}, size = {}, abstract = {Coevolutionary architectures have been shown to be effective ways to improve the performance of multiobjective (MO) optimization problems. This paper presents a cooperative coevolutionary algorithm for multiobjective particle swarm optimization (COMOPSO), which applies the divide-and-conquer approach to decompose decision vectors into smaller components and evolves multiple solutions in the form of cooperative subswarms. Representatives from each evolving subswarm are combined to form the solution to the whole system. The fitness of each individual is related to its ability to collaborate with individuals from other species, thereby encouraging the development of cooperative strategies. An adaptive niche sharing algorithm is introduced to handle the selection of the niche radius in a dynamic manner. Coupled with the adaptive niche sharing algorithm, COMOPSO demonstrates its effectiveness and efficiency in evolving highly competitive solution sets against various MO algorithms on benchmark problems characterized by different difficulties with consistent results.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Hasan:2007:cec, title={Modified Genetic Algorithm for Job-Shop Scheduling: A Gap-Utilization Technique}, author={S. M. Kamrul Hasan and Ruhul Sarker and David Cornforth}, pages = {3804--3811}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1617.pdf}, url = {}, size = {}, abstract = {The Job-Shop Scheduling Problem (JSSP) is one of the most critical combinatorial optimization problems. The objective of JSSP in this research is to minimize the makespan. In this paper, we propose two Genetic Algorithm (GA) based approaches for solving JSSP. Firstly, we design a simple heuristic to reduce the completion time of jobs on the bottleneck machines that we call the reducing bottleneck technique (RBT). This heuristic was implemented in conjunction with a GA. Secondly; we propose to fill any possible gaps left in the simple GA solutions by the tasks that are scheduled later. We call this process the gap-utilization technique (GUT). With GUT, we also apply a swapping technique that deals only with the bottleneck job. We study 35 test problems with known solutions, using the existing GA and our proposed two algorithms. We obtain optimal solutions for 23 problems, and the solutions are very close for the rest.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Žiška:2007:cec, title={Design of Analog All-Pass Filters with Equal-Ripple Group Delay Frequency Response}, author={Premysl \v{Z} i\v{s}ka and Milo\v{s} Laipert}, pages = {3482--3485}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1622.pdf}, url = {}, size = {}, abstract = {This paper provides an effective new method for design of analog all-pass filters with equal-ripple group delay frequency response. The method is based on combination of the proposed analytical polynomials with a numerical solution by using the Differential Evolution (DE) algorithm. The method is illustrated on practical example.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Nedjah:2007:cec, title={SoC-Based Implementation for Modular Exponentiation Using Evolutionary Addition Chains}, author={Nadia Nedjah and Luiza de Macedo Mourelle}, pages = {3539--3546}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1623.pdf}, url = {}, size = {}, abstract = {Modular exponentiation is an important operation in several public-key cryptosystems. It is performed using successive modular multiplications. For the sake of efficiency, one needs to reduce the total number of required modular multiplications. In this paper, we propose an efficient hardware implementation for computing modular exponentiations using the concept of addition chain. This implementation use an addition chain tailored for the exponent to compute the modular power and evolved by a genetic algorithm. The system-on-chip (SoC) methodology is used to yield a hardware/software co-design of the modular exponentiation that takes advantage of the evolved addition chain. We provide a comparison of the proposed implementation to three existing ones using the performance factor, which takes into account both space and time requirements.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Avigad:2007:cec, title={The Sequential Optimization-Constraint Multi-Objective Problem and its Applications for Robust Planning of Robot Paths}, author={Gideon Avigad and Kalyanmoy Deb}, pages = {2101--2108}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1625.pdf}, url = {}, size = {}, abstract = {In this paper a new approach to search for diverse solutions for a multi-objective problem is presented. Commonly, a search for solutions for a multi-objective problem, which is aimed at optimization, results in a set of Pareto optimal solutions. There are cases where more solutions should be also considered, nonetheless preserving the optimization inspiration. These solutions should not resemble the Pareto set, so as to provide diversity within the design space, and therefore they might not always be found by taking an epsilon-Pareto approach. With this motivation in mind, an already established method, which searches for diverse solutions, which are not all necessarily optimal, is herewith discussed and its shortages are highlighted. In contrast to the already established design method, the approach taken in this paper is to solve the multi-objective problem repeatedly, adding (automatically or interactively) at each run constraints, which are constructed, based on the obtained Pareto set. The motivation for the introduced approach comes from the need to generate a set of robot paths, which allow a mobile robot operator, flexibility in complying with different planning demands and a rapid response to a developing scenario. The methodology and the applicability of the approach are explained and demonstrated by utilizing multi-objective path planning problems.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Leung:2007:cec, title={Consumer Credit Scoring Using an Artificial Immune System Algorithm}, author={Kevin Leung and France Cheong and Christopher Cheong}, pages = {3377--3384}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1627.pdf}, url = {}, size = {}, abstract = {Credit scoring has become a very important task in the credit industry and its use has increased at a phenomenal speed through the mass issue of credit cards since the 1960s. This paper compares the performance of current classifiers against an artificial intelligence technique based on the natural immune system, named simple artificial immune system (SAIS). Experiments were performed on three benchmark credit datasets and SAIS was found to be a very competitive classifier.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Engelbrecht:2007:cec, title={Binary Differential Evolution Strategies}, author={A. P. Engelbrecht and G. Pampar\'a}, pages = {1942--1947}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1628.pdf}, url = {}, size = {}, abstract = {Differential evolution has shown to be a very powerful, yet simple, population-based optimization approach. The nature of its reproduction operator limits its application to continuous-valued search spaces. However, a simple discretization procedure can be used to convert floating-point solution vectors into discrete-valued vectors. This paper considers three approaches in which differential evolution can be used to solve problems with binary-valued parameters. The first approach is based on a homomorphous mapping [1], while the second approach interprets the floating-point solution vector as a vector of probabilities, used to decide on the appropriate binary value. The third approach normalizes solution vectors and then discretize these normalized vectors to form a bitstring. Empirical results are provided to illustrate the efficiency of both methods in comparison with particle swarm optimizers.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Graff:2007:cec, title={Linear Selection}, author={Mario Graff and Riccardo Poli and Alberto Moraglio}, pages = {2598--2605}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1629.pdf}, url = {}, size = {}, abstract = {In most forms of selection, when multiple individuals are needed for an operation, these are drawn independently from the population. So, for example, in the case of crossover, the probability of a particular pair of parents being selected is given by the product of the selection probabilities of each parent. In this paper we investigate a form of selection where pairs of parents are not selected independently. We show that a particular form of dependent selection, linear selection, leads a genetic algorithm with homologous crossover to become very similar to a genetic algorithm with standard (independent) selection and headless chicken crossover, i.e., it turns crossover into a type of mutation. In the paper we analyse this form of selection theoretically, and we compare it to ordinary selection with crossover and headless chicken crossover in real runs.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Cheong:2007:cec, title={A Hierarchical Fuzzy System with High Input Dimensions for Forecasting Foreign Exchange Rates}, author={France Cheong}, pages = {1642--1647}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1633.pdf}, url = {}, size = {}, abstract = {Fuzzy systems suffer from the curse of dimensionality as the number of rules increases exponentially with the number of input dimensions. Although several methods have been proposed for eliminating the combinatorial rule explosion, none of them is fully satisfactory as there are no known fuzzy systems that can handle a large number of inputs so far. In this paper, we describe a method for building fuzzy systems with high input dimensions based on the hierarchical architecture and the MacVicar-Whelan meta-rules. The proposed method is fully automated since a complete fuzzy system is generated from sample input-output data using an Evolutionary Algorithm. We tested the method by building fuzzy systems for two different applications, namely the forecasting of the Mexican and Argentinan pesos exchange rates. In both cases, our approach was successful as both fuzzy systems performed very well.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Li:2007:cec, title={On Performance Metrics and Particle Swarm Methods for Dynamic Multiobjective Optimization Problems}, author={Xiaodong Li and J\"urgen Branke and Michael Kirley }, pages = {576--583}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1635.pdf}, url = {}, size = {}, abstract = {This paper describes two performance measures for measuring an EMO (Evolutionary Multiobjective Optimization) algorithm's ability to track a time-varying Paretofront in a dynamic environment. These measures are evaluated using a dynamic multiobjective test function and a dynamic multiobjective PSO, maximinPSOD, which is capable of handling dynamic multiobjecytive optimization problems. maximinPSOD is an extension from a previously proposed multiobjective PSO, maximinPSO. Our results suggest that these performance measures can be used to provide useful information about how well a dynamic EMO algorithm performs in tracking a time-varying Pareto-front. The results also show that maximinPSOD can be made self-adaptive, tracking effectively the dynamically changing Pareto-front.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Chen:2007:cec, title={Genetic Network Programming with Sarsa Learning and Its Application to Creating Stock Trading Rules}, author={Yan Chen and Shingo Mabu and Kotaro Hirasawa and Jinglu Hu}, pages = {220--227}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1636.pdf}, keywords = {genetic algorithms, genetic programming}, url = {}, size = {}, abstract = {In this paper, trading rules on stock market using the Genetic Network Programming (GNP) with Sarsa learning is described. GNP is an evolutionary computation, which represents its solutions using graph structures and has some useful features inherently. It has been clarified that GNP works well especially in dynamic environments since GNP can create quite compact programs and has an implicit memory function. In this paper, GNP is applied to creating a stock trading model. There are three important points: The first important point is to combine GNP with Sarsa Learning which is one of the reinforcement learning algorithms. Evolution-based methods evolve their programs after task execution because they must calculate fitness values, while reinforcement learning can change programs during task execution, therefore the programs can be created efficiently. The second important point is that GNP uses candlestick chart and selects appropriate technical indices to judge the timing of the buying and selling stocks. The third important point is that sub-nodes are used in each node to determine appropriate actions (buying/selling) and to select appropriate stock price information depending on the situation. In the simulations, the trading model is trained using the stock prices of 16 brands in 2001, 2002 and 2003. Then the generalization ability is tested using the stock prices in 2004. From the simulation results, it is clarified that the trading rules of the proposed method obtain much higher profits than Buy&Hold method and its effectiveness has been confirmed.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Araújo:2007:cec, title={Hybrid Differential Evolutionary System for Financial Time Series Forecasting}, author={Ricardo de A. Ara\'ujo and Germano C. Vasconcelos and Tiago A. E. Ferreira}, pages = {4329--4336}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1637.pdf}, url = {}, size = {}, abstract = {This paper proposes a Hybrid Differential Evolutionary System (HDES) for financial time series forecasting, which performs a differential evolutionary search for the minimum dimension to determining the characteristic phase space that generates the time series phenomenon. It consists of an intelligent hybrid model composed of an Artificial Neural Network (ANN) combined with the Improved Differential Evolution (IDE). The proposed IDE searches for the relevant time lags for a correct time series characterization, the number of processing units in the ANN hidden layer, the ANN training algorithm and the modeling of ANN. Initially, the proposed HDES chooses the most tuned prediction model for time series representation, thus it performs a behavioral statistical test in the attempt to adjust forecast time phase distortions that appear in financial time series. An experimental analysis is conducted with the proposed HDES using two real world financial time series and five well-known performance metrics are used to assess its performance. The obtained results are compared to Time-delay Added Evolutionary Forecasting (TAEF) method.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Zielinski:2007:cec, title={Differential Evolution with Adaptive Parameter Setting for Multi-Objective Optimization}, author={Karin Zielinski and Rainer Laur}, pages = {3585--3592}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1638.pdf}, url = {}, size = {}, abstract = {Control parameter settings influence the convergence probability and convergence speed of evolutionary algorithms but it is often not obvious how to choose them. In this work an adaptive approach for setting the control parameters of a multi-objective Differential Evolution algorithm is presented. The adaptive approach is based on methods from Design of Experiments, so it is able to detect significant performance differences of individual parameters as well as interaction effects between parameters. It is evaluated based on 13 test functions and several performance measures.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Yamachi:2007:cec, title={A Solution Method Employing a Multi-Objective Genetic Algorithm to Search for Pareto Solutions of Series-Parallel System Component Allocation Problem}, author={Hidemi Yamachi and Hisashi Yamamoto and Yasuhiro Tsujimura and Yasushi Kambayashi}, pages = {3058--3064}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1641.pdf}, url = {}, size = {}, abstract = {We discuss the optimal system component allocation problem for series-parallel systems with interchangeable elements. A series-parallel system consists of subsystems that are connected in series and each subsystem consists of components in parallel. There are some heuristic methods to obtain quasi optimal solutions for the component allocation problem of series-parallel systems. Because this problem is one of the NP-complete problems, it is difficult to obtain the exact solutions for large scale problems. We had formulated this problem as a multi-objective optimization problem minimizing the system cost and maximizing the system reliability, and proposed an algorithm that obtains the exact solutions of the problems in an efficient way. The algorithm utilized the depth-first search method to eliminate useless searches and employs the branch-and-bound method to obtain the Pareto solutions. According to the results of our numerical experiments, the algorithm searches the Pareto solutions in practical time for not so large problems. In order to solve larger problems, in this paper, we propose a Multi-Objective Genetic Algorithm (MOGA). In comparison with the exact solution method we had proposed and the MOGA method, we assure the MOGA method produces the best compromised solutions and the later can solve large scale problems. We have conducted experiments for reasonably large scale problems and have demonstrated reasonably good performance of our method.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Cheong:2007:cec, title={A Multi-Objective Evolutionary Algorithm for Berth Allocation in a Container Port}, author={C. Y. Cheong and C. J. Lin and K. C. Tan and D. K. Liu}, pages = {927--934}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1644.pdf}, url = {}, size = {}, abstract = {This paper considers a berth allocation problem (BAP) which requires the determination of exact berthing times and positions of incoming ships in a container port. The problem is solved by optimizing the berth schedule so as to minimize concurrently the three objectives of make span, number of crossings, and waiting time. These objectives represent the interests of both port and ship operators. A multi-objective evolutionary algorithm (MOEA) that incorporates the concept of Pareto optimality is proposed for solving the multi-objective BAP. The MOEA is equipped with a novel solution decoding scheme which is specifically designed to optimize the use of berth space. The MOEA is also able to function in a dynamic context which is of more relevance to a real-world situation.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Mehnen:2007:cec, title={Introducing User Preference Using Desirability Functions in Multi-Objective Evolutionary Optimisation of Noisy Processes}, author={J\"orn Mehnen and Heike Trautmann and Ashutosh Tiwari}, pages = {2687--2694}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1645.pdf}, url = {}, size = {}, abstract = {Multi-Objective Evolutionary Algorithms (MOEAs) are generally designed to find a well spread Pareto-front approximation. Often, only a small section of this front may be of practical interest. Desirability Functions (DFs) are able to describe user preferences intuitively. Furthermore, DFs can be attached to any fitness function easily. This way, desirability functions can help in guiding MOEAs without introducing additional restrictions or changes to the algorithm. The application of noisy fitness functions is not straight forward but relevant to many real-world problems. Therefore, a variant of Harrington's one-sided desirability function using expectations is introduced which takes noise into account. A deterministic strategy as well as the NSGA-II are used in combination with DF to solve a noisy Binh problem and a noisy cost estimation problem for turning processes.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Sarker:2007:cec, title={An Evolutionary Algorithm for Machine Layout and Job Assignment Problems}, author={Ruhul Sarker and Tapabrata Ray and Jos\'e Barahona da Fonseca}, pages = {3991--3997}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1647.pdf}, url = {}, size = {}, abstract = {Machine layout and material flow between machines are crucial considerations for improving productivity in any manufacturing environment. The machine layout and the operations assignment problems are both known to be NP hard problems. In this paper, we introduce a new combined machine layout and operations assignment problem. We propose an evolutionary algorithm to solve the combined machine layout and operations assignment problem. The effectiveness of our approach is demonstrated through numerical examples.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Gomez:2007:cec, title={Using Evolution Strategies to Perform Stellar Population Synthesis for Galaxy Spectra from SDSS}, author={J. C. Gomez and O. Fuentes}, pages = {1808--1814}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1648.pdf}, url = {}, size = {}, abstract = {Current surveys from modern astronomical observatories contain a huge amount of data; in particular, the Sloan Digital Sky Survey (SDSS) has reached the order of terabytes of data in images and spectra. Such an amount of information needs to be exploited by sophisticated algorithms that automatically analyze the data in order to extract useful knowledge from the mega databases. In this work we employ Evolution Strategies (ES) to automatically extract a set of physical parameters corresponding to stellar population synthesis (ages, metallicities, reddening and relative contributions) from a sample of galaxy spectra taken from SDSS. Such parameters are useful in cosmological studies and for understanding galaxy formation, composition, and evolution. We pose this parameter extraction as an optimization problem and then solve it using ES. The idea is to reconstruct each galaxy spectrum from the sample by means of a linear combination of three similar theoretical models, each contributing in a different way to the stellar population synthesis. This linear combination produces a model spectrum that is compared with the original spectrum using a simple difference function. The goal is to find a model that minimizes this difference, using ES as the algorithm to explore the parameter space. We present experimental results using a set of 100 spectra from SDSS Data Release 2 that show that ES are very well suited to extract stellar population parameters from galaxy spectra.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Gong:2007:cec, title={Solving Multidimensional Knapsack Problems by an Immune-Inspired Algorithm}, author={Maoguo Gong and Licheng Jiao and Wenping Ma and Shuiping Gou}, pages = {3385--3391}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1652.pdf}, url = {}, size = {}, abstract = {This paper introduces a computational model simulating the dynamic process of human immune response to solve multidimensional knapsack problems. The new model is a quaternion (G, I, R, Al), where G denotes exterior stimulus or antigen, I denotes the set of valid antibodies, R denotes the set of reaction rules describing the interactions between antibodies, and Al denotes the dynamic algorithm describing how the reaction rules are applied to antibody population. The set of antibody-adjusting rules, the set of clonal selection rules, and a dynamic algorithm, named MKP-PAISA, are designed for solving multidimensional knapsack problems. The efficiency of the proposed algorithm was validated by testing on 57 benchmark problems and comparing with three genetic algorithms. The results indicated that the proposed algorithm was suitable for solving multidimensional knapsack problems.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Zielinski:2007:cec, title={Adaptive Parameter Setting for a Multi-Objective Particle Swarm Optimization Algorithm}, author={Karin Zielinski and Rainer Laur}, pages = {3019--3026}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1654.pdf}, url = {}, size = {}, abstract = {To avoid the effort associated with choosing control parameter settings, an adaptive approach for parameter setting of a multi-objective Particle Swarm Optimization algorithm is presented in this work. The adaptive parameter control relies on methods from Design of Experiments which are able to detect significant performance variations of parameter settings. Furthermore, interaction effects of different parameters can be discovered. The adaptive control is applied to the parameters which are incorporated in the update equations of PSO, so the movement of particles is adapted based on feedback about successes during the search. The adaptive approach is evaluated using 13 test functions and several performance measures.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Ishibuchi:2007:cec, title={Iterative Approach to Indicator-Based Multiobjective Optimization}, author={Hisao Ishibuchi and Noritaka Tsukamoto and Yusuke Nojima}, pages = {3967--3974}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1655.pdf}, url = {}, size = {}, abstract = {An emerging trend in the design of evolutionary multiobjective optimization algorithms is to directly optimize a quality indicator of non-dominated solution sets such as the hypervolume measure. Some algorithms have been proposed to search for a set of a pre-specified number of non-dominated solutions that maximizes the given quality indicator. In this paper, we propose an iterative approach to indicator-based evolutionary multiobjective optimization. The main feature of our approach is that only a single solution is obtained by its single run. Thus multiple runs are needed to find a solution set. In each run, our approach searches for a solution with the maximum contribution to the hypervolume of the solution set obtained by its previous runs. We discuss several issues related to the implementation of such an iterative approach.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Gong:2007:cec, title={Solving Multiobjective Clustering Using an Immune-Inspired Algorithm}, author={Maoguo Gong and Lining Zhang and Licheng Jiao and Shuiping Gou}, pages = {15--22}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1656.pdf}, url = {}, size = {}, abstract = {In this study, we introduced a novel multiobjective optimization algorithm, Nondominated Neighbor Immune Algorithm (NNIA), to solve the muliobjective clustering problems. NNIA solves multiobjective optimization problems by using a nondominated neighbor-based selection technique, an immune inspired operator, two heuristic search operators and elitism. The main novelty of NNIA is that the selection technique only selects minority isolated nondominated individuals in current population to clone proportionally to the crowding-distance values, recombine and mutate. As a result, NNIA pays more attention to the less-crowded regions in the current trade-off front. The experimental results on seven artificial data sets with different manifold structure and six real-world data sets show that the NNIA is an effective algorithm for solving multiobjective clustering problems, and the NNIA based multiobjective clustering technique is a cogent unsupervised learning method.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Ibrahim:2007:cec, title={A DNA Sequence Design for Molecular Computation of HPP with Output Visualization Based on Real-Time PCR}, author={Zuwairie Ibrahim and Tri Basuki Kurniawan and Marzuki Khalid}, pages = {1823--1828}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1657.pdf}, url = {}, size = {}, abstract = {Molecular computing has proved its possibility to solve weighted graph problem such as Hamiltonian Path Problem (HPP), Traveling Salesman Problem (TSP) and the Shortest Path Problem (SPP). Normally, in molecular computation, the DNA sequences used for the computation should be critically designed in order to reduce error that could occur during computation. In the previous paper, we have proposed a readout method tailored specifically to HPP in DNA Computing using real-time PCR for output visualization. Six nodes of HPP was considered. Based on the example instance, the method requires 11 oligonucleotides, where 6 oligonucleotides are for the nodes and 5 oligonucleotides are for the edges. Three TaqMan probes and five primers are required as well. In this study, a procedure for DNA sequence design is presented in order to obtain good sequences for those nodes, primers, and probes. The experiment is done based on the generated DNA sequences and the Hamiltonian Path can be determined successfully.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Walker:2007:cec, title={Success Effort and Other Statistics for Performance Comparisons in Genetic Programming}, author={Matthew Walker and Howard Edwards and Chris Messom}, pages = {4631--4638}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1658.pdf}, keywords = {genetic algorithms, genetic programming}, url = {}, size = {}, abstract = {This paper looks at the statistics used to compare variations to the genetic programming method. Previous work in this area has been dominated by the use of mean best-of-run fitness and Koza's minimum computational effort. This article re-introduces a statistic we name success effort and analyses two methods to produce condence intervals for the statistic. We then compare success effort and four other performance measures and conclude that success effort is a sometimes more powerful statistic than computational effort and a more desirable measure than the other statistics.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Lima:2007:cec, title={Influence of Selection and Replacement Strategies on Linkage Learning in BOA Claudio F. Lima, Martin Pelikan, David E. Goldberg, Fernando G. Lobo, Kumara Sastry and}, author={Mark Hauschild}, pages = {1083--1090}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1661.pdf}, url = {}, size = {}, abstract = {The Bayesian optimization algorithm (BOA) uses Bayesian networks to learn linkages between the decision variables of an optimization problem. This paper studies the influence of different selection and replacement methods on the accuracy of linkage learning in BOA. Results on concatenated m-k deceptive trap functions show that the model accuracy depends on a large extent on the choice of selection method and to a lesser extent on the replacement strategy used. Specifically, it is shown that linkage learning in BOA is more accurate with truncation selection than with tournament selection. The choice of replacement strategy is important when tournament selection is used, but it is not relevant when using truncation selection. On the other hand, if performance is our main concern, tournament selection and restricted tournament replacement should be preferred. These results aim to provide practitioners with useful information about the best way to tune BOA with respect to structural model accuracy and overall performance.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Kishor:2007:cec, title={Particle Swarm Optimization Based Neural-Network Model for Hydro Power Plant Dynamics}, author={Nand Kishor and Madhusudan Singh and A. S. Raghuvanshi}, pages = {2725--2731}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1663.pdf}, url = {}, size = {}, abstract = {This paper addresses the modeling of hydro power plant dynamics using neural network approach. The cost function as root mean square error is optimized by particle swarm optimization technique. The identification performance is compared with fuzzy models based on GK clustering algorithm in application to study hydro power plant dynamics. It is found that the response obtained from the NN model is comparable to those determined by fuzzy model with much significance to nature of input-output variables used for modeling.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Wijesinghe:2007:cec, title={Using Restricted Loops in Genetic Programming for Image Classification}, author={Gayan Wijesinghe and Vic Ciesielski}, pages = {4569--4576}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1664.pdf}, url = {}, size = {}, abstract = {Loops are rarely used in genetic programming due to issues such as detecting infinite loops and invalid programs. In this paper we present a restricted form of loops that is specifically designed to be evolved in image classifiers. Particularly, we evolve classifiers that use these loops to perform calculations on image regions chosen by the loops. We have compared this method to another classification method that only uses individual pixels in its calculations. These two methods are tested on two synthesised and one non-synthesised greyscale image classification problems of varying difficulty. The most difficult problem requires determining heads or tails of 320×320 pixel images of a US one cent coin at any angle. On this problem, the accuracy of the loops approach was 97.80% in contrast to the no-loop method accuracy of 79.46%. Use of loops also reduces overfitting of training data. We also found that loop methods overfit less when only a few training examples are available.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(:2007:cec, title={Enhancing the NichePSO}, author={A. P. Engelbrech and L. N. H. van Loggerenberg}, pages = {2297--2302}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1665.pdf}, url = {}, size = {}, abstract = {The NichePSO was developed as one of the first particle swarm optimization (PSO) approaches to locate multiple solutions to continuous optimization problems. The NichePSO forms subswarms from a main swarm, where each subswarm represents a single niche (or solution). Mechanisms are employed to merge subswarms if they converge to the same solution, and also to absorb any particle within a subswarm if that particle enters the area covered by the subswarm. The NichePSO has shown very good performance in locating a good number of solutions to multimodal problems. However, it was found that the current subswarm merging and particle absorption strategies are premature, and limits exploration in the main swarm. This paper proposes a number of different merging and absorption strategies, and shows that fine tuning of these processes improves the performance of NichePSO on lower dimensional problems.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(González:2007:cec, title={Analyzing Fault Tolerance on Parallel Genetic Programming by Means of Dynamic-Size Populations}, author={Daniel Lombra\~ua Gonz\'alez and Francisco Fern\'andez de Vega}, pages = {4392--4398}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1666.pdf}, url = {}, size = {}, abstract = {This paper presents an experimental research on the size of individuals when dynamic size populations are employed with Genetic Programming (GP). By analyzing the individual's size evolution, some ideas are presented for reducing the length of the best individual while also improving the quality. This research has been performed studying both individual's size and quality of solutions, considering the fixed-size populations and also dynamic size by means of the plague operator. We propose an improvement to the Plague operator, that we have called Random Plague, that positively affects the quality of solutions and also influences the individuals' size. The results are then considered from a quite different point of view, the presence of processors failures when parallel execution over distributed computing environments are employed. We show that results strongly encourage the use of Parallel GP on non fault-tolerant computing resources: experiments shows the fault tolerant nature of Parallel GP.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Burke:2007:cec, title={The Scalability of Evolved on Line Bin Packing Heuristics}, author={E. K. Burke and M. R. Hyde and G. Kendall and J. R. Woodward}, pages = {2530--2537}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1668.pdf}, keywords = {genetic algorithms, genetic programming}, url = {}, size = {}, abstract = {The on line bin packing problem concerns the packing of pieces into the least number of bins possible, as the pieces arrive in a sequential fashion. In previous work, we used genetic programming to evolve heuristics for this problem, which beat the human designed 'bestfit' algorithm. Here we examine the performance of the evolved heuristics on larger instances of the problem, which contain many more pieces than the problem instances used in training. In previous work, we concluded that we could confidently apply our heuristics to new instances of the same class of problem. Now we can make the additional claim that we can confidently apply our heuristics to problems of much larger size, not only without deterioration of solution quality, but also within a constant factor of the performance obtained by 'best fit'. Interestingly, our evolved heuristics respond to the number of pieces in a problem instance although they have no explicit access to that information. We also comment on the important point that, when solutions are explicitly constructed for single problem instances, the size of the search space explodes. How- ever, when working in the space of algorithmic heuristics, the distribution of functions represented in the search space reaches some limiting distribution and therefore the combinatorial explosion can be controlled.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Lukasiewycz:2007:cec, title={SAT-Decoding in Evolutionary Algorithms for Discrete Constrained Optimization Problems}, author={Martin Lukasiewycz and Michael Glaß and Christian Haubelt and J\"urgen Teich}, pages = {935--942}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1669.pdf}, url = {}, size = {}, abstract = {For complex optimization problems, several population-based heuristics like Multi-Objective Evolutionary Algorithms have been developed. These algorithms are aiming to deliver sufficiently good solutions in an acceptable time. However, for discrete problems that are restricted by several constraints it is mostly a hard problem to even find a single feasible solution. In these cases, the optimization heuristics typically perform poorly as they mainly focus on searching feasible solutions rather than optimizing the objectives.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Parent:2007:cec, title={Grid-Based Evolutionary Strategies Applied to the Conformational Sampling Problem Benjamin Parent, Alexandru Tantar, Nouredine Melab, El-Ghazali Talbi and}, author={Dragos Horvath}, pages = {291--296}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1671.pdf}, url = {}, size = {}, abstract = {Computational simulations of conformational sampling in general, and of macromolecular folding in particular represent one of the most important and yet one of the most challenging applications of computer science in biology and medicinal chemistry. The advent of GRID computing may trigger some major progress in this field. This paper presents our first attempts to design GRID-based conformational sampling strategies, exploring the extremely rugged energy response surface in function of molecular geometry, in search of low energy zones through phase spaces of hundreds of degrees of freedom. We have generalized the classical island model deployment of Genetic Algorithms (GA) to a "planetary" model where each node of the grid is assimilated to a "planet" harboring quasi-independent multi-island simulations based on a hybrid GA-driven sampling approach. Although different "planets" do not communicate to each other - thus minimizing inter-CPU exchanges on the GRID - each new simulation will benefit from the preliminary knowledge extracted from the centralized pool of already visited geometries, located on the dispatcher machine, and which is disseminated to any new "planet". This "panspermic" strategy allows new simulations to be conducted such as to either be attracted towards an apparently promising phase space zone (biasing strategies, intensification procedures) or to avoid already in-depth sampled (tabu) areas. Successful folding of mini-proteins typically used in benchmarks for allatoms protein simulations has been observed, although the reproducibility of these highly stochastic simulations in huge problem spaces is still in need of improvement. Work on two structured peptides (the "tryptophane cage" 1L2Y and the "tryptophane zipper" 1LE1) used as benchmarks for all-atom protein folding simulations has shown that the planetary model is able to reproducibly sample conformers from the neighborhood of the native geometries. However, within these neighborhoods (within ensembles of conformers similar to models published on hand of experimental geometry determinations), the energy landscapes are still extremely rugged. Therefore, simulations in general produce "correct" geometries (similar enough to experimental model for any practical purposes) which sometimes unfortunately correspond to relatively high energy levels and therefore are less stable than the most stable among misfolded conformers. The method thus reproducibly visits the native phase space zone, but fails to reproducibly hit the bottom of its rugged energy well. Intensifications of local sampling may in principle solve this problematic behavior, but is limited by computational ressources. The quest for the optimal time point at which a phase space zone should stop being intensively searched and declared tabu, a very difficult problem, is still awaiting for a practically useful solution.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Leguizamón:2007:cec, title={A Boundary Search based ACO Algorithm Coupled with Stochastic Ranking}, author={Guillermo Leguizam\'on and Carlos A. Coello Coello}, pages = {165--172}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1672.pdf}, url = {}, size = {}, abstract = {In this paper we present a boundary search based ACO algorithm for solving nonlinear constrained optimization problems. The aim of this work is twofold. Firstly, we present a modified search engine which implements a boundary search approach based on a recently proposed ACO metaheuristic for continuos problems. Secondly, we propose the incorporation of the stochastic ranking technique to deal with feasible and infeasible solutions during the search which focuses on the boundary region. In our experimental study we compare the overall performance of the proposed ACO algorithm by including two different complementary constraint-handling techniques: a penalty function and stochastic ranking. In addition, we include in our comparison of results the Stochastic Ranking algorithm, which was originally implemented using an Evolution Strategy as its search engine.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Nitschke:2007:cec, title={Emergent Specialization in the Extended Multi-Rover Problem}, author={G. S. Nitschke and M. C. Schut and A. E. Eiben}, pages = {3410--3417}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1673.pdf}, url = {}, size = {}, abstract = {This paper introduces the Collective Neuro Evolution (CONE) method, and compares its efficacy for designing specialization, with a conventional Neuro-Evolution (NE) method. Specialization was defined at both the individual agent, and at the agent group level. The CONE method was tested comparatively with the conventional NE method in an extension of the multirover task domain, where specialization exhibited at both the individual and group level is known to benefit task performance. In the multi-rover domain, the task was for many agents (rovers) to maximize the detection and evaluation of points of interest in a simulated environment, and to communicate gathered information to a base station. The goal of the rover group was to maximize a global evaluation function that measured performance (fitness) of the group. Results indicate that the CONE method was appropriate for facilitating specialization at both the individual and agent group levels, where as, the conventional NE method succeeded only in facilitating individual specialization. As a consequence of emergent specialization derived at both the individual and group levels, rover groups evolved by the CONE method were able to achieve a significantly higher task performance, comparative to groups evolved by the conventional NE method.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Bernardino:2007:cec, title={A Hybrid Genetic Algorithm for Constrained Optimization Problems in Mechanical Engineering}, author={H. S. Bernardino and H. J. C. Barbosa and A. C. C. Lemonge}, pages = {646--653}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1674.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 = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(:2007:cec, title={Adaptive Farming Strategies for Dynamic Economic Environment}, author={Nanlin Jin and Mette Termansen and Klaus Hubacek and Joseph Holden and Mike Kirkby}, pages = {1213--1220}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1675.pdf}, url = {}, size = {}, abstract = {This paper aims to forecast the economic impacts of changing land-use in UK uplands. We assume that farmers adaptively learn and respond to a dynamic economic environment. The main research approach is the use of evolutionary algorithms for dynamic optimization. We use this approach to study how the changes of agricultural subsidy policy (CAP reform) affect farmers' land-use decisions. We compare the experimental results from our simulated evolution versus the predictions made by agricultural experts. We have found that evolutionary algorithms for dynamic optimization forecast farmers' land-use decision in line with experts' predictions. This study also shows that maintenance of the diversity of the solution set is important for evolutionary algorithms to continuously track dynamic optimums. This work provides a framework to integrate other natural, social and economic factors in future.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Lucas:2007:cec, title={A Statistically Aligned Recombination Operator for Finite State Machines}, author={Simon M. Lucas}, pages = {4554--4560}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1676.pdf}, url = {}, size = {}, abstract = {Learning finite state machines from samples of data has been extensively studied within machine learning and since the dawn of evolutionary computation. Conventional crossover or recombination operators used for finite state machines suffer from the competing conventions problem, caused by the combinatorial number of isomorphisms of each distinct machine. This paper introduces an efficient alignment operator to counteract this phenomenon. Results show that when in the neighbourhood of the target machine, the aligned crossover operator reaches the optimum in far few steps (on average) than either a naive crossover operator or a standard flip-style mutation operator.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Emmendorfer:2007:cec, title={An Empirical Evaluation of Linkage Learning Strategies for Multimodel Optimization}, author={L. R. Emmendorfer and A. T. R. Pozo}, pages = {326--333}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1677.pdf}, url = {}, size = {}, abstract = {Diversity presentation has shown to be very important for allowing the identification of the problem structure as much as for keeping several global optima during the process of evoluationary computation. The most important evolutionary algorithms currently available in the literature adopt diversity preservation technique as supporting tools in the process, while they trust on more sophisticated models for the identification of the problem structure. This work evaluates a novel approach where a clustering algorithm plays a central role in the evoluationary process beyond maintaining the diversity. Empirical evoluation and comparison show the effectiveness of this new approach when solving multimodal optimization problems.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Chen:2007:cec, title={Multi-Objective Optimisation on Motorised Momentum Exchange Tether for Payload Orbital Transfer}, author={Yi Chen and Matthew P. Cartmell}, pages = {987--993}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1679.pdf}, url = {}, size = {}, abstract = {The symmetrical motorised momentum exchange tether, is intended to be excited by a continuous torque, so that, it can be applied as an orbital transfer system. The motor drive accelerates the tether, and increases the relative velocity of payloads fitted to each end. In order to access better tether performance, a higher efficiency index needs to be achieved. Meanwhile, the stress in each tether sub-span should stay within the stress limitations. The multi-objective optimisation methods of Genetic Algorithms can be applied for tether performance enhancement. The tether's efficiency index and stress are used as multi-objectives, and the analysis of the resulting Pareto front suggests a set of solutions for the parameters of the motorised momentum exchange tether when used for payload transfer, in order to achieve relative high transfer performance, and safe tether strength.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Xie:2007:cec, title={Genetic Programming for New Zealand CPI Inflation Prediction}, author={Huayang Xie and Mengjie Zhang and Peter Andreae}, pages = {2538--2545}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1682.pdf}, keywords = {genetic algorithms, genetic programming}, url = {}, size = {}, abstract = {Reserve Bank of New Zealand (RBNZ) is one of many inflation-targeting central banks. The effective conduct of monetary policy requires the capacity to make accurate short and medium term predictions about price inflation. The RBNZ's prediction system is very complex, requiring many iterations and significant input from human experts. This paper investigates the capability of Genetic Programming (GP) to predict price inflation over short and medium terms. By using un-preprocessed economic time series over small intervals, the experimental results demonstrate that GP can produce predictions of price inflation with accuracy comparable to the RBNZ's official prediction system, over both short and medium terms.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Kaji:2007:cec, title={Acceleration of Experiment-Based Evolutionary Multi-objective Optimization of Internal-Combustion Engine Controllers Using Fitness Estimation}, author={Hirotaka Kaji and Hajime Kita}, pages = {1777--1784}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1683.pdf}, url = {}, size = {}, abstract = {Control parameters of automotive engine have to be adjusted adequately and simultaneously to achieve plural criteria such as environmental emissions, fuel-consumption and engine torque. Evolutionary Multi-objective Optimization (EMO) is expected to be a powerful optimization framework for these engineering designs. Additionally, a smart environment called Hardware In the Loop Simulation (HILS) has recently become available for the engine calibration. To make Experiment-Based EMO (EBEMO) using the HILS environment feasible, the most important pre-requisite is reduction of the number of necessary fitness evaluations. In this paper, we apply an acceleration method using fitness estimation to overcome the aforementioned problem for EBEMO of real internal-combustion engines, and the effectiveness of our proposal is examined through real engine experiments.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Mabu:2007:cec, title={Stock Trading Rules Using Genetic Network Programming with Actor-Critic}, author={Shingo Mabu and Yan Chen and Kotaro Hirasawa and Jinglu Hu }, pages = {508--515}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1684.pdf}, keywords = {genetic algorithms, genetic programming}, url = {}, size = {}, abstract = {Genetic Network Programming (GNP) is an evolu- tionary computation which represents its solutions using graph structures. Since GNP can create quite compact programs and has an implicit memory function, it has been clarified that GNP works well especially in dynamic environments. In this paper, GNP is applied to creating a stock trading model. The first important point is to combine GNP with Actor-Critic which is one of the reinforcement learning algorithms. Evolution-based methods evolve their programs after task execution because they must calculate fitness values, while reinforcement learning can change programs during task execution, therefore the programs can be created efficiently. The second important point is that GNP with Actor-Critic (GNP-AC) can select appropriate technical indexes to judge the buying and selling timing of stocks using Importance Index especially designed for stock trading decision making. In the simulations, the trading model is trained using the stock prices of 20 brands in 2001, 2002 and 2003. Then the generalization ability is tested using the stock prices in 2004. From the simulation results, it is clarified that the trading rules of GNP-AC obtain higher profits than Buy&Hold method.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Ullah:2007:cec, title={An Agent-Based Memetic Algorithm (AMA) for Solving Constrained Optimization Problems}, author={Abu S. S. M. Barkat Ullah and Ruhul Sarker and David Cornforth and Chris Lokan}, pages = {999--1006}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1685.pdf}, url = {}, size = {}, abstract = {In recent years, memetic algorithms (MAs) have been proposed to enhance the performance of evolutionary algorithms by incorporating local search techniques with evolutionary algorithms' global search ability, and applied successfully to solve different type of optimization problems. This paper proposes a new memetic algorithm and then introduces an agent-based memetic algorithm (AMA), for the first time, to further enhance the ability of MA in solving constrained optimization problems. In a lattice-like environment, each of the agents represents a candidate solution of the problem. The agents are able to sense and act on the society, and their performances i.e. fitness of the solution improves through co-evolutionary adaptation of society with the individual learning of the agents. The proposed algorithm is tested on 13 benchmark problems and the experimental results show promising performance.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Bourmistrova:2007:cec, title={Control System Design Optimisation via Genetic Programming}, author={A. Bourmistrova and S. Khantsis}, pages = {1993--2000}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1691.pdf}, keywords = {genetic algorithms, genetic programming}, url = {}, size = {}, abstract = {This paper describes a stochastic approach for comprehensive diagnostics and validation of control system architecture for Unmanned Aerial Vehicle (UAV). Mathematically based diagnostics of a 6 DoF system provides capability for a complex evaluation of system components behaviour, but are typically both memory and computationally expensive. Design and optimisation of the flight controllers is a demanding task which usually requires deep engineering knowledge of intrinsic aircraft behaviour. Evolutionary Algorithms (EAs) are known for their robustness for a wide range of optimising functions, when no a priori knowledge of the search space is available. Thus it makes evolutionary approach a promising technique to design the task controllers for complex dynamic systems such as an aircraft. In this study, EAs are used to design a controller for recovery (landing) of a small fixed-wing UAV on a frigate ship deck. The control laws are encoded in a way common for Evolutionary Programming. However, parameters (numeric coefficients in the control equations) are optimised independently using effective Evaluation Strategies, while structural changes occur at a slower rate. The fitness evaluation is made via test runs on a comprehensive 6 degree-of-freedom non-linear UAV model. The need of a well defined approach to the control system validation is dictated by the nature of UAV application, where the major source of mission success is based on autonomous control system architecture reliability. The results show that an effective controller can be designed with little knowledge of the aircraft dynamics using appropriate evolutionary techniques. An evolved controller is evaluated and a set of reliable algorithm parameters is validated.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Hasegawa:2007:cec, title={Estimation of Distribution Algorithm Based on Probabilistic Grammar with Latent Annotations}, author={Yoshihiko Hasegawa and Hitoshi Iba}, pages = {1043--1050}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1692.pdf}, keywords = {genetic algorithms, genetic programming}, url = {}, size = {}, abstract = {Genetic Programming (GP) which mimics the natural evolution to optimize functions and programs, has been applied to many problems. In recent years, evolutionary algorithms are seen from the viewpoint of the estimation of distribution. Many algorithms called EDAs (Estimation of Distribution Algorithms) based on probabilistic techniques have been proposed. Although probabilistic context free grammar (PCFG) is often used for the function and program evolution, it assumes the independence among the production rules. With this simple PCFG, it is not able to induce the building-blocks from promising solutions. We have proposed a new function evolution algorithm based on PCFG using latent annotations which weaken the independence assumption. Computational experiments on two subjects (the royal tree problem and the DMAX problem) demonstrate that our new approach is highly effective compared to prior approaches.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Tai:2007:cec, title={An Enhanced Chromosome Encoding and Morphological Representation of Geometry for Structural Topology Optimization Using GA}, author={K. Tai and N. Wang}, pages = {4178--4185}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1694.pdf}, url = {}, size = {}, abstract = {The structural topology optimization approach can be used to generate the structural design for some desired input-output (force-deflection) requirements. Optimization methods based on genetic algorithms (GA) have recently been demonstrated to have the potential for overcoming the problems associated with gradient-based methods. The success of the GA depends, to a large extent, on the structural geometry representation scheme used. In this work, some enhancements are incorporated into the recently developed morphological geometric representation scheme coupled with a GA. Based on the morphology of living creatures, a geometric representation scheme had earlier been developed that works by specifying a skeleton which defines the underlying topology/connectivity of a structural continuum together with segments of material surrounding the skeleton. In this work, the flexibility to turn on or off parts of the skeleton is integrated into the scheme. This improves the variability of topological and shape characteristics in the evolutionary process and enhances the representation's versatility. The methodology is tested by solving a multicriterion 'target matching' problem : a simulated topology optimization problem where a 'target' geometry is first created and predefined as the optimum solution, and design solutions are evolved to converge towards this target shape.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Carreño:2007:cec, title={Evolution of Classification Rules for Comprehensible Knowledge Discovery}, author={Emiliano Carre\~uo and Guillermo Leguizam\'on and Neal Wagner}, pages = {1261--1268}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1695.pdf}, keywords = {genetic algorithms, genetic programming}, url = {}, size = {}, abstract = {This article, which lies within the data mining framework, proposes a method to build classifiers based on the evolution of rules. The method, named REC (Rule Evolution for Classifiers), has three main features: it applies genetic programming to perform a search in the space of potential solutions; a procedure allows biasing the search towards regions of comprehensible hypothesis with high predictive quality and it includes a strategy for the selection of an optimum subset of rules (classifier) from the rules obtained as the result of the evolutionary process. A comparative study between this method and the rule induction algorithm C5.0 is carried out for two application problems (data sets). Experimental results show the advantages of using the method proposed.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Xie:2007:cec, title={An Analysis of Depth of Crossover Points in Tree-Based Genetic Programming}, author={Huayang Xie and Mengjie Zhang and Peter Andreae}, pages = {4561--4568}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1696.pdf}, keywords = {genetic algorithms, genetic programming}, url = {}, size = {}, abstract = {The standard crossover operator in tree-based Genetic Programming (GP) is problematic in that it is most often destructive. Selecting crossover points with an implicit bias towards the leaves of a program tree aggravates its destructiveness and causes the code bloat problem in GP. Therefore, a common view has been developed that adjusting the depth of crossover points to eliminate the bias can improve GP performance, and many attempts have been made to create effective crossover operators according to this view. As there are a large number of possible depth-control strategies, it is very difficult to identify the strategy that provides the most significant improvement in performance. This paper explores depth-control strategies by analysing the depth of crossover points in evolutionary process logs of five different GP systems on problems in three different domains. It concludes that controlling the depth of crossover points is an evolutionary stage dependent and problem dependent task, and obtaining a significant performance improvement is not trivial.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Wang:2007:cec, title={Handling Objectives as Adaptive Constraints for Multiobjective Structural Optimization}, author={N. Wang and K. Tai}, pages = {3922--3929}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1697.pdf}, url = {}, size = {}, abstract = {Very often real-world applications involve multiple objectives. Research on multiobjective evolutionary optimization algorithms have amply demonstrated that they are capable of finding multiple and diverse non-dominated solutions which allows the user to choose among many solutions. Although it is difficult to evaluate the importance of the various objectives quantitatively during the conceptual/preliminary stages of the design process, usually qualitative preference can be specified. This paper presents a novel, simple and intuitive way to integrate the user's preference into the evolutionary algorithm. This approach treats relatively more important objectives as adaptive constraints whose ideal values will be adaptively changed during the optimization procedure. Such changes will affect the region feasibility of the objective space which results in the variation of problem type (unconstrained problem, moderately constrained problem or highly constrained problem). As the selection criteria for mating partner depends on the type of problem in the algorithm used here, more selection pressure is put on adaptive constraints. The algorithm is validated using a target matching test problem. The results obtained indicate that the approach can produce good results at reasonable computational costs. The proposed algorithm efficiently guides the population towards the (preferred) region of interest, allowing a faster convergence and a better coverage of the preferred area of the Pareto optimal front based on the relative importance of the objectives.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Shafi:2007:cec, title={Real Time Signature Extraction From a Supervised Classifier System}, author={Kamran Shafi and Hussein A. Abbass and Weiping Zhu}, pages = {2509--2516}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1698.pdf}, url = {}, size = {}, abstract = {Recently some algorithms have been proposed to clean post-training rule populations evolved by XCS, a state of the art Learning Classifier System (LCS). We present an algorithm to extract optimal rules, which we refer to as signatures, during the operation of UCS, a recent variant of XCS. In a benchmark binary valued dataset our method seconds the generalization and optimality hypotheses for UCS and provide mechanisms for retrieving all maximally general rules in real time. In real valued problems, where precise realization of decision boundaries is often not possible, our algorithm is able to retrieve near optimal representations with the help of a modified subsumption operator. The algorithm is able to reduce the processing time asymptotically and provides a mechanism for early stopping of the learning process.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Wang:2007:cec, title={A Hybrid Genetic Algorithm for Multiobjective Structural Optimization}, author={N. Wang and K. Tai}, pages = {2948--2955}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1699.pdf}, url = {}, size = {}, abstract = {The Genetic Algorithm (GA) is a potent multi-objective optimization method, and the effectiveness of hybridizing it with local search (LS) has recently been reported in the literature. In this work, the proposed hybrid algorithm integrates a simple local search strategy with an effective constrained multi-objective evolutionary algorithm. A novel constrained tournament selection is used as a single objective function in the local search strategy. The selection is utilized to determine whether a new solution generated in local search process will survive. Hooke and Jeeves method is applied to decide search path. Good initial solutions, the solutions to be mutated, are chosen for local search. This paper also examines the following strategies in the implementation of local search: applying local search only to final solutions, applying local search to solutions only in the early generations, and initializing local search when mutation gives rise to improvement in the solution. Simulation results from a target matching test problem indicate that the hybrid algorithm outperforms the multi-objective method without genetic local search when the implementation of local search is appropriate. It is also shown that the hybridization can improve the convergence speed.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Teoh:2007:cec, title={A Global-Local Hybrid Evolutionary Strategy (ES) for Recurrent Neural Networks (RNNs) in System Identification}, author={E. J. Teoh and C. Xiang}, pages = {1628--1635}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1701.pdf}, url = {}, size = {}, abstract = {Recurrent neural networks, through their unconstrained synaptic connectivity and resulting state-dependentnonlinear dynamics, offer a greater level of computational ability when compared with regular feedforward neural network (FFNs) architectures. A necessary consequence of this increased capability is a higher degree of complexity, which in turn leads to gradient-based learning algorithms for RNNs being more likely to be trapped in local optima, thus resulting in suboptimal solutions. This motivates the use of evolutionary computational methods which center about the use of population based global-search techniques as an optimization scheme. In this article, we propose the use of a hybrid evolutionary strategy (ES) approach together with an adaptive linear observer, acting as a local search operator, as a learning mechanism for general RNN applications. Illustrative examples, though largely preliminary in nature, in solving a few system identification problems, are encouraging.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Ghoneim:2007:cec, title={The Critical Point When Prisoners Meet the Minority: Local and Global Dynamics in Mixed Evolutionary Games}, author={Ayman Ghoneim and Hussein Abbass and Michael Barlow }, pages = {3711--3718}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1702.pdf}, url = {}, size = {}, abstract = {Evolutionary games are used to model and understand some complex real world situations in economics, defence, and industry. However, different games are usually studied independently and in isolation of each other. Notwithstanding, in real world situations, an agent is involved in multiple games simultaneously and her action in one game influences her utility in the others. This situation is far more complex when the utility functions for the different games are in conflict with each other. In this paper, we propose for the first time an analysis for the interaction between the Iterated Prisoner's Dilemma and the Minority Games. We empirically explore the relationship and clarify the mathematical conditions under which the Minority game won't change the dynamics of the Iterated Prisoner's Dilemma game.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Mochon:2007:cec, title={Bidding with Memory in the Presence of Synergies: A Genetic Algorithm Implementation}, author={A. Mochon and Y. Saez and D. Quintana and P. Isasi}, pages = {228--235}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1704.pdf}, url = {}, size = {}, abstract = {A genetic algorithm has been developed to solve bidding strategies in a dynamic multi-unit auction: the Ausubel auction. The genetic algorithm aims to maximize each bidder's payoff. To this end, a memory system about past experiences has been implemented. An extensive set of experiments have been carried out where different parameters of the genetic algorithm have been used in order to make a robust test bed. The present model has been studied for several environments that involve the presence or absence of synergies. For each environment, the bidding strategies, along with their effects on revenue and efficiency, are analyzed.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(:2007:cec, title={Modelling Cost into a Genetic Algorithm-Based Portfolio Optimization System by Seeding and Objective Sharing}, author={C. Aranha and H. Iba}, pages = {196--203}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1706.pdf}, url = {}, size = {}, abstract = {Portfolio Optimization by GA is a problem that has recently received a lot of attention. However, most works in this area have so far ignored the effects of cost on Portfolio Optimization, and haven't directly addressed the problem of portfolio management (continuous optimization of a portfolio over time). In this work, we use the Euclidean Distance between the portfolio selection in two consecutive time periods as measure of cost, and the objective sharing method to balance the goals of maximizing returns and minimizing distance over time. We also improve the GA method by adding genetic material from previous runs into the new population (seeding). We experiment our method on historical monthly data from the NASDAQ and NIKKEI indexes, and obtain a better result than pure GA, defeating the index under non-bubble market conditions.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Ang:2007:cec, title={Multi-Objective Evolutionary Recurrent Neural Networks for System Identification}, author={J. H. Ang and C. K. Goh and E. J. Teoh and A. A. Mamun}, pages = {1586--1592}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1707.pdf}, url = {}, size = {}, abstract = {This paper proposes a new multi-objective evolutionary approach for training recurrent neural networks (RNNs). The algorithm uses features of a variable length representation allowing easy adaptation of neural networks structures and a micro genetic algorithm (μGA) with an adaptive local search intensity scheme for local fine-tuning. In addition, a structural mutation (SM) operator for evolving the appropriate number of neurons for RNNs is used. Simulation results demonstrated the effectiveness of proposed method for system identification tasks.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Vasile:2007:cec, title={A Behavioral-Based Meta-Heuristic for Robust Global Trajectory Optimization}, author={Massimiliano L. Vasile}, pages = {2056--2063}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1709.pdf}, url = {}, size = {}, abstract = {This paper presents a behavioural-based meta-heuristic for black-box problems of global trajectory optimization. This approach is shown to perform an efficient exploration of the solution sapce without sacrificing local convergence. The proposed meta-heuristic models the search for a solution as an action-selection process: a number os agents, forming a population, are endowed with a number of individulistic and social behaviors. The combination of these behaviors drives the entire population toward a number of local optima and eventually to the golbal one. In order to improve the collection of local optima in different regions of the search space the behavioral-search has been hybridized with a domain decompositon technique. This approach was applied to two typical problems in trajectory design, demonstrating a remarkable robustness compared to the most common methods, both stochastic and deterministic, for global oganization.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Zhou:2007:cec, title={Adaptive Modelling Strategy for Continuous Multi-Objective Optimization}, author={Aimin Zhou and Qingfu Zhang and Yaochu Jin and Bernhard Sendhoff }, pages = {431--437}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1711.pdf}, url = {}, size = {}, abstract = {The Pareto optimal set of a continuous multi-objective optimization problem is a piecewise continuous manifold under some mild conditions.We have recently developed several multi-objective evolutionary algorithms based on this property. However, the modelling methods used in these algorithms are rather costly. In this paper, a cheap and effective modelling strategy is proposed for building the probabilistic models of promising solutions. A new criterion is proposed for measuring the convergence of the algorithm. The locality degree of each local model is adjusted according to the proposed convergence criterion. Experimental results show that the algorithm with the proposed strategy is very promising.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Al-Jaafreh:2007:cec, title={Training Type-2 Fuzzy System by Particle Swarm Optimization}, author={Moha'med O. Al-Jaafreh and Adel A. Al-Jumaily}, pages = {3442--3446}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1712.pdf}, url = {}, size = {}, abstract = {Many Intelligent techniques were established during last decades to handle nonlinear, multimode, noisy, nondifferentiable problems and to obtain optimum solution(s).}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Lin:2007:cec, title={Crowd Control with Swarm Intelligence}, author={Ying-Yin Lin and Ying-Ping Chen}, pages = {3321--3328}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1713.pdf}, url = {}, size = {}, abstract = {This paper presents a uniform conceptual model based on the particle swarm optimization (PSO) paradigm to simulate crowds in computer graphics. According to the mechanisms of PSO, each person (particle) in the crowd (swarm) can adopt the information to search a path from the initial position to the specified target (optimum) automatically. However, PSO aims to obtain the optimal solution, while the purpose of this study concentrates on the generated paths of particles. Hence, in order to generate appropriate paths of people in a crowd, we propose a method to employ the computational facilities provided in PSO. The proposed model is simple, uniform, and easy to implement. The results of simulations demonstrate that using PSO with the proposed technique can generate appropriate nondeterministic, non-colliding paths in several different scenarios, including static obstacles, moving targets, and multiple crowds.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Soliman:2007:cec, title={The Effect of a Stochastic Step Length on the Performance of the Differential Evolution Algorithm}, author={Omar S. Soliman and Lam T. Bui and Hussein A. Abbass}, pages = {2850--2857}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1714.pdf}, url = {}, size = {}, abstract = {In this paper, we present a novel efficient strategy to improve the performance of the differential evolution (DE) algorithm for real parameter optimization, by generating a variable step length based on a probability distribution, instead of using the conventional fixed step length approach. Previous studies investigated uniform and Gaussian distributions. In this study, we compare between these two distributions and a Cauchy distribution. The proposed strategy controls search parameters in a probabilistic manner. Experimental results are carried out on a wide range of fifteen standard test problems with different scenarios. The obtained results showed that the performance of the DE algorithm was best when using a cauchy distribution (CD); thanks to its thick tails that enable it to generate considerable changes more frequently than other probability distributions and to escape a local optima for multimodal optimization problems.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Naujoks:2007:cec, title={Evolutionary Optimization of Ship Propulsion Systems}, author={Boris Naujoks and Max Steden and Sven-BrianM\"uller and Jochen Hundemer}, pages = {2809--2816}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1715.pdf}, url = {}, size = {}, abstract = {The concept of a linear jet yields the potential to play a decisive role in modern ship propulsion systems technology. A linear jet consists of multiple components, namely a rotor, a stator, and a nozzle. The optimization of a complete linear jet is a complex and challenging task, which requires a couple of comprehensive simulation tools to work together. All required analysis and simulation issues are subject to ongoing research. The aspired optimization algorithm is already available and can be tested on the different components that can already be analyzed. In this way, the optimization is first limited to the propeller blade to determine good parameterizations for the optimization method. The results are analyzed in detail performing deeper investigations with additional optimization runs. Furthermore, one parameterization identified to perform best results is incorporated for the optimization of a complete propulsion system featuring rotor, hub, and nozzle.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Phienthrakul:2007:cec, title={GPES: An Algorithm for Evolving Hybrid Kernel Functions of Support Vector Machines}, author={Tanasanee Phienthrakul and Boonserm Kijsirikul}, pages = {2636--2643}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1717.pdf}, url = {}, size = {}, abstract = {The Support Vector Machine (SVM) is a popular approach to the classification of data. One problem of SVM is how to choose a kernel and the parameters for the kernel. This paper proposes a classification technique, called GPES, that combines Genetic Programming (GP) and Evolutionary Strategies (ES) to evolve a hybrid kernel for an SVM classifier. The hybrid kernels are represented as trees that have some adjustable parameters. These hybrid kernels are also the Mercer's kernels. The experimental results are compared with a standard SVM classifier using the polynomial and radial basis function kernels with various parameter settings.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Ruiz-Torrubiano:2007:cec, title={Use of Heuristic Rules in Evolutionary Methods for the Selection of Optimal Investment Portfolios}, author={Rub\'en Ruiz-Torrubiano and Alberto Su\'arez}, pages = {212--219}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1718.pdf}, url = {}, size = {}, abstract = {A novel hybrid algorithm that combines evolutionary algorithms, quadratic programming, and a specially devised pruning heuristic is proposed for the selection of cardinalityconstrained optimal portfolios. The framework used is the standard Markowitz mean-variance formulation for portfolio selection with constraints of practical interest, such as minimum and maximum investments per asset and/or on groups of assets. The use of cardinality constraints transforms portfolio selection into an NP-hard mixed-integer quadratic optimization problem that is difficult to solve by standard methods. An implementation of the algorithm that employs a genetic algorithm with a set representation, an appropriately defined mutation operator and Random Assortment Recombination for crossover (RAR-GA) is compared with implementations using various estimation of distribution algorithms (EDAs). Without the pruning heuristic, RAR-GA is superior to the implementations with EDAs in terms of both accuracy and efficiency. The incorporation of the pruning heuristic leads to a significant decrease in computation times and makes EDAs competitive with RAR-GA.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Fu:2007:cec, title={Time-Series Infectious Disease Data Analysis Using SVM and Genetic Algorithm}, author={Xiuju Fu and Christina Liew and Harold Soh and Gary Lee and Terence Hung and Lee-Ching Ng}, pages = {1276--1280}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1723.pdf}, url = {}, size = {}, abstract = {Dengue represents a serious health threat in the Tropics, owing to the year-round presence of Aedes mosquito vectors, and the lack of any anti-viral drugs or vaccines. Climatic factors are important in influencing the incidence of dengue. It is important to determine the relationships between climatic factors and disease incidence trends, which would be helpful for relevant environment and health agencies in planning appropriate pre-emptive control measures. Climatic factors and dengue case records vary over time. It is therefore difficult to justify the time-lag when a climatic factor affects the mosquito-to-human and human-to-mosquito loops. In this paper, we propose to use support vector machine (SVM) classifiers for analyzing the timeseries dengue data and genetic algorithm (GA), to determine the time-lags and subset of climatic factors as effective factors influencing the spread of dengue. It is shown that the proposed model is able to detect important climatic factors and their timelags which affect the disease, and the GA-based SVM classifiers could improve the classification accuracy significantly.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Friedrich:2007:cec, title={On Improving Approximate Solutions by Evolutionary Algorithms}, author={Tobias Friedrich and Jun He and Nils Hebbinghaus and Frank Neumann and Carsten Witt}, pages = {2614--2621}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1726.pdf}, url = {}, size = {}, abstract = {Hybrid methods are very popular for solving problems from combinatorial optimization. In contrast to this the theoretical understanding of the interplay of different optimization methods is rare. The aim of this paper is to make a first step into the rigorous analysis of such combinations for combinatorial optimization problems. The subject of our analyses is the vertex cover problem for which several approximation algorithms have been proposed. We point out specific instances where solutions can (or cannot) be improved by the search process of a simple evolutionary algorithm in expected polynomial time.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(:2007:cec, title={Optimal Location of FACTS Devices by A Bacterial Swarming Algorithm for Reactive Power Planning}, author={Z. Lu and M. S. Li and W. J. Tang and Q. H. Wu}, pages = {2344--2349}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1727.pdf}, url = {}, size = {}, abstract = {This paper proposes a reactive power planning model, which incorporates Flexible AC Transmission System (FACTS) devices. The optimal location of multi-type FACTS devices and their control parameters are optimized by a novel Bacterial Swarming Algorithm (BSA) to minimize the real power losses and also to improve voltage profile. The simulation studies have been undertaken on both IEEE 30-bus and 118-bus test systems. Simulation results show that the performance of the power system is improved with multi-type FACTS placed optimally in the reactive power planning model. The advantage of using BSA to determine the location of FACTS devices is demonstrated, in comparison with two other Evolutionary Algorithms (EAs), i.e., Genetic Algorithm (GA) and Particle Swarm Optimizer (PSO).}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Maddock:2007:cec, title={Optimal Design for a NEO Tracking Spacecraft Formation}, author={Christie Alisa Maddock and Massimiliano Vasile}, pages = {979--986}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1728.pdf}, url = {}, size = {}, abstract = {The following paper presents the design and methodology for developing an optimal set of spacecraft orbits for a NEO tracking mission. The spacecraft is designed to fly in close formation with the asteroid, avoiding the nonlinear gravity field produced by the asteroid. A periodic orbit is developed, and the initial conditions are optimized by use of a global optimizer for constrained nonlinear problems. The asteroid Apophis (NEO 2004 MN4) was used as the case study due the potential impact with Earth in 2036, and the need for more accurate ephemerides.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Watanabe:2007:cec, title={A Study on Application of Fitness Inference Method to PC-IGA}, author={Yoshinobu Watanabe and Tomohiro Yoshikawa and Takeshi Furuhashi}, pages = {1450--1455}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1729.pdf}, url = {}, size = {}, abstract = {This paper applies the fitness inference method to Interactive Genetic Algorithm based on Paired Comparison (PC-IGA). PC-IGA enables users to reduce the mental burden for evaluation by using paired comparison. Fitness inference method can decrease evaluation times on EC by using inferred fitness value instead of actual evaluation for candidate solutions based on the information of actually evaluated solutions in the past generations. However, PC-IGA does not give enough information to apply fitness inference method. This paper investigates effective application of the fitness inference method to PC-IGA through experiments with simulated evaluation. The experimental results show that fitness inference method can decrease the number of actual evaluation times by 40% comparing with the normal PC-IGA.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Zhang:2007:cec, title={A New Reduced Space Searching Algorithm (RSSA) and Its Application in Optimal Design of Alloy Steels}, author={Qian Zhang and Mahdi Mahfouf}, pages = {1815--1822}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1731.pdf}, url = {}, size = {}, abstract = {In this paper, a new search and optimisation algorithm based on a reduced space searching strategy, named RSSA, is presented. This algorithm originates from an idea which relates to a simple experience when humans search for an optimal solution to a 'real-life' problem, i.e. when humans search for a candidate solution given a certain objective, a large area tends to be scanned first; should one succeed in finding clues in relation to the predefined objective, then the search space is greatly reduced for a more detailed search. The proposed algorithm is validated via well-known benchmark functions and is found to be efficient. Furthermore, the algorithm is extended to include the multiobjective case. Simulation results of optimising some challenging benchmark multiobjective problems, including the ZDT and DTLZ series problems, suggest that the new algorithm can locate the Pareto-optimal front and performs better than some other salient optimisation algorithms. Then, this proposed algorithm is successfully applied to the optimal design of alloy steels, which aims at determining the optimal heat treatment regimes and the required weight percentages for the chemical composites in order to obtain the pre-defined mechanical properties of the material.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Steiner:2007:cec, title={Emergence of Feedback in Artificial Gene Regulatory Networks}, author={T. Steiner and L. Schramm and Y. Jin and B. Sendhoff}, pages = {867--874}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1732.pdf}, url = {}, size = {}, abstract = {In this paper, we present a model for simulating the evolution of development together with a method for the analysis of emergence of negative feedback inside the regulatory network. In order to record the development of feedback during evolution, we analyze both the static as well as the dynamic interactions between the transcription factors in the regulatory network. When perturbing the gene regulatory network using random mutations, we find that the evolved negative feedback is the main mechanism for robustness against such mutations. We argue that this robustness is the reason for the sustained emergence of negative feedback during evolution.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Nakano:2007:cec, title={Particle Swarm Optimization Based on the Concept of Tabu Search}, author={Shinichi Nakano and Atsushi Ishigame and Keiichiro Yasuda}, pages = {3258--3263}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1733.pdf}, url = {}, size = {}, abstract = {This paper presents a new Particle Swarm Optimization based on the concept of Tabu Search (TS-PSO). In PSO, when a particle finds a local optimal solution, all of the particles gather around the one, and cannot escape from it. On the other hand, TS can escape from the local optimal solution by moving away from the best solution at the present. The proposed TS-PSO is the method for combining the excellence of both PSO and TS. In this method, particles are divided into two categories called swarm1 and swarm2. And they play the key roles of intensification and diversification respectively. Swarm1 playing roles of intensification searches the area around the best solution at the present, and swarm2 playing roles of diversification intends to avoid local optimal solutions and to find global optimal one. Then, the proposed method is validated through numerical simulations with several functions which are well known as optimization benchmark problems comparing to the conventional PSO methods.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Alba:2007:cec, title={Gene Selection in Cancer Classification Using PSO/SVM and GA/SVM Hybrid Algorithms}, author={Enrique Alba and Jos\'e Garc\'ia-Nieto and Laetitia Jourdan and El-Ghazali Talbi}, pages = {284--290}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1735.pdf}, url = {}, size = {}, abstract = {In this work we compare the use of a Particle Swarm Optimization (PSO) and a Genetic Algorithm (GA) (both augmented with Support Vector Machines SVM) for the classification of high dimensional Microarray Data. Both algorithms are used for finding small samples of informative genes amongst thousands of them. A SVM classifier with 10-fold cross-validation is applied in order to validate and evaluate the provided solutions. A first contribution is to prove that PSOSVM is able to find interesting genes and to provide classification competitive performance. Specifically, a new version of PSO, called Geometric PSO, is empirically evaluated for the first time in this work using a binary representation in Hamming space. In this sense, a comparison of this approach with a new GASSVM and also with other existing methods of literature is provided. A second important contribution consists in the actual discovery of new and challenging results on six public datasets identifying significant in the development of a variety of cancers (leukemia, breast, colon, ovarian, prostate, and lung).}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(:2007:cec, title={Plateaus Can Be Harder in Multi-Objective Optimization}, author={Tobias Friedrich and Nils Hebbinghaus and Frank Neumann}, pages = {2622--2629}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1736.pdf}, url = {}, size = {}, abstract = {In recent years a lot of progress has been made in understanding the behavior of evolutionary computation methods for single- and multi-objective problems. Our aim is to analyze the diversity mechanisms that are implicitly used in evolutionary algorithms for multi-objective problems by rigorous runtime analyses. We show that, even if the population size is small, the runtime can be exponential where corresponding single-objective problems are optimized within polynomial time. To illustrate this behavior we analyze a simple plateau function in a first step and extend our result to a class of instances of the well-known SETCOVER problem.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Lluch-Revert:2007:cec, title={A New Two-Stage Approach to Solve the Linear Shelf Space Allocation Problem}, author={L. Lluch-Revert and A. I. Esparcia-Alc\'azar and J. M. Albarrac\'in-Guillem and M. E. Palmer-Gato}, pages = {4475--4482}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1737.pdf}, url = {}, size = {}, abstract = {In previous work we gave the first steps towards the development of a commercially viable tool that used evolutionary computation to address the Linear Shelf Space Allocation Problem (LiSSAP). This consists on allocating lengths of shelves in a given shop to specific products or groups of products. Here we present a procedure to solve the problem based on two stages. The first one relies on finding the "ideal" number of modules for every group of products. The second one uses an evolutionary algorithm (μ+ λ)-S to fulfill groups affinity requirements. Moreover, a new distance measure between modules is also introduced, which has an important effect on the fitness function. Finally, we analyse different choices that can be made in the evolutionary algorithm and how they affect the global performance of the algorithm. These involve the chromosome encoding and the λ parameter. The different combinations described are tested on two real problem configurations.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Mingo:2007:cec, title={Grammatical Evolution Guided by Reinforcement}, author={Jack Mario Mingo and Ricardo Aler}, pages = {1475--1482}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1738.pdf}, url = {}, size = {}, abstract = {Grammatical Evolution is an evolutionary algorithm able to develop, starting from a grammar, programs in any language. Starting from the point that individual learning can improve evolution, in this paper it is proposed an extension of Grammatical Evolution that looks at learning by reinforcement as a learning method for individuals. This way, it is possible to incorporate the Baldwinian mechanism to the evolutionary process. The effect is widened with the introduction of the Lamarck hypothesis. The system is tested in two different domains: a symbolic regression problem and an even parity Boolean function. Results show that for these domains, a system which includes learning obtains better results than a grammatical evolution basic system.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Fu:2007:cec, title={An Optimization Model for Container Transportation Network with ACO Approach}, author={Boxin Fu and Xiangqun Song and Zijian Guo and Peng Zhang}, pages = {4768--4775}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1740.pdf}, url = {}, size = {}, abstract = {The rapid growth of international container handling in recent years has led to an increased utilization of mega-containerships and the rebuild of container network. In this paper, we focus on the optimization of total cost for regional containers transportation network system of seaports, and propose a new reliable intelligent algorithm, named Ant Colony Optimization (ACO). ACO is to solve the nonlinear NP-complete problems, which is greatly inspired from real ants and their food seeking behavior. The paper constructs an optimization model for certain containers transportation network system and simulates the whole process. The results show the improved ACO Algorithm is of a credible and excellent probability accumulation searching method to reduce the integrated cost of container transportation network obviously. The optimization model that we constructed provides a reference model for containers transportation network layout.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Duan:2007:cec, title={Experimental Study of the Adjustable Parameters in Basic Ant Colony Optimization Algorithm}, author={Haibin Duan and Guanjun Ma and Senqi Liu}, pages = {149--156}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1741.pdf}, url = {}, size = {}, abstract = {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. Although basic ACO algorithm for the heuristic solution of hard combinational optimization problems enjoy a rapidly growing popularity, but little research is conducted on the optimum configuration strategy for the adjustable parameters in the ACO algorithm. In order to deeply study the optimum configuration strategy for the adjustable parameters in the ACO algorithm, an effective Matlab GUI(Graphical User Interface)-based ACO simulation platform is developed in this paper. In order to investigate the relative strengths and weaknesses of these adjustable parameters, series of experiments on EIL51TSP are conducted on the developed ACO simulation platform. On the basis of the experimental results presented above, a novel effective "three-step" optimum configuration strategy for the adjustable parameters in basic ACO algorithm is drawn. This "three-step" optimum configuration strategy for the adjustable parameters in basic ACO algorithm is also beneficial to the application and development of ACO algorithm in various kinds of optimization problems.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Gallagher:2007:cec, title={Bayesian Inference in Estimation of Distribution Algorithms}, author={Marcus Gallagher and Ian Wood and Jonathan Keith and George Sofronov}, pages = {127--133}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1744.pdf}, url = {}, size = {}, abstract = {Metaheuristics such as Estimation of Distribution Algorithms and the Cross-Entropy method use probabilistic modelling and inference to generate candidate solutions in optimization problems. The model fitting task in this class of algorithms has largely been carried out to date based on maximum likelihood. An alternative approach that is prevalent in statistics and machine learning is to use Bayesian inference. In this paper, we provide a framework for the application of Bayesian inference techniques in probabilistic model-based optimization. Based on this framework, a simple continuous Bayesian Estimation of Distribution Algorithm is described. We evaluate and compare this algorithm experimentally with its maximum likelihood equivalent, UMDAcG. }, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Cagnina:2007:cec, title={A Bi-Population PSO with a Shake-Mechanism for Solving Constrained Numerical Optimization}, author={Leticia C. Cagnina and Susana C. Esquivel and Carlos A. Coello Coello}, pages = {670--676}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1745.pdf}, url = {}, size = {}, abstract = {This paper presents an enhanced Particle Swarm Optimizer approach, which is designed to solve numerical constrained optimization problems. The approach uses a single method to handle different types of constraints (linear, nonlinear, equality or inequality) and it incorporates a shakemechanism and a dual population in an attempt to overcome the problem of premature convergence to local optima. The proposed algorithm is validated using standard test functions taken from the specialized literature and is compared with respect to algorithms representative of the state-of-the-art in the area. Our preliminary results indicate that our proposed approach is a highly competitive alternative to solve constrained optimization problems.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Kim:2007:cec, title={Evolving Hypernetworks for Pattern Classification}, author={Joo-Kyung Kim and Byoung-Tak Zhang}, pages = {1856--1862}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1746.pdf}, url = {}, size = {}, abstract = {Hypernetworks consist of a large number of hyperedges that represent higher-order features sampled from training patterns. Evolutionary algorithms have been used as a method for evolving hypernetworks. The order of a hyperedge is defined as the number of feature variables in the hyperedge and it is an important parameter of the hypernetwork model. Previous studies used fixed-order hyperedges which limit model spaces and, thus, the best performance achievable by hypernetworks. Here, we present a method for evolving variable-order hypernetwork models. To find the proper orders automatically, the fitness values are calculated for each hyperedge and the hyperedges with low fitness values are substituted by new hyperedges. The method was tested on three data sets from UCI machine learning repository. The results show that the evolutionary hypernetworks show classification accuracies comparable to those of other conventional algorithms, find appropriate orders of hyperedges automatically, and extract important rules in the hyperedges for the given pattern classification problems.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(:2007:cec, title={Multi-Population Approach to Approximate the Development of Neocortical Networks}, author={Andreas Herzog and Karsten Kube and Bernd Michaelis and Ana D. de Lima and Thomas Voigt}, pages = {628--633}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1747.pdf}, url = {}, size = {}, abstract = {Cultured natural cortical neurons form functional networks through a complex set of developmental steps during the first weeks in vitro. The dynamic behavior of the network in this early development period changes from spontaneous spiking of single neurons to slow synchronous activity and finally to a mature firing profile with complex high-order patterns of spikes and bursts. In the present modeling study we investigate the required properties of the networks during the development by biologic realistic simulations and use an evolutionary algorithm (EA) to fit the parameters to the results of biological experiments. For each day in vitro (DIV) during the development a population of individuals is defined, which determines the statistical parameters to generate the networks and set up neuron properties by genes. The fitness function and the recombination algorithm are extended for this multi-population approach to allow the EA to follow different parameter trajectories over time (which are possible solutions) and include several kinds of biologically a-priori knowledge with an adjustable uncertainty.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Abraham:2007:cec, title={Stability Analysis of the Ant System Dynamics with Non-Uniform Pheromone Deposition Rules}, author={Ajith Abraham and Amit Konar and Nayan R. Samal and Swagatam Das}, pages = {1103--1108}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1748.pdf}, url = {}, size = {}, abstract = {The paper extends the classical Ant Systems by considering non-uniform deposition by the ants, while constructing pheromone trails. A deterministic solution to the ant system dynamics for both uniform and non-uniform pheromone deposition rules has been obtained to determine the parameters of the dynamics that ensure stability in pheromone trails. Computer simulation confirmed the results of stability analysis. Performance of the extended ant system (with nonuniform pheromone deposition rule) is compared with the classical ant system using the well known Traveling Salesperson Problem. Simulation results reveal that the extended ant system outperforms the classical ant system by a large margin with respect to convergence speed without sacrificing the quality of solution.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Chong:2007:cec, title={On the Fuzzy Cognitive Map Attractor Distance}, author={Alex Chong and Kok WaiWong}, pages = {2652--2657}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1749.pdf}, url = {}, size = {}, abstract = {Fuzzy Cognitive Map (FCM) has commonly been used as a prediction tool. The FCM forward chains have been used to find answers to what-if questions. The process starts with the encoding of the what-if question into a stimulus vector. The vector goes through a series of vector-matrix multiplication until the FCM converges to one of the FCM attractors. The attractor is the answer to the initial question. There are several types of FCM attractors. The usefulness of the different types of attractors relies heavily on the user's objectives and interpretations. This paper presents the theoretical discussion on distance measurement among the various FCM attractor distances. Subsequently the FCM Attractor Distance (FCMAD) based on genetic algorithm is proposed. The use of this distance in FCM goal oriented analysis and FCM learning is discussed. Experiment results have confirmed the effectiveness of the proposed technique.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Zhang:2007:cec, title={Multi-Sub-Swarm Particle Swarm Optimization Algorithm for Multimodal Function Optimization}, author={Jun Zhang and De-Shuang Huang and Kun-Hong Liu}, pages = {3215--3220}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1750.pdf}, url = {}, size = {}, abstract = {This paper presents a novel multi-sub-swarm Particle Swarm Optimization (PSO) algorithm. The proposed algorithm can effectively imitate a natural ecosystem, in which the different sub-populations can compete with each other. After competing, the winner will continue to explore the original district, while the loser will be obliged to explore another district. Four benchmark multimodal functions of varying difficulty are used as test functions. The experimental results show that the proposed method has a stronger adaptive ability and a better performance for complicated multimodal functions with respect to other methods.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Dräger:2007:cec, title={Benchmarking Evolutionary Algorithms on Convenience Kinetics Models of the Valine and Leucine Biosynthesis in C. glutamicum}, author={Andreas Dr\"ager and Marcel Kronfeld and Jochen Supper and Hannes Planatscher and Jørgen B. Magnus and Marco Oldiges and Andreas Zell}, pages = {896--903}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1752.pdf}, url = {}, size = {}, abstract = {An important problem in systems biology is parameter estimation for biochemical system models. Our work concentrates on the metabolic subnetwork of the valine and leucine biosynthesis in Corynebacterium glutamicum, an anaerobic actinobacterium of high biotechnological importance. Using data of an in vivo experiment measuring 13 metabolites during a glucose stimulus-response experiment we investigate the performance of various Evolutionary Algorithms on the parameter inference problem in biochemical modeling. Due to the inconclusive information on the reversibility of the reactions in the pathway, we develop both a reversible and an irreversible differential equation model based on the recent convenience kinetics approach. As the reversible model allows better approximation on the whole, we use it to analyze the impact of different settings on four especially promising EAs. We show that Particle Swarm Optimization as well as Differential Evolution are useful methods for parameter estimation on convenience kinetics models outperforming Genetic Algorithm and Evolution Strategy approaches and nearly reaching the quality of independent spline approximations on the raw data.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(:2007:cec, title={N-gram Fitness Function with a Constraint in a Musical Evolutionary System}, author={Man Yat Lo and Simon M. Lucas}, pages = {4246--4251}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1753.pdf}, url = {}, size = {}, abstract = {This paper describes an evolutionary music composition system that combines trainable music critics with a bag of notes constraint. Unlike many evolutionary composition systems, there is no human interaction involved in the loop. The role of the human within our system is to select the set of melodies to train the critics on, and choose the bag of notes. The trainable critics are N-gram model. The system then evolves pleasant sounding melodies by permuting the order of the notes selected from the bag. The bag of notes constraint prevents a previously observed problem. We solve a prior problem where the Maximum Likelihood Sequence (MLS) generated by our N-gram model is repetitive using the bag of notes constraint. In this paper, two experiments are constructed. Both experiments are identical except for the melody representations, which are Absolute Pitch (AP) and Pitch Difference (PD) representations. In each case, the bag of notes constraint results in more pleasing melodies, and the melody shape of the melodies are more controllable and predictable with appropriate operators. We suggest that using absolute pitch representation leads to better sounding melodies than the pitch difference representation, especially if the user enjoys arpeggio effects.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Sedwell:2007:cec, title={Techniques for the Design of Molecules and Combinatorial Chemical Libraries}, author={A. N. Sedwell and I. C. Parmee}, pages = {2435--2442}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1755.pdf}, url = {}, size = {}, abstract = {A number of evolutionary based techniques have been investigated in the field of in silico drug discovery. This paper contrasts previous work carried out within Evotec and the ACDDM Lab in the area of chemical library design with techniques currently under investigation for de novo molecule design. These drug design spaces are highly complex, and present many difficulties applying standard search and optimisation techniques. The problems dealing with these search spaces, and the application of computational techniques have been described along with reasons for selecting the most appropriate techniques mirroring the user processes in the laboratory.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Zhang:2007:cec, title={Emergence of Small-World Networks via Local Interaction Using Prisoner's Dilemma Game}, author={Sihai Zhang and Zhiwei Song and Xufa Wang and Wuyang Zhou}, pages = {3706--3710}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1756.pdf}, url = {}, size = {}, abstract = {The mechanism for the formation of small-world networks is important but still unsolved. We proposed a network evolution model based on local interaction among rational individuals with fixed network dimensions. This model extends Barabási's preferential attachment mechanism to consider two more realistic factors when choosing opponent to interact. Prisoner's dilemma game are utilized to model such local interaction between individuals. The edges of the network are regulated by one simple rule proposed which strengthen the edges with good interaction while weaken those with bad ones. Numerical results show that small-world network structure could be evolved.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Fillon:2007:cec, title={Symbolic Regression of Discontinuous and Multivariate Functions by Hyper-Volume Error Separation (HVES)}, author={Cyril Fillon and Alberto Bartoli}, pages = {23--30}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1757.pdf}, keywords = {genetic algorithms, genetic programming}, url = {}, size = {}, abstract = {Symbolic regression is aimed at discovering mathematical expressions, in symbolic form, that fit a given sample of data points. While Genetic Programming (GP) constitutes a powerful tool for solving this class of problems, its effectiveness is still severely limited when the data sample requires different expressions in different regions of the input space - i.e., when the approximating function should be discontinuous. In this paper we present a new GP-based approach for symbolic regression of discontinuous functions in multivariate data-sets. We identify the portions of the input space that require different approximating functions by means of a new algorithm that we call Hyper-Volume Error Separation (HVES). To this end we run a preliminary GP evolution and partition the input space based on the error exhibited by the best individual across the data-set. Then we partition the data-set based on the partition of the input space and use each such partition for driving an independent, preliminary GP evolution. The populations resulting from such preliminary evolutions are finally merged and evolved again. We compared our approach to the standard GP search and to a GP search for discontinuous functions in univariate data-sets. Our results show that coupling HVES with GP is an effective approach and provides significant accuracy improvements while requiring less computational resources.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Acampora:2007:cec, title={A Proposal of Multi-Agent Simulation System for Membrane Computing Devices}, author={Giovanni Acampora and Vincenzo Loia}, pages = {4100--4107}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1759.pdf}, url = {}, size = {}, abstract = {Membrane Computing (or P-System theory) is a recent area of Natural Computing, the field of computer science that works with computational techniques inspired by nature and natural systems. Particularly, Membrane computing investigates models of computation inspired by the structure and functioning of biological cells focusing attention on their distributed and parallel transformations. Different software applications which have been developed in imperative languages, like Java, or in declaratives languages, as Prolog, work in the framework of Membrane Computing systems. These applications simulate the behavior of P-System focusing on details about computational power of different Membrane devices without exploiting the distributed nature of simulated cellular structures. This paper presents a parallel and distributed application, based on Multi-Agent System technology, able to simulate Membrane Computing devices. The aim is to show how the theoretical distributed nature of P-Systems can be mapped into a real distributed Multi-Agent System in order to achieve two important goals: 1) to define a theoretical computational model for Multi-Agent System architectures; 2) to design a software application able to simulate Membrane Computing devices in a real fashion by exploiting the distributed nature of Multi-Agent System technology.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Cococcioni:2007:cec, title={A New Multi-Objective Evolutionary Algorithm Based on Convex Hull for Binary Classifier Optimization}, author={Marco Cococcioni and Pietro Ducange and Beatrice Lazzerini and Francesco Marcelloni}, pages = {3150--3156}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1761.pdf}, url = {}, size = {}, abstract = {In this paper, we propose a novel population-based multi-objective evolutionary algorithm (MOEA) for binary classifier optimization. The two objectives considered in the proposed MOEA are the false positive rate (FPR) and the true positive rate (TPR), which are the two measures used in the ROC analysis to compare different classifiers. The main feature of our MOEA is that the population evolves based on the properties of the convex hulls defined in the FPR-TPR space. We discuss the application of our MOEA to determine a set of fuzzy rule-based classifiers with different trade-offs between FPR and TPR in lung nodule detection from CT scans. We show how the Pareto front approximation generated by our MOEA is better than the one generated by NSGA-II, one of the most known and used population-based MOEAs.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Brockhoff:2007:cec, title={Improving Hypervolume-based Multiobjective Evolutionary Algorithms by Using Objective Reduction Methods}, author={Dimo Brockhoff and Eckart Zitzler}, pages = {2086--2093}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1762.pdf}, url = {}, size = {}, abstract = {Hypervolume based multiobjective evolutionary algorithms (MOEA) nowadays seem to be the first choice when handling multiobjective optimization problems with many, i.e., at least three objectives. Experimental studies have shown that hypervolume-based search algorithms as SMS-EMOA can outperform established algorithms like NSGA-II and SPEA2. One problem remains with most of the hypervolume based algorithms: the best known algorithm for computing the hypervolume needs time exponentially in the number of objectives. To save computation time during hypervolume computation which can be better spent in the generation of more solutions, we propose a general approach how objective reduction techniques can be incorporated into hypervolume based algorithms. Different objective reduction strategies are developed and then compared in an experimental study on two test problems with up to nine objectives. The study indicates that the (temporary) omission of objectives can improve hypervolume based MOEAs drastically in terms of the achieved hypervolume indicator values.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(:2007:cec, title={The Limitations of Distribution Sampling for Linkage Learning }, author={D. J. Coffin and R. E. Smith}, pages = {364--369}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1763.pdf}, url = {}, size = {}, abstract = {This paper investigates the performance of estimation of distribution algorithms (EDAs) over binary test problems containing parity functions. We describe two test problems; the concatenated parity function (CPF), and the concatenated parity/trap function (CP/TF). Although these functions are separable, with bounded complexity and uniformly scaled subfunction contributions, the hierarchical Bayesian Optimization Algorithm (hBOA) scales exponentially on both. hBOA is able to solve large CPFs with small population sizes when it is unable to solve them with larger population sizes. We argue that test problems containing parity functions are hard for EDAs because there are no interactions in the contribution to fitness between any strict subset of a parity function's bits. This means that as population sizes increase the dependency between variable values for any strict subset of a parity function's bits decreases. Unfortunately most EDAs including hBOA search for their models by looking for dependencies between pairs of variables (at least at first). We make suggestions on how EDAs could be adjusted to handle parity problems, but also comment on the apparently inevitable computational cost.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Bercachi:2007:cec, title={Evolving Dynamic Change and Exchange of Genotype Encoding in Genetic Algorithms for Difficult Optimization Problems}, author={Maroun Bercachi and Philippe Collard and Manuel Clergue and Sebastien Verel}, pages = {4516--4523}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1764.pdf}, url = {}, size = {}, abstract = {The application of genetic algorithms (GAs) to many optimization problems in organizations often results in good performance and high quality solutions. For successful and efficient use of GAs, it is not enough to simply apply simple GAs (SGAs). In addition, it is necessary to find a proper representation for the problem and to develop appropriate search operators that fit well to the properties of the genotype encoding. The representation must at least be able to encode all possible solutions of an optimization problem, and genetic operators such as crossover and mutation should be applicable to it. In this paper, serial alternation strategies between two codings are formulated in the framework of dynamic change of genotype encoding in GAs for function optimization. Likewise, a new variant of GAs for difficult optimization problems denoted Split-and-Merge GA (SM-GA) is developed using a parallel implementation of an SGA and evolving a dynamic exchange of individual representation in the context of Dual Coding concept. Numerical experiments show that the evolved SM-GA significantly outperforms an SGA with static single coding.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Samal:2007:cec, title={A Closed Loop Stability Analysis and Parameter Selection of the Particle Swarm Optimization Dynamics for Faster Convergence}, author={Nayan R. Samal and Amit Konar and Swagatam Das and Ajith Abraham}, pages = {1769--1776}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1765.pdf}, url = {}, size = {}, abstract = {This paper presents an alternative formulation of the PSO dynamics by a closed loop control system, and analyzes the stability behavior of the system by using Jury's test and root locus technique. Previous stability analysis of the PSO dynamics was restricted because of no explicit modeling of the non-linear element in the feedback path. In the present analysis, the non-linear element model of the non-linear element is considered for closed loop stability analysis. Unlike the previous works on stability analysis, where the acceleration coefficients have been combined into a single term, this paper considered their separate existence for determining their suitable range to ensure stability of the dynamics. The range of parameters of the PSO dynamics, obtained by Jury's test and root locus technique were also confirmed by computer simulation of the PSO algorithm.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(:2007:cec, title={Co-Evolution of Test Programs and Stimuli Vectors for Testing of Embedded Peripheral Cores}, author={L. Bolzani and E. Sanchez and M. Schillaci and G. Squillero}, pages = {3474--3481}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1766.pdf}, url = {}, size = {}, abstract = {Research community has not investigated as deeply as necessary the test generation problem of peripheral modules inside a system-on-a-chip (SoC), yet. Testing process for a peripheral core requires two distinct but highly correlated tasks: peripheral configuration and peripheral exercising. The configuration task is usually performed by an assembly program executed by the microprocessor within the SoC; whereas peripheral exercising directly concerns to the use of the device, which may be activated by both the executed program and a carefully devised set of external stimuli. When embedded in a SoC, peripheral cores introduce new issues for their testing. In this paper an automatic approach able to coevolve assembly programs and stimuli sets for peripheral cores embedded in a SoC is described. The presented approach is based on an evolutionary algorithm that exploits high-level simulation and gathers coverage metrics information to produce the test sets. The proposed method considerably reduces the required efforts to produce a suitable test set with respect to the previous approaches, broadening its applicability and increasing its usefulness.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Ravotto:2007:cec, title={A Local Analysis of an Incremental Evolutionary Tool for Processor Diagnosis}, author={D. Ravotto and E. Sanchez and M. Schillaci and G. Squillero}, pages = {3467--3473}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1767.pdf}, url = {}, size = {}, abstract = {This paper details an evolutionary tool targeted at increasing the diagnostic power of a set of assembly programs. The underlying evolutionary scheme is quite peculiar in some aspect and present interesting characteristics The effectiveness of the generated set has recently been demonstrated. Here the use of the tool is further motivated through a deep experimental analysis that provides insight on the obtainable results and better explains the design choices. The use of the tool is validated against a widely used microprocessor core and results are provided.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Mezura-Montes:2007:cec, title={Comparing Bio-Inspired Algorithms in Constrained Optimization Problems}, author={Efr\'en Mezura-Montes and Blanca Cecilia L\'opez-Ramīrez}, pages = {662--669}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1768.pdf}, url = {}, size = {}, abstract = {This paper presents a comparison of four bio-inspired algorithms (all seen as search engines) with a similar constraint-handling mechanism (Deb's feasibility rules) to solve constrained optimization problems. The aim is to analyze the performance of traditional versions of each algorithm based on both, final results and on-line behavior. A set of 24 wellknown benchmark problems are used in the experiments. Quality and consistency of results per each algorithm are investigated. Furthermore, two performance measures (number of evaluations to reach a feasible solution and progress ratio inside the feasible region) are utilized to compare the on-line behavior of each approach. Based on the obtained results, some conclusions are established.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Ashlock:2007:cec, title={A Fractal Representation for Real Optimization}, author={Daniel Ashlock and Justin Schonfeld}, pages = {87--94}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1770.pdf}, url = {}, size = {}, abstract = {The chaos game, in which a moving point is repeatedly averaged toward randomly selected vertices of a triangle, is one method of generating the fractal called the Sierpinski triangle. The sequence of vertices, called generators, used to reach a given point of the Sierpinski triangle yields a map from strings over a three-character alphabet to points in the plane. This study generalizes that representation to give a character-string representation for points in Rn. This is a novel representation for evolutionary optimization. With the correct generating points the method is proven to search its entire target domain at an easily controlled resolution. The representation can be used to achieve the same goals as niche specialization at a far lower computational cost because the optima located are specified by strings which can be stored and searched in standard string dictionaries. An implementation of the algorithm called the multiple optima Sierpinski searcher(MOSS) is found to be substantially faster at locating diverse collections of optima than a standard optimizer. The Sierpinski representation has a number of natural mathematical properties that are described in the paper. These include the ability to adapt both its search domain and its resolution on the fly during optimization.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Becerra:2007:cec, title={Search Space Pruning and Global Optimization of Multiple Gravity Assist Trajectories with Deep Space Manoeuvres}, author={V. M. Becerra and S. J. Nasuto and J. Anderson and M. Ceriotti and C. Bombardelli}, pages = {957--964}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1771.pdf}, url = {}, size = {}, abstract = {This paper deals with the design of optimal multiple gravity assist trajectories with deep space manoeuvres. A pruning method which considers the sequential nature of the problem is presented. The method locates feasible vectors using local optimization and applies a clustering algorithm to find reduced bounding boxes which can be used in a subsequent optimization step. Since multiple local minima remain within the pruned search space, the use of a global optimization method, such as Differential Evolution, is suggested for finding solutions which are likely to be close to the global optimum. Two case studies are presented.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Kubota:2007:cec, title={Learnability in Human Gesture Recognition for a Partner Robot Based on Computational Intelligence}, author={Naoyuki Kubota and Yu Tomioka}, pages = {2517--2522}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1772.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 selforganizing map. We show several experimental results and discuss their effectiveness.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Kubota:2007:cec, title={Evolutionary Robot Vision for Human Tracking of Partner Robots in Ambient Intelligence}, author={Naoyuki Kubota and Yu Tomioka}, pages = {1491--1496}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1773.pdf}, url = {}, size = {}, abstract = {This paper discusses the role of evolutionary computation in visual perception for partner robots. The search of evolutionary computation has many analogies with human visual search. First of all, we discuss the analogies between the evolutionary search and human visual search. Next, we propose the concept of evolutionary robot vision, and a human tracking method based on the evolutionary robot vision. The proposed method is composed of human detection and the update of human tracking positions. Finally, we show experimental results of the human tracking to discuss the effectiveness of our proposed method.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Kubota:2007:cec, title={Trajectory Generation Based on a Steady-State Genetic Algorithm for Imitative Learning of a Partner Robot}, author={Naoyuki. Kubota and Toshiyuki. Shimizu}, pages = {1497--1502}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1776.pdf}, url = {}, size = {}, abstract = {This paper proposes a steady-state genetic algorithm for trajectory generation used in the imitation of a partner robot interacting with a human. Various types of genetic algorithms have been applied for the trajectory generation of robot manipulators. In this paper, we propose a trajectory generation method for the partner robot by a steady-state genetic algorithm based on the human motions pattern, and compare the proposed method with its related methods. Finally, we show experimental results of trajectory generation through interaction with a human.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Shevertalov:2007:cec, title={A Genetic Algorithm for Solving the Binning Problem in Networked Applications Detection}, author={Maxim Shevertalov and Edward Stehle and Spiros Mancoridis}, pages = {713--720}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1778.pdf}, url = {}, size = {}, abstract = {Network administrators need a tool that detects the kind of applications running on their networks, in order to allocate resources and enforce security policies. Previous work shows that applications can be detected by analyzing packet size distributions. Detection by packet size distribution is more efficient and accurate if the distribution is binned. An unbinned packet size distribution considers the occurrences of each packet size individually. In contrast, a binned packet size distribution considers the occurrences of packets within packet size ranges. This paper reviews some of the common methods for binning distributions and presents an improved approach to binning using a Genetic Algorithms to assist the detection of network applications.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Riff:2007:cec, title={Towards an Immune System that Solves CSP}, author={Mar\'ia-Cristina Riff and Marcos Z\'u\~uiga}, pages = {2837--2841}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1779.pdf}, url = {}, size = {}, abstract = {Constraint satisfaction problems (CSPs) widely occur in artificial intelligence. In the last twenty years, many algorithms and heuristics were developed to solve CSP. Recently, bio-inspired algorithms have been proposed to solve CSP. They have shown to be more efficient than systematic approaches in solving hard instances. Given that recent publications indicate that Immune systems offer advantages to solve complex problems, our aim here is to propose an efficient immune system which can solve CSPs. We propose an immune system which is able to solve hard constraint satisfaction problems. The tests were carried out using random generated binary constraint satisfaction problems on the transition phase.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Duda:2007:cec, title={A Genetic Algorithm for Lot Sizing Optimization with a Capacity Loading Criterion}, author={Jerzy Duda and Andrzej Osyczka}, pages = {3790--3795}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1780.pdf}, url = {}, size = {}, abstract = {The lot sizing problems are one of the basic optimization problems, which have to be solved during production planning. Only a few nature-inspired algorithms have been proposed for solving such problems. In this study the authors propose a genetic algorithm for a discrete lot sizing problem with so called small buckets and the criterion of capacity utilization. The results are compared with CPLEX MIP solver and other heuristics. The genetic algorithm proposed here gives solutions, which are 0.4 to 2.9 percent away from the theoretical lower bound.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Simões:2007:cec, title={Improving Memory's Usage in Evolutionary Algorithms for Changing Environments}, author={A. Sim.oes and E. Costa}, pages = {276--283}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1783.pdf}, url = {}, size = {}, abstract = {When using Evolutionary Algorithms (EAs) in dynamic environments some extensions have been introduced in order to avoid the premature convergence of the population towards a non-optimal point of the search space. One of these improvements consists in adding an explicit memory used for storing good individuals from the search population. When the environment is cyclic and previous environments reappear later memory should maintain EA's performance by keeping individuals' fitness at an acceptable level. But in most situations this purpose is not achieved and the typical behavior of an EA when a change occurs involves the decrease of the best individual's fitness and some time is necessary to readapt to the new conditions. The key problem when using explicit memory is the restriction usually imposed on its size. So, when it is necessary to store a new individual and memory is full we need to replace some individuals. This replacement can lead to the destruction of information that might be useful in the future. In this paper we are interested in the enhancement of memory's usage and we propose two new replacing methods to apply when memory is full. The investigated methods were tested in several memory-based EAs and the results show that memory can be used in a more effective way so algorithms' performance is strongly improved.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Cobb:2007:cec, title={Case-Based Reasoning and Object-Oriented Data Structures Exploit Biological Analogs to Generate Virtual Evolutionary Linkages}, author={Corie L. Cobb and Ying Zhang and Alice M. Agogino and Jennifer Mangold}, pages = {334--341}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1784.pdf}, url = {}, size = {}, abstract = {Multiobjective Genetic Algorithms (MOGA) and Case-based Reasoning (CBR) have proven successful in the design of MEMS (Microelectromechanical Systems) suspension systems. Object-oriented data structures of primitive and complex genetic algorithm (GA) elements have been developed to restrict genetic operations to produce feasible design combinations as required by physical limitations or practical constraints. Thus, virtual linkage between genes and chromosomes are coded into the properties of pre-defined GA objects. A new design problem requires selecting the right primitive elements, associated data structures, and linkages that promise to produce the best gene pool for new functional requirements. In this paper, biomimetics is proposed as a means to examine and classify functional requirements so that case-based reasoning algorithms can be used to map design requirements to promising initial conceptual designs and appropriate GA primitives. The concept is demonstrated using micro-mechanical resonators.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Kukkonen:2007:cec, title={Ranking-Dominance and Many-Objective Optimization}, author={Saku Kukkonen and Jouni Lampinen}, pages = {3983--3990}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1785.pdf}, url = {}, size = {}, abstract = {An alternative relation to Pareto-dominance is studied. The relation is based on ranking a set of solutions according to each separate objective and an aggregation function to calculate a scalar fitness value for each solution. The relation is called as ranking-dominance and it tries to tackle the curse of dimensionality commonly observed in multi-objective optimization. Ranking-dominance can be used to sort a set of solutions even for a large number of objectives when the Pareto-dominance relation cannot distinguish solutions from one another anymore. This permits the search to advance even with a large number of objectives.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Tan:2007:cec, title={Clonal Particle Swarm Optimization and Its Applications}, author={Y. Tan and Z. M. Xiao}, pages = {2303--2309}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1786.pdf}, url = {}, size = {}, abstract = {Particle swarm optimization (PSO) is a stochastic global optimization algorithm inspired by social behavior of bird flocking in search for food, which is a simple but powerful, and widely used as a problem-solving technique to a variety of complex problems in science and engineering. A novel particle swarm optimization algorithm based on immunityclonal strategies, called as clonal particle swarm optimization (CPSO), is proposed at first in this paper. By cloning the best individual of ten succeeding generations, CPSO has better optimization solving capability and faster convergence performance than the conventional standard particle swarm optimization (SPSO) based on a number of simulations. A detailed description and explanation of the CPSO algorithm are given in the paper. Several experiments on six benchmark test functions are conducted to demonstrate that the proposed CPSO algorithm is able to speedup the evolution process and improve the performance of global optimizer greatly, while avoiding the premature convergence on the multidimensional variable space.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Téllez-Enríquez:2007:cec, title={An Ant System with Steps Counter for the Job Shop Scheduling Problem}, author={Emanuel T\'ellez-Enr\'iquez and Efrīen Mezura-Montes and Carlos A. Coello Coello }, pages = {477--484}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1787.pdf}, url = {}, size = {}, abstract = {In this paper, we present an ant system algorithm variant designed to solve the job shop scheduling problem. The proposed approach is based on a recent biological study which showed that natural ants can count their steps when they build the path between the nest and their food source. Experiments using a set of well-known job shop scheduling problems and a comparison against state-of-the-art techniques show that the proposed approach can reduce the number of evaluations performed without a degradation of performance. Additionally, our proposed approach reduces the number of parameters that need to be tuned by the user (specifically the parameters that balance the importance between the pheromone trail and heuristic values), with respect to the original ant system algorithm.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Wong:2007:cec, title={Effects of Program Simplification on Simple Building Blocks in Genetic Programming}, author={Phillip Wong and Mengjie Zhang}, pages = {1570--1577}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1789.pdf}, keywords = {genetic algorithms, genetic programming}, url = {}, size = {}, abstract = {This paper investigates the effects on building blocks of using simplification in a Genetic Programming (GP) system to combat the problem of code bloat. The evolved genetic programs are simplified online during the evolutionary process using algebraic simplification rules and hashing techniques. A simplified form of building block (numerical-nodes) is tracked throughout individual GP runs both when using or not using online simplification of evolved genetic programs. The results suggest that online simplification disrupts existing potential building blocks during the evolution process. However, GP with simplification is capable of creating new building blocks which are used to form a more accurate solution, when compared to the standard GP. The effectiveness of GP systems utilising simplification can be correlated to the creation of these new building blocks.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Tapia:2007:cec, title={Applications of Multi-Objective Evolutionary Algorithms in Economics and Finance: A Survey}, author={Ma. Guadalupe Castillo Tapia and Carlos A. Coello Coello }, pages = {532--539}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1792.pdf}, url = {}, size = {}, abstract = {This paper provides a state-of-the-art survey of applications of multi-objective evolutionary algorithms in economics and finance reported in the specialized literature. A taxonomy of applications within this area is proposed, and a brief review of the most representative research reported to date is then provided. In the final part of the paper, some potential paths for future research within this area are identified.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Jumonji:2007:cec, title={A Novel Distributed Genetic Algorithm Implementation with Variable Number of Islands}, author={Takuma Jumonji and Goutam Chakraborty and Hiroshi Mabuchi and Masafumi Matsuhara}, pages = {4698--4705}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1794.pdf}, url = {}, size = {}, abstract = {Genetic Algorithm (GA) has some inherent drawbacks which become apparent while trying to solve complex multimodal problems. They are slow and the efficiency depends on parameter values. Some methods were proposed for alleviating these problems. But they did not address all the drawbacks. In this work, we propose a new distributed implementation strategy named Variable Island GA (VIGA), where the number of islands vary. In VIGA, where the number of individuals in every island is 2, the parameter population size in an island is fixed. Other parameters like number of islands, crossover/mutation probabilities, also need not be set. As the generation progresses, islands are created or erased based on the convergence status of searching in each island. Experiments were done with different function optimization problems. For all experiments VIGA delivered better or at least as good results as obtained by other competitive algorithms, at the expense of less computation and communication costs.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Ibershoff:2007:cec, title={Making the SAT Decision Based on a DNA Computation}, author={Joseph Ibershoff and Jerzy W. Jaromczyk and Danny van Noort}, pages = {1835--1842}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1795.pdf}, url = {}, size = {}, abstract = {Much of the recent research in DNA computing has focused on designing better overall techniques for computation, or implementing the techniques in simulation or a wet-lab in order to show the viability of these techniques for solving small SAT problems. In this paper, we examine a major obstacle to using DNA computing to solve larger, real-world SAT problems for which the correct answer is not already known. In particular, we ask the following question: Given the results of a DNA computation, how does one determine the answer to the underlying SAT problem, and how does one examine the confidence of this answer? We examine this question in detail for selection-based DNA computing, and show that it is non-trivial to answer. We then introduce a method we call "decision thresholds" for answering it which can be applied to any variation of selection-based DNA computing. Furthermore, we provide an example by applying this method to the technique of using a network of microreactors employing negative selection of ssDNA.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Valdés:2007:cec, title={Virtual Reality High Dimensional Objective Spaces for Multi-Objective Optimization: An Improved Representationg}, author={Julio J. Vald\'es and Alan J. Barton and Robert Orchard}, pages = {4191--4198}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1796.pdf}, keywords = {genetic algorithms, genetic programming}, url = {}, size = {}, abstract = {This paper presents an approach for constructing improved visual representations of high dimensional objective spaces using virtual reality. These spaces arise from the solution of multi-objective optimization problems with more than 3 objective functions which lead to high dimensional Pareto fronts. The 3-D representations of m-dimensional Pareto fronts, or their approximations, are constructed via similarity structure mappings between the original objective spaces and the 3-D space. Alpha shapes are introduced for the representation and compared with previous approaches based on convex hulls. In addition, the mappings minimizing a measure of the amount of dissimilarity loss are obtained via genetic programming. This approach is preliminarily investigated using both theoretically derived high dimensional Pareto fronts for a test problem (DTLZ2) and practically obtained objective spaces for the 4 dimensional knapsack problem via multi-objective evolutionary algorithms like HLGA, NSGA, and VEGA. The improved representation captures more accurately the real nature of the m-dimensional objective spaces and the quality of the mappings obtained with genetic programming is equivalent to those computed with classical optimization algorithms.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Zhao:2007:cec, title={Particle Swarm Optimization for Optimal Deployment of Relay Nodes in Hybrid Sensor Networks}, author={Chunhua Zhao and Peng Chen}, pages = {3316--3320}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1797.pdf}, url = {}, size = {}, abstract = {In order to solve the problem of optimal deployment in static hybrid sensor networks, an particle swarm optimization for integer planning is adopted to optimally deploy the relay nodes so as to obtain the optimal energy efficiency by minimizing the average path length. The modified particle swarm optimization can improve calculation efficiency, because the method restricts the evolution within integer space has decreased the searching probability in many unwanted non-feasible solution field. The simulations showed that the optimal deployment of relay nodes can largely improve the energy efficiency and the location of sink has direct affect on the optimal efficiency.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(:2007:cec, title={A Versatile Quantum-Inspired Evolutionary Algorithm}, author={Micha\"el Defoin Platel and Stefan Schliebs and Nikola Kasabov }, pages = {423--430}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1798.pdf}, url = {}, size = {}, abstract = {This study points out some weaknesses of existing Quantum-Inspired Evolutionary Algorithms (QEA) and explains in particular how hitchhiking phenomenons can slow down the discovery of optimal solutions and encourage premature convergence. A new algorithm, called Versatile Quantum inspired Evolutionary Algorithm (vQEA), is proposed. With vQEA, the attractors moving the population through the search space are replaced at every generation without considering their fitness. The new algorithm is much more reactive. It always adapts the search toward the last promising solution found thus leading to a smoother and more efficient exploration. In this paper, vQEA is tested and compared to a Classical Genetic Algorithm CGA and to a QEA on several benchmark problems. Experiments have shown that vQEA performs better than both CGA and QEA in terms of speed and accuracy. It is a highly scalable algorithm as well. Finally, the properties of the vQEA are discussed and compared to Estimation of Distribution Algorithms (EDA).}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Lin:2007:cec, title={A Bicriteria Shortest Path Routing Problems by Hybird Genetic Alogrithm in Communication Networks}, author={Lin Lin and Mitsuo Gen}, pages = {4577--4582}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1799.pdf}, url = {}, size = {}, abstract = {Routing problem is one of the important research issues in communication network fields. In this paper, we consider a bicriteria shortest path routing (bSPR) model dedicated to calculating nondominated paths for (1) the minimum total cost and (2) the minimum transmission delay. To solve this bSPR problem, we propose a new multiobjective genetic algorithm (moGA): (1) an efficient chromosome representation using the priority-based encoding method; (2) a new operator of GA parameters auto-tuning, is adaptively regulation of exploration and exploitation based on the change of the average fitness of parents and offspring which is occurred at each generation; and (3) an interactive adaptive-weight fitness assignment mechanism is implemented that assigns weights to each objective and combines the weighted objectives into a single objective function. Numerical experiments with various scales of network design problems show the effectiveness and the efficiency of our approach by comparing with the recent researches.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Tanev:2007:cec, title={On Human Competitiveness of the Evolved Agent Operating a Scale Model of a Car}, author={Ivan T. Tanev and Katsunori Shimoharap}, pages = {3646--3653}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1800.pdf}, url = {}, size = {}, abstract = {We present an approach for evolutionary design of the driving style of an agent, remotely operating a scale model of a car in a human competitive way. The agent perceives the environment from an overhead video camera and conveys its actions to the car via standard radio remote control transmitter. In order to cope with the video feed latency we propose an anticipatory modeling in which the agent considers its current actions based on the anticipated intrinsic (rather than currently available, outdated) state of the car and its surrounding. We formalized the notion of driving style by defining the key parameters, which describe it, and demonstrated the feasibility of applying genetic algorithms to evolve the optimal values of these parameters. The optimized driving style, employed by the agent, is human competitive in that it yields both faster and more consistent lap times than those of a human around a predefined circuit. Presented work can be viewed as a step towards the automated design of the control software of remotely operated vehicles capable to find an optimal solution to various tasks in a priori known environmental situations. Also, the results can be seen as a verification of the feasibility of developing a framework of adaptive racing games in which the human competes against a computerized opponent with matching capabilities, both operating physical, scale models of cars.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(McNabb:2007:cec, title={Parallel PSO Using Map Reduce}, author={Andrew W. McNabb and Christopher K. Monson and Kevin D. Seppi}, pages = {7--14}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1802.pdf}, url = {}, size = {}, abstract = {In optimization problems involving large amounts of data, such as web content, commercial transaction information, or bioinformatics data, individual function evaluations may take minutes or even hours. Particle Swarm Optimization (PSO) must be parallelized for such functions. However, large-scale parallel programs must communicate efficiently, balance work across all processors, and address problems such as failed nodes.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Cornforth:2007:cec, title={An Investigation into Dynamic Problem Solving in a Hybird Evolutionary Market-Based Multi-Agent System}, author={D. J. Cornforth}, pages = {1732--1739}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1803.pdf}, url = {}, size = {}, abstract = {Static resources allocation problems have been widely studied. More recently some of this attention has changed to focus on dynamic problems, where problem specications, constraints or resources may change before a solution is obtained. This work examines an approach that combines a multi agent system based on a simulated market with evolutionary optimization. Previous work has showed the efcacy of such a hybrid approach, where the characteristics of agents are subject to evolutionary optimization. This work compares the multi agent only, and the hybrid system, when the problem is subject to random change during the attempt to find a solution. Results confirm the advantages of evolutionary optimization of agent rules in a static or dynamic environment, both in terms of tasks completed within a given time, and the cost per task completed. Surprisingly, an optimum amount of noise exists, that improves the performance of the multi agent or hybrid trading model.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Parasuraman:2007:cec, title={Multiple Sensors Data Integration Using MFAM for Mobile Robot Navigation}, author={S. Parasuraman and V. Ganapathy and Bijan Shirinzadeh}, pages = {2421--2427}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1805.pdf}, url = {}, size = {}, abstract = {The mobile robot navigation with complex environment needs more input space to match the environmental data into robot outputs in order to perform realistic task. At the same time, the number of rules at the rule base needs to be optimized to reduce the computing time and to provide the possibilities for real time operation. In this paper, the optimization of fuzzy rules using a Modified Fuzzy Associative Memory (MFAM) is designed and implemented. MFAM provides good flexibility to use multiple input space and reduction of rule base for robot navigation. This paper presents the MFAM model to generate the rule base for robot navigation. The behavior rules obtained from MFAM model are tested using simulation and real world experiments, and the results are discussed in the paper and compared with the existing methods.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Kotecha:2007:cec, title={MultiObjective Genetic Algorithm based Adaptive QoS Routing in MANET}, author={Ketan Kotecha and Sonal Popat}, pages = {1423--1428}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1806.pdf}, url = {}, size = {}, abstract = {Areas in which Genetic Algorithm (GA) excel is their ability to manipulate many parameters simultaneously, their use of parallelism enables them to produce multiple equally good solutions to the same problem. So GAs are most appropriate for multi objective optimization problems, in which there is no single value to be minimized or maximized, but having multiple objectives, usually with tradeoffs involved: one can only be improved at the expense of another. By looking at this strength of GA, we have applied MultiObjective GA to support QoS Routing in Mobile Ad-hoc Network (MANET).}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Skolpadungket:2007:cec, title={Portfolio Optimization Using Multi-Objective Genetic Algorithms}, author={Prisadarng Skolpadungket and Keshav Dahal and Napat Harnpornchai }, pages = {516--523}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1807.pdf}, url = {}, size = {}, abstract = {A portfolio optimisation problem involves allocation of investment to a number of different assets to maximize yield and minimize risk in a given investment period. The selected assets in a portfolio not only collectively contribute to its yield but also interactively define its risk as usually measured by a portfolio variance. In this paper we apply various techniques of multiobjective genetic algorithms to solve portfolio optimization with some realistic constraints, namely cardinality constraints, floor constraints and round-lot constraints. The algorithms experimented in this paper are Vector Evaluated Genetic Algorithm (VEGA), Fuzzy VEGA, Multiobjective Optimization Genetic Algorithm (MOGA), Strength Pareto Evolutionary Algorithm 2nd version (SPEA2) and Non-Dominated Sorting Genetic Algorithm 2nd version (NSGA2). The results show that using fuzzy logic to combine optimization objectives of VEGA (in VEGA_Fuz1) for this problem does improve performances measured by Generation Distance (GD) defined by average distances of the last generation of population to the nearest members of the true Pareto front but its solutions tend to cluster around a few points. MOGA and SPEA2 use some diversification algorithms and they perform better in terms of finding diverse solutions around Pareto front. SPEA2 performs the best even for comparatively small number of generations. NSGA2 performs closed to that of SPEA2 in GD but poor in distribution.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Olorunda:2007:cec, title={Differential Evolution in High-Dimensional Search Spaces}, author={Olusegun Olorunda and Andries P. Engelbrecht}, pages = {1934--1941}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1812.pdf}, url = {}, size = {}, abstract = {A possible way of dealing with a high dimensional problem space is to divide it up into smaller parts, and to have each part optimized by a separate population. A mechanism is then defined to construct a complete solution from the subpopulations, and to evaluate the entities contained in the subpopulations. This form of cooperation has been successfully applied to Particle Swarm Optimization (PSO), by [1] in the cooperative split PSO, and to genetic algorithms, in the cooperative coevolutionary genetic algorithm, developed by [2], on which the cooperative split PSO is based.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Seredynski:2007:cec, title={Preventing Selfish Behavior in Ad Hoc Networks}, author={M. Seredynski and P. Bouvry and M. A. Klopotek}, pages = {3554--3560}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1813.pdf}, url = {}, size = {}, abstract = {Cooperation enforcement is one of the key issues in ad hoc networks. In this paper we proposes a new strategy driven approach that aims at discouraging selfish behavior among network participants. Each node is using a strategy that defines conditions under which packets are being forwarded. Such strategy is based on the notion of trust and activity of the source node of the packet. This way network participants are forced to forward packets and to reduce the amount of time spent in a sleep mode. To evaluate strategies we use a new game theory based model of an ad hoc network. A genetic algorithm (GA) is applied to find good strategies. Experimental results show that our approach makes selfish behavior unattractive.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(:2007:cec, title={Interactive Composition Aid System by Means of Tree Representation of Musical Phrase}, author={Daichi Ando and Hitoshi Iba}, pages = {4258--4265}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1814.pdf}, keywords = {genetic algorithms, genetic programming}, url = {}, size = {}, abstract = {Research on the application of Interactive Evolutionary Computation(IEC) to the field of musical computation has been improved in recent years, marking an interesting parallel to the current trend of applying human characteristics or sensitivities to computer systems. However, past techniques developed for IEC-based composition have not necessarily proven very effective for professional use. This is due to the large difference between data representation used by IEC and authored classical music composition. To solve this difficulties, the authors purpose a new IEC approach to music composition based on classical music theory. In this paper, the authors describe an established system according to the above idea, and detail of making success of composition a piece.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Kim:2007:cec, title={Evolving Hypernetwork Classifiers for microRNA Expression Profile Analysis}, author={Sun Kim and Soo-Jin Kim and Byoung-Tak Zhang}, pages = {313--319}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1815.pdf}, url = {}, size = {}, abstract = {High-throughput microarrays inform us on different outlooks of the molecular mechanisms underlying the function of cells and organisms.While computational analysis for the microarrays show good performance, it is still difficult to infer modules of multiple co-regulated genes. Here, we present a novel classification method to identify the gene modules associated with cancers from microarray data. The proposed approach is based on 'hypernetworks', a hypergraph model consisting of vertices and weighted hyperedges. The hypernetwork model is inspired by biological networks and its learning process is suitable for identifying interacting gene modules. Applied to the analysis of microRNA (miRNA) expression profiles on multiple human cancers, the hypernetwork classifiers identified cancer-related miRNA modules. The results show that our method performs better than decision trees and naive Bayes. The biological meaning of the discovered miRNA modules has been examined by literature search.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(He:2007:cec, title={A Soft Computing Method to Estimate the Effect of Production Factors on Economic Growth}, author={Yong He and Kejun Zhu}, pages = {4337--4343}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1821.pdf}, url = {}, size = {}, abstract = {This paper utilizes soft computing to estimate the effects of production factors on economic growth. Using GA-ISODATA algorithm to categorize China(which contains 31 regions) according to the level of Science and Technology (S&T), then sets up the fuzzy mapping relation from production factors (fixed assets, human capital and plowland) to economic output, and the result shows that: during the year 1999 to 2003, the effects of the production factors on economic growth are remarkably different in the regions which have dissimilar levels of S&T, the effects of fixed asset, human capital on economy in Developed S&T regions are greater than developing or underdeveloped S&T regions, but the effect of plowland in Developed S&T regions is less than developing or underdeveloped S&T regions; the effect of human capital on economic growth is greater than fixed assets for all regions; S&T progress and institutional innovation are playing fundamental roles on economic growth.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Hong:2007:cec, title={Experimental Analysis of Dynamic Migration Intervals on 0/1 Knapsack Problems}, author={Tzung-Pei Hong and Wen-Yang Lin and Shu-Min Liu and Jiann-Horng Lin}, pages = {1163--1167}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1822.pdf}, url = {}, size = {}, abstract = {In this paper, the effects of adapting the migration intervals on the performance and solution quality of dynamic multi-population genetic algorithms (MGAs) are investigated. An adaptive scheme to evolve the appropriate migration intervals for MGAs is presented. An adaptive multi-population genetic algorithm which can adjust the migration intervals is proposed. Experiments on the 0/1 knapsack problem are conducted to show the effectiveness of the proposed approach. The experimental results have illustrated the effectiveness of the self-adaptation of migration intervals for MGAs.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Mohemmed:2007:cec, title={A New Particle Swarm Optimization Based Algorithm for Solving Shortest-Paths Tree Problem}, author={Ammar W. Mohemmed and Nirod Chandra Sahoo and Tan Kim Geok}, pages = {3221--3225}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1823.pdf}, url = {}, size = {}, abstract = {This paper presents an efficient particle swarm optimization (PSO) based search algorithm for solving the single source all destination shortest paths or what is called the shortest-paths tree (SPT), commonly encountered in graph theory. A new particle encoding/decoding scheme has been devised for particle-representation of the SPT parameters. This encoding/decoding exploits the sub-optimality feature of the shortest path. In the proposed algorithm, the solution, the shortest path tree, is not represented by one particle, but it is the solution that is contributed by the complete swarm population. Numerical computation results on several networks with random topologies illustrate the efficiency of the proposed PSO method for computation of shortest paths in networks.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Neri:2007:cec, title={An Adaptive Prudent-Daring Evolutionary Algorithm for Noise Handling in On-line PMSM Drive Design}, author={Ferrante Neri and Giuseppe L. Cascella and Nadia Salvatore and Silvio Stasi }, pages = {584--591}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1824.pdf}, url = {}, size = {}, abstract = {This paper studies the problem of the optimal control design of Permanent Magnet Synchronous Motor (PMSM) drives taking into account the noise due to sensors and measurement devices. The problem is analyzed by means of an experimental approach which considers noisy data returned by the real plant (on-line). In other words, each fitness evaluation does not come from a computer but from a real laboratory experiment. In order to perform the optimization notwithstanding presence of the noise, this paper proposes an Adaptive Prudent- Daring Evolutionary Algorithm (APDEA). The APDEA is an evolutionary algorithm with a dynamic parameter setting. Furthermore, the APDEA employs a dynamic penalty term and two cooperative-competitive survivor selection schemes. The numerical results show that the APDEA robustly executes optimization in the noisy environment. In addition, comparison with other meta-heuristics shows that behavior of the APDEA is very satisfactory in terms of convergence velocity. A statistical test confirms the effectiveness of the APDEA.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Pathak:2007:cec, title={Multi-Resource-Constrained Discrete Time-Cost Tradeoff with MOGA Based Hybrid Method}, author={Bhupendra Kumar Pathak and Harish Kumar Singh and Sanjay SrivastavaA}, pages = {4425--4432}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1825.pdf}, url = {}, size = {}, abstract = {This work focuses on solving multi-resource-constrained discrete time-cost tradeoff (MRCDTCT) problems in project scheduling using a novel methodology which hybridizes a heuristic with a multi-objective genetic algorithm ― a hybrid MOGA. The proposed method is pertinent for the real world project scheduling where the resources are constrained, and where generation of complete Pareto-optimal front is essential for a decision-maker. Accordingly, the entire time-cost tradeoff (TCT) profile is identified, wherein the decision-maker basically makes a sequence of decisions, which optimizes the overall performance of the project in terms of time and cost by satisfying not only the precedence constraints but also the resource constraints. A MOGA, is devised to search for the optimal profile, and a heuristic is developed and hybridized with MOGA to ensure the availability of the resource requirements for each instance of the project schedule of MOGA by adjusting the start time of non-critical activities exploiting their float values. To demonstrate the efficacy of MOGA employed in this work, two standard test problems from the literature are attempted. The hybrid MOGA is employed to solve two test problems of TCT, one with a single resource constraint, and the other with three resource constraints. The results are also compared with those of exhaustive enumeration technique - the close proximity between these results validates the suitability and accuracy of hybrid MOGA to solve MRCDTCT problems.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Raghuraj:2007:cec, title={Genetic Programming Models for Classification of Data from Biological Systems}, author={K. Rao Raghuraj and S. Lakshminarayanan and Kyaw Tun}, pages = {4154--4161}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1827.pdf}, url = {}, size = {}, abstract = {Data classification problems especially for biological systems pose serious challenges mainly due to the presence of multivariate and highly nonlinear interactions between variables. Specimens that need to be distinguished from one another show similar profiles and cannot be separated easily based on decision boundaries or available discriminating rules. Alternatively, inter-relations among the feature vectors can be exploited for distinguishing samples into specific classes. Such variable interaction models are difficult to establish given the nature of biological systems. Genetic Programming, a data driven evolutionary modeling approach is proposed here to be a potential tool for designing variable dependency models and exploiting them further for class discrimination. A new and alternative GP model based classification approach is proposed. Analysis is carried out using illustrative datasets and the performance is benchmarked against well established linear and nonlinear classifiers like LDA, kNN, CART, ANN and SVM. It is demonstrated that GP based models can be effective tools for separating unknown biological systems into different classes. The new classification method has the potential to be effectively and successfully extended to many systems biology applications of recent interest.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Ram:2007:cec, title={A Guided Genetic Algorithm for Learning Gene Regulatory Networks}, author={Ramesh Ram and Madhu Chetty}, pages = {3862--3869}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1828.pdf}, url = {}, size = {}, abstract = {In the post-genomic era, understanding the interactions of genes plays a vital role in the analysis of complex biological systems. Recently, we developed a causal model approach for learning gene regulatory networks from microarray data. The optimization process for this learning was implemented by using genetic algorithm (GA) as a search technique to find the best candidate over the space of possible networks. In this paper, we propose a genetic algorithm which is guided by exploiting certain characteristics of diversity and high level heuristics in order to generate good networks as quickly as possible. A comparison of this algorithm to the standard genetic algorithms implemented in our earlier work is also presented in this paper. The Guided GA (GGA) is tested on both synthetic and real-world microarray data. The novel approach of GGA shows superiority of the solutions, computational efficiency along with accuracy improvement compared to standard GA.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Hughes:2007:cec, title={MSOPS-II: A General-Purpose Many-Objective Optimiser}, author={Evan J. Hughes}, pages = {3944--3951}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1835.pdf}, url = {}, size = {}, abstract = {Existing evolutionary methods capable of true Many-Objective optimisation have been limited in their application: for example either initial search directions need to be specified a-priori, or the use of hypervolume limits the search in practice to less than 10 objective dimensions. This paper describes two extensions to the Multiple Single Objective Pareto Sampling (MSOPS) algorithm. The first provides automatic target vector generation, removing the requirement for initial a-priori designer intervention; and secondly redefines the fitness assignment method to simplify analysis and allow more comprehensive constraint handling. The significant enhancements allow the new MSOPS-II ranking process to be used as part of a general-purpose multi/many objective optimisation algorithm, requiring minimal initial configuration.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Lu:2007:cec, title={Conditional Value-at-Risk Based Mid-Term Generation Operation Planning in Electricity Market Environment}, author={Gang Lu and FushuanWen and C. Y. Chung and K. P. Wong}, pages = {2745--2750}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1837.pdf}, url = {}, size = {}, abstract = {In the electricity market environment, it is very important for generation companies (GENCOs) to make the optimal mid-term generation operation planning (MTGOP) which includes the trading strategies in the spot market and the contract market as well as the suitable unit maintenance scheduling (UMS). In making the decision of MTGOP, GENCOs are subject to risk due to uncertain factors, and hence should manage the inevitable risk rationally. Given this background, a new MTGOP model is first developed for a GENCO as a price taker so as to maximize its profit and minimize its risk measured by the Conditional Value-at-Risk (CVaR). In this model, the bilateral physical contracts are taken into consideration, together with the transmission congestion and the operation constraints of generating units. Then, a solving method is given by integrating the Genetic Algorithm and the Monte Carlo method. Finally, a numerical example is used to show the features of the proposed method.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Masaru:2007:cec, title={Genetic Algorithm Approach to Design Covariates of Binomial Logit Model for Estimation of Default Probability}, author={Tezuka Masaru and Ito Yoichi and Munakata Satoshi}, pages = {4344--4349}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1839.pdf}, url = {}, size = {}, abstract = {Credit risk management is one of the most important tasks of financial institutes. Default probability is the probability that a company will go into default, or be unable to fulfill an obligation, and it is a critical information for credit administration. Binomial logit model is widely used for default probability estimation. The formulas for computing covariates used in the model are designed by human experts in trial-and-error way, based on their experience. In this paper, we propose a method to design covariates. Integer-coded GA is employed and a representation of the chromosome is proposed for the purpose of optimizing the covariate. The method optimizes the covariates using the GA and estimates the coefficient of binomial logit model using Broyden-Fletcher-Goldfarb-Shanno method. The method is tested on an actual data provided for evaluation by a bank. The result of the experiment shows the method outperformed the human design.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Xu:2007:cec, title={Optimal Parameter Setting of Performance Based Regulation with Reward and Penalty}, author={Nuo Xu and Fushuan Wen and Minxiang Huang and Zhaoyang Dong}, pages = {2740--2744}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1844.pdf}, url = {}, size = {}, abstract = {The employment of performance based regulation (PBR) in distribution systems could provide some incentive for improving operating efficiency and reducing electricity prices. However, if the PBR mechanism is not properly designed, the enforcement of the PBR may have a negative effect on the supply reliability. In this paper, a mathematical model for optimally setting the parameters of the PBR with a reward/penalty structure is presented, with the minimization of the costs associated with the enforcement of the PBR as the objective and the required reliability level for the distribution system operation as the constraint. Finally, the well-known genetic algorithm is employed for solving the optimization problem. The effectiveness of the approach is demonstrated on a sample example.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Soltoggio:2007:cec, title={Evolving Neuromodulatory Topologies for Reinforcement Learning-Like Problems}, author={Andrea Soltoggio and Peter D\"urr and Claudio Mattiussi and Dario Floreano}, pages = {2471--2478}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1847.pdf}, url = {}, size = {}, abstract = {Environments with varying reward contingencies constitute a challenge to many living creatures. In such conditions, animals capable of adaptation and learning derive an advantage. Recent studies suggest that neuromodulatory dynamics are a key factor in regulating learning and adaptivity when reward conditions are subject to variability. In biological neural networks, specific circuits generate modulatory signals, particularly in situations that involve learning cues such as a reward or novel stimuli. Modulatory signals are then broadcast and applied onto target synapses to activate or regulate synaptic plasticity.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Acharya:2007:cec, title={Constrained Genetic Algorithm Based Independent Component Analysis}, author={D. P. Acharya and G. Panda and Y. V. S. Lakshmi}, pages = {2443--2449}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1849.pdf}, url = {}, size = {}, abstract = {Independent Component Analysis, a computationally efficient statistical signal processing technique, has been an area of interest for researchers for many practical applications in various fields of science and engineering. The present paper proposes a constrained genetic algorithm optimization based independent component analysis assuming a noise free Independent Component Analysis (ICA) model. It investigates on the application and performance of the popular evolutionary computation technique GA in independent component analysis problem. It is observed that the proposed constrained genetic algorithm optimization based ICA overcomes the long standing permutation ambiguity and recovers the independent components in a fixed order which is dependent on the statistical characteristics of the signals to be estimated. The constrained GA based ICA has also been compared with the most popular fast ICA algorithm.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Jaroš:2007:cec, title={Parallel BMDA with Probability Model Migration}, author={Ji\v{r}\'i Jaro\v{s} and Josef Schwarz}, pages = {1059--1066}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1851.pdf}, url = {}, size = {}, abstract = {The paper presents a new concept of parallel bivariate marginal distribution algorithm using the stepping stone based model of communication with the unidirectional ring topology. The traditional migration of individuals is compared with a newly proposed technique of probability model migration. The idea of the new xBMDA algorithms is to modify the learning of classical probability model (applied in the sequential BMDA [1]). In the first strategy, the adaptive learning of the resident probability model is used. The evaluation of pair dependency, using Pearson's chi-square statistics is influenced by the relevant immigrant pair dependency according to the quality of resident and immigrant subpopulation. In the second proposed strategy, the evaluation metric is applied for the diploid mode of the aggregated resident and immigrant subpopulation. Experimental results show that the proposed adaptive BMDA outperforms the traditional concept of individual migration.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Lai:2007:cec, title={An Effective Genetic Algorithm to ImproveWireless Sensor Network Lifetime for Large-Scale Surveillance Applications}, author={Chih-Chung Lai and Chuan-Kang Ting and Ren-Song Ko}, pages = {3531--3538}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1852.pdf}, url = {}, size = {}, abstract = {Wireless sensor network lifetime for large-scale surveillance systems is defined as the time span that all targets can be covered. One approach to extend the lifetime is to divide the deployed sensors into disjoint subsets of sensors, or sensor covers, such that each sensor cover can cover all targets and work by turns. The more sensor covers can be found, the longer sensor network lifetime can be prolonged. Finding the maximum number of sensor covers can be solved via transformation to the Disjoint Set Covers (DSC) problem, which has been proved to be NP-complete. For this optimization problem, existing heuristic algorithms either get unsatisfactory solutions in some cases or take exponential time complexity. This paper proposes a genetic algorithm to solve the DSC problem. The simulation results show that the proposed algorithm can get near-optimal solutions with polynomial computation time and can improve the performance of the most constrained-minimum constraining heuristic algorithm by 16% in solution quality.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Ataka:2007:cec, title={Solving Exclusionary Side Constrained Logistics Network by Using Priority-Based GA}, author={Shinichiro Ataka and Mitsuo Gen}, pages = {4483--4488}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1853.pdf}, url = {}, size = {}, abstract = {Logistics network design is one of the most important fields in supply chain management (SCM). For some real-world applications, it is often that the Transportation Problem (TP) is extended to satisfy several other additional constraints or performed in several stages. There is Transportation Problem with exclusionary side constraints (escTP) proposed by Sun as the example. In this model, the TP is extended to satisfy the additional constraints in which the simultaneous shipment from some pairs of plants are prohibited. The restrictions which this model has are deeply concerned with TP in the real world. In this paper, we consider the exclusionary side constrained Logistics Network (esc-LN). It is the network model which combined two-stage Transportation Problem (tsTP) and escTP. To solve the problem, we used a priority-based Genetic Algorithm (priGA), and introduce the designing method of a delivery course.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Bärecke:2007:cec, title={Memetic Algorithms for Inexact Graph Matching}, author={Thomas B\"arecke and Marcin Detyniecki}, pages = {4238--4245}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1854.pdf}, url = {}, size = {}, abstract = {The noise-robust matching of two graphs is a hard combinatorial problem with practical importance in several domains. In practical applications, a unique solution for a given instance can not be defined, i.e. the actual solution may be outscored by some other due to noise effects arising during feature extraction. Soft computing approaches in general provide fast but not necessarily globally optimal solutions. In this case, the lack of guarantee of the global optimum is not a real drawback, since the uncertainty already arises in the problem definition. This paper discusses the application of memetic algorithms on the error-correcting graph isomorphism problem. We show that permutation encoding is robust enough to allow addressing both, the matching problem for graphs of the same size, and the subgraph matching problem. Since gene order information is meaningless in this particular case, a strict position based crossover is applied providing better performance than the popular PMX. We evaluate our algorithm on a synthetic data set with larger graph sizes than used in traditional, exact approaches and other permutation-based genetic approaches.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Oh:2007:cec, title={Multiobjective Optimization of Sensor Network Deployment by a Genetic Algorithm Shao Chong Oh, Chung Huat Tan, Fook Wai Kong, Yuan Sin Tan, Khin Hua Ng, Gee Wah Ng}, author={and K. Tai}, pages = {3917--3921}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1855.pdf}, url = {}, size = {}, abstract = {Decision support tools for assisting the human mission planner in the deployment of sensor networks is an important component of sensor management. The optimal selection of the number and types of sensors available from a suite of sensors, and their optimal placement in the terrain, is typically a multiobjective optimization problem with objectives defined based on the mission and scenario. One of the key advantages in applying multiobjective genetic algorithms for solving such problems is their ability to find multiple Pareto optimal solutions in a single run which then allows for the mission planner to select a final optimal solution based on higher-level considerations. The aim in this work is an effective genetic algorithm implementation of such a decision support tool for the deployment of sensor networks.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(:2007:cec, title={Combining Exhaustive Search with Evolutionary Computation via Computational Resource Allocation}, author={S. Y. Zhao and K. Y. Szeto}, pages = {1878--1881}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1857.pdf}, url = {}, size = {}, abstract = {The division of the solution space into several subspaces and the subsequent search restricted to individual subspace have the advantage that effort in one subspace will not be repeated in the other subspace. This feature of exhaustive search is combined with evolutionary computation in each subspace via an adaptive allocation of computational resource to subspace search. A recent version of Genetic algorithm, called MOGA [1], [2], [3] is used as the evolutionary computation. Chromosomes evolve in a given subspace only. The computational resource allocation will be based on the quality of the search results: the subspace expected to contain the true solution will be given more computational resource. In this way, a quasi-parallelism is provided to evolutionary computation in different subspace in terms of computational time [4]. Various ways of resource allocation have been tried on the knapsack problem and the Weierstrass's Function problem. Results show that in general, division of solution space into subspace provides a higher efficiency.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Misra:2007:cec, title={Simplified Polynomial Neural Network for Classification Task in Data Mining }, author={B. B. Misra and B. N. Biswal and P. K. Dash and G. Panda}, pages = {721--728}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1858.pdf}, url = {}, size = {}, abstract = {In solving classification task of data mining the traditional Polynomial Neural Network (PNN) algorithm takes longer time while generating complex mathematical models. PNN algorithm takes the combinations two or three inputs to generates one Partial Description (PD) for the next layer. The output of the PDs becomes the input to the next layer. The number of PDs in each layer increases very fast, which consume lot of time for evaluation of the coefficients of the PDs, consume huge memory and increase complexity of the model. We propose Simplified Polynomial Neural Network (SPNN) for the task of classification. PDs for a single layer of the PNN model are developed. The outputs of these PDs along with the original inputs from the dataset are fed to a single perception model of Artificial Neural Network (ANN) without any hidden layers. The ANN is trained with gradient descent method as well as with Particle Swarm Optimization (PSO) technique. The results of both techniques for training are considered for the comparison of the performance. Simulation and result shows that the performance of SPNN is better than PNN model.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Anand:2007:cec, title={A Novel Fuzzy and Multiobjective Evolutionary Algorithm Based Gene Assignment for Clustering Short Time Series Expression Data}, author={Ashish Anand and P. N. Suganthan and Kalyanmoy Deb}, pages = {297--304}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1859.pdf}, url = {}, size = {}, abstract = {Conventional clustering algorithms based on Euclidean distance or Pearson correlation coefficient are not able to include order information in the distance metric and also unable to distinguish between random and real biological patterns. We present template based clustering algorithm for time series gene expression data. Template profiles are defined based on up-down regulation of genes between consecutive time points. Assignment of genes to templates is based on fuzzy membership function. Multi-objective evolutionary algorithm is used to determine compact clusters with varying number of templates. Statistical significance of each template is determined using permutation based non-parametric test. Statistically significant profiles are further tested for their biological relevance using gene ontology analysis. The algorithm was able to distinguish between real and noisy pattern when tested on artificial and real biological data. The proposed algorithm has shown better or similar performance compared to STEM and better than k-means on a real biological data.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Yamashita:2007:cec, title={A Practical Framework to Utilize Quantum Search}, author={Shigeru Yamashita and Masaki Nakanishi}, pages = {4086--4093}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1865.pdf}, url = {}, size = {}, abstract = {In this paper we propose a practical framework to utilize quantum computers in the future. To the best of our knowledge, this is the first paper to show a concrete usage of quantum computation in general programming. In our framework, we can utilize a quantum computer as a coprocessor to speed-up some parts of a program that runs on a classical computer. To do so, we propose several new ideas and techniques, such as a practical method to design a large quantum circuits for search problems and an efficient quantum comparator.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Ma:2007:cec, title={Pipeline Defect Detection and Sizing Based on MFL Data Using Immune RBF Neural Networks}, author={Zhongli Ma and Hongda Liu}, pages = {3399--3403}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1866.pdf}, url = {}, size = {}, abstract = {Corrosion inspection tools, or pipeline pigs, based on Magnetic Flux Leakage (MFL) are commonly used in oil-gas pipeline inspections. One of the difficult areas in applying this type of technology is how to recognize and quantify the corrosion characteristics. A Radial Basis Function Neural Network (RBFNN) has been found to be a suitable technique for such purposes. The RBFNN has excellent local closing ability and the Immune algorithm has self-organization ability. Utilizing the advantages of both, an Immune RBFNN (IRBFNN) algorithm was proposed to process the MFL data. In this paper, steps of the algorithm are shown in detail, and the model built was applied to lab data to assess this technique's ability to determine the location and size of the corrosion spots on the pipeline. A segment of metal pipeline was taken as an object for inspection. Several corrosion spots were artificially made on it. The sizes of the square corrosion spots were first computed by using standard 3D finite-element analysis and this was then compared to the MFL data collected. These sizes were regarded as training inputs to the neural network. Experimental results show that the method successfully identified the location and size of corrosion on the pipe. It is fast in convergence, responds to the geometrical characteristics of corrosion correctly and, thus provides a promising new method for accurately detecting and sizing oil-gas pipeline corrosion.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Quek:2007:cec, title={An Evolutionary, Game Theoretic Approach to the Modeling, Simulation and Analysis of Public Goods Provisioning under Asymmetric Information}, author={H. Y. Quek and A. Tay}, pages = {4735--4742}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1868.pdf}, url = {}, size = {}, abstract = {The paper presents an agent-based, computational model to simulate and analyze the collective outcome of public goods provisioning under asymmetric information. Agents that embrace different information types are conceptualized and configured to interact in an N-player public goods game where each agent group adapts to the dynamic environment using co-evolutionary learning. The impact of information type, number of players, rate of interaction, group size, and the scheme of game play are studied under different settings. The simulated results reveal interesting dynamics of strategy profiles, level of public goods provisioned, and the evolution of cooperation. Analysis of these simulated attributes provides a more holistic understanding of collective action and insights into how the effects of social dilemma can be mitigated.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Lay:2007:cec, title={Applying Artificial Immune Systems to Real-Time Embedded Systems}, author={Nicholas Lay and Iain Bate}, pages = {3743--3750}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1869.pdf}, url = {}, size = {}, abstract = {Real-time systems are becoming more complex at the same time as the expectation of adaptability and dependability rises. Traditional methods for ensuring no faults in the design or identifying their source are labour intensive and overly restrictive. In this paper we explore how concepts of AIS can be applied to RTS to aid their design and maintenance. Contributions are made to both the AIS and RTS communities.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Cupertino:2007:cec, title={Compact GAs for Neural Network Online Training in Tubular Linear Motor Control}, author={F. Cupertino and E. Mininno and D. Naso and B. Turchiano and L. Salvatore}, pages = {1542--1547}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1872.pdf}, url = {}, size = {}, abstract = {This paper describes a control system for a tubular synchronous linear motor based on a combination of a linear PID controller and a nonlinear neural network. The nonlinear part of the controller is introduced to progressively augment the tracking performance of the system and is trained online by a compact GA. We implement a variant of a known compact GA that well lends itself to practical implementations in low capacity microcontrollers, thanks to its reduced memory requirements and better distributed computational loads. The potential of the proposed approach is assessed by means of a simulation study on a detailed model of a linear motor. The control system obtained through genetic search outperforms alternative schemes obtained with linear design techniques.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(:2007:cec, title={Obtaining Better Non-Dominated Sets Using Volume Dominance}, author={Khoi Le and Dario Landa-Silva}, pages = {3119--3126}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1874.pdf}, url = {}, size = {}, abstract = {An important goal in multiobjective optimisation is to find a good set of non-dominated solutions that is both well-distributed and well-converged. Most multiobjective optimisation algorithms use the conventional Pareto dominance relationship. Over recent years, new approaches for the dominance relationship such as relaxed Pareto dominance, have been proposed. The interest in relaxed forms of Pareto dominance has been increasing due to their capability to find extreme values in the objective space. This paper conducts a short review on relaxed forms of Pareto dominance in the literature. A new form of relaxed Pareto dominance, called volume dominance, is presented afterward. The results based on comparison between volume dominance and Pareto dominance using SEAMO2, SPEA2 and NSGA2 show that the proposed volume dominance is capable of obtaining a better and smoother trade-off front, and it is more robust than Pareto dominance.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Kanyakam:2007:cec, title={Passive Vibration Suppression of a Walking Tractor Handlebar Structure Using Multiobjective PBIL}, author={Siwadol Kanyakam and Sujin Bureerat}, pages = {4162--4169}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1876.pdf}, url = {}, size = {}, abstract = {This paper is concerned with vibration suppression of a walking tractor handler structure using multiobjective population-based incremental learning (PBIL). Two bi-objective optimisation problems are assigned aiming at vibration alleviation as well as structure mass reduction. Design variable are structure shape and sizing parameters Whereas the objective functions include structural weight, natural frequencies, and frequency response function. The problems are posed to minimise the objecives whilst meeting structural safety requirement. The PBIL multiobjective optimiser is detailed and implemented to solve the optimization problems. The optimum results obtained are compared, illustrated and discussed. It is shown that simple but effective passive vibration control of a handlebar structure can be achieved through the implementation of the proposed mutiobjective PBIL.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Jebalia:2007:cec, title={Identification of the Isotherm Function in Chromatography Using CMA-ES}, author={M. Jebalia and A. Auger and M. Schoenauer and F. James and M. Postel}, pages = {4289--4296}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1879.pdf}, url = {}, size = {}, abstract = {This paper deals with the identification of the flux for a system of conservation laws in the specific example of analytic chromatography. The fundamental equations of chromatographic process are highly non linear. The state-of-the-art Evolution Strategy, CMA-ES (the Covariance Matrix Adaptation Evolution Strategy), is used to identify the parameters of the so-called isotherm function. The approach was validated on different configurations of simulated data using either one, two or three components mixtures. CMA-ES is then applied to real data cases and its results are compared to those of a gradient-based strategy.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Atashpaz-Gargari:2007:cec, title={Imperialist Competitive Algorithm: An Algorithm for Optimization Inspired by Imperialistic Competition}, author={Esmaeil Atashpaz-Gargari and Caro Lucas}, pages = {4661--4667}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1880.pdf}, url = {}, size = {}, abstract = {This paper proposes an algorithm for optimization inspired by the imperialistic competition. Like other evolutionary ones, the proposed algorithm starts with an initial population. Population individuals called country are in two types: colonies and imperialists that all together form some empires. Imperialistic competition among these empires forms the basis of the proposed evolutionary algorithm. During this competition, weak empires collapse and powerful ones take possession of their colonies. Imperialistic competition hopefully converges to a state in which there exist only one empire and its colonies are in the same position and have the same cost as the imperialist. Applying the proposed algorithm to some of benchmark cost functions, shows its ability in dealing with different types of optimization problems.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(:2007:cec, title={Evolutionary Simulation of Hedging Pressure in Futures Markets}, author={J. Duke and C. D. Clack}, pages = {782--789}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1882.pdf}, url = {}, size = {}, abstract = {We present a real world application that models a financial futures market. The agent-based simulation includes speculator agents each of which uses a Genetic Algorithm to improve its profitability in the market. This is a realistic simulation whose rates-of-return distribution is similar to those of real futures markets such as corn and FTSE100 futures.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Chandrasekaran:2007:cec, title={Multi-Objective Particle Swarm Optimization Algorithm for Scheduling in Flowshops to Minimize Makespan, Total Flowtime and Completion Time Variance}, author={S. Chandrasekaran and S. G. Ponnambalam and R. K. Suresh and N. Vijayakumar}, pages = {4012--4018}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1883.pdf}, url = {}, size = {}, abstract = {The present work deals with the development of particle swarm optimization algorithm to solve the multi-objective flowshop scheduling problem. In this paper, minimization of makespan, total flowtime and completion time variance are considered simultaneously. Performance of the proposed methodology has been tested by solving benchmark scheduling problems available in the literature. The proposed methodology is guided to search a set of nondominated solutions close to the Pareto front. The search capability of the proposed PSO algorithm is enhanced using a local search mechanism. This work is a preliminary step in our research to identify the reference or Pareto solution sets for the benchmark FSPs proposed in the literature, when (Cmax), (tft) and (ctv) are to be simultaneously optimized.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Michelitsch:2007:cec, title={Designing Memetic Algorithms for Real-World Applications Using Self-Imposed Constraints}, author={T. Michelitsch and T. Wagner and D. Biermann and C. Hoffmann}, pages = {3050--3057}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1886.pdf}, url = {}, size = {}, abstract = {Memetic algorithms (MAs) combine the global exploration abilities of evolutionary algorithms with a local search to further improve the solutions. While a neighborhood can be easily defined for discrete individual representations, local search within real-valued domains requires an appropriate choice of the local search method. If the subject of optimization shows discontinuous behavior, a standard hill-climbing routine cannot be successfully applied. Thus, in this paper we present a general approach that defines a quasi-discrete neighborhood for real-valued variables by applying problem-specific self-imposed constraints. Thereby, knowledge about properties of good solutions can be easily integrated into the search process and discontinuous parts can be found. Satisfying results can be obtained faster while all important issues in the design of MAs are preserved.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Balaji:2007:cec, title={Uncertainties Reducing Techniques in Evolutionary Computation}, author={P. G. Balaji and D. Srinivasan and C. K. Tham }, pages = {556--563}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1887.pdf}, url = {}, size = {}, abstract = {Real-world applications are bound to have certain level of uncertainty inherent in them. Among this noise is one of the most predominant factors affecting the optimization process whether it is conventional or evolutionary techniques. The evolutionary optimization techniques are found to be inherently stronger and robust to noisy environments but they are robust for lower noise levels, higher noise requires corrections to be made to the algorithm. This paper attempts to provide a comprehensive overview of the different correction methods used for optimizing noisy objective functions or fitness functions that creates uncertain environment and also provide with an brief overview of the other issues involved while using evolutionary computational methods for optimizing applications in uncertain environment.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(:2007:cec, title={On the Influence of Selection Operators on Performances in Cellular Genetic Algorithms}, author={D. Simoncini and P. Collard and S. Verel and M. Clergue}, pages = {4706--4713}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1888.pdf}, url = {}, size = {}, abstract = {In this paper, we study the influence of the selective pressure on the performance of cellular genetic algorithms. Cellular genetic algorithms are genetic algorithms where the population is embedded on a toroidal grid. This structure makes the propagation of the best so far individual slow down, and allows to keep in the population potentially good solutions. We present two selective pressure reducing strategies in order to slow down even more the best solution propagation. We experiment these strategies on a hard optimization problem, the Quadratic Assignment Problem, and we show that there is a threshold value of the control parameter for both which gives the best performance. This optimal value does not find explanation on the selective pressure only, measured either by takeover time or diversity evolution. This study makes us conclude that we need other tools than the sole selective pressure measures to explain the performance of cellular genetic algorithms.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Pant:2007:cec, title={A Simple Diversity Guided Particle Swarm Optimization}, author={M. Pant and T. Radha and V. P. Singh}, pages = {3294--3299}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1889.pdf}, url = {}, size = {}, abstract = {In this paper we have proposed a new diversity guided Particle Swarm Optimizer (PSO), namely ATRE-PSO, which is a modification of attractive and repulsive PSO (ARPSO), suggested by Riget and Vesterstorm [1]. Depending on the diversity of the population the ATRE-PSO switches alternately between three phases of attraction, repulsion and a combination of attraction and repulsion, called the phase of positive conflict [2]. The performance of ATRE-PSO is compared with Basic PSO (BPSO) and ARPSO. The numerical results show that besides preserving the rapid convergence of the BPSO, ATRE-PSO also maintains a good diversity in the population. Under most of the test cases, simulations show that ATRE-PSO finds a better solution than BPSO as well as ARPSO.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Azevedo:2007:cec, title={An Approach to Feature Selection for Keystroke Dynamics Systems Based on PSO and Feature Weighting}, author={Gabriel L. F. B. G. Azevedo and George D. C. Cavalcanti and E. C. B. Carvalho Filho}, pages = {3577--3584}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1890.pdf}, url = {}, size = {}, abstract = {Techniques based on biometrics have been successfully applied to personal identification systems. One rather promising technique uses the keystroke dynamics of each user in order to recognize him/her. In the present study, we present the development of a hybrid system based on support vector machines and stochastic optimization techniques. The main objective is the analysis of these optimization algorithms for feature selection. We evaluate two optimization techniques for this task: genetic algorithms (GA) and particle swarm optimization (PSO). We use the standard GA and we created a PSO variation, where each particle is represented by a vector of probabilities that indicate the possibility of selecting a particular feature and directly affects the original values of the features. In the present study, PSO outperformed GA with regard to classification error, processing time and feature reduction rate.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Dolívka:2007:cec, title={Using the Differential Evolution Algorithm for the Multi-Objective Optimization of a Switched-Current Circuit}, author={Luk\'a\v{s} Dol\'ivka and Ji\v{r}\'i Hospodka}, pages = {1351--1358}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1891.pdf}, url = {}, size = {}, abstract = {The optimization of a circuit based on the switched-current technique is described in this paper. The differential evolution – one of evolutionary algorithms – is used for the optimization. The circuit is applied to low-pass filter realization. The optimization takes into account three requirements. Hence, it is multi-objective (multi-criteria) optimization. The first requirement is the elimination of the effect of nonidealities in the circuit on its magnitude frequency response. The nonidealities are the nonzero output conductance of the fieldeffect transistors realizing the current sources and the nonzero on-state resistance of the switches. The second and third fact respected by the optimization is concerned with the values of transconductances of the transistors in the circuit. The ratio between the highest and lowest one (the spread of transconductance values) and the sum of all of them should be as small as possible. The reason is a better circuit design. The aim of the optimization is to obtain suitable values of the transconductances satisfying the three requirements.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Pilato:2007:cec, title={Fitness Inheritance in Evolutionary and Multi-Objective High-Level Synthesis Christian Pilato, Gianluca Palermo, Antonino Tumeo, Fabrizio Ferrandi, Donatella Sciuto}, author={and Pier Luca Lanzi}, pages = {3459--3466}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1893.pdf}, url = {}, size = {}, abstract = {The high-level synthesis process allows the automatic design and implementation of digital circuits starting from a behavioral description. Evolutionary algorithms are very widely adopted to approach this problem or just part of it. Neverthless, some concerns regarding execution times exist. In evolutionary high-level synthesis, design solutions have to be evaluated to extract information about some figures of merit (such as performance, area, etc.) and to allow the genetic algorithm to evolve and converge to Pareto-optimal solutions. Since the execution time of such evaluations increases with the complexity of the specification, the overall methodology could lead to unacceptable execution time. This paper presents a model to exploit fitness inheritance in a multi-objective optimization algorithm (i.e. NSGA-II) by substituting the expensive real evaluations with estimations based on closeness in an hypothetical design space. The estimations are based on the measure of the distance between individuals and a weighted average of the fitnesses of the closest ones. The results shows that the Pareto-optimal set obtained by applying the proposed model well approximates the set obtained without fitness inheritance. Moreover, the overall execution time is reduced up to the 25% in average.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Ting:2007:cec, title={The Effects of Supermajority on Multi-Parent Crossover}, author={Chuan-Kang Ting and Chun-Cheng Chen}, pages = {4524--4530}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1895.pdf}, url = {}, size = {}, abstract = {Multi-parent crossover allows more than two parents participating in crossover. The increase of parents in crossover intensifies exploitation or exploration or both; however, the intensification is often unbalanced and consequently causes deterioration in performance. In this paper we propose the use of supermajority to address the issue of lopsided intensification on exploitation and exploration in multi-parent crossover. The proposed crossover, called biased occurrencebased scanning crossover (bOB), controls the tendency toward exploitation or exploration by the threshold in supermajority. Two adaptive strategies are developed to adjust the threshold of bOB. Experimental results indicate that bOB can achieve significant improvement on uniform crossover and occurrence-based scanning crossover in both solution quality and convergence speed. Precisely, the improvement in mean best fitness ranges from 4–89% on our test problems. The preferable results validate that bOB crossover can not only enhance the performance but also provide an effective way to control the exploitation and exploration in crossover.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(:2007:cec, title={Generic GA-Based Meta-Level Parameter Optimization for Pattern Recognition Systems}, author={Ernest Lumanpauw and Michel Pasquier and Richard J. Oentaryo}, pages = {1593--1600}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1896.pdf}, url = {}, size = {}, abstract = {This paper proposes a novel generic meta-level parameter optimization framework to address the problem of determining the optimal parameters of pattern recognition systems. The proposed framework is currently implemented to control the parameters of neuro-fuzzy system, a subclass of pattern recognition system, by employing a genetic algorithm (GA) as the core optimization technique. Two neuro-fuzzy systems i.e., Generic Self-Organizing Fuzzy Neural Network realizing Yager inference (GenSoFNN-Yager) and Reduced Fuzzy Cerebellar Model Articulation Computer realizing the Yager inference (RFCMAC-Yager), are employed as the test prototypes to evaluate the proposed framework. Experimental results on several classification and regression problems have shown the efficacy and robustness of the proposed approach.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(:2007:cec, title={An Improvement of the .Quotient Construction. Method and Further Asymptotic Results on the Stationary Distributions of the Markov Chains Modeling Evolutionary Algorithms}, author={Boris Mitavskiy and Chris Cannings}, pages = {2606--2613}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1901.pdf}, url = {}, size = {}, abstract = {In earlier work of the first author, a method has been presented for analysis of Markov chains modeling evolutionary algorithms through use of a suitable quotient construction. Asymptotic bounds for the rate of concentration of the stationary distribution of the Markov chains modeling evolutionary algorithms have been obtained. The bounds obtained in the previous work involve the increase of selection pressure depending on the decaying mutation rate. The rate of concentration of the stationary distribution of the Markov chain in the reverse order (i.e. decay of mutation rate with respect to the increase in selection pressure) remained an open question. We address this question in the current paper.More informative results obtained with the use of the "quotient construction" method will appear in the sequel papers.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Du:2007:cec, title={Pattern Classification with a PSO Optimization based Elliptical Basis Function Neural Networks}, author={Ji-Xiang Du and De-Shuang Huang and Zeng-FuWang}, pages = {1654--1661}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1902.pdf}, url = {}, size = {}, abstract = {In this paper, a novel model of elliptical basis function neural networks (EBFNN) based on a hybrid optimization algorithm is proposed. Firstly, a geometry analytic algorithm is applied to construct the hyper-ellipsoid units of hidden layer of the EBFNN, i.e., an initial structure of the EBFNN, which is further pruned by the particle swarm optimization (PSO) algorithm. And the shape parameters of kernel function for the hidden layer are also optimized by the PSO simultaneously. Finally, the hybrid learning algorithm (HLA) is further applied to adjust the hidden centers and the shape parameters of kernel function for the hidden layer. The experimental results demonstrated the proposed hybrid optimization algorithm for the EBFNN model is feasible and efficient, and the EBFNN is not only parsimonious but also has better generalization performance than the RBFNN.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Saleem:2007:cec, title={A Framework for Empirical Evaluation of Nature Inspired Routing Protocols forWireless Sensor Networks}, author={Muhammad Saleem and Muddassar Farooq}, pages = {751--758}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1906.pdf}, url = {}, size = {}, abstract = {The major contribution of the work presented in this paper is a comprehensive performance evaluation framework for conducting an unbiased evaluation of routing protocols for Wireless Sensor Networks (WSNs). The framework is primarily designed for agent based Nature inspired routing protocols but is general enough to be used for evaluation of classical WSN routing protocols as well. The other important contribution of the work is implementation of two state-of-the-art routing protocols, EEABR and BeeSensor, in a well known WSN simulator RMASE and their thorough comparative analysis, using our proposed framework, with one another and with Flooded Piggybacked Ant Routing (FP-Ant) and a well known classical routing protocol, Ad-hoc On-demand Distance Vector (AODV). To the best of our knowledge, Nature inspired WSN routing protocols have never been evaluated in such an extensive manner either with one another or with AODV. The results obtained from the simulations, using the framework, have given us the opportunity to report new behavioral patterns of the protocols that were never previously reported. We expect that the proposed framework can act as a significant evaluation tool that protocol designers can utilize to identify serious shortcomings in their protocols and rectify them at an earlier protocol engineering cycle.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Zhang:2007:cec, title={SVMs Ensemble for Radar Target Recognition Based on Evolutionary Feature Selection}, author={Xiangrong Zhang and Licheng Jiao and Shuiping Gou}, pages = {2804--2808}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1908.pdf}, url = {}, size = {}, abstract = {A novel radar target recognition method based on SVMs ensemble is presented, in which a set of suitable feature subsets are selected for component SVMs by Immune Clonal Algorithm, a new artificial immune system algorithm. With Immune Clonal Algorithm, high quality and high diversity of the components for SVMs ensemble are ensured. Experimental results on one-dimension radar high resolution range profiles demonstrate the validity and reliability of this new radar target recognition method.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Balaji:2007:cec, title={Multi-Agent System based Urban Traffic Management}, author={P. G. Balaji and Gaurav Sachdeva and D. Srinivasan and Chen-Khong Tham}, pages = {1740--1747}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1909.pdf}, url = {}, size = {}, abstract = {Road Traffic congestion can occur anywhere from normal city roads, freeways to even highways. Traffic congestion can also be accentuated by incidents like terrorist attacks, accidents and breakdowns. This paper summarizes the use of various evolutionary techniques for traffic management and congestion avoidance in Intelligent Transportation Systems. Evolutionary algorithms with their inherent strength as optimization techniques are good candidates for solutions to road traffic management and congestion avoidance problems. A number of approaches involving the use of Genetic algorithms, Learning Classifier Systems and Genetic programming have been discussed for solutions to different problems in this domain. This paper proposes a multi-agent based real-time centralized evolutionary optimization technique for urban traffic management in the area of traffic signal control. This scheme uses evolutionary strategy for the control of traffic signal. The total vehicle mean delay in a six junction network was reduced by using evolutionary strategy. In order to achieve this the green signal time was optimized in an online manner. Comparison with a fixed time based traffic controller has been made and was found to produce better results.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Rimcharoen:2007:cec, title={Optimal Stopping Time of Compact Genetic Algorithm on Deceptive Problem Using Real Options Analysis}, author={Sunisa Rimcharoen and Daricha Sutivong and Prabhas Chongstitvatana}, pages = {4668--4675}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1910.pdf}, url = {}, size = {}, abstract = {This paper proposes using a decision contour derived from real options analysis, which is an evaluation tool for investment under uncertainty, to suggest an optimal stopping time of the compact genetic algorithm on the trap problem. The proposed criterion provides a stopping boundary, where termination is optimal on one side and continuation is on the other. A generic stopping function is formulated with an exercise region that scales well. The new stopping policy helps save on computational effort, and the evolutionary process reaches a higher solution quality when the reset method is incorporated. The proposed technique can be applied to analyze other problems.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(:2007:cec, title={HiCEFS - A Hierarchical Coevolutionary Approach for the Dynamic Generation of Fuzzy System}, author={Haoming Huang and Michel Pasquier and Chai Quek}, pages = {3426--3433}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1911.pdf}, url = {}, size = {}, abstract = {A novel hierarchical coevolutionary approach called HiCEFS for the dynamic generation of a fuzzy system from data is presented. This paper is focused on using the proposed hierarchical coevolutionary approach to generate a form of generic membership function (MF) called Irregular Shaped Membership Function (ISMF). This approach divides the ISMFs generation task into several subtasks of finding ISMFs for each input, which are co-evolved in separate genetic populations. The approach is able automatically allocate proper number of accurate ISMFs to fully represent the data distribution. Experimental results show that the fuzzy systems adopting the ISMFs generated by the proposed approach generally outperform those derived by the previous work both in accuracy and structure compactness and compare favorably against other well known systems.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Petalas:2007:cec, title={Entropy.Based Memetic Particle Swarm Optimization for Computing Periodic Orbits of Nonlinear Mappings}, author={Y. G. Petalas and K. E. Parsopoulos and M. N. Vrahatis}, pages = {2040--2047}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1912.pdf}, url = {}, size = {}, abstract = {The computation of periodic orbits of nonlinear mappings is very important for studying and better understanding the dynamics of complex systems. Evolutionary algorithms have shown to be an efficient alternative for the computation of periodic orbits in cases where the inherent properties of the problem at hand render gradient–based methods invalid. Such cases usually involve nondifferentiable mappings or poorly behaved partial derivatives. We propose a Memetic Particle Swarm Optimization algorithm that exploits Shannon's information entropy for decision making in swarm level, as well as a probabilistic decision making scheme in particle level, for determining when and where local search is applied. These decisions have a significant impact on the required number of function evaluations, especially in cases where high accuracy is desirable. Experimental results are performed on well-known problems and useful conclusions are derived.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(:2007:cec, title={Implementation of New Evolutionary Techniques for Transmission Loss Reduction}, author={S. Mishra and G. Dilip Reddy and P. Eswar Rao and K. Santosh}, pages = {2331--2336}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1914.pdf}, url = {}, size = {}, abstract = {This paper presents a comparison between bacteria foraging algorithm (BFA), Conventional Genetic algorithm (CGA) and differential genetic algorithm (DGA) with regard to transmission loss minimization considering the New England 39-bus power system as a test case. Considering all the standard equality and inequality constraints, solutions are obtained that minimize losses by changing the tap settings of various transformers. It has already been demonstrated from the previous research work that there are certain buses in the system where reactive power injections from capacitor banks can improve the voltage profile and reduce the transmission losses.This paper employs bacteria foraging algorithm (BFA) with some modifications so as to expidite the convergence of the optimization problem. A comparison between the three methods suggests the superiority of the Bacteria foraging algorithm.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Guerci:2007:cec, title={Learning Agents in a Monopolistic Competition Framework}, author={Eric Guerci and Stefano Ivaldi and Marco Raberto and Silvano Cincotti }, pages = {524--531}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1915.pdf}, url = {}, size = {}, abstract = {We propose an agent-based computational model in order to study a general equilibrium macro-economic system within a monopolistic competition setting. We address the framework of monopolistic competition introduced by a seminal contribution of Blanchard-Kiyotaki. We model a number of price-setting firms producing differentiated goods characterized by a constant elasticity of substitution. A representative buyer participates in the goods' markets, supplies labor to firms and sets the level of wage. We assume a bounded rationality framework where economic agents learn to optimize their own utility in a strategic economic context. The model is suited to be represented by a normal-form game in order to perform a convergence analysis of learning dynamics with respect to game-theoretical solutions. Results point out that the general equilibrium solution la Blanchard-Kiyotaki results to be both a Nash equilibrium and Pareto optima allocation, but it is not the most frequently played solution. Indeed, the most played solution is characterized by the highest competition on the side of producers, resembling the Bertrand-like solution on price competition, and by the highest representative buyer's welfare. A possible explanation of this finding is that in our setting the highest competition occurs in the price-setting behavior of firms who face residual demands, whereas the single representative buyer has not any opponent on wage setting thus being able to reach her optimal utility.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Ghoneim:2007:cec, title={Investigating Alliance Dynamics Using a Co-Evolutionary Iterated Prisoner's Dilemma with an Exit Option}, author={Ayman Ghoneim and Hussein Abbass and Michael Barlow}, pages = {4065--4072}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1916.pdf}, url = {}, size = {}, abstract = {Evolutionary games are used in understanding the complex dynamics of real life situations. The iterated prisoner's dilemma game with exit option was used in modeling strategic alliances. The model was solved as an optimization problem, and several observations were considered as to how partners are behaving in the alliance, and what factors are affecting the alliance's cooperation level. In this paper we introduce an evolutionary version of the iterated prisoner's dilemma game with exit option to better understand alliance activity and investigate whether the observations from the mathematical solution continue to hold in the evolutionary environment. The results reveal that in some cases there is a significant difference in the evolutionary dynamics from that predicted by the mathematical solution. The mathematical model was found to be inadequate for alliances which use history information to make their future decisions.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings((Bob):2007:cec, title={Using Compression to Understand the Distribution of Building Blocks in Genetic Programming Populations}, author={R. I. McKay (Bob) and Shin Jungseok and Hoang Tuan Hao and Nguyen Xuan Hoai and Mori Naoki}, pages = {2501--2508}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1917.pdf}, keywords = {genetic algorithms, genetic programming}, url = {}, size = {}, abstract = {Compression algorithms generate a predictive model of data, using the model to reduce the number of bits required to transmit the data (in effect, transmitting only the differences from the model). As a consequence, the degree of compression achieved provides an estimate of the level of regularity in the data. Previous work has investigated the use of these estimates to understand the replication of building blocks within Genetic Programming (GP) individuals, and hence to understand how different GP algorithms promote the evolution of repeated common structure within individuals. Here, we extend this work to the population level, and use it to understand the extent of similarity between sub-structures within individuals in GP populations.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Quiroz:2007:cec, title={Interactive Genetic Algorithms for User Interface Design}, author={Juan C. Quiroz and Sushil J. Louis and Anil Shankar and Sergiu M. Dascalu}, pages = {1366--1373}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1919.pdf}, url = {}, size = {}, abstract = {We attack the problem of user fatigue in using an interactive genetic algorithm to evolve user interfaces in the XUL interface definition language. The interactive genetic algorithm combines computable user interface design metrics with subjective user input to guide evolution. Individuals in our population represent interface specifications and we compute an individual's fitness from a weighted combination of user input and user interface design guidelines. Results from our preliminary study involving three users indicate that users are able to effectively bias evolution towards user interface designs that reflect both user preferences and computed guideline metrics. Furthermore, we can reduce fatigue, defined by the number of choices needing to be made by the human designer, by doing two things. First, asking the user to pick just two (the best and worst) user interfaces from among a subset of nine shown. Second, asking the user to make the choice once every t generations, instead of every single generation. Our goal is to provide interface designers with an interactive tool that can be used to explore innovation and creativity in the design space of user interfaces.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Wu:2007:cec, title={Improving ANN Classification Accuracy for the Identification of Students with LDs Through Evolutionary Computation}, author={Tung-KuangWu and Shian-Chang Huang and Ying-Ru Meng}, pages = {4358--4364}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1921.pdf}, url = {}, size = {}, abstract = {Due to the implicit characteristics of learning disabilities (LD), the identification and diagnosis of students with learning disabilities has long been a difficult issue. Identification of LD usually requires extensive man power and takes a long time. Our previous studies using smaller size samples have shown that ANN classifier can be a good predictor to the identification of students with learning disabilities. In this study, we focus on the genetic algorithm based feature selection method and evolutionary computation based parameters optimization algorithm on a larger size data set to explore the potential limit that ANN can achieve in this specific classification problem. In addition, a different procedure in combining evolutionary algorithms and neural networks is proposed. We use the back propagation method to train the neural networks and derive a good combination of parameters. Evolutionary algorithm is then used to search around the neighborhood of the previously acquired parameters to further improve the classification accuracy. The outcomes show that the procedure is effective, and with appropriate combinations of features and parameters setting, the accuracy of the ANN classifier can reach the 88.2% mark, the best we have achieved so far. The result again indicates that AI techniques do have their roles on the identification of students with learning disabilities.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Correia:2007:cec, title={How Redundancy and Neutrality May Affect Evolution on NK Fitness Landscapes}, author={Marisol B. Correia and Carlos M. Fonseca}, pages = {2842--2849}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1922.pdf}, url = {}, size = {}, abstract = {An experimental study was performed to determine whether it is neutrality itself or the larger neighborhoods associated with neutral representations that allow good results to be achieved on NK fitness landscape problems. Markov chains were used to model a stochastic hill climber on NK fitness landscapes, using three types of representation: a neutral network representation, a redundant representation without neutrality which exhibits the same neighborhood of the neutral representation and a non-redundant representation.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Chuang:2007:cec, title={Linkage Identification by Perturbation and Decision Tree Induction}, author={Chung-Yao Chuang and Ying-ping Chen}, pages = {357--363}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1923.pdf}, url = {}, size = {}, abstract = {The purpose of linkage identification in genetic and evolutionary algorithms is to detect the strongly related variables of the fitness function. If such linkage information can be acquired, the crossover or recombination operator can accordingly mix the discovered sub-solutions effectively without disrupting them. In this paper, we propose a new linkage identification technique, called inductive linkage identification (ILI), employing perturbation with decision tree induction. With the proposed scheme, the linkage information can be obtained by first constructing an ID3 decision tree to learn the mapping from the population of solutions to their corresponding fitness differences caused by perturbations and then inspecting the constructed decision tree for variables exhibiting strong interdependencies with one another. The numerical results show that the proposed technique can accomplish the identical linkage identification task with a lower number of function evaluations compared to similar methods proposed in the literature. Moreover, the proposed technique is also shown being able to handle both uniformly scaled and exponentially scaled problems.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Çakar:2007:cec, title={Towards a Quantitative Notion of Self-Organisation Emre Cakar, Moez Mnif, Christian M\"uller-Schloer, Urban Richter and}, author={Hartmut Schmeck}, pages = {4222--4229}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1925.pdf}, url = {}, size = {}, abstract = {Organic Computing (OC) and other research initiatives like Autonomic Computing or Proactive Computing have developed the idea of systems that possess life-like properties, that self-organise, that adapt to their dynamically changing environments, and that establish other so-called self-x properties, like self-healing, self-configuration, self-optimisation etc. What we are searching for in OC are not concepts for systems that simply self-organise, but systems that self-organise to achieve a well defined system goal. Therefore we talk in OC about controlled self-organisation.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Srinivas:2007:cec, title={A New Elitist Multi-Objective Stochastic Search Technique and its Application to Economic-Emission Dispatch Problem in Power Systems}, author={K. Srinivas and C. Patvardhan and D. Bhagwan Das}, pages = {2988--2995}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1926.pdf}, url = {}, size = {}, abstract = {In this paper, a new Multi-Objective Hybrid Evolutionary Algorithm (MOHEA) dubbed as Elitist Multi-Objective Stochastic Search Technique – II (EMOSST-II) which is capable of finding multiple Pareto-optimal solutions with good diversity in a single run is presented. It is applied for the solution of two-objective Economic- Emission Dispatch Problem (EED) in Power Systems. The features of EMOSST-II ensure better diversity and prevent premature convergence to ensure better non-dominated solutions and faster convergence. The computational performance of EMOSST-II for EED is investigated on the IEEE 30 bus 6 generator system, IEEE 57 bus 13 generator system. The results indicate that the performance of EMOSST-II is competitive when compared to the other state-of-the–art elitist Multi-objective Evolutionary Algorithms in terms of convergence to true Pareto-optimal front, maintenance of good spread in Pareto solutions, speed of convergence and scalability.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Bull:2007:cec, title={Anticipation Mappings for Learning Classifier Systems}, author={Larry Bull and Pier Luca Lanzi and Toby O'hara}, pages = {2133--2140}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1927.pdf}, url = {}, size = {}, abstract = {In this paper, we study the use of anticipation mappings in learning classifier systems. At first, we enrich the eXtended Classifier System (XCS) with two types of anticipation mappings: one based on array of perceptrons array, one based on neural networks. We apply XCS with anticipation mappings (XCSAM) to several multistep problems taken from the literature and compare its anticipatory performance with that of the Neural Classifier System X-NCS which is based on a similar approach. Our results show that, although XCSAM is not a "true" Anticipatory Classifier System like ACS, MACS, or X-NCS, nevertheless XCSAM can provide accurate anticipatory predictions while requiring smaller populations than those needed by X-NCS.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Echegoyen:2007:cec, title={Exact Bayesian Network Learning in Estimation of Distribution Algorithms}, author={Carlos Echegoyen and Jose A. Lozano and Roberto Santana and Pedro Larra\~uaga}, pages = {1051--1058}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1928.pdf}, url = {}, size = {}, abstract = {This paper introduces exact learning of Bayesian networks in estimation of distribution algorithms. The estimation of Bayesian network algorithm (EBNA) is used to analyze the impact of learning the optimal (exact) structure in the search. By applying recently introduced methods that allow learning optimal Bayesian networks, we investigate two important issues in EDAs. First, we analyze the question of whether learning more accurate (exact) models of the dependencies implies a better performance of EDAs. Second, we are able to study the way in which the problem structure is translated into the probabilistic model when exact learning is accomplished.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Handa:2007:cec, title={Estimation of Distribution Algorithms with Niche Separation Mechanism}, author={Hisashi Handa}, pages = {119--126}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1929.pdf}, url = {}, size = {}, abstract = {Estimation of Distribution Algorithms are a promising method in Evolutionary Computation. In the Estimation of Distribution Algorithms, instead of using conventional crossover and mutation operations, probabilistic models are used to sample the genetic information in the next population. Although the use of probabilistic models enables EDAs to have several schema simultaneously, it sometimes causes worse performance due to converse schemata. In this paper, Estimation of Distribution Algorithms with Niche Separation Mechanism is proposed. Coexistence schemata are also newly introduced, where schemata at the same loci but converse information. The proposed method splits population into two subpopulations if such coexistence schemata are found. One of subpopulations is used to constitute the next generation while the probabilistic model of another subpopulation is enqueued. The queued probabilistic model is used if the current population is converged. The experimental results on Max-Sat problems and Ising Spin Glass problems show the effectiveness of the proposed method.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Loiacono:2007:cec, title={Support Vector Machines for Computing Action Mappings in Learning Classifier Systems}, author={Daniele Loiacono and Andrea Marelli and Pier Luca Lanzi}, pages = {2141--2148}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1931.pdf}, url = {}, size = {}, abstract = {XCS with Computed Action, briefly XCSCA, is a recent extension of XCS to tackle problems involving a large number of discrete actions. In XCSCA the classifier action is computed with a parameterized function learned in a supervised fashion. In this paper, we introduce XCSCAsvm that extends XCSCA using Support Vector Machines to compute classifier action. We compared XCSCAsvm and XCSCA on the learning of several binary functions. The experimental results show that XCSCAsvm reaches the optimal performance faster than XCSCA.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Tufte:2007:cec, title={Achieving Environmental Tolerance through the Initiation and Exploitation of External Information}, author={Gunnar Tufte and Pauline C. Haddow}, pages = {2485--2492}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1933.pdf}, url = {}, size = {}, abstract = {It is often believed that biological organisms have an inherent tolerance to environmental changes. This is a seductive concept if transferred to artificial organisms. An experimental approach is taken to investigate if tolerance to the environmental is an inherent property of developing organisms. The environment used is an external environment which the phenotype have to develop and survive in. As such, it is the phenotype that needs to adapt to its surrounding. The results show that tolerance is a property that can be sought by evolution rather than an inherent property of the organism, i.e. genomes that have explicitly been exposed to environmental changes. Further, to exploit this property a definition of what environmental changes mean in an artificial development setting is needed. The article classifies the roles of environment in artificial development models and suggests an external environment wherein the organism develops and function.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Lanzi:2007:cec, title={An Analysis of Generalization in XCS with Symbolic Conditions}, author={Pier Luca Lanzi}, pages = {2149--2156}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1934.pdf}, keywords = {genetic algorithms, genetic programming}, url = {}, size = {}, abstract = {We analyze generalization in the eXtended Classifier System (XCS) with symbolic conditions, based on genetic programming, briefly XCSGP. We start from the results presented in the literature, which showed that XCSGP could not reach optimality in Boolean problems when classifier conditions involved logical disjunctions.We apply a new implementation of XCSGP to the learning of Boolean functions and show that our version can actually reach optimality even when disjunctions are allowed in classifier conditions. We analyze the evolved generalizations and explain why logical disjunctions can make the learning more difficult in XCS models and why our version performs better than the earlier one. Then, we show that in problems that allow many generalizations, so that or clauses are less "convenient", XCSGP tends to develop solutions that do not exploit logical disjunctions as much as one might expect. However, when the problems allow few generalizations, so that or clauses become an interesting way to introduce simple generalizations, XCSGP exploit them so as to evolve more compact solutions.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Flórez-Revuelta:2007:cec, title={Specific Crossover and Mutation Operators for a Grouping Problem Based on Interaction Data in a Regional Science Context}, author={Francisco Fl\'orez-Revuelta and Jos\'e M. Casado-D\'iaz and Lucas Mart\'inez-Bernabeu }, pages = {378--385}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1935.pdf}, url = {}, size = {}, abstract = {This paper proposes a set of specific crossover and mutation operators for the delineation of functional regions through evolutionary computation. We consider a problem of dividing a given territory into local labor market areas based on spatial interaction data. Such areas are defined so that a high degree of inter-regional separation and intraregional integration -in both cases in terms of commuting flows- exist. A genetic algorithm has been designed based on the maximization of a fitness function that measures aggregate intra-region interaction under constraints of inter-region separation and minimum size. Additional requirements, typical of any functional regionalization, include the absence of overlapping between delineated regions and an exhaustive coverage of the whole territory (so all basic spatial units must be allocated to one -and only one- region). The complex set of restrictions results in conventional operators often generating invalid solutions, impeding or delaying the evolutionary process. This is the reason why an extensive set of operators has been designed that incorporates knowledge about the problem, allowing the evolution of the set of solutions towards the final result.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(:2007:cec, title={A Tight Analysis of the (1 + 1)-EA for the Single Source Shortest Path Problem}, author={Benjamin Doerr and Edda Happ and Christian Klein}, pages = {1890--1895}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1936.pdf}, url = {}, size = {}, abstract = {We conduct a rigorous analysis of the (1 + 1) evolutionary algorithm for the single source shortest path problem proposed by Scharnow, Tinnefeld and Wegener (Journal of Mathematical Modelling and Algorithms, 2004). We prove a tight bound of ⊖ (n2 max{log(n), l}) on the optimization time, where l is the maximum number of edges of a shortest path with minimum number of edges from the source to another vertex. Using various tools from probability theory we show that these bounds not only hold in expectation, but also with high probability. We are optimistic that these tools can also be used to analyze the run-time of evolutionary algorithms for other problems.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Das:2007:cec, title={Annealed Differential Evolution}, author={Swagatam Das and Amit Konar and Uday K. Chakraborty}, pages = {1890--1897}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1938.pdf}, url = {}, size = {}, abstract = {Differential Evolution (DE) has recently emerged as a leading methodology for global search and optimization over continuous, high-dimensional spaces. It has been successfully applied to a wide variety of nearly intractable engineering problems. However, the DE and its variants usually employ a deterministic selection mechanism that always allows the better solution to survive to the next generation. This often prevents DE from escaping local optima at the early stages of search over a multi-modal fitness landscape and leads to a premature convergence. The present work proposes to improve the accuracy and convergence speed of DE by introducing a stochastic selection mechanism. The idea of a conditional acceptance function (that allows accepting inferior solutions with a gradually decaying probability) is borrowed from the realm of the Simulated Annealing (SA). In addition, the work proposes a center of mass based mutation operator and a decreasing crossover rate in DE. Performance of the resulting hybrid algorithm has been compared with three state-of-the-art adaptive DE schemes. The method is shown to be statistically significantly better on a six-function testbed and one difficult engineering optimization problem with respect to the following performance measures: solution quality, time to find the solution, frequency of finding the solution, and scalability.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Daum:2007:cec, title={Reliability-Based Optimization for Multiple Constraints with Evolutionary Algorithms}, author={David A. Daum and Kalyanmoy Deb and J\"urgen Branke}, pages = {911--918}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1939.pdf}, url = {}, size = {}, abstract = {In this paper, we combine reliability-based optimization with a multi-objective evolutionary algorithm for handling uncertainty in decision variables and parameters. This work is an extension to a previous study by the second author and his research group to more accurately compute a multiconstraint reliability. This means that the overall reliability of a solution regarding all constraints is examined, instead of a reliability computation of only one critical constraint. First, we present a brief introduction into this so-called 'structural reliability' aspects. Thereafter, we introduce a method for identifying inactive constraints according to the reliability evaluation. With this method, we show that with less number of constraint evaluations, an identical solution can be achieved. Furthermore, we apply our approach to a number of problems including a real-world car side impact design problem to illustrate our method.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Bosman:2007:cec, title={Inventory Management and the Impact of Anticipation in Evolutionary Stochastic Online Dynamic Optimization}, author={P. A. N. Bosman and H. La Poutr\'e}, pages = {268--275}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1940.pdf}, url = {}, size = {}, abstract = {Inventory management (IM) is an important area in logistics. The goal is to manage the inventory of a vendor as efficiently as possible. Its practical relevance also makes it an important real-world application for research in optimization. Because inventory must be managed over time, IM optimization problems are dynamic and online (i.e. they must be solved as time goes by). Dynamic optimization is typically harder than non-dynamic optimization. Much research in IM is devoted to finding specific algorithms that solve specific abstractions. For each new aspect to be taken into account, a new algorithm must be designed. In this paper, we aim at a more general approach. We employ general insights into online dynamic problem solving. A recently proposed framework is also employed. We point out how IM problems can be solved in a much more general fashion using evolutionary algorithms (EAs). Here, time-dependence (i.e. decisions taken now have consequences in the future) is an important practical type of problem difficulty that is characteristic of practical dynamic optimization problems. Time-dependence is usually not taken into account in the literature and myopic (i.e. blind to future events) algorithms are often designed. We show that time-dependence is automatically tackled by our novel approach. We extend the common definition of IM problems with time-dependence by introducing customer satisfaction. We show that customer satisfaction for IM problems with superior solutions can be achieved when this form of time-dependence is properly taken into account. This also demonstrates our conclusion that taking into account the existence of time-dependence in practical online dynamic optimization problems such as IM is very important.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(El-Alfy:2007:cec, title={Solving the Minimum-Cost Constrained Multipath Routing with Load Balancing in MPLS Networks Using an Evolutionary Method}, author={El-Sayed M. El-Alfy and Shokri Z. Selim and Syed N. Mujahid}, pages = {4433--4438}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1942.pdf}, url = {}, size = {}, abstract = {This paper presents a flexible evolutionary method for minimum-cost multipath constrained routing with load balancing problem in MPLS networks. The proposed solution approach combines genetic algorithms with linear multi-commodity flow to enhance the efficiency of the solution attained. The goal is to determine the distribution of traffic demands over a given capacitated network topology to minimize the routing cost while balancing loads on various links. The constraints that should be satisfied are the maximum hop count, the total number of virtual paths and the link capacities. This problem is a highly constrained multiobjective optimization for which exact optimization methods become helpless to deal with such complexity. Using a case study from the literature, the proposed approach is evaluated and compared with the standard genetic algorithm. We also show how the proposed approach can be used to determine approximate Pareto points and compare them with the exact Pareto front.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Lehre:2007:cec, title={Runtime Analysis of (1+1) EA on Computing Unique Input Output Sequences}, author={Per Kristian Lehre and Xin Yao}, pages = {1882--1889}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1945.pdf}, url = {}, size = {}, abstract = {Computing unique input output (UIO) sequences is a fundamental and hard problem in conformance testing of finite state machines (FSM). Previous experimental research has shown that evolutionary algorithms (EAs) can be applied successfully to find UIOs on some instances. However, before EAs can be recommended as a practical technique for computing UIOs, it is necessary to better understand the potential and limitations of these algorithms on this problem. In particular, more research is needed in determining for what instances of the problem EAs are feasible.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(:2007:cec, title={ALPS Evaluation in Financial Portfolio Optimisation}, author={S. Patel and C. D. Clack}, pages = {813--819}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1947.pdf}, keywords = {genetic algorithms, genetic programming}, url = {}, size = {}, abstract = {Hornby's Age-Layered Population Structure claims to reduce premature convergence in Evolutionary Algorithms. We provide the first evaluation of ALPS on a realworld problem ― the evolution of non-linear factor models for financial portfolio optimisation. We incorporate ALPS into our GP system, coupled to an investment simulator, and provide a head-to-head comparison between ALPS GP and Standard GP.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Benkhelifa:2007:cec, title={Towards Evolving Fault Tolerant Biologically Inspired Hardware Using Evolutionary Algorithms}, author={Elhadj Benkhelifa and Anthony Pipe and Gabriel Dragffy and Mokhtar Nibouche}, pages = {1548--1554}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1948.pdf}, url = {}, size = {}, abstract = {Embryonic Hardware Systems satisfy the fundamental characteristics found in nature which contribute to the development of any multi-cellular living being. Attempts of researchers' in this field to learn from nature have yielded promising results; they proved the feasibility of applying nature-like mechanisms to the world of digital electronics with self-diagnostic and self-healing characteristics, Design by humans however often results in very complex hardware architectures, requiring a large amount of manpower and computational resources. A wider objective is to find novel solutions to design such complex architectures for Embryonic Systems, by problem decomposition and unique design methodologies so that system functionality and performance will not be compromised. Design automation using reconfigurable hardware and EA (Evolutionary Algorithm), such as GA (Genetic Algorithms), is one way to tackle this issue. This concept applies the notion of EHW (Evolvable Hardware) to the problem domain. Unlocking the power of EHW for both novel design solutions and for circuit optimisation has attracted many researchers since the early '90s. The promise of using Genetic Algorithms through Evolvable Hardware design will, in this paper, be demonstrated by the authors by evolving a relatively simple combinatorial logic circuit (full-adder).}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Ellis:2007:cec, title={Evolving Cooperation in the Non-Iterated Prisoner's Dilemma: A Social Network Inspired Approach}, author={T. S. Ellis and X. Yao}, pages = {736--743}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1949.pdf}, url = {}, size = {}, abstract = {Online service provision is becoming increasingly decentralized as system designers pursue the benefits gained from utilizing nodes at the periphery of the network. However, distributing control means relying on the cooperation of participating agents, and it is a significant challenge to design mechanisms that incentivise optimal global behavior in a population of selfish, rational agents. This is particularly evident in peer-to-peer file-sharing, where a high incidence of selfish behavior in the form of downloading without uploading, leads to the network losing the benefits of a decentralized network. In this paper a notion of reputation based on simple social network analysis is used to significantly improve cooperation rates in the one-shot game of prisoner's dilemma, where without such a technique the dominant strategy would be for all agents to defect.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Bradstreet:2007:cec, title={Incrementally Maximising Hypervolume for Selection in Multi-Objective Evolutionary Algorithms}, author={Lucas Bradstreet and Lyndon While and Luigi Barone}, pages = {3203--3210}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1950.pdf}, url = {}, size = {}, abstract = {Several multi-objective evolutionary algorithms compare the hypervolumes of different sets of points during their operation, usually for selection or archiving purposes. The basic requirement is to choose a subset of a front such that the hypervolume of that subset is maximised. We describe and evaluate three new algorithms based on incremental calculations of hypervolume using the new Incremental Hypervolume by Slicing Objectives (IHSO) algorithm: two greedy algorithms that respectively add or remove one point at a time from a front, and a local search that assesses entire subsets. Empirical evidence shows that using IHSO, the greedy algorithms are generally able to out-perform the local search and perform substantially better than previously published algorithms.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Monson:2007:cec, title={A Utile Function Optimizer}, author={Christopher K. Monson and Kevin D. Seppi and James L. Carroll}, pages = {1067--1074}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1951.pdf}, url = {}, size = {}, abstract = {We recast the problem of unconstrained continuous evolutionary optimization as inference in a fixed graphical model. This approach allows us to address several pervasive issues in optimization, including the traditionally difficult problem of selecting an algorithm that is most appropriate for a given task. This is accomplished by placing a prior distribution over the expected class of functions, then employing inference and intuitively defined utilities and costs to transform the evolutionary optimization problem into one of active sampling. This allows us to pose an approach to optimization that is optimal for each expressly stated function class. The resulting solutionmethodology can optimally navigate exploration-exploitation tradeoffs using well-motivated decision theory, while providing the process with a natural stopping criterion. Finally, the model naturally accommodates the expression of dynamic and noisy functions, setting it apart from most existing algorithms that address these issues as an afterthought. We demonstrate the characteristics and advantages of this algorithm formally and with examples.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Hourdakis:2007:cec, title={A Biologically Inspired Approach for the Control of the Hand}, author={Emmanouil Hourdakis and Michail Maniadakis and Panos Trahanias}, pages = {1503--1510}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1952.pdf}, url = {}, size = {}, abstract = {The control of the hand in primate species is characterized by a high dimensionality, due to the large number of joints in the fingers. In this study we present how its manipulation can be simplified without compromising its usage, through a constraint methodology that is inspired from recent neurobiological findings. We further develop a computational model, consisting of several brain areas related to hand control, using a co-evolutionary architecture. Due to its neurobiological basis the methodology gives rise to a number of emergent properties that have been shown to occur in primate species during reach-to-grasp tasks.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Pavlidis:2007:cec, title={Computational Intelligence Algorithms for Risk-Adjusted Trading Strategies}, author={N. G. Pavlidis and E. G. Pavlidis and M. G. Epitropakis and V. P. Plagianakos and M. N. Vrahatis }, pages = {540--547}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1953.pdf}, keywords = {genetic algorithms, genetic programming}, url = {}, size = {}, abstract = {This paper investigates the performance of trading strategies identified through Computational Intelligence techniques. We focus on trading rules derived by Genetic Programming, as well as, Generalized Moving Average rules optimized through Differential Evolution. The performance of these rules is investigated using recently proposed risk-adjusted evaluation measures and statistical testing is carried out through simulation. Overall, the moving average rules proved to be more robust, but Genetic Programming seems more promising in terms of generating higher profits and detecting novel patterns in the data.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(:2007:cec, title={JADE: Self-Adaptive Differential Evolution with Fast and Reliable Convergence Performance}, author={Jingqiao Zhang and Arthur C. Sanderson}, pages = {2251--2258}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1957.pdf}, url = {}, size = {}, abstract = {A new differential evolution algorithm, JADE, is proposed to improve the rate and the reliability of convergence performance by implementing a new mutation strategy 'DE/current-to-p-best' and controlling the parameters in a self-adaptive manner. The 'DE/current-to-p-best' is a generalization of 'DE/current-to-best'. It diversifies the population but still inherits the fast convergence property. Self-adaptation is beneficial for performance improvement. Also, it avoids the requirement of prior knowledge about parameter settings and thus works well without user interaction. Compared to other self-adaptive DE algorithms, JADE converges faster and reliably in at least 10 out of a set of 13 benchmark problems and shows competitive results in other cases as well. Simulations results also clearly show that there is no single parameter value suitable for various problems or even at different optimization stages of a single problem.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Zhang:2007:cec, title={An Approximate Gaussian Model of Differential Evolution with Spherical Fitness Functions}, author={Jingqiao Zhang and Arthur C. Sanderson}, pages = {2220--2228}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1958.pdf}, url = {}, size = {}, abstract = {An analytical method is proposed to study the evolutionary stochastic properties of the population in Differential Evolution (DE) for a spherical function model. Properties of mutation and selection are developed, based on which a Gaussian approximate model of DE is introduced to facilitate mathematical derivations. The evolutionary dynamics and the convergence behavior of DE are investigated based on the derived analytical formulae and their appropriateness is verified by experimental results. It is shown that the lower limit of mutation factor should be as high as 0.68 to avoid premature convergence if the initial population is isotropically normally distributed and infinitely far from the optimum (i.e., the function landscape becomes a hyper- plane). The lower limit, however, may be decreased if the population becomes closer to the optimum and an accordingly smaller mutation factor is beneficial to speed up the convergence. This motivates future research to improve DE by dynamically adapting control parameters as evolution search proceeds.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Nagata:2007:cec, title={Efficient Evolutionary Algorithm for the Vehicle Routing Problem with TimeWindows: Edge Assembly Crossover for the VRPTW}, author={Yuichi Nagata}, pages = {1175--1182}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1959.pdf}, url = {}, size = {}, abstract = {We propose an evolutionary algorithm (EA) for the vehicle routing problem with time windows (VRPTW). In particular, a crossover operator suitable for solving the VRPTW are presented. The proposed crossover is designed by extending edge assembly crossover (EAX) which was originally designed for the traveling salesman problem (TSP). Moreover, we propose an efficient modification procedure to eliminate violations of the capacity constraints and the time window constraints because intermediate solutions generated by EAX violate these constraints. The EA is composed of EAX, the modification procedure and a very simple local search. The EA is applied to the well-known Gering and Homberger benchmarks for the VRPTW. The EA finds new best-known solutions in 119 of the 180 instances (200 – 600 customer benchmarks).}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Aguirre:2007:cec, title={Constraint Handling Techniques for a Non-Parametric Real-valued Estimation Distribution Algorithm}, author={Arturo Hern\'andez Aguirre and Enrique Villa Diharce and Carlos Coello Coello}, pages = {654--661}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1961.pdf}, url = {}, size = {}, abstract = {This article introduces the Non-Parametric Realvalued Estimation Distribution Algorithm (NOPREDA), and its application to constrained optimization problems. NOPREDA approximates the target probability density function by building the cumulative empirical distribution of the decision variables. Relationships and structure among the data is modeled through a rank correlation matrix (Spearmans statistics). The procedure to induce a target rank correlation matrix into the new population is described. NOPREDA is used to solve constrained optimization problems. Three constraint handling techniques are investigated: truncation selection, feasibility tournament, and Stochastic Ranking. NOPREDA's performance is competitive in problems with inequality constraints. However, a mechanism for properly handling equality constraints remains as part of our future research work.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Schonfeld:2007:cec, title={The Effect of Selection on the Development of Mutational Robustness}, author={Justin Schonfeld and Sushil J. Louis}, pages = {4714--4721}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1962.pdf}, url = {}, size = {}, abstract = {This paper investigates the role of selection in the acquisition of mutational robustness for two test problems: rONEMAX and SAW. Three different selection methods: tournament, fitness proportionate, and ranking, were implemented in a geerational genetic algorithm and applied to both problems. The effect of altering the selection pressure for the tournament selection method was investigated by varying the tournament size. For the rONEMAX problem the tournament and ranking selection based algorithms found optimal solutions which were significantly more robust to point mutation than those found by either the fitness proportionate selection algorithm or random sampling of the optimal solution space. Altering the selection pressure had no significant effect on the robustness of the solutions located by tournament selection algorithm for the rONEMAX problem. For the SAW problem, however, tournament selection with a tournament size of four found solutions which were significantly more robust than those located by larger tournament sizes. For the majority of the problem variants explored here the tournament and ranking selection methods proved more effective at locating robust optimal solutions than fitness proportionate selection.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Preuss:2007:cec, title={Solving Multimodel Problems via Multiobjective Technique with Application to Phase Equilibrium Detection}, author={Mike Preuss and G\"unter Rudolph and Feelly Tumakaka}, pages = {2703--2710}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1964.pdf}, url = {}, size = {}, abstract = {For solving multimodel problems by means of evoluationary algorithms, one often resorts or niching methods. The latter approach the question: 'What is else-where?' by an implicit second criterion in order to keep population distributed over the search space Induced by a practical problem that appears to be simple but is not easily solved, a multiobjective algorithm is proposed for solving multimodel problems. It employs an explicit diversity criterion as second objective. Experimental comparison with standard methods suggests that the multiobjective algorithm is fast and relaible and that coupling it with a local search technique is straightforward and leads to enormous quality valuable gain. The combined algorithm is still fast and may be especially valuable for practical problems with costly target funtion evaluations.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Kimbrough:2007:cec, title={On Gray-Coded Binary Representation for Supporting a (Repair-by-Interpolation) Genetic Operator for Constrained Optimization Problems}, author={Steven Orla Kimbrough and David Harlan Wood}, pages = {1141--1148}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1967.pdf}, url = {}, size = {}, abstract = {A new genetic operator is proposed in the context of Genetic Algorithms that are applied to constrained optimization problems with binary decision variables. A solution is said to be feasible if it satisfies the constraints, and infeasible otherwise. Provided the two inputs of the operator are of differing feasibility, the operator outputs a infeasible/feasible pair that differ by only a single bit. This is valuable because any optimal solution is within a single bit of transitioning between infeasibility and feasibility, unless the constraints are irrelevant. The operator is implemented by binary search along a path connecting the two inputs. The path is a portion of a randomly selected Gray code (an ordered list of all binary strings having the property that adjacent strings differ by a single bit).}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Danoy:2007:cec, title={Coevolutionary Genetic Algorithms for Ad Hoc Injection Networks Design Optimization}, author={Gr\'egoire Danoy and Pascal Bouvry and Luc Hogie}, pages = {4273--4280}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1971.pdf}, url = {}, size = {}, abstract = {When considering realistic mobility patterns, nodes in mobile ad hoc networks move in such a way that the networks most often get divided in a set of disjoint partitions. This presence of partitions is an obstacle to communication within these networks. Ad hoc networks are generally based on technologies allowing nodes in a geographical neighborhood to communicate for free, in a P2P manner. These technologies include IEEE802.11 (Wi-Fi), Bluetooth, etc. In most cases a communication infrastructure is available. It can be a set of access point as well as GMS/UMTS network. The use of such an infrastructure is billed, but it permits distant nodes to get in communication, through what we call "bypass links". The objective of our work is to improve the network connectivity by defining a set of long distance connections. To do this we consider the number of bypass links, as well as the two properties that build on the "small-world" graph theory: the clustering coefficient, and the characteristic path length. A fitness function, used for genetic optimization, is processed out of these three metrics. In this paper we investigate the use of two Coevolutionary Genetic Algorithms (LCGA and CCGA) and compare their performance to a generational and a steadystate genetic algorithm (genGA and ssGA) for optimizing one instance of this topology control problem and present evidence of their capacity to solve it.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Fung:2007:cec, title={A Multi-Objective Algorithm for the Design of High Performance Reconfigurable Architectures with Embedded Decoding}, author={Wing On Fung and Tughrul Arslan}, pages = {4399--4404}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1972.pdf}, url = {}, size = {}, abstract = {The increasing demand for FPGAs and reconfigurable hardware targeting high performance low power applications has lead to an increasing requirement for new computer aided design methodology for rapid design of such high performance reconfigurable embedded FPGA cores. In this paper, a power aware genetic algorithm is presented for automatic generation of target specific embedded reconfigurable cores from a library of logic blocks. The paper compares four decoding techniques within the algorithm, and explores the impact they have on the optimality of the architecture.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Gilkerson:2007:cec, title={The Genetic Algorithm Scheme for Consensus Sequences}, author={Joshua W. Gilkerson and Jerzy W. Jaromczyk}, pages = {3870--3878}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1973.pdf}, url = {}, size = {}, abstract = {A consensus sequence is a single sequence that represents characteristics of a family of sequences. Such synopses are most commonly used in the bioinformatics for sequence analysis. For example, algorithms that determine high quality consensus sequences are useful to construct a multiple alignment and consequently, a sequence logo (another representation that attempts to capture the important features of sequences). The determination of optimal consensus sequences is NP-hard (Gusfield). We present two new algorithms and compare them to earlier, published methods of determining consensus sequences. The first, CONSENSIZE, is an application of the Genetic Algorithm Scheme (GAS). The other is a simple steepest descent search, usually not very useful for NP-Hard problems, but surprisingly successful for this application. We discuss both algorithms and experimentally compare their accuracy and efficiency with the Simulated Annealing, Multiple Alignment and Center String approaches. Test results are presented on both synthetic data and biological sequences.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Someya:2007:cec, title={Theoretical Analysis on an Inversion Phenomenon of Convergence Velocity in a Real-Coded GA}, author={Hiroshi Someya}, pages = {4531--4537}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1976.pdf}, url = {}, size = {}, abstract = {The aims of this paper are to analyze an inversion phenomenon theoretically and discussion on appropriateness of combination of a crossover operator and a selection model. In the previous study, the author designed a crossover operator that worked well on various kinds of objective functions. One of the features of the objective functions is "the optimum exists near a boundary much more than the other." On such objective functions, with recommended selection model, the proposed crossover operator set with an appropriate parameter has shown the fastest convergence speed. However, with another selection model, its convergence speed has been the slowest. In order to understand this inversion phenomenon, a theoretical analysis quantified the selection pressures of the selection models and estimated the expected positions of the center of gravity of the population. The theoretical results corresponded to empirical verifications and successfully explained. Finally, a guideline for designing RCGAs was obtained.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Agapitos:2007:cec, title={Multiobjective Techniques for the Use of State in Genetic Programming Applied to Simulated Car Racing}, author={Alexandros Agapitos and Julian Togelius and Simon M. Lucas}, pages = {1562--1569}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1977.pdf}, keywords = {genetic algorithms, genetic programming}, url = {}, size = {}, abstract = {Multi-objective optimisation is applied to encourage the effective use of state variables in car controlling programs evolved using Genetic Programming. Three different metrics for measuring the use of state within a program are introduced. Comparisons are performed among multi- and single-objective fitness functions with respect to learning speed and final fitness of evolved individuals, and attempts are made at understanding whether there is a trade-off between good performance and stateful controllers in this problem domain.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Kikuchi:2007:cec, title={Efficient Assembling of Genome Fragments Using Genetic Algorithm Enhanced by Heuristic Search}, author={Satoko Kikuchi and Goutam Chakraborty}, pages = {305--312}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1978.pdf}, url = {}, size = {}, abstract = {Shotgun sequencing is the state-of-the-art to decode genome sequence. However this technique needs a lot of fragments. Combining those fragments correctly requires enormous computational cost. In our previous work we have shown how Genetic Algorithm (GA) could solve this problem efficiently. In this work, we added two heuristic ideas with GA to make it more efficient. One is Chromosome Reduction (CRed) step which shorten the length of the chromosomes, participating in genetic search, to improve the efficiency. The other is Chromosome Refinement (CRef) step which is a greedy heuristics, rearranging the bits using domain knowledge, to locally improve the fitness of chromosomes. With this hybridization and simple scaffold list, we could obtain longer contigs and scaffolds using GA. We experimented using three actual genome data to test our algorithm. We succeed in restructuring contigs covering about 90% of target genome sequences, and assembling about 500 ∼ 1,000 fragments into 3 ∼ 11 scaffolds. All the experiments were done using common desktop machines.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Eskandari:2007:cec, title={Handling Uncertainty in Evolutionary Multiobjective Optimization: SPGA}, author={Hamidreza Eskandari and Christopher D. Geiger and Robert Bird}, pages = {4130--4137}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1982.pdf}, url = {}, size = {}, abstract = {This paper presents an extension of the previously developed approach to solve multiobjective optimization problems in deterministic environments by incorporating a stochastic Pareto-based solution ranking procedure. The proposed approach, called stochastic Pareto genetic algorithm (SPGA), employs some statistical analysis on the solution dominance in stochastic problem environments to better discriminate among the competing solutions. Preliminary computational results on three published test problems for different levels of noise with SPGA and NSGA-II are discussed.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Liu:2007:cec, title={Optimization ofWarship Degaussing Decision Based on Poly-Population Particle Swarm Algorithm}, author={Hongda Liu and Zhongli Ma}, pages = {2719--2724}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1985.pdf}, url = {}, size = {}, abstract = {In order to disguise and hide themselves better, warships and submarines are equipped with degaussing windings. But, with the development of warship degaussing technology, more and more degaussing coils are being adopted. Thus, conventional methods for calibrating the degaussing system are increasingly difficult to implement. Combining incomplete evolution of every particle swarm and the idea of compressing the space, the evolution of LDW-PSO and space compression is done alternately, until the global optimization solution is got. Through the incomplete evolution of LDW-PSO algorithm, adequate preferable information is got to provide the foundation to reduce the searching space; the collective decision-making of the elite particle group is used to decide the scope of the new space. Taking some warship as an example, the proposed method was applied to adjust the degaussing system and the optimized calibrating mathematical model and optimization results are given. Simulation results are presented, which shows that this algorithm is advantageous to others, in terms of minimizing the vessel degaussing magnetic signature RMS value and peak value, and that this method can be used to optimize one parameter or combinational parameters that relate to the vessel or submarine's magnetic signature, so the vessel or submarine degaussing system is calibrated to an optimal state, and the vessel or submarine's magnetic safety is ensured. Furthermore, this optimization algorithm has many advantages, such as intuitional algorithm, simple programming, high calculating speed, which makes easy collaborating multi-computer and applying it to real project.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Garcia-Almanza:2007:cec, title={Repository Method to Suit Different Investment Strategies}, author={Alma Lilia Garcia-Almanza and Edward P. K. Tsang}, pages = {790--797}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1986.pdf}, keywords = {genetic algorithms, genetic programming}, url = {}, size = {}, abstract = {This work is motivated by the interest in finding significant movements in financial stock prices. The detection of such movements is important because these could represent good opportunities for invest. However, when the number of profitable opportunities is very small the prediction of these cases is very difficult.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Sulistijono:2007:cec, title={Evolutionary Robot Vision and Particle Swarm Optimization for Multiple Human Heads Tracking of A Partner Robot}, author={Indra Adji Sulistijono and Naoyuki Kubota}, pages = {1535--1541}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1987.pdf}, url = {}, size = {}, abstract = {This paper discusses the advantage and disadvantage of evolutionary robot vision and particle swarm optimization for multiple human heads tracking. Evolutionary robot vision combines the technologies of the evolutionary computation and robot vision. Both of evolutionary computation and particle swarm optimization can perform the multiple human heads tracking well for feasible solution in a dynamic movement. This paper compares their performance. Finally, the proposed method is applied to a partner robot, and we discuss the effectiveness of the multiple human heads tracking in the natural communication with humans.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Sagarna:2007:cec, title={Estimation of Distribution Algorithms for Testing Object Oriented Software}, author={Ram\'on Sagarna and Andrea Arcuri and Xin Yao }, pages = {438--444}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1988.pdf}, url = {}, size = {}, abstract = {One of the main tasks software testing involves is the generation of the test cases to be used during the test. Due to its expensive cost, the automation of this task has become one of the key issues in the area. While most of the work on test data generation has concentrated on procedural software, little attention has been paid to object oriented programs, even so they are a usual practice nowadays. We present an approach based on Estimation of Distribution Algorithms (EDAs) for dealing with the test data generation of a particular type of objects, that is, containers. This is the first time that an EDA has been applied to testing object oriented software. In addition to automated test data generation, the EDA approach also offers the potential of modelling the fitness landscape defined by the testing problem and thus could provide some insight into the problem. Firstly, we show results from empirical evaluations and comment on some appealing properties of EDAs in this context. Next, a framework is discussed in order to deal with the generation of efficient tests for the container classes. Preliminary results are provided as well.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(König:2007:cec, title={Genetic Programming.A Tool for Flexible Rule Extraction}, author={R. K\"onig and U. Johansson and L. Niklasson}, pages = {1304--1310}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1989.pdf}, keywords = {genetic algorithms, genetic programming}, url = {}, size = {}, abstract = {Although data mining is performed to support decision making, many of the most powerful techniques, like neural networks and ensembles, produce opaque models. This lack of interpretability is an obvious disadvantage, since decision makers normally require some sort of explanation before taking action. To achieve comprehensibility, accuracy is often sacrificed by the use of simpler, transparent models, such as decision trees. Another alternative is rule extraction; i.e. to transform the opaque model into a comprehensible model, keeping acceptable accuracy. We have previously suggested a rule extraction algorithm named G-REX, which is based on genetic programming. One key property of G-REX, due to the use of genetic programming, is the possibility to use different representation languages. In this study we apply G-REX to estimation tasks. More specifically, three representation languages are evaluated using eight publicly available data sets. The quality of the extracted rules is compared to two standard techniques producing comprehensible models; multiple linear regression and the decision tree algorithm C&RT. The results show that G-REX outperforms the standard techniques, but that the choice of representation language is important.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Montero:2007:cec, title={Calibrating Strategies for Evolutionary Algorithms}, author={Elizabeth Montero and Mar\'ia-Cristina Riff }, pages = {394--399}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1990.pdf}, url = {}, size = {}, abstract = {The control of parameters during the execution of evolutionary algorithms is an open research area. In this paper, we propose new parameter control strategies for evolutionary approaches, based on reinforcement learning ideas. Our approach provides efficient and low cost adaptive techniques for parameter control. Moreover, it is a general method, thus it could be applied to any evolutionary approach having more than one operator. We contrast our results with tuning techniques and HAEA a random parameter control.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Cazangi:2007:cec, title={An Evolutionary Approach for Autonomous Robotic Tracking of Dynamic Targets in Healthcare Environments}, author={Renato R. Cazangi and Craig Feied and Michael Gillam and Jonathan Handler and Mark Smith and Fernando J. Von Zuben}, pages = {3654--3661}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1993.pdf}, url = {}, size = {}, abstract = {Despite thousands of years of changes in medical practice, healthcare delivery remains highly dependent on manual human effort. Mobile robots can help clinicians by automating the execution of tasks that do not directly demand medical knowledge (e.g. transporting medications to nurses). Yet, healthcare is a dynamic environment with a constantly mobile workforce. The present work describes a solution to the problem of autonomous robotic tracking of mobile targets in large, dynamic environments supported by a high-resolution, real-time, ultra wideband radio-frequency localization technology. The solution consists of a navigation system able to perform both global and local path planning simultaneously based on an evolutionary computation approach. A priori information and instant sensorial stimuli are integrated by the system in order to evolve efficient trajectories in real-time. The system proposed was tested with dynamic obstacles and targets and was demonstrated to be both highly adaptive and responsive.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(:2007:cec, title={A Realistic Approach to Evolutionary Multiobjective Portfolio Optimization}, author={S. C. Chiam and A. Al Mamun and Y. L. Low}, pages = {204--211}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1994.pdf}, url = {}, size = {}, abstract = {This paper aims to address a more realistic model of the portfolio optimization problem, unlike other previous evolutionary multiobjective optimization approaches. For this purpose, an order-based representation is proposed, which can be easily extended to handle various realistic constraints like floor and ceiling constraint and cardinality constraint. Furthermore, the current experimental platform for evolutionary multiobjective portfolio optimization will be improved by introducing diversity measures and statistical analysis that are commonly used in performance assessment of multiobjective optimizers. Comparative study with other conventional representations, based on benchmark problems obtained from the OR-library, demonstrated that the proposed representation is able to attain a better approximation of the efficient frontier in terms of proximity and diversity. Experimental results also validated its viability and practicality in handling the various realistic constraints. Lastly, preference based techniques are considered also, allowing the evolutionary search to be focused on specific region of the efficient frontier. Future work includes improving the algorithmic model with more sophisticated variation operators and local search operators for better exploration and exploitation of the search space.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Fossati:2007:cec, title={A Simple Real-Coded Extended Compact Genetic Algorithm}, author={Luca Fossati and Pier Luca Lanzi and Kumara Sastry and David E. Goldberg and Osvaldo Gomez}, pages = {342--348}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1995.pdf}, url = {}, size = {}, abstract = {This paper presents a simple real-coded estimation of distribution algorithm (EDA) design using X-ary extended compact genetic algorithm (XECGA) and discretization methods. Specifically, the real-valued decision variables are mapped to discrete symbols of user-specified cardinality using discretization methods. The XECGA is then used to build the probabilistic model and to sample a new population based on the probabilistic model. The effect of alphabet cardinality and the selection pressure on the scalability of the real-coded ECGA (rECGA) method is investigated. The results show that the population size required by rECGA―to successfully solve a class of additivelyseparable problems―scales sub-quadratically with problem size and the number of function evaluations scales sub-cubically with problem size. The proposed rECGA is simple, making it amenable for further empirical and theoretical analysis. Moreover, the probabilistic models built in the proposed realcoded ECGA are readily interpretable and can be easily visualized. The proposed algorithm and the results presented in this paper are first step towards conducting a systematic analysis of real-coded EDAs and towards developing a design theory for development of scalable and robust real-coded EDAs.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Subramanyam:2007:cec, title={A Dual Layered PSO Algorithm for Evolving an Artificial Neural Network Controller}, author={V. Subramanyam and D. Srinivasan and R. Oruganti}, pages = {2350--2357}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1997.pdf}, url = {}, size = {}, abstract = {This paper introduces a Dual layered Particle Swarm Optimization Algorithm (DLPSO), an evolutionary algorithm proposed to design an Artificial Neural Network (ANN). The algorithm evolves the architecture of the ANN and trains its weights simultaneously. Different from the other techniques previously used, the proposed algorithm evolves the architecture along with the weights in two different layers. Tested on a non-linear system, typically a boost converter, the DLPSO evolves an optimal ANN controller to produce more efficient and robust results than the conventional control techniques used. The performance of the DLPSO based ANN controller is compared to that of a conventional PI controller at different operating points of the non-linear system. The tests show that the evolved controller performs equal to or better than the conventional techniques in terms of overshoot voltages and settling times for small and large signal input transients. Also, a comparison between the applicability of a PSO and a Real-Valued Genetic Algorithm for the training of weights is presented which shows that the PSO is faster and more efficient as a learning algorithm. Moreover, the proposed approach fully automates the neural network generation process, thus removing the need for time consuming manual design.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Sharma:2007:cec, title={Evolutionary Computation and Economic Time Series Forecasting}, author={V. Sharma and D. Srinivasan}, pages = {188--195}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {1998.pdf}, url = {}, size = {}, abstract = {This paper summarizes the collective work done in the application of Evolutionary Computation for financial time series forecasting. These are mainly stock market indices and foreign exchange rate prediction. The time series corresponding to these indices is a non-linear dynamic stochastic system different from other static patterns which are independent of time. Evolutionary techniques have capabilities of efficient search space exploration with population models corresponding to the problem. Their ability to capture the non linear dependencies among the system variables has invited economic analysts towards their use in the field of financial time series prediction. In this paper, previous research done in the application of evolutionary techniques for economic time series prediction and resolving the issues involved has been presented.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Zhang:2007:cec, title={Target Shape Design Optimization by Evolving Splines}, author={Pan Zhang and Xin Yao and Lei Jia and B. Sendhoff and T. Schnier}, pages = {2009--2016}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {2000.pdf}, url = {}, size = {}, abstract = {Target shape design optimization problem (TSDOP) is a miniature model for real world design optimization problems. It is proposed as a test bed to design and analyze optimization approaches for design optimization with tremendously reducing the running period of optimization process, while, the merit can be only achieved by correctly approximating the real design situation and satisfying the causality of design and evaluation. The representation of the designed object is mostly described by parameterization techniques. To realize the design optimization, is to vary the parameterized object by means of operating the relevant parameters. The solution of design optimization often involved the choice of suitable description for the designed object, which can be obtained by expanding the design freedom. When changing the description length, the original parameters of the designed object will then varied. This bring about the requirements for optimization algorithms to self-adapt their strategy parameters and related variables to perform consistently searching. We first put forwards a revised fitness evaluation mechanism for the TSDOP in order to more reasonably check the designed shape and direct optimization procedures. Based on the revised TSDOP framework, we further discuss the parameter setting problem for algorithms, especially evolution strategies, to adapt and initial their search strategy parameters. A solution method is proposed with solving a linear equations by a recursive way with linear time complexity. All discussions are limited with the B-spline parameterization framework, but may generally suit other parameterization techniques. Experiments are used to verify the causality of the revised fitness evaluation mechanism and to study the significance of the proposed method for suitable parameter settings of optimization algorithms during the adaptation of the description length for design optimization.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Rad:2007:cec, title={A Recommender System Based on Invasive Weed Optimization Algorithm}, author={Hoda Sepehri Rad and Caro Lucas}, pages = {4297--4304}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {2001.pdf}, url = {}, size = {}, abstract = {Recommender systems intend to help users find their interested items from among a large number of items. We continue our previous work that emphasizes on "prioritized user-profile" approach as an effective approach to increase the quality of the recommendations. Prioritized user-profile is an approach that tries to implement more personalized recommendation by assigning different priority importance to each of the features of the user-profile for different users. In order to find the optimal priorities for each user an optimization algorithm is needed. In this paper, we employ a new optimization algorithm namely Invasive Weed Optimization (IWO) for this purpose. IWO is a relatively new and simple algorithm inspired from the invasive habits of growth of weeds in nature. Experimental results showed that IWO achieved the best accuracy in predicting users' interests compared to two other prioritized approaches which were based on Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) and to standard user-based Pearson algorithm on a movie dataset.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Sasaki:2007:cec, title={Behavior Learning of Multiple Mobile Robots Based on Spiking Neural Networks with A Parallel Genetic Algorithm}, author={Hironobu Sasaki and Naoyuki Kubota}, pages = {1456--1461}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {2002.pdf}, url = {}, size = {}, abstract = {Recently, various types of artificial neural networks are applied for behavioral learning of mobile robots in unknown and dynamic environments. In this research, the behavioral learning method based on a spiking neural networks for multiple mobile robots are proposed. The robots learn the forward relationship from sensory inputs to motor outputs. However, the behavioral leaning capability of the robots depends strongly on the network structure and the environments. Therefore, we use a parallel genetic algorithm for updating the network structure through the interaction among robots suitable to the environment. Finally, the effectiveness of the proposed method is discussed through experimental results on behavioral learning for collision avoidance.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Santos:2007:cec, title={Crew Scheduling Urban Problem: An Exact Column Generation Approach Improved by a Genetic Algorithm}, author={Andr\'e G. Santos and Geraldo R. Mateus}, pages = {1725--1731}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {2005.pdf}, url = {}, size = {}, abstract = {Many papers state that one of the best approaches to solve Crew Scheduling problems is by Column Generation. Generally a large number of columns must be handled, then the problem is decomposed and a subproblem is solved to generate the columns iteratively. This paper shows a successful application of genetic algorithm to solve the subproblem, improving the performance of the column generation algorithm, reaching the solution faster than using an integer programming package. The genetic algorithm is combined with an exact method, assuring the optimality of the final solution. The usual way to solve the subproblem is using integer programming. We compare this approach, the genetic algorithm, and a heuristic based on the linear relaxation of the subproblem formulation. We apply these algorithms to a crew scheduling problem that arises in the public transportation of a specific city. The results show that the genetic algorithm outperforms them.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Neruda:2007:cec, title={Hybrid Evolutionary Algorithm for Multilayer Perceptron Networks with Competitive Performance}, author={Roman Neruda}, pages = {1620--1627}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {2006.pdf}, url = {}, size = {}, abstract = {Hybrid models combining neural networks and genetic algorithms have been studied recently with the goal of achieving either better performance of the resulting network or faster training. In this paper we deal with variants of genetic learning applied for the structure optimization and weights evolution of multi-layer perceptron networks. Several genetic operators are tested, including memetic-type local search, that produce good results in terms of network performace. It is shown, that combining evolutionary algorithms with neural networks can lead to better results than relying on neural networks alone in terms of the quality of the solution (both training and generalization error). Comparison to gradient algorithms in terms of time complexity is discussed which does not bring overly optimistic results sometimes met in literature}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Ishibuchi:2007:cec, title={An Empirical Study on the Specification of the Local Search Application Probability in Multiobjective Memetic Algorithms}, author={Hisao Ishibuchi and Yasuhiro Hitotsuyanagi and Yusuke Nojima}, pages = {2788--2795}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {2009.pdf}, url = {}, size = {}, abstract = {This paper empirically examines the effect of the specification of the local search application probability on the performance of multiobjective memetic algorithms. In each generation of multiobjective memetic algorithms, local search is probabilistically applied to each solution. We handle the local search application probability as a controllable parameter. In computational experiments in this paper, we examine the effect of dynamically changing the probability using the five control strategies: constant, step-wise increase, step-wise decrease, linear increase, and linear decrease. Better results are obtained for almost all test problems by changing the local search application probability than specifying it as a constant value. An interesting observation is that the choice of an appropriate control strategy is problem-dependent. Gradually decreasing its value leads to good results for many problems. Such a control strategy, however, does not work on some test problems.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Kononova:2007:cec, title={Fitness Diversity Based Adaptive Memetic Algorithm for Solving Inverse Problems of Chemical Kinetics}, author={Anna V. Kononova and Kevin J. Hughes and Mohamed Pourkashanian and Derek B. Ingham}, pages = {2366--2373}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {2010.pdf}, url = {}, size = {}, abstract = {This paper proposes the Fitness Diversity based Adaptive Memetic Algorithm (FIDAMA) for solving the problem of the inverse type consisting of retrieving chemical kinetics reaction rate coefficients in the generalised Arrhenius form based on the observed concentrations in a given range of temperatures of a limited set of species which describe the reaction mechanism. FIDAMA consists of the Evolutionary Framework and three Local Searchers adaptively governed by a novel fitness diversity based measure. Moreover, a certain simplification of the decision space was carried out without any deterioration in the result obtained. The numerical results preseted show the superiority of FIDAMA compared to the other published computational intelligence methods.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(:2007:cec, title={Refined Runtime Analysis of a Basic Ant Colony Optimization Algorithm}, author={Benjamin Doerr and Daniel Johannsen }, pages = {501--507}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {2012.pdf}, url = {}, size = {}, abstract = {Neumann and Witt (2006) analyzed the runtime of the basic ant colony optimization (ACO) algorithm 1-ANT on pseudo-boolean optimization problems. For the problem ONEMAX they showed how the runtime depends on the evaporation factor. In particular, they proved a phase transition from exponential to polynomial runtime. In this work, we simplify the view on this problem by an appropriate translation of the pheromone model. This results in a profound simplification of the pheromone update rule and, by that, a refinement of the results of Neumann and Witt. In particular, we show how the exponential runtime bound gradually changes to a polynomial bound inside the phase of transition.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Lup:2007:cec, title={A Hybrid Evolutionary Algorithm for Dynamic Route Planning}, author={Lai Wei Lup and Dipti Srinivasan}, pages = {4743--4749}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {2016.pdf}, url = {}, size = {}, abstract = {This paper considers a dynamic route planning problem (DRPP) involving the optimization of a route for a single vehicle traveling between a given source and given destination. Although route planning has been widely studied, most of the available applications are primarily targeted at finding the shortest path (SP) routes, which is insufficient for dynamic route planning in real life scenario. For example, the travel time for the SP may not correspond to the overall shortest time (ST) route due to varying road conditions. In this paper, the proposed Hybrid Evolutionary Algorithm for solving the Dynamic Route Planning Problem (HEADRPP) is believed to be capable of solving this problem. The proposed HEADRPP comprises a Fuzzy Logic Implementation (FLI) and a Graph Partitioning Algorithm (GPA) incorporated into a Genetic Algorithm (GA) core, and offers both optimized SP and ST routes to the user. In this paper, the proposed HEADRPP is successfully tested on a 138 node network extracted from the Singapore Map, and its performance on SP optimization is compared with a pure GA and an ant based algorithm. Overall the performance of the proposed HEADRPP is shown to be robust to the dynamic nature of the DRPP.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Rachmawati:2007:cec, title={Dynamic Resizing for Grid-Based Archiving in Evolutionary Multi-Objective Optimization}, author={L. Rachmawati and D. Srinivasan}, pages = {3975--3982}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {2017.pdf}, url = {}, size = {}, abstract = {Archival of elite solutions is widespread practice in Evolutionary Multi-Objective Optimization. Grid-based archiving presents a compromise between accuracy and computational cost. Most grid-based archiving algorithms require apriori knowledge of the span of the Pareto front for pre-setting of the grid length or the associated parameter, grid number. Unfortunately the knowledge is often unavailable beforehand in practice. The quality of the attained nondominated front can be very sensitive to the dimension of the grids. This paper presents a dynamic grid resizing strategy, capable of shrinking or expanding hyper grids as necessity dictates. Empirical study on two- and three-objective test functions demonstrates robust performance with respect to the initial grid sizes. Applied in the context of PAES, the adaptive archiving strategy performed well for initial grid sizes determined from a uniform random distribution. In comparison to AGA, the dynamic strategy presents improved non-dominated solutions in terms of proximity to the Pareto front and diversity for selected test problems.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Zhi-gang:2007:cec, title={Research and Application of a New Predictive Control Based on State Feedback Theory in Power Plant Control System}, author={Hua Zhi-gang and Hua Hua and Lu Jian-hong and Zhang Tie-jun}, pages = {4378--4385}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {2018.pdf}, url = {}, size = {}, abstract = {Reheat steam temperature of the boiler in power plant is the important parameter of unit security and economical run. This paper proposed a new dynamic matrix control based on state feedback theory, for the large inertia and large delay characteristic of the reheat steam temperature plant. The principle is to compensate the large inertia and delay characteristic of the plant by state feedback theory, then to control the generalized plant by predictive control. In this paper the design of controller is introduced. The simulation results demonstrate that the new control system has good robustness and transient performance. And the practical results show that the satisfactory control results have been achieved. So it is an effective control strategy for large delay industry process. Now it has been successfully applied to reheat steam temperature control system in power plant.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Junyou:2007:cec, title={Stock Price Forecasting Using PSO-Trained Neural Networks}, author={Boo Junyou}, pages = {2879--2885}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {2021.pdf}, url = {}, size = {}, abstract = {This paper discusses the performance an artificial neural network (ANN) utilizing particle swarm optimization (PSO), to forecast the Singapore stock market index. The particle swarm optimized feed forward neural network (PSO FFNN) program which was developed in C++ will also be discussed. The Straits Times Index (STI) is the primary time series data set and the California electricity market price data will be used as a secondary data set to validate the results obtained from the STI data set. An initial overview of the results obtained from the back propagation neural network (BPNN) optimized parameters will be discussed and used as a benchmark for the PSO FFNN. Subsequently, the improvement in forecasting accuracy after replacing the traditional backpropagation algorithm with particle swarm optimization (PSO) will be shown. Finally, the performance of the PSO FFNN is evaluated by optimizing the PSO parameters and the results are illustrated to show the success of implementation of the particle swarm algorithm in the training of neural network weights.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Ciftcioglu:2007:cec, title={Building Performance Analysis Supported by GA}, author={O\" zer Ciftcioglu and I. Sevil Sariyildiz and Michael S. Bittermann}, pages = {859--866}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {2023.pdf}, url = {}, size = {}, abstract = {A neural tree structure is considered with nodes of neuronal type which is a Gaussian function and it plays the role of membership function. The total tree structure effectively works as a fuzzy logic system having system inputs and outputs. In this system the locations of the Gaussian membership functions of non-terminal nodes are unity so that the system has several desirable features and it represents a fuzzy model maintaining the transparency and effectiveness while dealing with complexity. The research is described in detail and its outstanding merits are pointed out in a framework having transparent fuzzy modelling properties and addressing complexity issues at the same time. A demonstrative real-life application of this model is presented and the favourable performance for similar applications is highlighted.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Doerr:2007:cec, title={A Rigorous View on Neutrality}, author={Benjamin Doerr and Michael Gnewuch and Nils Hebbinghaus and Frank Neumann}, pages = {2591--2597}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {2024.pdf}, url = {}, size = {}, abstract = {Motivated by neutrality observed in natural evolution often redundant encodings are used in evolutionary algorithms. Many experimental studies have been carried out on this topic. In this paper we present a first rigorous runtime analysis on the effect of using neutrality. We consider a simple model where a layer of constant fitness is distributed in the search space and point out situations where the use of neutrality significantly influence the runtime of an evolutionary algorithm.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Saxena:2007:cec, title={Trading on Infeasibility by Exploiting Constraint's Criticality Through Multi-Objectivization: A System Design Perspective}, author={Dhish Kumar Saxena and Kalyanmoy Deb}, pages = {919--926}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {2027.pdf}, url = {}, size = {}, abstract = {Preferences are ubiquitous in real life - many problems are over-constrained and would not be solvable if we insist that all our requirements are strictly met. This paper portrays 'constraints' in an unconventional perspective, based on the realization that in order to truly maximize/minimize objective function(s), one has to optimize the feasible set. On a broader level, this paper acknowledges the need to move from 'optimizing the given', towards 'designing the optimal'. Here, the constraints are treated as objectives over and above the stated objective(s) and no other restrictions are used to 'constrain' the search space. We then evaluate this enhanced set of objectives, in terms of their criticality or redundancy. To this effect, we utilize our earlier proposed dimensionality reduction procedures [3], [7], to obtain a minimal set of objectives, which would characterize the original system with reasonable accuracy (from dimensionality reduction perspective) but with enhanced effectiveness (from a 'system design' perspective).}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Teytaud:2007:cec, title={On the Adaptation of Noise Level for Stochastic Optimization}, author={O. Teytaud and A. Auger}, pages = {3027--3034}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {2030.pdf}, url = {}, size = {}, abstract = {This paper deals with the optimization of noisy fitness functions, where the noise level can be reduced by increasing the computational effort. We theoretically investigate the question of the control of the noise level. We analyse two different schemes for an adaptive control and prove sufficient conditions ensuring the existence of an homogeneous Markov chain, which is the first step to prove linear convergence when dealing with non-noisy fitness functions. We experimentally validate the relevance of the homogeneity criterion. Large-scale experiments conclude to the efficiency in a difficult framework.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Moon:2007:cec, title={Evolutionary Algorithm Based on Topological Sort for Precedence Constrained Sequencing}, author={Chiung Moon and Young Su Yun and Choonseong Leem}, pages = {1325--1332}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {2032.pdf}, url = {}, size = {}, abstract = {In this paper we suggest an efficient evolutionary approach based on topological sort techniques for precedence constrained sequencing. The determination of optimal sequence has much to offer to downstream project management and opens up new opportunities for supply chains and logistics.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Heni:2007:cec, title={Approximate Inference in Dynamic Possibilistic Networks}, author={Abdelkader Heni and Adel M. Alimi}, pages = {1240--1246}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {2034.pdf}, url = {}, size = {}, abstract = {This paper describes an approximate algorithm for inference in dynamic possibilistic networks (DPNs). DPNs provide a succinct and expressive graphical language for representing sequential data and factoring joint possibility distributions and they are powerful models using only the concepts of random variables and conditional possibilities. The proposed algorithm, to perform inference in such networks, is an approximate one and it is based mainly on the standard Boyen-Koller (BK) algorithm well defined for dynamic probabilistic networks. The new possibilistic framework, proposed in this paper, is notable because it gives a counterpart of traditional probability framework, generally used to represent uncertainty in sequential data. The possibilistic BK algorithm is based on the junction tree technique where inference is done via an interface clusters that decrease the size of the dynamic network structured and amenable to a very simple form of inference. We present this algorithm in terms of two possibilistic conditioning; the product based and the minbased one.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Deb:2007:cec, title={Light Beam Search Based Multi-Objective Optimization Using Evolutionary Algorithms}, author={Kalyanmoy Deb and Abhay Kumar}, pages = {2125--2132}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {2035.pdf}, url = {}, size = {}, abstract = {For the past decade or so, evolutionary multiobjective optimization (EMO) methodologies have earned wide popularity for solving complex practical optimization problems, simply due to their ability to find a representative set of Pareto-optimal solutions for mostly two, three, and some extent to four and five-objective optimization problems. Recently, emphasis has been made in addressing the decision-making activities in arriving at a single preferred solution. The multiple criteria decision making (MCDM) literature offers a number of possibilities for such a task involving user preferences which can be supplied in different forms. This paper presents an interactive methodology for finding a preferred set of solutions, instead of the complete Pareto-optimal frontier, by incorporating preference information of the decision maker. Particularly, we borrow the concept of light beam search and combine it with the NSGA-II procedure. The working of this procedure has been demonstrated on a set of test problems and on engineering design problems having two to ten objectives, where the obtained solutions are found to match with the true Pareto-optimal solutions. The results highlight the utility of this approach towards eventually facilitating a better and more reliable optimization-cum-decision-making task.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(:2007:cec, title={Application of Differential Evolution in 2-Dimensional Electromagnetic Inverse Problems}, author={Krishna Agarwal and Xudong Chen}, pages = {4305--4312}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {2036.pdf}, url = {}, size = {}, abstract = {Electromagnetic inverse techniques are non-destructive techniques to investigate an unknown region. These techniques use the principle of scattering to determine the number of objects present in the domain, their properties and shapes. However, the scattered field is non-linear function of the objects' parameters. Direct search methods prove beneficial in solving such problems. In this paper, we study a two-dimensional domain having dielectric elliptic cylinders of infinite length. We try to estimate the location, contour and relative permittivity of the each of the cylinders. The previous works have majorly contributed to optimization of shapes of cylinders made of perfect electric conductor. Here, we investigate cases of domain having single dielectric elliptic cylinder in different orientations and in noisy/noise-free scenarios. We also present results for a noise-free domain containing two dielectric elliptic cylinders. We use Multiple Signal Classification algorithm to find the exact number of cylinders in the domain and their locations. Then, Differential Evolution is used to estimate the relative permittivities and contours of the cylinders.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Xu:2007:cec, title={A New Evolutionary Neural Network For Forecasting Net Flow of A Car Sharing System}, author={J.-X. Xu and J. S. Lim}, pages = {1670--1676}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {2037.pdf}, url = {}, size = {}, abstract = {In this work, an evolutionary neural network (ENN) is proposed for forecasting net flow of a car sharing system. This work consists mainly of two contributions. The first is to develop a mixed optimization approach with genetic algorithm (GA) and back propagation (BP) training for the ENN. In particular, the crossover operator of the genetic algorithm is performed with multiple neural networks that have heterogeneous structures: either different number of nodes in a hidden layer or different number of hidden layers. Hence, this optimization process enables co-evolution of multiple NN structures which present different nonlinear models, and facilitates the selection of the most suitable forecasting model from multiple candidates. To expedite the searching process for ENN and meanwhile retain an efficient learning rate, the backpropagation training is applied only to the best or the second best chromosome in each generation. The second contribution of this work is the application of the ENN to a real forecasting problem arising from a car-sharing system. Despite the presence of randomness, nonlinearity and complexity in the forecasting process, the ENN demonstrates superior performance when comparing with both classics time series forecasting approaches and other soft-computing approaches.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Tang:2007:cec, title={Hierarchical Model Parallel Memetic Algorithm in Heterogeneous Computing Environment}, author={J. Tang and M. H. Lim and Y. S. Ong and L. Q. Song}, pages = {2758--2765}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {2038.pdf}, url = {}, size = {}, abstract = {Distributed computing environments offer vast amounts of computational power for use in parallel memetic algorithms. However, they consist of heterogeneous computing nodes, in terms of computational power, operating platform, network connectivity and latency. The behavior of parallel memetic algorithms in such environment is poorly understood: the vast majority of current parallel MAs assumes homogeneous environment. To deal with the heterogeneity of the computing resources, a hierarchical model PMA (hPMA-DLS) is proposed to provide the speed-up regardless of the heterogeneity in the distributed environment while preserving the standard behavior of the PMA. The empirical study on several large scale quadratic assignment problems (QAPs) shows that hPMA-DLS can enhance the efficiency of the island model PMA-DLS [22] search without deterioration in the solution quality.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Chua:2007:cec, title={GA Optimisation of Non-Singleton Fuzzy Logic System for ECG Classification}, author={Teck Wee Chua and Woei Wan Tan}, pages = {1677--1684}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {2039.pdf}, url = {}, size = {}, abstract = {This paper studies the ability of a non-singleton fuzzy logic system (NSFLS) that is evolved using Genetic Algorithm (GA) to handle the uncertainties in pattern classification problems. The performance of non-singleton and singleton systems for cardiac arrhythmias classification is compared. Results show that NSFLS can deal with uncertainty within its framework more efficiently, thereby enabling classification to be performed using features that are easier to extract.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Lee:2007:cec, title={Improving Generalization Capability of Neural Networks Based on Simulated Annealing}, author={Yeejin Lee and Jong-Seok Lee and Sun-Young Lee and Cheol Hoon Park}, pages = {3447--3453}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {2042.pdf}, url = {}, size = {}, abstract = {This paper presents a single-objective and a multiobjective stochastic optimization algorithms for global training of neural networks based on simulated annealing. The algorithms overcome the limitation of local optimization by the conventional gradient-based training methods and perform global optimization of the weights of the neural networks. Especially, the multiobjective training algorithm is designed to enhance generalization capability of the trained networks by minimizing the training error and the dynamic range of the network weights simultaneously. For fast convergence and good solution quality of the algorithms, we suggest the hybrid simulated annealing algorithm with the gradient-based local optimization method. Experimental results show that the performance of the trained networks by the proposed methods is better than that by the gradient-based local training algorithm and, moreover, the generalization capability of the networks is significantly improved by preventing overfitting phenomena.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Srinivasan:2007:cec, title={Co-Evolutionary Algorithms for Evolving Buyers' Bidding Strategies in an Electrical Power Market}, author={Dipti Srinivasan and Chen Khong Tham and Chengyu Wu}, pages = {774--781}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {2043.pdf}, url = {}, size = {}, abstract = {This paper presents the application of two co-evolutionary algorithms for evolving buyers' bidding strategies in a restructured pool-type electrical power market. A "greedy" algorithm which always aims to get higher power and pay less locational marginal price, as well as a "demand-driven" algorithm which aims to follow closely the individual demand, have been analyzed and implemented in simulations under different market scenarios. The two distinctive algorithms were compared against each other in a simulated power market of a reasonably large scale with 7 buyers and 20 sellers in an IEEE 14 bus network. The PowerWorld® simulator has been used as a tool to ensure that the system validity and various constraints have been met. The simulation results suggest that a "demand-driven" co-evolutionary algorithm is more effective as it does not only help buyers to save cost when supply in the market is sufficient, but also enables them to outbid their opponents easily during tougher situations, such as when supply is in great shortage.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Do:2007:cec, title={Memetic Algorithm Based Fuzzy Clustering}, author={Anh-Duc Do and Siu-Yeung Cho}, pages = {2398--2404}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {2044.pdf}, url = {}, size = {}, abstract = {This paper presents a Memetic Algorithm (MA) based Fuzzy C-Means (FCM) clustering algorithm. Traditional FCM algorithm suffers from the problem of local optimal, whereas the proposed MA-based FCM algorithm is able to overcome this problem and produce good performance in various ways. Experimental results showed that the proposed clustering algorithm outperforms traditional fuzzy clustering algorithms significantly on a wide variety of datasets with overlapping class boundaries and spread data distributions.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Harnpornchai:2007:cec, title={Dynamic Adjustment of Age Distribution in Human Resource Management by Genetic Algorithms}, author={Napat Harnpornchai and Nopasit Chakpitak and Tirapot Chandarasupsang and Tuang-Ath Chaikijkosi and Keshav Dahal}, pages = {1234--1239}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {2045.pdf}, url = {}, size = {}, abstract = {Adjustment of a given age distribution to a desired age distribution within a required time frame is dynamically performed for the purpose of Human Resource (HR) planning in Human Resource Management (HRM). The adjustment process is carried out by adding the adjustment magnitudes to the existing number of employees at the selected age groups on the yearly basis. A model of a discrete dynamical system is employed to emulate the evolution of the age distribution used under the adjustment process. Genetic Algorithms (GA) is applied for determining the adjustment magnitudes that influence the dynamics of the system. An interesting aspect of the problem lies in the high number of constraints; though the constraints are fundamental, they are considerably higher in number than in many other optimization problems. An adaptive penalty scheme is proposed for handling the constraints. Numerical examples show that GA with the utilized adaptive penalty scheme provides potential means for HR planning in HRM.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(:2007:cec, title={Type-2 GA-TSK Fuzzy Neural Network}, author={Alvin Cai and Chai Quek and Douglas L. Maskell}, pages = {1578--1585}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {2046.pdf}, url = {}, size = {}, abstract = {A novel fuzzy-neural network, the type-2 GATSKfnn (T2GA-TSKfnn), combining a type-2 fuzzy logic system (FLS) and a Genetic Algorithm (GA) based Takagi-Sugeno- Kang fuzzy neural network (GA-TSKfnn), is presented. The rational for this combination is that type-2 fuzzy sets are better able to deal with rule uncertainties, while the optimal GA-based tuning of the T2GA-TSKfnn parameters achieves better classification results. However, a general T2GA-TSKfnn is computationally very intensive due to the complexity of the type-2 to type-1 reduction. Therefore, we adopt an interval T2GA-TSKfnn implementation to simplify the computational process. Simulation results are provided to compare the T2GA-TSKfnn against other fuzzy neural networks. These results show that the proposed system is able to achieve a higher classification rate when compared against a number of other traditional neuro-fuzzy classifiers.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Chen:2007:cec, title={Evolutionary Random Neural Ensembles Based on Negative Correlation Learning}, author={Huanhuan Chen and Xin Yao}, pages = {1468--1474}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {2049.pdf}, url = {}, size = {}, abstract = {This paper proposes to incorporate bootstrap of data, random feature subspace and evolutionary algorithm with negative correlation learning to automatically design accurate and diverse ensembles. The algorithm utilizes both bootstrap of training data and random feature subspace techniques to generate an initial and diverse ensemble and evolves the ensemble with negative correlation learning. The idea of generating ensemble by simultaneous randomization of data and feature is to promote the diversity within the ensemble and encourage different individual NNs in the ensemble to learn different parts or aspects of the training data so that the ensemble can learn better the entire training data. Evolving the ensemble with negative correlation learning emphasizes not only the accuracy of individual NNs but also the cooperation among different individual NNs and thus improves the generalization. As a byproduct of bootstrap, out-of-bag (OOB) estimation, which can estimate the generalization performance without any extra data points, serves another benefit of this algorithm. The proposed algorithm is evaluated by several benchmark problems and in these cases the performance of our algorithm is better than the performance of other ensemble algorithms.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Yamamura:2007:cec, title={An Implementation of Aqueous Memory Molecules with Light Responsive DNAs}, author={Masayuki Yamamura and Noriko Hirayama and Ken Komiya}, pages = {1843--1848}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {2050.pdf}, url = {}, size = {}, abstract = {Early explosion of DNA computing to solve combinatorial problems is now shrinking by three hardness; (1) code set design, (2) scalability and (3) speed and reliability. This paper proposes an implementation of Aqueous memory molecules by using light responsive modification of DNAs and show a series of feasibility experiments. We expect to overcome three difficulties since Aqueous computing is code design free, DNA sequence provides arbitrary size of address space, and light responsive reaction is fast and reliable.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(:2007:cec, title={A Distributed Co-Evolutionary Particle Swarm Optimization Algorithm}, author={D. S. Liu and K. C. Tan and W. K. Ho}, pages = {3831--3838}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {2052.pdf}, url = {}, size = {}, abstract = {This paper introduces a distributed co-evolutionary particle swarm optimization algorithm (DCPSO). In DCPSO, the population is decomposed into the subpopulations that are each responsible for optimizing one parameter, and co-evolve in a competitive manner. Such co-evolution mechanism with both cooperation and competition are designed to be effective and efficient in solving multi-objective (MO) problems under distributed computation. The competition mechanism also indirectly helps DCPSO to overcome the fault-tolerance constraint in distributed computation. DCPSO shows substantial speedup from non-distributed version by sharing workload among computational nodes. In addition, a dynamic load balancing mechanism is used to further speed up the total runtime by minimizing the amount of idle time in each node.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Zhang:2007:cec, title={A Novel Selection-Learning Algorithm for Multi-Satellite Scheduling Problems}, author={Yan Zhang and Feng Yang and YongXuan Huang}, pages = {1318--1324}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {2053.pdf}, url = {}, size = {}, abstract = {In this paper, a novel Selection-Learning algorithm is proposed to solve multi-satellite scheduling problems, which are proved to be equivalent to Maximum Independent Set problems. Based on prior evolutionary algorithms, a selection operator is designed to assign each individual in the group with cognitive ability, resulting in a higher tendency for an individual to select information that are useful to its growth, thereby decreasing waste searches. Extensive simulations are performed, and the results show that the proposed algorithm works better than Ants Colony Systems on benchmark problems.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Okabe:2007:cec, title={Theoretical Analysis of Selection Operator in Genetic Algorithms}, author={Tatsuya Okabe}, pages = {4676--4683}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {2054.pdf}, url = {}, size = {}, abstract = {Genetic algorithms (GAs) have gathered much attention by researchers and practitioners because of successful results in many real-world optimization problems [4], [5], [6]. However, to our knowledge, the working mechanism of GAs, in particular dynamics of GAs, is still unclear and should be investigated. Since we believe that the theoretical analysis of dynamics will help us not only to understand the working mechanism of GAs but also to develop more efficient algorithms, we proposed the theoretical framework to analyze the dynamics of GAs in [14]. Since we formulated only crossover and mutation in [14], we formulate selection operator in this paper as an extension of our previous paper. Based on the proposed theoretical framework, this paper analyzes the dynamics of selection and derives several theorems.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Shaker:2007:cec, title={Investigating Hybrids of Evolutionary Search and Linear Discriminant Analysis for Authorship Attribution}, author={Kareem Shaker and David Corne and Richard Everson}, pages = {2071--2077}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {2055.pdf}, url = {}, size = {}, abstract = {Authorship Attribution is the problem of determining who is (or was) the author of one or more texts, in cases where authorship is disputed. There are many wellknown cases of disputed authorship; in this paper we consider the Federalist Papers, and the 15th Book of Oz. We treat the problem as a supervised classification problem, and use evolutionary algorithms to search through subsets of function words, which in turn form the basis of predicting authorship via linear discriminant analysis. We compare two approaches (due to the size of the text corpora in dispute, extensive experimentation is difficult), both centred around the optimization of ROC curves. On both datasets, the hybrid EA approach was able to classify the disputed works with 100% accuracy, using small sets of function words comparable to or better than previous works on these cases.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Yin:2007:cec, title={Evolutionary Computation Enabled Game Theory Based Modelling of Electricity Market Behaviours and Applications}, author={Jin Yin and Wei Chen and Yun Li}, pages = {1896--1903}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {2057.pdf}, url = {}, size = {}, abstract = {The collapse of the Californian electricity market system in 2001 has highlighted urgency in research in intelligent electricity trading systems and strategies involving both suppliers and customs. In their trading systems, power generation companies under the New Electricity Trading Arrangement (NETA) of the UK are now developing gaming strategies. However, modelling of such "intelligent" market behaviours is extremely challenging, because traditional mathematical and computer modelling techniques cannot cope with the involvement of game theory. In this paper, evolutionary computation enabled modelling of such system is presented. Both competitive and cooperative game theory strategies are taken into account in evolving the intelligent model. The model then leads to intelligent trading strategy development and decision support. Experimental tests, verification and validation are carried out with various strategies, using different model scales and data published by NETA. Results show that evolutionary computation enabled game theory involved modelling and decision making provides an effective tool for NETA trading analysis, prediction and support.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Reynolds:2007:cec, title={Exploring Knowledge and Population Swarms via an Agent-Based Cultural Algorithms Simulation Toolkit (CAT)}, author={Robert G. Reynolds and Mostafa Z. Ali}, pages = {2711--2718}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {2058.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 optimization problems. While many successful real-world applications of Cultural Algorithms have been produced, we are interested in studying the fundamental computational processes involved in the use of Cultural Systems as problem solvers. Here we describe a Java-based toolkit system, the Cultural Algorithm Toolkit (CAT) developed in the Repast Symphony Simulation environment. The system allows users to easily configure and visualize the problem solving process of a Cultural Algorithm. Currently the system supports predator/prey problem solving in a "Cones World" environment as well as a suite of benchmark problems in engineering design. Example runs of a predator prey example are presented to demonstrate the systems' capabilities.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(An:2007:cec, title={Evolving the Best-Response Strategy to Decide When to Make a Proposal}, author={Bo An and Kwang Mong Sim and Victor Lesser}, pages = {1035--1042}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {2059.pdf}, url = {}, size = {}, abstract = {This paper designed and developed negotiation agents with the distinguishing features of (1) conducting continuous time negotiation rather than discrete time negotiation, (2) learning the response times of trading parties using Bayesian learning and, (3) deciding when to make a proposal using a multi-objective genetic algorithm (MOGA) to evolve their best-response proposing time strategies for different negotiation environments and constraints. Results from a series of experiments suggest that (1) learning trading parties' response times helps agents achieve more favorable trading results, and (2) on average, when compared with SSAs (Static Strategy Agents), BRSAs (Best-Response proposing time Strategy Agents) achieved higher average utilities, higher success rates in reaching deals, and smaller average negotiation time.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Komuro:2007:cec, title={Using the Pareto Frontier to Detect Deficiencies in a Biological Simulation Model}, author={Ri\'e Komuro and Joel H. Reynolds and E. David Ford}, pages = {888--895}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {2060.pdf}, url = {}, size = {}, abstract = {We introduce the use of multiobjective optimization methods to assess mechanistic simulation models. Such models are often developed to investigate processes underlying phenomena in biology and other fields. The proposed model structure must be assessed to reveal inadequacies in its simulation of the phenomenon. Objective functions are defined to measure how well the model reproduces specific phenomenon features. The objectives may be continuous or binary-valued, e.g. constraints, depending on the quality and quantity of phenomenon data. Assessment requires estimating and exploring the model's Pareto frontier. The problem is illustrated with the assessment of a model of shoot growth in pine trees using an elitist Multiobjective Evolutionary Algorithm (MOEA). The algorithm uses the partition induced on the parameter space by binary-valued objectives. The assessment revealed the need for hysteresis in the model structure to more accurately simulate shoot growth.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Sim:2007:cec, title={Adaptive Bargaining Agents That Negotiate Optimally and Rapidly}, author={Kwang Mong Sim and Yuanyuan Guo and Benyun Shi}, pages = {1007--1014}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {2061.pdf}, url = {}, size = {}, abstract = {Whereas many extant works only adopt utility as the performance measure for evaluating negotiation agents, this work formulates strategies that optimize combined negotiation outcomes in terms of utilities, success rates, and negotiation speed. In some applications (e.g., Grid resource management), negotiation agents should be designed such that they are more likely to acquire resources more rapidly and with more certainty (in addition to optimizing utility). For negotiations with complete information, mathematical proofs show that the negotiation strategy set in this work optimizes the utilities of agents while guaranteeing that agreements are reached. A novel algorithm BLGAN is devised to guide agents in negotiations with incomplete information. BLGAN adopts (1) a Bayesian learning (BL) approach for estimating the reserve price of an agent's opponent, and (2) a multi-objective genetic algorithm (GA) for generating a proposal at each negotiation (N) round. In bilateral negotiations with incomplete information, empirical results show that when both agents adopt BLGAN to learn each other's reserve price, they are both guaranteed to reach agreements, and complete negotiations with much fewer negotiation rounds. When only one agent adopts BLGAN, the agent was highly successful in reaching agreements, achieved average utilities that were much closer to optimal, and used fewer negotiation rounds than the agent that did not adopt BLGAN.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Dürr:2007:cec, title={LisBON: A Framework for Parallelisation and Hybridisation of Optimisation Algorithms}, author={C. D\"urr and T. F\"uhner and P. N. Suganthan}, pages = {1717--1724}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {2064.pdf}, url = {}, size = {}, abstract = {This paper introduces LisBON, a novel framework for distributed, hybrid optimisation algorithms. LisBON aims at simplifying the development of memetic algorithms-a combination of heuristic, population-based search approaches with local optimisers. Moreover, LisBON's design allows for an integration of virtually any optimisation algorithm. It could hence be used to implement a large variety of different hybrid approaches, multiple-restart methods in local search routines, and multiple populations and meta-evolution in evolutionary algorithms. With LisBON, it is not only possible to distribute optimisers onto different computing nodes, but also the concurrent evaluation of merit functions can be defined in a straightforward manner.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Kandavanam:2007:cec, title={A Multi-Layered Solution for Supporting ISP Traffic Demand Using Genetic Algorithm}, author={G. Kandavanam and D. Botvich and S. Balasubramaniam and P. N. Suganthan and W. Donnelly}, pages = {2032--2039}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {2066.pdf}, url = {}, size = {}, abstract = {This paper proposes a unique feedback governed multi layered architectural model to support ISP's traffic demands with multiple Quality of Service (QoS) constraints. The proposed model consists of different modules each responsible for a particular set of tasks. The most challenging task involved in satisfying the demands is routing the traffic subject to multiple QoS constraints for multiple Internet Service Providers (ISP). Routing the traffic subject to multiple constraints itself is known to be an NP-hard problem. This paper addresses the problem of finding the optimum routes to satisfy the demands of different ISPs, where different ISPs have different demands and their priority of QoS keep changing. A genetic algorithm (GA) which makes use of heuristic technique is proposed in this paper. All the optimum routes are found in one run of the program, therefore the chromosome selected encodes all the demanded routes. This paper also makes use of employing a tournament selection mechanism where the diversity of the population is preserved while the best chromosomes are carried to the next generation. The evolutionary property of GA is utilised in this paper to evolve to suit the changing demands. The performance and the evolutionary property of the proposed solution are shown with the simulation tests.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Wahab:2007:cec, title={Speech Emotion Recognition Using Auditory Cortex}, author={Abdul Wahab and Chai Quek and Sussan De}, pages = {2658--2664}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {2067.pdf}, url = {}, size = {}, abstract = {The importance of recognizing emotions from human speech has grown with the increasing role of spoken language interfaces in human-computer interaction applications. The extraction of emotional features from human speech and the classification of different emotion would require a more complex architecture of the human brain. This paper study novel neuropsychologically inspired computational intelligence techniques that are able to mimic the learning process of the brain in formulating the emotion exhibited by the human under observation using the auditory cortex. We first extract the emotion features using Mel frequency cepstral coefficients (MFCC) from the sampled speech signal and detecting cross cultural emotions from the speech with a high degree of accuracy. Experimental results shows the capability of the architecture to detect and distinguish the emotional state of happiness from anger using data obtained from real-life, unobtrusive environment and an online call centre archive. }, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Phee:2007:cec, title={A Personalised Approach to Insulin Regulation Using Brain-Inspired Neural Semantic Memory in Diabetic Glucose Control}, author={H. K. Phee and W. L. Tung and C. Quek}, pages = {2644--2651}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {2069.pdf}, url = {}, size = {}, abstract = {Diabetes mellitus is a chronic disease with a high incidence rate worldwide. In Type-1 diabetes, the failure to produce sufficient pancreatic insulin leads to an uncontrolled increase in blood glucose. Prolong elevated blood glucose level poses significant risks of acute and chronic medical complications. Human assisted insulin injection, either through a fixed regime under the close supervision of a physician or through compartmental model schedules, is fundamentally an open-loop control system. Currently, a large amount of research has been conducted to treat Type-1 diabetes using a closed-loop insulin delivery system. The objective of this work is to investigate the use of a brain-inspired neural fuzzy system as a controller to deliver insulin in a closed-loop system for the treatment of Type-1 diabetes. In this paper, the Pseudo-Outer Product based Fuzzy Neural Network using the Yager rule of inference (i.e. POP-Yager) is employed as an intelligent controller to dispense the appropriate amount of insulin in the presence of varying meal disturbances to achieve normoglycemia for a simulated Type-1 diabetic patient.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Victoire:2007:cec, title={Improved MOCLPSO Algorithm for Environmental/Economic Dispatch}, author={T. A. A. Victoire and P. N. Suganthan}, pages = {3072--3076}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {2071.pdf}, url = {}, size = {}, abstract = {This article proposes a Multi-Objective Comprehensive Learning Particle Swarm Optimization (MOCLPSO) approach for multi-objective environmental/economic dispatch (EED) problem in electric power system. The EED problem is a non-linear constrained multi-objective optimization problem where the power generation cost and emission are treated as competing objectives. The proposed MOCLPSO approach handles the problem with competing and noncommensurable fuel cost and emission objectives and has a diversity-preserving mechanism using an external memory (called "repository") and Pareto dominance concept to find widely different Pareto-optimal solutions. Simulations are conducted on typical power system problems. The superiority of the algorithm in converging to the better Pareto optimal front with fewer fitness function evaluations is shown in general.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Korjani:2007:cec, title={Optimal Model Detection in Distributed Sensor Networks Using Genetic-Fuzzy Clustering}, author={Mohammad Mehdi Korjani and Ahmad Afshar and Mohammad Bagher Menhaj and Mohammad Reza Rajati}, pages = {2817--2821}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {2072.pdf}, url = {}, size = {}, abstract = {This paper presents an accurate method to determine the environment model for decentralized detection in sensor networks. We develop a clustering algorithm to classify sensor data and to achieve this model. Then we further enhance the performance of the method in case of noisy sensors, nonidentical observations and unreliable communication links. The enhanced algorithm performs an unsupervised hierarchical grouping of the model, threshold detection and outlier removal. The proposed algorithm is verified through simulation.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Tseng:2007:cec, title={Multiple Trajectory Search for Multiobjective Optimization}, author={Lin-Yu Tseng and Chun Chen}, pages = {3609--3616}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {2074.pdf}, url = {}, size = {}, abstract = {In recent years, many algorithms for solving multiobjective (MO) optimization problems were proposed. Some studies on the design of multiobjective test problems and the performance evaluation of algorithms were also suggested. In this paper, we proposed a novel searching algorithm called the multiple trajectory search (MTS). The MTS uses multiple agents to search the solution space concurrently. Each agent does an iterated local search using one of four 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 multiobjective optimization and tested it on the 13 benchmark problems provided for competition in the Special Session & Competition on Performance Assessment of Multi-Objective Optimization Algorithms in CEC2007.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(:2007:cec, title={Experiments with Monte Carlo Othello}, author={P. Hingston and M. Masek}, pages = {4059--4064}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {2076.pdf}, url = {}, size = {}, abstract = {In this paper, we report on our experiments with using Monte Carlo simulation (specifically the UCT algorithm) as the basis for an Othello playing program. Monte Carlo methods have been used for other games in the past, most recently and notably in successful Go playing programs. We show that Monte Carlo-based players have potential for Othello, and that evolutionary algorithms can be used to improve their strength.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(:2007:cec, title={A Foreign Exchange Portfolio Management Mechanism Based on Fuzzy Neural Networks}, author={Shuo Yao and Michel Pasquier and Chai Quek}, pages = {2576--2583}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {2078.pdf}, url = {}, size = {}, abstract = {The key in foreign exchange (Forex) trading is to pick the right currency to trade at the right time, primarily based on accurate forecast of future exchange rates. This paper presents a novel neuro-fuzzy approach in foreign exchange (Forex) portfolio management to pick the right pairs of currencies to buy and sell with optimized market timing. The proposed mechanism forecasts future BUY/SELL signals before matching these offsetting signals across different currencies to maximize trade returns. This mechanism makes use of fuzzy neural network (FNNs) as a forecasting tool, technical indicators such as moving averages and a novel Portfolio Trade Timing Optimization (PTTO) algorithm to produce an optimized BUY-SELL schedule for the Forex portfolio under management. Experimental results on real world Forex market data shows that the proposed mechanism yields significantly higher profits against various popular benchmarks.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Ohara:2007:cec, title={Effects of Spatial Structures on Evolution of Iterated Prisoner's Dilemma Game Strategies with Probabilistic Decision Making}, author={Ken Ohara and Yusuke Nojima and Yumeka Kitano and Hisao Ishibuchi}, pages = {4051--4058}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {2079.pdf}, url = {}, size = {}, abstract = {We have examined the effect of spatial structures on the evolution of iterated prisoner's dilemma (IPD) game strategies. In our former study, we used two neighborhood structures, which follow the concept of structured demes. One is for the interaction among players through the IPD game. A player in each cell in a grid-world plays against its neighbors defined by this neighborhood structure. The other is for the mating of strategies by genetic operations. A new strategy for a player is generated by genetic operations from a pair of parent strings, which are selected from its neighbors defined by the second neighborhood structure. In this paper, we extend our IPD game simulation to a more realistic problem while keeping the simplicity of the original IPD game. We employ a stochastic strategy represented by a string of real numbers between 0 and 1. Each real number in the string denotes the probability of cooperation. We examine the effects of spatial structures on the evolution of IPD game strategies with probabilistic decision making in various payoff matrices. From simulation results, it is shown that cooperative behavior is evolved only when the interaction neighborhood is small and the mating neighborhood is also small for some payoff matrices.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Doan:2007:cec, title={An Effective Ant-Based Algorithm for the Degree-Constrained Minimum Spanning Tree Problem}, author={Minh N. Doan }, pages = {485--491}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {2080.pdf}, url = {}, size = {}, abstract = {The minimum spanning tree problem with an added constraint that no node in the spanning tree has the degree more than a specified integer d, is known as the degree-constrained minimum spanning tree problem. Finding the degree-constrained minimum spanning tree of a graph is a well-studied NP-hard problem. This paper presents an effective ant-based algorithm for the degree-constrained minimum spanning tree problem. Experimental results on a benchmark set of problem instances show that the algorithm performs very well against previous algorithms.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Theofanis:2007:cec, title={An Optimization Based Genetic Algorithm Heuristic for the Berth Allocation Problem}, author={Sotirios Theofanis and Maria Boile and Mihalis Golias}, pages = {4439--4445}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {2084.pdf}, url = {}, size = {}, abstract = {Genetic Algorithms have been used extensively in the studies on the Berth Allocation Problem (BAP). In this paper we present an optimization based Genetic Algorithm heuristic for the Discrete and Dynamic BAP that is independent of the objective function of the problem. It is evaluated by considering the problem of allocating space at a berth for vessels with the objective of minimizing the total weighted service time of all the vessels. The problem is formulated as a linear mixed integer program. Computational experiments are reported to evaluate the efficiency of the proposed heuristic.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Tzafestas:2007:cec, title={Attraction and Cooperation in Space}, author={Elpida S. Tzafestas}, pages = {3698--3705}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {2085.pdf}, url = {}, size = {}, abstract = {This paper presents an attraction mechanism and a study of its behavior in noisy spatial IPD games. The attraction mechanism is coupled with a regular IPD strategy to produce the final agent's behavior. We study the mechanism in 2D space to understand how it influences the emerging spatial structures. We find that any agent strategy, even a plainly irrational one, may become stable in a spatial game given appropriate attraction relations with the neighbors. Also, often the removal of attraction after stabilization causes no qualitative effect. Then we study the effect of introduction of "extreme" agents, such as a "Don Juan" agent that is attracted by all others. Such agents can change dramatically the structure of emerging spatial strategy blocks. Especially the addition or removal of agents after stabilization almost always leads to re-organization and re-stabilization to a new configuration. Various parameters of this model are studied and finally the notion of mobility in relation to evolution is rediscussed. All our results suggest that psychological mechanisms external to the actual "problem" (here IPD) can interfere with it and can actually lead to enhanced cooperation and social stability despite environmental noise and agent irrationality. Moreover, all social/spatial variants that induce more interactions between agents with non-reciprocal attraction relations (such as the extreme agents), are bound to lead to a better social average and to more complex structures.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Smorodkina:2007:cec, title={Greedy Population Sizing for Evolutionary Algorithms}, author={Ekaterina Smorodkina and Daniel Tauritz}, pages = {2181--2187}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {2086.pdf}, url = {}, size = {}, abstract = {The number of parameters that need to be manually tuned to achieve good performance of Evolutionary Algorithms and the dependency of the parameters on each other make this potentially robust and efficient computational method very time consuming and difficult to use. This paper introduces a Greedy Population Sizing method for Evolutionary Algorithms (GPS-EA), an automated population size tuning method that does not require any population size related parameters to be specified or manually tuned a priori. Theoretical analysis of the number of function evaluations needed by the GPSEA to produce good solutions is provided. We also perform an empirical comparison of the performance of the GPS-EA to the performance of an EA with a manually tuned fixed population size. Both theoretical and empirical results show that using GPS-EA eliminates the need for manually tuning the population size parameter, while finding good solutions. This comes at the price of using twice as many function evaluations as needed by the EA with an optimal fixed population size; this, in practice, is a low price considering the amount of time and effort it takes to find this optimal population size manually.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(:2007:cec, title={GA Based FCMAC-BYY Model for Bank Solvency Analysis}, author={K. S. Lum and M. N. Nguyen and D. Shi}, pages = {1221--1225}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {2087.pdf}, url = {}, size = {}, abstract = {Since the collapse or failure of a bank could trigger an adverse financial repercussion and generate negative impacts, it is desirable to have an Early Warning System (EWS) that identifies potential bank failures or high-risk banks through the traits of financial distress. This research is aimed to construct a novel GA-FCMAC-BYY model as an alternative to analyze bank solvency. The proposed model attempts to advance our previous work which uses Fuzzy Cerebellar Model Arithmetic Controller - Bayesian Ying-Yang (FCMAC-BYY) network. Inspired by the ancient Chinese Ying Yang philosophy, FCMAC-BYY obtains optimal solution by achieving harmony between inputs and fuzzy clusters sets. However, it optimizes the fuzzy sets in the individual dimensions, resulting in the lost of relative binding data and global optimization may not be achieved. Genetic Algorithm (GA) is introduced here to look into the issue. GA operates on a population of potential solutions based on the principle of survival of the fittest to produce better approximations to a solution. Populations of candidate solutions are evaluated using fitness functions to determine the best solution. Thereafter, chromosomes would be evolved to produces new genes in the search of the optimal solution. The performance of the proposed GA-FCMAC-BYY model as a bank failure classification and early warning system is very encouraging.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Quek:2007:cec, title={Predicting Impact of News on Stock Price: An Evaluation of Neuro Fuzzy Systems}, author={C. Quek and P. Cheng and A. Jain}, pages = {1226--1233}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {2089.pdf}, url = {}, size = {}, abstract = {Investors react to news, particularly to earnings and dividend announcements released by respective firms, and consequently stock prices move. Thus, news has an impact on stock prices. However, the price adjustment process is a complex one. While neural fuzzy systems have advantages over statistical methods in modeling and predicting complex relationships generally, not many neural fuzzy systems share the same level of competence and capabilities. In this study, we evaluate the effectiveness of four neural fuzzy systems - Feed Forward Neural Network (FFNN), Adaptive Neuro Fuzzy Inference System (ANFIS), Radial Basis Function Network (RBFN) and Rough Set Based Pseudo Outer Product Rule (RSPOP), respectively - in predicting the impact of news on stock price movements. We found that Rough Set Based Pseudo Outer Product Rule (RSPOP) is the most effective system in the study undertaken, and is a candidate for further evaluation as a financial intelligence system.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Zamuda:2007:cec, title={Differential Evolution for Multiobjective Optimization with Self Adaptation}, author={Ale\v{s}s Zamuda and Janez Brest and Borko Bo\v{s}kovicī and Viljem \v{Z}umer}, pages = {3617--3624}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {2092.pdf}, url = {}, size = {}, abstract = {This paper presents performance assessment of Differential Evolution for Multiobjective Optimization with Self Adaptation algorithm, which uses the self adaptation mechanism from evolution strategies to adapt F and CR parameters of the candidate creation in DE. Results for several runs on CEC2007 special session test functions are presented and assessed with different performance metrics. Based on these metrics, algorithm strengths and weaknesses are discussed.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(:2007:cec, title={A Computational Intelligence System Based on Memory Consolidation Model}, author={T. Z. Tan and G. S. Ng and C. Quek}, pages = {2630--2635}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {2093.pdf}, url = {}, size = {}, abstract = {Humans are effective in learning and problem solving. Thus, a computational intelligence method that models human being not only increases in credibility, but also increases in performance. One of the reasons behind effective learning is that the brain employs an effective memory consolidation mechanism. Hence, a method that functionally models the memory consolidation mechanism is proposed. It is named pseudo associative complementary learning. Preliminary analysis shows that this system is capable of giving competent performance in pattern recognition.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(:2007:cec, title={MNFS-FPM: A Novel Memetic Neuro-fuzzy System Based Financial Portfolio Management}, author={Ernest Lumanpauw and Michel Pasquier and Chai Quek}, pages = {2554--2561}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {2094.pdf}, url = {}, size = {}, abstract = {Portfolio management consists of deciding what assets to include in a portfolio given the investor's objectives and changing market and economic conditions. The always difficult selection process includes identifying which assets to purchase, how much, and when. This paper presents a novel Memetic Neuro-fuzzy System for Financial Portfolio Management (MNFS-FPM) which emulates the thinking process of a rational investor and generates the optimal portfolio from a collection of assets based on a chosen investment style. The system consists mainly of two modules: the Generic Self-Organizing Fuzzy Neural Network realizing Yager inference (GenSoFNN-Yager), to predict the expected return of each asset, and a memetic algorithm using simplex local searches (MA-NM/SMD) to determine the optimal investment weight allocation for all assets in the portfolio. Experimental results on Dow Jones Industrial Average (DJIA) stocks show that the proposed system yields better performance compared to that of existing financial models: statistical mean-variance analysis and Capital Asset Pricing Model (CAPM).}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Chen:2007:cec, title={A Memetic Algorithm Configured via a Problem Solving Environment for the Hamiltonian Cycle Problems}, author={X. S. Chen and M. H. Lim and D. C. Wunsch II}, pages = {2766--2773}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {2097.pdf}, url = {}, size = {}, abstract = {Algorithm Development Environment for Permutation-based problems (ADEP) is a software environment for configuring meta-heuristics for solving combinatorial optimization problems. This paper describes the key features of ADEP and how the environment was used to generate a Memetic Algorithm (MA) solution for Hamiltonian Cycle Problems (HCP). The effectiveness of the MA algorithm is demonstrated through computer simulations and its performance is compared with backtracking and other heuristic techniques such as Simulated Annealing, Tabu Search, and Ant Colony Optimization.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Arakawa:2007:cec, title={Range Juggling for Better Convergence in Genetic Range Genetic Algorithms}, author={Masao Arakawa}, pages = {1359--1365}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {2098.pdf}, url = {}, size = {}, abstract = {One of the most important things in evolutional algorithms is to find global solution as stable as possible. As we know that it cost too much computational cost, even if we are not sure the results is really global or not, we do not want to re-run evolutional algorithms to make sure its final results. Therefore, evolutional algorithms need to include as much effort as possible to let the user feel relieved that they got close to global solution. In this paper, development of range juggling in Genetic Range Genetic Algorithms (GRGA) for better convergence is described. Genetic Range Genetic Algorithms is updated version of Adaptive Range Genetic Algorithms (ARGA). In ARGA, searching range differ every generation, in the initial stages, searching range try to find the range that include global optimum solution. In the very last stages, it tries to converge to global solution, and the searching range is beginning to shrink in order to raise the accuracy of the solution. Therefore it is very important to choose system parameter especially for the initial stage not to trap into local solution and also not to move too fast to overshoot global solution. Not like ARGA, GRGA is free from critical settings of parameters, but it has some short comings in first convergence, because once searching range is given it does not change before the range is diminished. For better convergence, techniques of range juggling is proposed and examined in this paper. Through numerical experiments, it turned out that it has better convergence and accuracy in simple problem. Even in the case that has a large number of design variables, it can reach close to global optimum solutions.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Murata:2007:cec, title={Developing Control Table for Multiple Agents Using GA-Based Q-Learning with Neighboring Crossover}, author={Tadahiko Murata and Yusuke Aoki}, pages = {1462--1467}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {2099.pdf}, url = {}, size = {}, abstract = {In this paper, we show the effectiveness of a GA-based Q-learning method to develop a control table for multiple agents. As a GA-based Q-learning method, we employ a method called "Q-learning with Dynamic Structuring of Exploration Space Based on Genetic Algorithm (QDSEGA)". In QDSEGA, Q-table for Q-learning is dynamically restructured by a genetic algorithm. QDSEGA combines Q-learning and genetic algorithm effectively, however, it has just employed simple genetic operations in their QDSEGA. We have proposed a crossover for QDSEGA to accelerate the convergence speed to develop a control table for multi-legged robot. In this paper, we show the effectiveness of the proposed neighboring crossover to develop a compact control table for multiple agents.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Goth:2007:cec, title={Initial Results with EpiSwarm, A Swarm-Based System for Investigating Genetic Epistasis}, author={Thomas Goth and Chia-Ti Tsai and Fu-Tien Chiang and Clare Bates Congdon}, pages = {3855--3861}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {2100.pdf}, url = {}, size = {}, abstract = {Many genetic diseases are not caused by the effects of a single gene, but rather, are due to multiple genes acting in concert. For complex diseases, looking at the effect of variation in a single gene may predict one disease outcome, while looking at the interactions of genetic variations across multiple genes gives us a richer understanding of the risk of disease, and may predict different outcomes. EpiSwarm is designed to model genetic epistasis (nonlinear effects among genes) using the Swarm system. EpiSwarm rule agents act both as rules to explain epistatic phenomena as well as the machinery to organize the data into clusters of similar etiologies. The preliminary results reported here indicate that the system is a promising approach for visualizing and understanding clusters of disease outcomes for complex genetic diseases.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Huang:2007:cec, title={Multi-Objective Optimization Based on Self-Adaptive Differential Evolution Algorithm}, author={V. L. Huang and A. K. Qin and P. N. Suganthan and M. F. Tasgetiren}, pages = {3601--3608}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {2101.pdf}, url = {}, size = {}, abstract = {In this paper, our recently developed Self-adaptive Differential Evolution algorithm (SaDE) is extended to solve numerical optimization problems with multiple conflicting objectives. The performance of the proposed MOSaDE algorithm is evaluated on a suit of 19 benchmark problems provided for the CEC2007 special session (http://www.ntu.edu.sg/home/epnsugan/) on Performance Assessment of Multi-Objective Optimization Algorithms.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Cook:2007:cec, title={Preliminary Results with GAUGUIN, an Evolutionary Computation Approach to Creating Art in the Suprematist Style}, author={Thomas Cook and Clare Bates Congdon}, pages = {4252--4257}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {2102.pdf}, url = {}, size = {}, abstract = {This project explores the use of evolutionary computation to design two-dimensional artwork in the Suprematist style. Using OpenGL libraries for rendering and GAUL for the evolutionary component, GAUGUIN allows the user to create art, without requiring any technical or artistic training. In using an evolutionary process to create the composition, all the user needs to do is evaluate a number of possible creations to explore the design space. The work presented here illustrates the prototype system and examples of the art that may be created with it. Future work will extend the design space to allow a wider range of possibilities and enhance the user-interface to allow more flexibility in design.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(:2007:cec, title={Performance Assessment of Generalized Differential Evolution 3 (GDE3) with a Given Set of Problems}, author={Saku Kukkonen and Jouni Lampinen}, pages = {3593--3600}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {2104.pdf}, url = {}, size = {}, abstract = {This paper presents results for the CEC 2007 Special Session on Performance Assessment of Multi-Objective Optimization Algorithms where Generalized Differential Evolution 3 (GDE3) has been used to solve a given set of test problems. The set consist of 19 problems having two, three, or five objectives. Problems have different properties in the sense of separability, modality, and geometry of the Pareto-front According to the results, a near optimal set of solutions was found in the majority of the problems. Rotated problems given caused more difficulty than the other problems. Performance metrics indicate that obtained approximation sets were even better than provided reference sets for many problems.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(:2007:cec, title={Multilevel Optimization Strategies Based on Metamodel.Assisted Evolutionary Algorithms, for Computationally Expensive Problems}, author={I. C. Kampolis and A. S. Zymaris and V. G. Asouti and K. C. Giannakoglou}, pages = {4116--4123}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {2105.pdf}, url = {}, size = {}, abstract = {In this paper, three multilevel optimization strategies are presented and applied to the design of isolated and cascade airfoils. They are all based on the same general-purpose search platform, which employs Hierarchical, Distributed Metamodel-Assisted Evolutionary Algorithms (HDMAEAs). The core search engine is an Evolutionary Algorithm (EA) assisted by local metamodels (radial basis function networks) which, for each population member, are trained anew on a "suitable" subset of the already evaluated solutions. The hierarchical scheme has a two-level structure, although it may accommodate any number of levels. At each level, the user may link (a) a different evaluation tool, such as low or high fidelity discipline-specific software, (b) a different optimization method, selected amongst stochastic and deterministic algorithms and/or (c) a different set of design variables, according to coarse and fine problem parameterizations. In the aerodynamic shape optimization problems presented in this paper, the three aforementioned techniques resort on (a) Navier-Stokes and integral boundary layer solvers, (b) evolutionary and gradient-descent algorithms where the adjoint method computes the objective function gradient and (c) airfoil parameterizations with different numbers of Bézier control points. The EAs used at any level are coarse-grained distributed EAs with a different MAEA at each deme. The three variants of the HDMAEA can be used either separately or in combination, in order to reduce the CPU cost. The optimization software runs in parallel, on multiprocessor systems.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Nguyen:2007:cec, title={A Study on the Design Issues of Memetic Algorithm}, author={Q. H. Nguyen and Y. S. Ong and N. Krasnogo}, pages = {2390--2397}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {2106.pdf}, url = {}, size = {}, abstract = {Over the recent years, there has been increasing research activities made on improving the efficacy of Memetic Algorithm (MA) for solving complex optimization problems. Particularly, these efforts have revealed the success of MA on a wide range of real world problems. MAs not only converge to high quality solutions, but also search more efficiently than their conventional counterparts. Despite the success and surge in interests on MAs, there is still plenty of scope for furthering our understanding on how and why synergy between populationbased and individual learning searchers would lead to successful Memetic Algorithms. In this paper we outline several important design issues of Memetic Algorithms and present a systematic study on each. In particular, we conduct extensive experimental studies on the impact of each individual design issue and their relative impacts on memetic search performances by means of three commonly used synthetic problems. From the empirical studies obtained, we attempt to reveal the behaviors of several MA variants to enhance our understandings on MAs.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Sharma:2007:cec, title={Hybridization of SBX Based NSGA-II and Sequential Quadratic Programming for Solving Multi-Objective Optimization Problems}, author={Deepak Sharma and Abhay Kumar and Kalyanmoy Deb and Karthik Sindhya}, pages = {3003--3010}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {2107.pdf}, url = {}, size = {}, abstract = {Most real-world search and optimization problems involve multiple conflicting objectives and results in a Pareto-optimal set. Various multi-objective optimization algorithms have been proposed for solving such problems with the goals of finding as many trade-off solutions as possible and maintaining diversity among them. Since last decade, Evolutionary Multi-objective Optimization (EMO) algorithms have been applied successfully to various test and real-world optimization problems. These population based algorithms provide a diverse set of non-dominated solutions. The obtained non-dominated set is close to the true Pareto-optimal front but it's convergence to the true Pareto-optimal front is not guaranteed. Hence to ensure the same, a local search method using classical algorithm can be applied. In the present work, SBX based NSGA-II is used as a population based approach and the sequential quadratic programming (SQP) method is used as a local search procedure. This hybridization of evolutionary and classical algorithms approach provides a confidence of converging near to the true Pareto-optimal set with a good diversity. The proposed procedure is successfully applied to 13 test problems consisting two, three and five objectives. The obtained results validate our motivation of hybridizing evolutionary and classical methods.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, ) @inproceedings(Kumar:2007:cec, title={A Hybrid Multi-Objective Optimization Procedure Using PCX Based NSGA-II and Sequential Quadratic Programming}, author={Abhay Kumar and Deepak Sharma and Kalyanmoy Deb}, pages = {3011--3018}, booktitle = "2007 IEEE Congress on Evolutionary Computation", year = 2007, editor = "Dipti Srinivasan and Lipo Wang", address = "Singapore", month = "25-28 September", organization ="IEEE Computational Intelligence Society", publisher = "IEEE Press", note = {}, ISBN = "1-4244-1340-0", file = {2109.pdf}, url = {}, size = {}, abstract = {Despite the existence of a number of procedures for multi-objective optimization using evolutionary algorithms, there is still the need for a systematic and unbiased comparison of different approaches on a carefully chosen set of test problems. In this paper, a hybrid approach using PCX based NSGA-II and Sequential Quadratic Programming (SQP) is applied on 19 benchmark test problems consisting of two, three and five objectives. PCX-NSGA-II is used as a population based algorithm where SQP is used as a local search procedure. A population based approach helps in finding the non-dominated set of solutions with a good spread, whereas SQP improves the obtained set of non-dominated solutions locally. The results obtained by the present approach shows mixed performance on the chosen test problems.}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE Catalog Number: 07TH8963C}, )