%Converted with wcci2008.awk $Revision: 1.00 $ http://bioinformatics.essex.ac.uk/users/wlangdon/rnanet
%WBL 27 May 2010 Add pages and doi to Prabhu:2008:cec,Harvey:2008:cec, tweak Tirronen:2008:cec
%WBL 21 May 2009 Add pages and doi to Pang:2008:cec

@inproceedings(Carpentieri:2008:cec,
  author = "Marco Carpentieri  ",
  title = "On the Relationships Between Genetic Algorithms and Neural Systems",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0006.pdf},
  url = {},
  size = {},
  abstract =	{We consider a marginal distribution genetic model based on crossover of sequences of genes and provide relations between the associated infinite population genetic system and the neural networks. A lower bound on population size is exhibited stating that the behaviour of the finite population system, incase of sufficiently large sizes, can be approximated by the behaviour of the corresponding infinite population system. The attractors (with binary components) of the infinite population genetic system are characterized as equilibrium points of a discrete(neural network) system that can be considered  as a variant of a Hopfield's network; it is shown that the fitness is a Lyapunov function for the variant of the discrete Hopfield's net. Our main result can be summarized by stating that the relation between marginal distribution genetic systems and neural nets is much more general than that already shown elsewhere for other simpler models. },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Candela:2008:cec,
  author = "R. Candela and E. Riva Sanseverino  ",
  title = "Partial Discharges Analysis and Parameters Identification by Continuous Ant Colony Optimization",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0007.pdf},
  url = {},
  size = {},
  abstract =	{The technique of Ant Colony Optimization has been employed in this paper to efficiently deal with the problem of parameters identification in partial discharge, PD, analysis. The latter is a continuous optimization problem. From the technical point of view the identification of these parameters allows the modeling of the phenomenon of Partial Discharges in dielectrics. In this way it is possible the early diagnosis of defects in Medium Voltage cable lines and components and thus it is possible to prevent possible outages and service interruptions. Analytically, the problem consists of finding the Weibull parameters of the Pulse Amplitude Distribution (PAD) distributions allowing the identification and classification of the defects in dielectrics. The accuracy in this identification is crucial for correct classification of defects.   The proposed algorithm, called DACS, Dynamic Ant Colony Search, allows the easy investigation of complex problems both in discrete and continuous search spaces. It dynamically redefines the search tree through which the ants (agents) move using an adaptive parameter in order to increase exploration or exploitation. In order to check the efficiency of the proposed algorithm in solving continuous optimization problems, many Partial Discharges, PD, experimental tests at various temperatures have been performed on some lumped capacity specimens. In this way, the experimental cumulative probability of amplitude histograms has been compared with those attained using the Weibull analysis. All the applications show that the error is quite limited and that the calculation times are considerably low compared to other techniques employed for the same purpose.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Huang:2008:cec,
  author = "Fu-Zhuo Huang and Ling Wang and Qie He",
  title = "A Hybrid Differential Evolution with Double Populations for Constrained Optimization",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0008.pdf},
  url = {},
  size = {},
  abstract =	{How to balance the objective and constraints is always the key point of solving constrained optimization problems. This paper proposes a hybrid differential evolution with double populations (HDEDP) to handle it. HDEDP uses a two-population mechanism to decouple constraints from objective function: one population evolves by Differential Evolution only according to either objective function or constraint, while the other stores feasible solutions which are used to repair some infeasible solutions in the former population. Thus, this technique allows objective function and constraints to be treated separately with little costs involved in the maintenance of the double population. In addition, to enhance the exploitation ability, simplex method (SM) is applied as a local search method to the best feasible solution of the first population. Simulation results based on three well-known engineering design problems as well as comparisons with some existed methods demonstrate the effectiveness, efficiency and robustness of the proposed method.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Nickabadi:2008:cec,
  author = "Ahmad Nickabadi and Mohammad Mehdi Ebadzadeh and Reza Safabakhsh  ",
  title = "DNPSO: A Dynamic Niching Particle Swarm Optimizer for Multi-Modal Optimization",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0009.pdf},
  url = {},
  size = {},
  abstract =	{In this paper, a new variant of the PSO algorithm called Dynamic Niching Particle Swarm Optimizer (DNPSO) is proposed. Similar to basic PSO, DNPSO is a global optimization algorithm in which the main population of the particles is divided into some sub-swarms and a group of free particles. A new sub-swarm forming algorithm is proposed. This new form of sub-swarm creation, combined with free particles which implement a cognition-only model of PSO, brings about a great balance between exploration and exploitation characteristics of the standard PSO. DNPSO is tested with some well-known and widely used benchmark functions and the results are compared with several PSO-based multi-modal optimization methods. The results show that in all cases, DNPSO provides the best solutions.    },
  keywords = { PSO, multi-modal function optimization, niching, dynamic, DNPSO.},
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Zhang:2008:cec,
  author = "Rui Zhang and Cheng Wu  ",
  title = "Decomposition and Immune Genetic Algorithm for Scheduling Large Job Shops",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0016.pdf},
  url = {},
  size = {},
  abstract =	{A decomposition and optimization algorithm is presented for large-scale job shop scheduling problems in which the total weighted tardiness must be minimized. In each iteration, a new subproblem is first defined by a heuristic approach and then solved using a genetic algorithm. We construct a fuzzy controller to calculate the characteristic values which describe the the bottleneck jobs in different optimization stages. Then, these characteristic values are used to guide the process of subproblem-solving in an immune mechanism. Numerical computational results show that the proposed algorithm is effective for solving large-scale scheduling problems.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Cheng:2008:cec,
  author = "Ba-Yi Cheng and Hua-Ping Chen and Hao Shao and Rui Xu and George Q. Huang  ",
  title = "A Chaotic Ant Colony Optimization Method for Scheduling a Single Batch-processing Machine with Non-Identical Job Sizes",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0017.pdf},
  url = {},
  size = {},
  abstract =	{The problem of minimizing makespan on a single batch-processing machine with non-identical job sizes is strongly NP-hard. This paper proposes an Ant Colony Optimization (ACO) algorithm with chaotic control to solve the problem. The Metropolis criterion is adopted to select the paths of ants to escape immature convergence. In order to improve the solutions of ACO, a chaotic optimizer is designed and integrated into ACO to reinforce the capacity of global optimization. Batch First Fit is introduced to decode the paths into feasible solutions of the problem. In the experiment, the instances of 24 levels are simulated and the results show that the proposed CACO outperforms Genetic Algorithm and Simulated Annealing on all the instances. },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Shen:2008:cec,
  author = "Shuhan Shen and Haolong Deng and Yuncai Liu",
  title = "Probability Evolutionary Algorithm Based Human Motion Tracking Using Voxel Data",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0022.pdf},
  url = {},
  size = {},
  abstract =	{A novel evolutionary algorithm called Probability Evolutionary Algorithm (PEA), and a method based on PEA for visual tracking of human body using voxel data are presented. PEA is inspired by the Quantum computation and the Quantum-inspired Evolutionary Algorithm, and it has a good balance between exploration and exploitation with very fast computation speed. The individual in PEA is encoded by the probabilistic compound bit, defined as the smallest unit of information, for the probabilistic representation. The observation step is used in PEA to obtain the observed states of the individual, and the update operator is used to evolve the individual. In the PEA based human tracking framework, tracking is considered to be a function optimization problem, so the aim is to optimize the matching function between the model and the image observation. Since the matching function is a very complex function in high-dimensional space, PEA is used to optimize it. Experiments on 3D human motion tracking using voxel data demonstrate the effectiveness, significance and computation efficiency of the proposed human tracking method.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Han:2008:cec,
  author = "Xue Han and Ma Hong-Xu",
  title = "Maximum Lifetime Data Aggregation in Distributed Intelligent Robot Network Based on ACO",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0025.pdf},
  url = {},
  size = {},
  abstract =	{Providing multimedia traffic support in distributed intelligent robot network (DIRN) as a kind of wireless sensor and actor network (WSAN) is addressed. Since multimedia traffic has stringent bounds on end-to-end delay resource reservation for transmitting such traffic has to be done. The existing methods used for multimedia traffic provide inefficient use of network resources and affect call acceptance and drop ratio of multimedia traffic severely. Hence a data aggregation scheme based on ant optimization algorithm using bionic swarm intelligence for supporting multimedia traffic is proposed to overcome those limitations and to help reduce the traffic to the sink node in turn reducing the power consumption of intermediate node. Lifetime maximization can balance the traffic across the network so as to avoid overwhelming the bottleneck nodes. Key issues and configurations are discussed and studied, such as influence of location of aggregation point, impact of network shape and balance based energy-efficient methods. Extensive simulations are done to assess the performance of the scheme under varying network condition for carrying multimedia traffic. A practical implementation with real DIRN has been carried out to validate the enhanced efficiency, stability and accuracy of the proposed algorithm, which is proved to lead to longer network lifetime in comparison to other traditional data aggregation schemes such as Minimum Energy Gathering Algorithm (MEGA) for supporting multimedia traffic in real time.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Yuan:2008:cec,
  author = "Xiao-Lei Yuan and Yan Bai and Ling Dong",
  title = "Identification of Linear Time-invariant, Nonlinear and Time Varying Dynamic Systems Using Genetic Programming",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0029.pdf},
  url = {},
  size = {},
  abstract =	{An improved genetic programming (GP) algorithm was developed in order to use a unified way to identify both linear and nonlinear, both time-invariant and time-varying discrete dynamic systems. 'D' operators and discrete time 'n' terminals were used to construct and evolve difference equations. Crossover operations of the improved GP algorithm were different from the conventional GP algorithm. Two levels of crossover operations were defined. A linear time-invariant system, a nonlinear time-invariant system and a time-varying system were identified by the improved GP algorithm, good models of object systems were achieved with accurate and simultaneous identification of both structures and parameters. GP generated obvious mathematical descriptions (difference equations) of object systems after expression editing, showing correct input-output relationships. It can be seen that GP is good at handling different kinds of dynamic system identification problems and is better than other artificial intelligence (AI) algorithms like neural network or fuzzy logic which only model systems as complete black boxes.  },
  keywords = {genetic algorithms, genetic programming},
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Khor:2008:cec,
  author = "Susan Khor  ",
  title = "Where Genetic Drift, Crossover and Mutation Play Nice in a Freemixing Single-Population Genetic Algorithm",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0030.pdf},
  url = {},
  size = {},
  abstract =	{A variant of the HIFF problem called HIFF-M is compared with HIFF-D- the discrete version of the original HIFF problem. By the SWO statistic, HIFF-M is less epistatic than HIFF-D. Using operator specific FDC measurements, we find that HIFF-M is less crossover-easy and less mutation-hard than HIFF-D. Nevertheless, from our experiments, HIFF-M is still difficult for an unspecialized hill climber and for a mutation-only multi-individual stochastic search algorithm to solve efficiently and reliably. HIFF-M also has a more symmetrical fitness distribution than HIFF-D thus increasing the possibility of useful neutral spaces at higher levels of fitness. Notably, explicit mechanisms to reduce diversity loss made it more difficult for crossover-only GAs to solve HIFF-M than HIFF-D. Over all configurations that we experimented with, the best search performance for HIFF-M was obtained with upGA- a single-population, steady-state GA which uses random parent selection, 1-2 point crossover and no explicit diversity preservation mechanism. This result suggests that HIFF-M has the kind of epistasis to create fitness landscapes where genetic drift, crossover and mutation work well together to balance the exploitative and explorative facets of a GA.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Tang:2008:cec,
  author = "H. Tang and W. Zhang and C. Fan and S. Xue",
  title = "Parameter Estimation Using a CLPSO Strategy",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0031.pdf},
  url = {},
  size = {},
  abstract =	{As a novel evolutionary computation technique, particle swarm optimisation (PSO) has attracted much attention and wide applications for solving complex optimisation problems in different fields mainly for various continuous optimisation problems. However, it may easily get trapped in a local optimum when solving complex multimodal problems. This paper uses an improved PSO by incorporating a comprehensive learning strategy into original PSO to discourage premature convergence, namely CLPSO strategy to estimate parameters of structural systems, which could be formulated as a multi-modal optimisation problem with high dimension. Simulation results for identifying the parameters of a structural system under conditions including limited output data and no prior knowledge of mass, damping, or stiffness are presented to demonstrate the effectiveness of the proposed method.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Pullan:2008:cec,
  author = "Wayne Pullan  ",
  title = "A Population Based Hybrid Metaheuristic for the $p$-median Problem",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0032.pdf},
  url = {},
  size = {},
  abstract =	{The p-median problem is one of choosing p facilities from a set of candidates to satisfy the demands of n clients such that the overall cost is minimised. In this paper, PBS, a population based hybrid search algorithm for the p-median problem is introduced. The PBS algorithm uses a genetic algorithm based meta-heuristic, primarily based on cut and paste crossover operators, to generate new starting points for a hybrid local search. For larger p-median instances, PBS is able to effectively use a number of computer processors. It is shown empirically that PBS is able to effectively solve p-median problems for a large range of the commonly used p-median benchmark instances.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(He:2008:cec,
  author = "Qie He and Ling Wang and Fu-Zhuo Huang",
  title = "Nonlinear Constrained Optimization by Enhanced Co-evolutionary PSO",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0035.pdf},
  url = {},
  size = {},
  abstract =	{Penalty function methods have been the most popular methods for nonlinear constrained optimisation due to their simplicity and easy implementation. However, it is often not easy to set suitable penalty factors or to design adaptive mechanisms. By employing the notion of co-evolution to adapt penalty factors, we present a co-evolutionary particle swarm optimisation approach (CPSO) for nonlinear constrained optimisation problems, where PSO is applied with two kinds of swarms for evolutionary exploration and exploitation in spaces of both solutions and penalty factors. To enhance the performance of our proposed algorithm, three improvement strategies are proposed. The proposed algorithm is population-based and easy to implement in parallel, in which the penalty factors to evolve in a self-tuning way. Simulation results based on three famous engineering constrained optimisation problems demonstrate the effectiveness, efficiency and robustness of the proposed enhanced CPSO (EC PSO). },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Chen:2008:cec,
  author = "Mingquan Chen  ",
  title = "Second Generation Particle Swarm Optimization",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0040.pdf},
  url = {},
  size = {},
  abstract =	{Second Generation Particle Swarm Optimization (SGPSO) is a new swarm intelligence optimization algorithm. SGPSO is based on the PSO. But the SGPSO will sufficiently use the information of the optimum swarm. The optimum swarm consists of the local optimum solution of every particle. In the SGPSO, every particle in the swarm not only moves to the local optimum solution and the global optimum solution, but also moves to the geometric center of optimum swarm. SGPSO, PSO and PSO with Time-Varying Acceleration Coefficients(PSO_TVAC) are compared on some benchmark functions. And experiment results show that SGPSO performs better in the accuracy and in getting red of the premature than PSO and PSO_TVAC. And according to the different swarm centers which every particle moves to, I will show some kinds of the variation of SGPSO. },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Wai:2008:cec,
  author = "Rong-Jong Wai and Kun-Lun Chuang and  Jeng-Dao Lee",
  title = "Supervisory Particle-Swarm-Optimization Control Design for Maglev Transportation System",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0041.pdf},
  url = {},
  size = {},
  abstract =	{This study focuses on the design of an on-line levitation and propulsion control for a magnetic-levitation (maglev) transportation system. First, the dynamic model of a maglev transportation system including levitated electromagnets and a propulsive linear induction motor (LIM) based on the concepts of mechanical geometry and motion dynamics is developed. Then, a total sliding-mode (TS) control strategy is introduced, and the concept of TS control is incorporated into particle swarm optimization (PSO) to form an on-line TSPSO control framework with varied inertial weights for preserving the robust control characteristics and reducing the chattering control phenomena of TS control. In this TSPSO control scheme, a PSO control system is used to be the major controller, and the stability can be indirectly ensured by the concept of TS control without strict constraint and detailed system knowledge. In order to further directly stabilize the system states around a predefined bound region and effectively accelerate the searching speed of the PSO control, a supervisory mechanism is embedded into the TSPSO control to constitute a supervisory TSPSO (STSPSO) control strategy. The effectiveness of the proposed control schemes for the maglev transportation system is verified by numerical simulations, and the superiority of the STSPSO control scheme is indicated in comparison with the TS and TSPSO control strategies.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Gao:2008:cec,
  author = "Jiaquan Gao and Guixia He and Yushun Wang and Feng Liu",
  title = "Multi-Objective Scheduling Problems Subjected to Special Process Constraint",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0042.pdf},
  url = {},
  size = {},
  abstract =	{The problem of parallel machine multi-objective scheduling subjected to special process constraint in the textile industries, as one of the most important combinational optimization problems, is different from other parallel machine scheduling problems in the following characteristics. On one hand, processing machines are non-identical; on the other hand, the sort of job processed on every machine can be restricted. Considering one of the multi-objective problems, either minimizing the maximum completion time among all the machines(makespan) or minimizing the total earliness/tardiness penalty of all the jobs has been cornerstone of most studies done so far. However, under special process constraint, taking them into account as a multi-objective problem has not been well studied. Therefore, in this paper, a multi-objective model based on them is presented and a new parallel genetic algorithm based on a vector group coding method is also proposed in order to effectively solve this model. The algorithm shows the following advantages: the coding method is simple and can effectively reflect the virtual scheduling policy, which can vividly reflect the numbers and sequences of these processed jobs on every machine, and then enables the individuals generated by crossover and mutation to satisfy process constraint. Numerical experiments show that it is efficient, and is better than the common genetic algorithm, and has the better parallel efficiency. A much better prospect of application can be optimistically expected.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Beadle:2008:cec,
  author = "Lawrence Beadle and Colin G. Johnson",
  title = "Semantically Driven Crossover in Genetic Programming",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0044.pdf},
  url = {},
  size = {},
  abstract =	{Crossover forms one of the core operations in genetic programming and has been the subject of many different investigations. We present a novel technique, based on semantic analysis of programs, which forces each crossover to make candidate programs take a new step in the behavioural search space. We demonstrate how this technique results in better performance and smaller solutions in two separate genetic programming experiments.  },
  keywords = {genetic algorithms, genetic programming},
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Tanimoto:2008:cec,
  author = "Jun Tanimoto  ",
  title = "Co-Evolution Model of Networks and Strategy in a 2×2 Game Emerges Cooperation",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0048.pdf},
  url = {},
  size = {},
  abstract =	{A 2×2 game model implemented by co-evolution of both networks and strategies is established. Several numerical experiments considering various 2×2 game classes, including Prisoner's Dilemma (PD), Chicken, Leader, and Hero, reveal that the proposed co-evolution mechanism can solve dilemmas in the PD game class. The result of solving a dilemma is the development of mutual-cooperation reciprocity (R reciprocity), which arises through the emergence of several cooperative hub agents, which have many links in a heterogeneous and assortative social network. However, the co-evolution mechanism seems counterproductive in case of the Leader and Hero game classes, where alternating reciprocity (ST reciprocity) is more demanding. It is also suggested that the assortative and cluster coefficients of a network affect the emergence of cooperation for R reciprocity. },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Dudley:2008:cec,
  author = "James Dudley and Luigi Barone and Lyndon While  ",
  title = "Multi-Objective Spam Filtering Using an Evolutionary Algorithm",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0051.pdf},
  url = {},
  size = {},
  abstract =	{SpamAssassin is a widely-used open source heuristic-based spam filter that applies a large number of weighted tests to a message, sums the results of the tests, and labels the message as spam if the sum exceeds a user-defined threshold. Due to the large number of tests and the interactions between them, defining good weights for SpamAssassin is difficult: moreover, users with different needs may desire different sets of weights to be used. We have built a multiobjective evolutionary algorithm MOSF that evolves weights for the tests in SpamAssassin according to two independent objectives: minimising the number of false positives (legitimate messages mislabeled as spam), and minimising the number of false negatives (spam messages mislabeled as legitimate). We show that MOSF returns a set of solutions offering a range of setups for SpamAssassin satisfying different users' needs, and also that MOSF can derive solutions which beat the existing SpamAssassin weights in both objectives simultaneously. Applying these ideas could substantially increase the usefulness of SpamAssassin and similar systems.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Fang-Ying:2008:cec,
  author = "Xiao Fang-Ying and Chen Han-Wu and Liu Wen-Jie and Li Zhi-Qiang",
  title = "Fault Detection for Single and Multiple Missing-Gate Faults in Reversible Circuits",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0054.pdf},
  url = {},
  size = {},
  abstract =	{To ensure the validity and reliability of reversible circuits, fault detection is necessarily. Two methods to get complete test set with respect to missing-gate fault (MGF) in reversible circuits were introduced. They are the method that divided the circuit into subcircuit to get the complete test set which is not minimal and the set covering method to get the minimal complete test set. Comparing to DFT detection method, the methods introduced in this paper do not need additional gates; they do not change the structure of the circuits and do not depend on implement technologies. So, it can be widely applied.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Cheng2:2008:cec,
  author = "Wei Cheng  ",
  title = "Different Systems, Same Matrix Representation, Similar Properties",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0056.pdf},
  url = {},
  size = {},
  abstract =	{In recent paper [W. Cheng, Phys. Lett. A 364: 517-521 (2007)] we take the original 4×4 bound entangled states in [S.-M. Fei, et al., Phys. Lett. A 352: 321-325 (2006)] as 2×8 states and show that it is still bound entangled. Whether other bound entangled states, especially 4×4 bound entangled states, have the same property or not? In this paper, we consider another 4×4 bound entangled state in [F. Benatti, et al., Phys. Lett. A 326: 187-198 (2004)]. The result again affirms our observation that different systems, having the same matrix representation, have similar properties. },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Yu:2008:cec,
  author = "Jianping Yu and Yaping Lin and Jinhua Zheng",
  title = "Ant-Based Query Processing for Replicated Events in Wireless Sensor Networks",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0057.pdf},
  url = {},
  size = {},
  abstract =	{Wireless sensor networks are often deployed in diverse application specific contexts and one unifying view is to treat them essentially as distributed databases. The simplest mechanism to obtain information from this kind of database is to flood queries for named data within the network and obtain the relevant responses from sources. However, if the queries are issued for replicated data, the simple approach can be highly inefficient. As sensor networks are uniquely characterized by limited energy availability and low memory, alternative strategies need to be examined for this kind of queries. A novel query processing approach using distributed Multiple Ant Colonies algorithm with positive interaction is presented in this paper, in which ants adjust individual behavior via cooperation to make colony behavior intelligent, demanding merely local information to find named data efficiently and determine the number and allocation of event replicas adaptively. Theore- tically and experimentally, the results clearly show that the proposed protocol is more flexible and energy-efficient than existing algorithms.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Liu:2008:cec,
  author = "Yu-Hsin Liu  ",
  title = "A Memetic Algorithm for the Probabilistic Traveling Salesman Problem",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0059.pdf},
  url = {},
  size = {},
  abstract =	{The probabilistic traveling salesman problem (PTSP) is an important theoretical and practical topic in the study of stochastic network problems. It provides researchers with a modeling framework for exploring the stochastic effects in routing problems. This paper focuses on developing a memetic algorithm (MA) by incorporating the nearest neighbor algorithm to generate initial solutions, 1-shift and/or 2-opt exchanges for local search, and edge recombination (ER) crossover to efficiently and effectively solve the PTSP. In addition, a mixed local search strategy by randomly selecting two different local search methods (i.e., 1-shift and 2-opt exchanges) is introduced to further enhance the effectiveness of the proposed MA for solving the PTSP. A set of numerical experiments based on both heterogeneous and homogeneous PTSP instances were conducted to test the validity of the proposed MA. The numerical results showed that the newly proposed MA enhanced the performance in terms of objective function value and/or computation time in most of the tested cases as compared to existing methods tested in previous studies. Moreover, the results indicated that incorporating mixed local search strategy into the MA can significantly increase the solution quality. These findings show the potential of the proposed MA in effectively and efficiently solving the large-scale PTSP.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Rada:2008:cec,
  author = "Juan Rada and Ruben Parma and Wilmer Pereira",
  title = "Path Optimization for Multiple Objectives in Directed Graphs Using Genetic Algorithms",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0060.pdf},
  url = {},
  size = {},
  abstract =	{This paper presents a genetic algorithmic approach for finding efficient paths in directed graphs when optimizing multiple objectives. Its aim is to provide solutions for the game of Animat where an agent must evolve paths to achieve the greatest amount of bombs in the fewest moves as possible. The nature of this problem suggests agents with memory abilities to choose different edges from a vertex v such that each time v is reached, the agent can avoid cycles and be encouraged to keep searching for bombs all over the directed graph. This approach was tested on several random scenarios and also on specially designed ones with very encouraging results. The multi-objective genetic algorithm chosen to evolve paths was SPEA2 using one-point crossover and low mutation to allow genetic diversity of the population and an enhanced convergence rate. Results are compared with an implementation for the same game using Ant Colony Optimization.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Liu2:2008:cec,
  author = "Qingbo Liu and Yueqing Yu and Liying Su and Qixiao Xia  ",
  title = "A Fast Collision-Free Motion Planning Method for Underactuated Robots Based On Genetic Algorithm",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0061.pdf},
  url = {},
  size = {},
  abstract =	{A new approach of fast collision-free motion planning for underactuated robots based on genetic algorithm is proposed. The collision avoidance problem is formulated and solved as a position-based force control problem. Virtual generalized force representing the intrusion of the arm into the obstacle dangerous zone is computed in real time using a virtual spring-damper model. The partly stable controllers are adopted and the energy based fitness function is built, then the best switching sequence of partly stable controllers is obtained by genetic algorithm. Because the proposed method does not make any hypothesis about the degree of freedom, it can be used without modification for arms with a large number of degree of freedom. At last, numerical simulations which are carried on the planar 3R underactuated robots show the effectiveness of the proposed method.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Arcuri:2008:cec,
  author = "Andrea Arcuri and Xin Yao",
  title = "A Novel Co-Evolutionary Approach to Automatic Software Bug Fixing",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0063.pdf},
  url = {},
  size = {},
  abstract =	{Many tasks in Software Engineering are very expensive, and that has led the investigation to how to automate them. In particular, Software Testing can take up to half of the resources of the development of new software. Although there has been a lot of work on automating the testing phase, fixing a bug after its presence has been discovered is still a duty of the programmers. In this paper we propose an evolutionary approach to automate the task of fixing bugs. This novel evolutionary approach is based on Co-evolution, in which programs and test cases co-evolve, influencing each other with the aim of fixing the bugs of the programs. This competitive co-evolution is similar to what happens in nature for predators and prey. The user needs only to provide a buggy program and a formal specification of it. No other information is required. Hence, the approach may work for any implementable software. We show some preliminary experiments in which bugs in an implementation of a sorting algorithm are automatically fixed.  },
  keywords = {genetic algorithms, genetic programming},
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Li:2008:cec,
  author = "Kangshun Li and Weifeng Pan and Wensheng Zhang and Zhangxin Chen",
  title = "A Sequence Cipher Producing Method Based on Two-layer Ranking Multi-Objective Evolutionary Algorithm",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0064.pdf},
  url = {},
  size = {},
  abstract =	{Aiming at designing a high safe and high efficiency cryptosystem, the period of the sequence cipher can not be too long, and the cipher sequence produced should approach random numbers. But the key sequence produced by traditional methods sometimes does not have randomness, which makes insecurity the system using this key sequence. Considering this, in this paper, we take two criteria usually used to evaluate the randomness of a key sequence as two objectives of Multi-Objective Evolutionary Algorithm (MOEA), and a new sequence cipher producing method based on two-layer MOEA is proposed (called TLEASCP). Because of TLEASCP is based on the randomness of crossover operator and mutation operator of the high efficient MOEA, the key sequences produced by TLEASCP have the merits of high randomness, chaos and long period.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Lopez:2008:cec,
  author = "Oscar Javier {Romero Lopez} and Angelica {de Antonio}",
  title = "Hybrid Behaviour Orchestration in a Multilayered Cognitive Architecture Using an Evolutionary Approach",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0065.pdf},
  url = {},
  size = {},
  abstract =	{Managing and arbitrating behaviours, processes and components in multilayered cognitive architectures when a huge amount of environmental variables are changing continuously with increasing complexity, ensue in a very comprehensive task. The presented framework proposes an hybrid cognitive architecture that relies on subsumption theory and includes some important extensions. These extensions can be condensed in inclusion of learning capabilities through bioinspired reinforcement machine learning systems, an evolutionary mechanism based on gene expression programming to self-configure the behaviour arbitration between layers, a co-evolutionary mechanism to evolve behaviour repertories in a parallel fashion and finally, an aggregation mechanism to combine the learning algorithms outputs to improve the learning quality and increase the robustness and fault tolerance ability of the cognitive agent. The proposed architecture was proved in an animat environment using a multi-agent platform where several learning capabilities and emergent properties for selfconfiguring internal agent's architecture arise.},
  keywords = {genetic algorithms, genetic programming, gene expression programming},
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Gong:2008:cec,
  author = "Wenyin Gong and Zhihua Cai",
  title = "A Multiobjective Differential Evolution Algorithm for Constrained Optimization",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0067.pdf},
  url = {},
  size = {},
  abstract =	{Recently, using multiobjective optimization concepts to solve the constrained optimization problems (COPs) has attracted much attention. In this paper, a novel multiobjective differential evolution algorithm, which combines several features of previous evolutionary algorithms (EAs) in a unique manner, is proposed to COPs. Our approach uses the orthogonal design method to generate the initial population; also the crossover operator based on the orthogonal design method is employed to enhance the local search ability. In order to handle the constraints, a novel constraint-handling method based on Pareto dominance concept is proposed. An archive is adopted to store the nondominated solutions and a relaxed form of Pareto dominance, called <font face="symbol">e</font>-dominance, is used to update the archive. Moreover, to use the archive solution to guide the search, a hybrid selection mechanism is proposed. Experiments have been conducted on 13 benchmark COPs. And the results prove the efficiency of our approach. Compared with five state-ofthe- art EAs, our approach provides very good results, which are highly competitive with those generated by the compared EAs in constrained evolutionary optimization. Furthermore, the computational cost of our approach is relatively low.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Bi:2008:cec,
  author = "Chengpeng Bi  ",
  title = "Evolutionary Metropolis Sampling in Sequence Alignment Space",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0068.pdf},
  url = {},
  size = {},
  abstract =	{Metropolis sampling is the earliest Markov chain Monte Carlo (MCMC) method and MCMC has been widely used in motif-finding via sequence local alignment. A key issue in the design of MCMC algorithms is to improve the proposal mechanism and the mixing behavior. To overcome these difficulties, it is common either to run a population of chains or incorporate the evolutionary computing techniques into the MCMC framework. This paper combines a simple evolutionary (genetic) algorithm (GA) with the Metropolis sampler and proposes the new motif algorithm GAMS to carry out motif heuristic search throughout the multiple alignment space. GAMS first initializes a population of multiple local alignments (initial MCMC chains) each of which is encoded on a chromosome that represents a potential solution. GAMS then conducts a genetic algorithm-based search in the sequence alignment space using a motif scoring function as the fitness measure. The genetic algorithm gradually moves this population towards the best alignment from which the motif model is derived. Experimental results show that the new algorithm compares favorably to the simple multiple-run MCMC in some difficult cases, and it also exhibits higher precision than some popular motif-finding algorithms while testing on the annotated prokaryotic and eukaryotic binding sites data sets.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Cheng3:2008:cec,
  author = "Wei Cheng  ",
  title = "Trace Norm Related to Concurrence",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0069.pdf},
  url = {},
  size = {},
  abstract =	{By investigating the property of the trace norm which appeared in the analytical lower bound for the concurrence of arbitrary dimensional bipartite quantum states [Phys. Rev. Lett. 95, 040504 (2005)], we demonstrate the limitation of the bound in that paper and provide several concrete examples.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Jian:2008:cec,
  author = "Li Jian and Wang Cheng  ",
  title = "Resource Planning and Scheduling of Payload for Satellite with Genetic Particles Swarm Optimization",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0070.pdf},
  url = {},
  size = {},
  abstract =	{The resource planning and scheduling technology of payload is a key technology to realize an automated control for earth observing satellite with limited resources on satellite, which is implemented to arrange the works states of various payloads to carry out missions by optimizing the scheme of the resources. The scheduling task is a difficult constraint optimization problem with various and mutative requests and constraints. Based on the analysis of the satellite's functions and the payload's resource constraints, a proactive planning and scheduling strategy based on the availability of consumable and replenishable resources in time-order is introduced along with dividing the planning and scheduling period to several pieces, where then the planning and scheduling is modeled as a combinatorial optimization. The genetic particle swarm optimization algorithm (GPSO) is proposed to address the problem, which was derived from the original continuous particle swarm optimization (PSO) and incorporated with the genetic reproduction mechanisms, namely crossover and mutation. The simulation results have shown that GPSO significantly improved the search efficacy of PSO for the combinatorial optimizations.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Wang:2008:cec,
  author = "Yu-Xuan Wang and Qiao-Liang Xiang",
  title = "Exploring New Learning Strategies in Differential Evolution Algorithm",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0071.pdf},
  url = {},
  size = {},
  abstract =	{In the field of evolutionary algorithm, Differential Evolution (DE) has gained a great focus due to its strong global optimization capability and simple implementation. In DE, mutant vector, which plays the role of leading individuals to explore the search space, is generated by combining a base vector and a difference vector. However, these two vectors are selected either randomly or greedily according to the conventional strategies. In this paper, we propose three different learning strategies for conventional DE, one is for selecting the base vector and the other two are for constructing the difference vector. Experimental results on six benchmark functions validate the effectiveness of the proposed strategies.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Ma:2008:cec,
  author = "Patrick C. H. Ma and Keith C. C. Chan and Xin Yao",
  title = "An Effective Evolutionary Algorithm for Discrete-valued Data Clustering",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0072.pdf},
  url = {},
  size = {},
  abstract =	{Clustering is concerned with the discovery of interesting groupings of records in a database. Of the many algorithms have been developed to tackle clustering problems in a variety of application domains, a lot of effort has been put into the development of effective algorithms for handling spatial data. These algorithms were originally developed to handle continuous-valued attributes, and the distance functions such as the Euclidean distance measure are often used to measure the pair-wise similarity/distance between records so as to determine the cluster memberships of records. Since such distance functions cannot be validly defined in non-Euclidean space, these algorithms therefore cannot be used to handle databases that contain discrete-valued data. Owing to the fact that data in the real-life databases are always described by a set of descriptive attributes, many of which are not numerical or inherently ordered in any way, it is important that a clustering algorithm should be developed to handle data mining tasks involving them. In this paper, we propose an effective evolutionary clustering algorithm for this problem. For performance evaluation, we have tested the proposed algorithm using several real data sets. Experimental results show that it outperforms the existing algorithms commonly used for discrete-valued data clustering, and also, when dealing with mixed continuous- and discrete-valued data, its performance is also promising.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Chan:2008:cec,
  author = "K. Y. Chan and H. L. Zhu and C. C. Lau and S. H. Ling",
  title = "Gene Signature Selection for Cancer Prediction Using an Integrated Approach of Genetic Algorithm and Support Vector Machine",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0073.pdf},
  url = {},
  size = {},
  abstract =	{Classification of tumor types based on genomic information is essential for improving future cancer diagnosis and drug development. Since DNA microarray studies produce a large amount of data, effective analytical methods have to be developed to sort out whether specific cancer samples have distinctive features of gene expression over normal samples or other types of cancer samples. In this paper, an integrated approach of support vector machine (SVM) and genetic algorithm (GA) is proposed for this purpose. The proposed approach can simultaneously optimize the feature subset and the classifier through a common solution coding mechanism. As an illustration, the proposed approach is applied in searching the combinational gene signatures for predicting histologic response to chemotherapy of osteosarcoma patients, which is the most common malignant bone tumor in children. Cross-validation results show that the proposed approach outperforms other existing methods in terms of classification accuracy. Further validation using an independent dataset shows misclassification of only one of fourteen patient samples suggesting that the selected gene signatures can reflect the chemoresistance in osteosarcoma. },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Li2:2008:cec,
  author = "Bi Li and Tu-Sheng Lin and Liang Liao and Ce Fan",
  title = "Genetic Algorithm Based on Multipopulation Competitive Coevolution",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0075.pdf},
  url = {},
  size = {},
  abstract =	{Coevolutionary algorithms assess individuals by their performance in relation to others. The assessing offers the possibility of subduing premature convergence which is a long-standing problem of standard genetic algorithms (SGA's). This paper presents a novel genetic algorithm based on multipopulation competitive coevolution (GAMCC) with inter-population assessment. GAMCC comprises three simultaneously coevolving populations: the learner population, the evaluator population and the fame hall. Learners are assessed by their competitive performance relative to evaluators. Learners and evaluators take turns learning and evaluating, reciprocally driving one another to increase levels of performance. The fame hall saves the elites selected from the learner population. The competitive exclusion principle in ecological theory is applied in the fame hall to maintain the chromosome diversity. Different mutation probabilities are employed to balance the tradeoff between exploration and exploitation. Experimental results show that GAMCC is more likely to avoid the occurrence of premature convergence, and maintains the chromosome diversity more effectively, outperforming the competing genetic algorithms.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Plessis:2008:cec,
  author = "Mathys C. du Plessis and Andries P. Engelbrecht",
  title = "Improved Differential Evolution for Dynamic Optimization Problems",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0076.pdf},
  url = {},
  size = {},
  abstract =	{This article reports improvements on DynDE, a approach to using Differential Evolution to solve dynamic optimization problems. Three improvements are suggested, namely favored populations, migrating individuals and a combination of these approaches. The effects of varying the change frequency, peak widths and the number of dimensions of the dynamic environment are investigated. Experimental results are presented that indicate that the suggested approaches constitute considerable improvements on previous research.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Sung:2008:cec,
  author = "Chi Wan Sung and Shiu Yin Yuen",
  title = "On the Analysis of the (1+1) Evolutionary Algorithm with Short-Term Memory",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0077.pdf},
  url = {},
  size = {},
  abstract =	{Given any randomized search algorithm, we can avoid re-evaluating the fitness of previously visited points by storing the information in memory. This idea is applied to the (1+1) Evolutionary Algorithm with standard mutation and the Randomized Local Search (RLS) algorithm. Our analysis shows that a large reduction in running time can be obtained if we store recently visited points and execute those algorithms on some pseudo-boolean functions. Besides, the stored information can also be used to affect the generation of new search points. We illustrate this idea by designing an algorithm called Progressive Randomized Local Search. In contrary to RLS, it is capable of escaping from local maxima.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Gras:2008:cec,
  author = "Robin Gras  ",
  title = "How Efficient are Genetic Algorithms to Solve High Epistasis Deceptive Problems?",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0078.pdf},
  url = {},
  size = {},
  abstract =	{We present an overview of the properties that are involved in the complexity of global combinatorial optimization problems with a focus on epistasis and deceptiveness. As the complexity of a problem is linked to the exploration operators and algorithm used, we propose at first a bibliography of genetic algorithms. We discuss their efficiency to solve global combinatorial optimization problems following the canonical and the statistical approaches. We propose two strategies to handle such problems. In order to evaluate the capabilities and limitations of each of them, we undertake a comparison on a set of problems with varying levels of epistasis and deceptiveness.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Mouhoub:2008:cec,
  author = "Malek Mouhoub and Zhijie Wang  ",
  title = "Improving the Ant Colony Optimization Algorithm for the Quadratic Assignment Problem",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0079.pdf},
  url = {},
  size = {},
  abstract =	{The Quadratic Assignment Problem (QAP) is a well known important combinatorial problem. Indeed, many real world applications such as backboard wiring, typewriter keyboard design and scheduling can be formulated as QAPs. Recently, tackling this problem has been addressed by Ant Colony Optimization (ACO) Algorithms. To do so, ACOs, and more precisely Min-Max Ant System (MMAS) Algorithms, are usually combined with two kinds of Stochastic Local Search (SLS) methods: the 2-opt local search and the tabu local search. We talk then respectively about MMAS<sub>2opt</sub> and MMAS<sub>tabu</sub>. In this paper, we propose an improvement of these two methods according to the properties of ACO and QAP. In the case of MMAS<sub>2opt</sub>, a new random walk strategy is used to avoid a quick stagnation into local optima. Moreover, a forwardlooking strategy is proposed to explore the neighborhood more thoroughly. In the case of MMAS<sub>tabu</sub>, a random walk strategy is also employed to avoid getting stuck at local optima. In order to show the merits of our proposed techniques we have conducted experimental tests comparing respectively MMAS<sub>2opt</sub> and MMAS<sub>tabu</sub> with and without the improvements. The results demonstrate that the improved local method, have better performance in terms of the quality of the solution returned than the original ones. Moreover, we also noticed that the improved methods outperform each other for different classes of problems.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Ding:2008:cec,
  author = "Nan Ding and Shude Zhou and Ji Xu and Zengqi Sun",
  title = "A Bayesian View on the Polynomial Distribution Model in Estimation of Distribution Algorithms",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0083.pdf},
  url = {},
  size = {},
  abstract =	{Estimation of distribution algorithms (EDA) are a class of recently-developed evolutionary algorithms in which the probabilistic model are used to explicitly characterize the distribution of the population and to generate new individuals. The polynomial distribution is applied by discrete EDAs and continuous EDAs based on discretization of the domain such as histogram-based EDA. We can unify those kinds of EDA from their distribution and call them PolyEDA. In this paper, we theoretically analyze PolyEDA from a Bayesian analysis view. Our analysis is based on the assumption that the prior distribution of the parameters satisfies a Dirichlet Distribution, because under this assumption the formulation can be analytically solved. Furthermore, we notice that the prior distribution is always overlooked by previous algorithms, so we follow this way and propose some strategies to improve the PolyEDA. The experimental results show that these new strategies can help the polynomial model based estimation of distribution algorithms achieve better convergence and diversity. },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Hong:2008:cec,
  author = "Yi Hong and Sam Kwong and Hanli Wang and Zhihui Xie and Qingsheng Ren",
  title = "SVPCGA: Selection on Virtual Population Based Compact Genetic Algorithm",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0084.pdf},
  url = {},
  size = {},
  abstract =	{This paper describes a novel virtual population based truncation selection operator that extends our previously proposed virtual population based tournament selection operator [1]. Moreover, two extensions of compact genetic algorithm (CGA) that make use of virtual population based selection operators are presented in this paper: one is the tournament selection on virtual population based compact genetic algorithm (SVPCGA-TO); the other is the truncation selection on virtual population based compact genetic algorithm (SVPCGA-TR). Both SVPCGA-TO and SVPCGA-TR are tested on several benchmark problems and their results are compared with those obtained by CGA [2] and ne-CGA [3]. Some superiorities of SVPCGA in search reliability can be achieved. },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Zhang2:2008:cec,
  author = "Qing Zhang and Sanyou Zeng and Rui Wang and Hui Shi and Guang Chen and Lixin Ding and Lishan Kang ",
  title = "Constrained Optimization by the Evolutionary Algorithm with Lower Dimensional Crossover and Gradient-Based Mutation",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0086.pdf},
  url = {},
  size = {},
  abstract =	{This paper proposes a new evolutionary algorithm with lower dimensional crossover and gradient-based mutation for real-valued optimization problems with constraints. The crossover operator of the new algorithm searches a lower dimensional neighbor of the parent points where the neighbor center is the barycenter of the parents, and therefore the new algorithm converges fast. The gradient-based mutation is used to converge fast for the problems with equality constraints and active inequality constraints. And the new algorithm is simple and easy to be implemented. We have used 24 constrained benchmark problems to test the new algorithm. The experimental results show it works better than or competitive to a known effective algorithm.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Greenfield:2008:cec,
  author = "Gary Greenfield  ",
  title = "Evolutionary Computation for Aesthetic Purposes Involving an Interacting Particle Simulation",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0087.pdf},
  url = {},
  size = {},
  abstract =	{We present the results of our efforts to evolve ``fountain paintings'' &#8212; interacting streams consisting of several hundred encapsulated virtual paint particles that move under the influence of artificial gravity, are subject to collision detection and resolution, and burst open when they impact a virtual canvas. Using algorithmic art examples to motivate the orchestration of such fountains, we explore the formulation of computational criteria for aesthetic fitness evaluation which then lead indirectly to evolved compositions in a new and unexpected style.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Huang2:2008:cec,
  author = "Min Huang and Guihua Bo and Wei Tong and W. H. Ip and Xingwei Wang",
  title = "A Hybrid Immune Algorithm for Solving Fourth-Party Logistics Routing Optimizing Problem",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0088.pdf},
  url = {},
  size = {},
  abstract =	{Recently, Fourth-Party Logistics (4PL) is receiving considerable attention in the manufacturing and retail industries. However, due to the complexity, the research of routing problem in 4PL is in an initial stage. The existing study does not consider the complicated problem with node-edge property. This paper studies the node-to-node routing problem in 4PL. A mathematical model is set up based on nonlinear integer programming and multigraph. With respect to the problem's characteristics a hybrid immune algorithm is designed. The simulation shows that the hybrid immune algorithm is effective for solving the problem and provides an efficient method for making decision on routing in 4PL.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Wei:2008:cec,
  author = "Wei Wei and Huiyu Zhou and Kaoru Shimada and Shingo Mabu and Kotaro Hirasawa",
  title = "Comparative Association Rules Mining Using Genetic Network Programming (GNP) with Attributes Accumulation Mechanism and its Application to Traffic Systems",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0090.pdf},
  url = {},
  size = {},
  abstract =	{In this paper, we present a method of comparative association rules mining using Genetic Network Programming (GNP) with attributes accumulation mechanism in order to uncover association rules between different datasets. GNP is an evolutionary approach which can evolve itself and find the optimal solutions. The motivation of the comparative association rules mining method is to use the data mining approach to check two or more databases instead of one, so as to find the hidden relations among them. The proposed method measures the importance of association rules by using the absolute difference of confidences among different databases and can get a number of interesting rules. Association rules obtained by comparison can help us to find and analyse the explicit and implicit patterns among a large amount of data. For the large attributes case, the calculation is very time-consuming, when the conventional GNP based data mining is used. So, we have proposed an attribute accumulation mechanism to improve the performance. Then, the comparative association rules mining using GNP has been applied to a complicated traffic system. By mining and analysing the rules under different traffic situations, it was found that we can get interesting information of the traffic system.},
  keywords = {genetic algorithms, genetic programming},
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Tagawa:2008:cec,
  author = "Kiyoharu Tagawa  ",
  title = "Evolutionary Computation Techniques for the Optimum Design of Balanced Surface Acoustic Wave Filters",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0091.pdf},
  url = {},
  size = {},
  abstract =	{Balanced Surface Acoustic Wave (SAW) filters play a key role in the modern Radio Frequency (RF) circuits of cellular phones. The frequency response characteristics of balanced SAW filters depend on their geometrical structures. Therefore, in order to find desirable balanced SAW filters' structures, the design of them is formulated as an optimization problem. Then two types of Evolutionary Algorithms (EAs), namely Differential Evolution (DE) and Genetic Algorithm (GA), are applied to the optimization problem respectively. Experimental results indicate that DE is superior to famous GA in the quality of solution obtained with the same cost.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Zhou:2008:cec,
  author = "Huiyu Zhou and Wei Wei and Kaoru Shimada and Shingo Mabu and Kotaro Hirasawa",
  title = "Time Related Association Rules Mining with Attributes Accumulation Mechanism and its Application to Traffic Prediction",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0092.pdf},
  url = {},
  size = {},
  abstract =	{We propose a method of association rule mining using Genetic Network Programming (GNP) with time series processing mechanism and attribute accumulation mechanism in order to find time related sequence rules efficiently in association rule extraction systems. We suppose that, the database consists of a large number of attributes based on time series. In order to deal with databases which have a large number of attributes, GNP individual accumulates better attributes in it gradually round by round, and the rules of each round are stored in the Small Rule Pool using hash method, and the new rules will be finally stored in the Big Rule Pool. The aim of this paper is to better handle association rule extraction of the database in many time-related applications especially in the traffic prediction problem. In this paper, the algorithm capable of finding the important time related association rules is described and experimental results considering a traffic prediction problem are presented.  },
  keywords = {genetic algorithms, genetic programming},
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Wang2:2008:cec,
  author = "Zhenzhen Wang and Hancheng Xing",
  title = "Dynamic-Probabilistic Particle Swarm Synergetic Model: A New Framework for a More In-depth Understanding of Particle Swarm Algorithms",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0093.pdf},
  url = {},
  size = {},
  abstract =	{There always exists a phenomenon in human society that elitists lead certain progressive force and under their leadship, the whole multitude will go to some structure. So this paper presents a novel dynamic-probabilistic particle swarm algorithm by using mind on Synergetics developed by H. Haken. In this model we discuss how to produce the particles having the global optimal or the local optimal, how to propagate these particles' influences and how the whole particle swarm constructs its structure. This model is a relatively complicated PSO variant that seems to be important for us to better understand the emergence and the creative process. Indepth theoretical analysis of this model is provided. Besides the probability evolution of the swarm structure is studied with the use of the stochastic difference equations. Especially, it provides a novel framework for extending the idea of particle swarm algorithms to social realm.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Hong2:2008:cec,
  author = "Yi Hong and Sam Kwong and Hanli Wang and Qingsheng Ren and Yuchou Chang",
  title = "Probabilistic and Graphical Model Based Genetic Algorithm Driven Clustering with Instance-level Constraints",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0094.pdf},
  url = {},
  size = {},
  abstract =	{Clustering is traditionally viewed as an unsupervised method for data analysis. However, several recent studies have shown that some limited prior instance-level knowledge can significantly improve the performance of clustering algorithm. This paper proposes a semi-supervised clustering algorithm termed as the Probabilistic and Graphical Model based Genetic Algorithm Driven Clustering with Instance-level Constraints (Cop-CGA). In Cop-CGA, all prior knowledge about pairs of instances that should or should not be classified into the same groups is denoted as a graph and all candidate clustering solutions are sampled from this graph with different orders to assign instances into a certain number of groups. We illustrate how to design the Cop-CGA to guarantee that all candidate solutions satisfy the given constraints and demonstrate the usefulness of background knowledge for genetic algorithm driven clustering algorithm through experiments on several real data sets with artificial hard constraints. One advantage of Cop- CGA is both positive and negative instance-level constraints can be easily incorporated. Moreover, the performance of Cop-CGA is not sensitive to the order of assignment of instances to groups.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Yang:2008:cec,
  author = "Qingyun Yang  ",
  title = "A Comparative Study of Discrete Differential Evolution on Binary Constraint Satisfaction Problems",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0095.pdf},
  url = {},
  size = {},
  abstract =	{There are some variants and applications of the discretization of differential evolution. Performances of discrete differential evolution algorithms on random binary constraint satisfaction problem are studied in this paper, and a novel discrete differential evolution algorithm based on exchanging elements is proposed. We compare the proposed discrete differential evolution, evolutionary algorithms and discrete particle swarm optimization on random binary constraint satisfaction problems. Experimental results indicate though the proposed algorithm is simpler, it is competitive with other evolutionary algorithms solving constraint satisfaction problems.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Chu:2008:cec,
  author = "Dominique Chu and Jonathan E. Rowe",
  title = "Crossover Operators to Control Size Growth in Linear GP and Variable Length GAs",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0096.pdf},
  url = {},
  size = {},
  abstract =	{In various nuances of evolutionary algorithms it has been observed that variable sized genomes exhibit large degrees of redundancy and corresponding undue growth. This phenomenon is commonly referred to as ``bloat.'' The present contribution investigates the role of crossover operators as the cause for length changes in variable length genetic algorithms and linear GP. Three crossover operators are defined; each is tested with three different fitness functions. The aim of this article is to indicate suitable designs of crossover operators that allow efficient exploration of designs of solutions of a wide variety of sizes, while at the same time avoiding bloat.  },
  keywords = {genetic algorithms, genetic programming},
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Zeng:2008:cec,
  author = "Sanyou Zeng and Guang Chen and Rui Wang and Hui Li and Hui Shi and Lixin Ding and Lishan Kang",
  title = "A New Technique for Assessing the Diversity of Close-Pareto-Optimal Front",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0099.pdf},
  url = {},
  size = {},
  abstract =	{The quality of an approximation set usually includes two aspects &#8212; approaching distance and spreading diversity. This paper introduces a new technique for assessing the diversity of an approximation to an exact Pareto-optimal front. This diversity is assessed by using an ''exposure degree'' of the exact Pareto-optimal front against the approximation set. This new technique has three advantages: Firstly, The ''exposure degree'' combines the uniformity and the width of the spread into a direct physical sense. Secondly, it makes the approaching distance independent from the spreading diversity at the most. Thirdly, the new technique works well for problems with any number of objectives, while the widely used diversity metric proposed by Deb would work poor in problems with 3 objectives or over. Experimental computational results show that the new technique assesses the diversity well.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(On:2008:cec,
  author = "Chin Kim On and Jason Teo and Azali Saudi",
  title = "Multi-Objective Artificial Evolution of RF-Localization Behavior and Neural Structures in Mobile Robots",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0102.pdf},
  url = {},
  size = {},
  abstract =	{This paper investigates the use of a multiobjective approach for evolving artificial neural networks (ANNs) that act as a controller for radio frequency (RF)-localization behavior of a virtual Khepera robot simulated in a 3D, physics-based environment. The non-elitist and elitist Pareto-frontier Differential Evolution (PDE) algorithm are used to generate the Pareto optimal sets of ANNs that optimize the conflicting objectives of maximizing the virtual Khepera robot's behavior for homing towards a RF signal source and minimizing the number of hidden neurons used in its feedforward ANNs controller. A new fitness function which involved maximizing average wheels speed and detection of the RF signal source is also proposed. The experimentation results showed that the virtual Khepera robot was able to move towards to the target with using only a small number of hidden neurons. Furthermore, the testing results also showed that the success rate for the robot to achieve the signal source was higher when the elitist PDE-EMO algorithm was used. The path analysis of the Pareto controllers elucidated many different behaviors in terms of providing a successful homing behavior for the robot to attain the RF signal source.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Azcarraga:2008:cec,
  author = "Arnulfo P. Azcarraga and Ming-Huei Hsieh and Rudy Setiono",
  title = "Market Research Applications of Artificial Neural Networks",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0107.pdf},
  url = {},
  size = {},
  abstract =	{Even in an increasingly globalized market, the knowledge about individual local markets could still be invaluable. In this cross-national study of brand image perception of cars, survey data from buyers in the top 20 automobile markets have been collected, where each respondent has been asked to associate a car brand with individual brand images and corporate brand images. These data can be used as tool for decision making at the enterprise level. We describe an algorithm for constructing auto-associative neural networks which can be used as a tool for knowledge discovery from this data. It automatically determines the network topology by adding hidden units as they are needed to improve accuracy and by removing irrelevant input attributes. Two market research applications are presented, the first is for classification, and the second is for measuring similarities in the perceptions of the respondents from the different markets. In the first application, the constructed networks are shown to be more accurate than a decision tree. In the second application, the constructed networks are able to reproduce the training data very accurately and could be used to identify country-level (i.e. local) markets which share similar perceptions about the car brands being studied.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Li3:2008:cec,
  author = "Hang Li and Minqiang Li and Jiezhi Wang",
  title = "The Performance of Genetic Algorithms in Dynamic Optimization Problems",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0108.pdf},
  url = {},
  size = {},
  abstract =	{In dynamic optimization problems, both the falls and the attraction basins of local optima are time-varying. By the infinite population model and the further experiments, the influence of the dynamic environment on the performance of genetic algorithms is analyzed. The results show that genetic algorithms should keep dynamic balance between the exploitation capacity and the exploration capacity so as to keep excellent performance in the dynamic optimization problems.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Chen2:2008:cec,
  author = "Yan Chen and Shingo Mabu and Kaoru Shimada and Kotaro Hirasawa",
  title = "Real Time Updating Genetic Network Programming for Adapting to the Change of Stock Prices",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0109.pdf},
  url = {},
  size = {},
  abstract =	{The key in stock trading model is to take the right actions for trading at the right time, primarily based on accurate forecast of future stock trends. Since an effective trading with given information of stock prices needs an intelligent strategy for the decision making, we applied Genetic Network Programming (GNP) to create a stock trading model. In this paper, we present a new method called Real Time Updating Genetic Network Programming (RTU-GNP) for adapting to the change of stock prices. There are two important points in this paper: First, the RTU-GNP method makes a stock trading decision considering both the recommendable information of technical indices and the change of stock prices according to the real time updating. Second, we combine RTU-GNP with a reinforcement learning algorithm to create the programs efficiently. The experimental results on the Japanese stock market show that the trading model with the proposed RTU-GNP method outperforms other models without time updating method. It yielded significantly higher profits than the traditional trading model without time updating. We also compare the experimental results using the proposed method with Buy&amp;Hold method to confirm its effectiveness, and it is clarified that the proposed trading model can obtain much higher profits than Buy&Hold method.  },
  keywords = {genetic algorithms, genetic programming},
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Wilson:2008:cec,
  author = "Garnett Wilson and Wolfgang Banzhaf",
  title = "Linear Genetic Programming GPGPU on Microsoft's Xbox 360",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0110.pdf},
  url = {},
  size = {},
  abstract =	{We describe how to harness the graphics processing abilities of a consumer video game console (Xbox 360) for general programming on graphics processing unit (GPGPU) purposes. In particular, we implement a linear GP (LGP) system to solve classification and regression problems. We conduct inter- and intra-platform benchmarking of the Xbox 360 and PC, using GPU and CPU implementations on both architectures. Platform benchmarking confirms highly integrated CPU and GPU programming flexibility of the Xbox 360, having the potential to alleviate typical GPGPU decisions of allocating particular functionalities to CPU or GPU.  },
  keywords = {genetic algorithms, genetic programming},
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Jin:2008:cec,
  author = "Yaochu Jin and Bernhard Sendhoff  ",
  title = "Evolving  in silico  Bistable and Oscillatory Dynamics for Gene Regulatory Network Motifs",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0111.pdf},
  url = {},
  size = {},
  abstract =	{Autoregulation, toggle switch and relaxation oscillators are important regulatory motifs found in biological gene regulatory networks and interesting results have been reported on theoretical analyses of these regulatory units. However, it is so far unclear how evolution has shaped these motifs based on elementary biochemical reactions. This paper presents a method of designing important dynamics such as bistability and oscillation with these network motifs using an artificial evolutionary algorithm. The evolved dynamics of the network motifs are then verified when the initial states and the parameters of the network motifs are perturbed. It has been found that while it is straightforward to evolve the switching behaviour, it is difficult to evolve stable oscillatory dynamics. We show that a higher Hill coefficient will facilitate the generation of undamped oscillation, however, an evolutionary path that can lead to a high Hill coefficient remains an open question for future research.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Wang3:2008:cec,
  author = "Hui Wang and Yong Liu and Zhijian Wu and Hui Sun and Sanyou Zeng and Lishan Kang",
  title = "An Improved Particle Swarm Optimization with Adaptive Jumps",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0113.pdf},
  url = {},
  size = {},
  abstract =	{Particle Swarm Optimisation (PSO) has shown its fast search speed in many complicated optimisation and search problems. However, PSO could often easily fall into local optima. This paper presents an improved PSO with adaptive jump. The proposed method combines a novel jump strategy and an adaptive Cauchy mutation operator to help escape from local optima. The new algorithm was tested on a suite of well-known benchmark functions with many local optima. Experimental results were compared with some similar PSO algorithms based on Gaussian distribution and Cauchy distribution, and showed better performance on those test functions.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Meng:2008:cec,
  author = "Yan Meng and Jing Gan",
  title = "Self-Adaptive Distributed Multi-Task Allocation in a Multi-Robot System",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0114.pdf},
  url = {},
  size = {},
  abstract =	{Some common issues exist in the bio-inspired algorithms for a multi-robot system include considerable randomness of the robot movement during coordination and unevenly distributed robots in a multi-task environment. To address these issues, a self-adaptive distributed multi-task allocation method in a multi-robot system is proposed in this paper. In this method, each robot only communicates with its neighbours through a virtual stigmergy mechanism and makes its local movement decision based on a balance between the exploration and exploitation inspired from particle swarm optimisation (PSO) method. To further reduce the random movement, a new task utility function is developed, where not only the current available task weight and the travel cost are considered, but also the potential number of robot redundancy around the task, as well as the task/robot distribution ratio. The proposed algorithm has been implemented on the embodied robot simulator, Player/Stage, in a searching task. The simulation results demonstrate the efficiency and robustness in a power-efficient manner with the real-world constraints.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Zheng:2008:cec,
  author = "Yuhua Zheng and Yan Meng",
  title = "Swarm Intelligence Based Dynamic Object Tracking",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0115.pdf},
  url = {},
  size = {},
  abstract =	{This paper presents a new object tracking algorithm by using the particle swarm optimisation (PSO), which is a bio-inspired population-based searching algorithm. Firstly the potential solutions of the problem are projected into a state space called solution space where every point in the space presents a potential solution. Then a group of particles are initialised and start searching in this solution space. The swarm particles search for the best solution within this solution space using the Particle Swarm Optimisation (PSO) algorithm. An accumulative histogram of the object appearance is applied to build up the fitness function for the interested object pattern. Eventually the swarming particles driven by the fitness function converge to the optimal solution. Experimental results demonstrate that the proposed PSO method is efficient and robust in visual object tracking under dynamic environments.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Ye:2008:cec,
  author = "Fengming Ye and Shigo Mabu and Kaoru Shimada and Kotaro Hirasawa",
  title = "Genetic Network Programming with Rules",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0116.pdf},
  url = {},
  size = {},
  abstract =	{Genetic Network Programming (GNP) is an evolutionary approach which can evolve itself and find the optimal solutions. As many papers have demonstrated that GNP which has a directed graph structure can deal with dynamic environments very efficiently and effectively. It can be used in many areas such as data mining, forecasting stock markets, elevator system problems, etc. In order to improve GNP's performance further, this paper proposes a method called GNP with Rules. The aim of the proposal method is to balance exploitation and exploration, that is, to strengthen exploitation ability by using the exploited information extensively during the evolution process of GNP. The proposal method consists of 4 steps: rule extraction, rule selection, individual reconstruction and individual replacement. Tile-world was used as a simulation environment. The simulation results show some advantages of GNP with Rules over conventional GNPs.  },
  keywords = {genetic algorithms, genetic programming},
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Wang4:2008:cec,
  author = "Yu-Xuan Wang and Qiao-Liang Xiang",
  title = "Particle Swarms with Dynamic Ring Topology",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0118.pdf},
  url = {},
  size = {},
  abstract =	{Particle Swarm Optimiser (PSO) is a recently proposed population-based evolutionary algorithm, which exhibits good performance in many fields, and now it's becoming more and more popular due to its strong global optimisation capability and simple implementation. To achieve better performance, some variants investigated the use of different topologies in PSO. However, particles are only ``conceptually'' connected in the topology, and the neighbourhoods of a certain particle never change (i.e. the neighbourhood structure is fixed). In this paper, we propose a dynamically changing ring topology, in which particles are connected unidirectionally with respect to their personal best fitness. Meanwhile, two strategies, namely the ``Learn From Far and Better Ones'' strategy and the ``Centroid of Mass'' strategy are used to enable certain particle to communicate with its neighbours. Experimental results on six benchmarks functions validate the effectiveness of the proposed algorithm.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Worasucheep:2008:cec,
  author = "Chukiat Worasucheep  ",
  title = "A Particle Swarm Optimization with Stagnation Detection and Dispersion",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0119.pdf},
  url = {},
  size = {},
  abstract =	{Particles or candidate solutions in the standard Particle Swarm Optimisation (PSO) algorithms often face the problems of being trapped into local optima. To solve such a problem, this paper proposes a modified PSO algorithm with the stagnation detection and dispersion (PSO-DD) mechanism, which can detect a probable stagnation and is able to disperse particles. This mechanism will be described and its performance is evaluated using eight well-known 30-dimensional benchmark functions that are widely used in literature. The results show a promising alternative path for solving the common problem of local optima in PSO algorithms.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Worasucheep2:2008:cec,
  author = "Chukiat Worasucheep  ",
  title = "Trading Index Mutual Funds with Evolutionary Forecasting",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0120.pdf},
  url = {},
  size = {},
  abstract =	{This paper proposes an intuitive strategy for trading index mutual funds via the prediction of the next-day closing index of a stock market. The prediction model is built from a set of basic technical indicators. The model is optimised with a self-adaptive differential evolution algorithm in which users require no expertise in parameter settings. The proposed strategy is evaluated using Nikkei, FTSE, S&amp;P500, Dow Jones Industrial Average, and NASDAQ indices. The experiment demonstrates that the proposed strategy results in higher returns than those from buy-and-hold strategy, which is generally employed by index mutual funds.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Qing-Hua:2008:cec,
  author = "Zhang Qing-Hua and Xu Bu-Gong",
  title = "A New Model of Self-Adaptive Network Intrusion Detection",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0122.pdf},
  url = {},
  size = {},
  abstract =	{A new model of self-adaptive network intrusion detection based on negative selection algorithm is presented to tackle the problem of self continuously changeable in network intrusion detection. The evolvement of self is fully expounded; a new method that generates and evolves detectors is put forward, which can update automatically to keep synchronisation with self. The result shows that the model has the properties of self-adaptability &amp; dynamics, and can identify the intrusion effectively.    },
  keywords = { intrusion detection, artificial immune, negative selection algorithm, Self-adaptive},
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Wolff:2008:cec,
  author = "Krister Wolff and David Sandberg and Mattias Wahde  ",
  title = "Evolutionary Optimization of a Bipedal Gait in a Physical Robot",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0123.pdf},
  url = {},
  size = {},
  abstract =	{Evolutionary Optimization of a gait for a bipedal robot has been studied, combining structural and parametric modifications of the system responsible for generating the gait. The experiment was conducted using a small 17 DOF humanoid robot, whose actuators consist of 17 servo motors. In the approach presented here, individuals representing a gait consisted of a sequence of set angles (referred to as states) for the servo motors, as well as ramping times for the transition between states. A hand-coded gait was used as starting point for the Optimization procedure: A population of 30 individuals was formed, using the hand-coded gait as a seed. An evolutionary procedure was executed for 30 generations, evaluating individuals on the physical robot. New individuals were generated using mutation only. There were two different mutation operators, namely (1) parametric mutations modifying the actual values of a given state, and (2) structural mutations inserting a new state between two consecutive states in an individual. The best evolved individual showed an improvement in walking speed of approximately 65percent.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Majhi:2008:cec,
  author = "Babita Majhi and G. Panda and A. Choubey",
  title = "Efficient Scheme of Pole-Zero System Identification Using Particle Swarm Optimization Technique",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0124.pdf},
  url = {},
  size = {},
  abstract =	{This paper introduces the application of Particle Swarm Optimization (PSO) technique to identify the parameters of pole-zero plants or infinite impulse response (IIR) systems. The PSO is one of the evolutionary computing tools that performs a structured randomized search of an unknown parameter space by manipulating a population of parameter estimates to converge to a suitable solution with low computational complexity. This paper applies this powerful PSO tool to identify the parameters of standard IIR systems and compares the results with those obtained using the Genetic Algorithm (GA). The comparative results reveal that the PSO shows faster convergence, involves low complexity, yields minimum MSE level and exhibits superior identification performance in comparison to its GA counterpart.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Ji:2008:cec,
  author = "T. Y. Ji and M. S. Li and Z. Lu and Q. H. Wu",
  title = "Optimal Morphological Filter Design Using a Bacterial Swarming Algorithm",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0125.pdf},
  url = {},
  size = {},
  abstract =	{Noise removal is an underlying issue of image processing. This paper proposes a generic approach to design an optimal filter which combines linear and morphological filtering techniques, so that both Gaussian and non-Gaussian noise can be rejected. The optimisation process is performed by a bacterial swarming algorithm (BSA), which is derived from the bacterial foraging algorithm (BFA) and involves underlying mechanisms of bacterial chemotaxis and quorum sensing. The performance of the combined filter optimised by BSA is analysed in comparison with the filter optimised by the genetic algorithm (GA), as well as with other commonly used filters. The simulation results demonstrated in this paper have shown the merits of the proposed filtering technique and the optimisation algorithm.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Langdon:2008:cec,
  author = "W. B. Langdon  ",
  title = "A Fast High Quality Pseudo Random Number Generator for Graphics Processing Units",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0126.pdf},
  url = {},
  size = {},
  abstract =	{Limited numerical precision of nVidia GeForce 8800 GTX and other GPUs requires careful implementation of PRNGs. The Park-Miller PRNG is programmed using G80's native Value4f floating point in RapidMind C++. Speed up is more than 40. Code is available via ftp cs.ucl.ac.uk genetic/gpcode/random-numbers/gpu_park-miller.tar.gz  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Yang2:2008:cec,
  author = "Ming Yang and Lishan Kang and Jing Guan",
  title = "Multi-Algorithm Co-evolution Strategy for Dynamic Multi-Objective TSP",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0128.pdf},
  url = {},
  size = {},
  abstract =	{Dynamic Multi-Objective TSP (DMOTSP), a new research filed of evolutionary computation, is an NP-hard problem which comes from the applications of mobile computing and mobile communications. Because the characters of DMOTSP change with time, the method of designing a single algorithm can not effectively solve this extremely complicated and diverse optimization problem according to NFLTs for optimization. In this paper, a new approach to designing algorithm, multi-algorithm co-evolution strategy (MACS), for DMOTSP is proposed. Through multi-algorithm co-evolution, MACS can accelerate algorithm's convergence, make Pareto set maintain diversity and make Pareto front distribute evenly with a complementary performance of these algorithms and avoiding the limitations of a single algorithm. In experiment, taking the three-dimensional benchmark problem CHN144+5 with two-objective for example, the results show that MACS can solve DMOTSP effectively with faster convergence, better diversity of Pareto set and more even distribution of Pareto front than single algorithm.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Kang:2008:cec,
  author = "Zhuo Kang and Lishan Kang and Changhe Li and Yuping Chen and Minzhong Liu",
  title = "Convergence Properties of E-Optimality Algorithms for Many Objective Optimization Problems",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0129.pdf},
  url = {},
  size = {},
  abstract =	{In the paper, for many-objective optimization problems, the authors pointed out that the Pareto Optimality is unfair, unreasonable and imperfect for Many-objective Optimization Problems (MOPs) underlying the hypothesis that all objectives have equal importance and propose a new evolutionary decision theory. The key contribution is the discovery of the new definition of optimality called E-optimality for MOP that is based on a new conception, so called E-dominance, which not only considers the difference of the number of superior and inferior objectives between two feasible solutions, but also considers the values of improved objective functions underlying the hypothesis that all objectives in the problem have equal importance. Two new evolutionary algorithms for E-optimal solutions are proposed. Because the new relation  &#947;<sub>E</sub> of E-dominance is not transitive, so a new way must be found for consideration of convergence properties of algorithms. A Boolean function better used as a select strategy is defined. The convergence theorems of the new evolutionary algorithms are proved. Some numerical experiments show that the new evolutionary decision theory is better than Pareto decision theory for many-objective function optimization problems.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Carpentieri2:2008:cec,
  author = "Marco Carpentieri  ",
  title = "Hybrid Genetic Models Based on Recombination of Allele Permutations Based on Shift and Rotations for DHCP",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0131.pdf},
  url = {},
  size = {},
  abstract =	{We introduce a genetic model to solve the Directed Hamiltonian Cycle Problem (DHCP) for random directed graphs (digraphs) containing a (hidden) superposed random Hamiltonian cycle. The model represents a scheme for hybrid techniques that recombine the genetic material of allele permutation chromosomes merging ideas coming from the most recent progress in the evolutionary algorithm and the traditional combinatorial optimization areas. The methods are interpreted by rephrasing DHCP as determining the compatibility of some quadratic systems over the finite field GF(2). Genetic algorithms implementing some instances of the model and in which the recombination of the alleles is based on shift and rotations of connected traits of the chromosomes are compared with the classic Angulin and Valiant technique designed to find Hamiltonian cycles in random digraphs. The comparison is interpreted taking also into account the results about the main combinatorial techniques, for which theoretical analysis has been developed, to solve DHCP for random digraphs.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Yang3:2008:cec,
  author = "Guangfei Yang and Kaoru Shimada and Shingo Mabu and Kotaro Hirasawa",
  title = "A Personalized Association Rule Ranking Method Based on Semantic Similarity and Evolutionary Computation",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0132.pdf},
  url = {},
  size = {},
  abstract =	{Many methods have been studied for mining association rules efficiently. However, because these methods usually generate a large number of rules, it is still a heavy burden for the users to find the most interesting ones. In this paper, we propose a novel method for finding what the user is interested in by assigning several keywords, like searching documents on the WWW by search engines. We build an ontology to describe the concepts and relationships in the research domain and mine association rules by Genetic Network Programming from the database where the attributes are concepts in ontology. By considering both the semantic similarity between the rules and the keywords, and the statistical information like support, confidence, chi-squared value, we could rank the rules by a new method named RuleRank, where genetic algorithm is applied to adjust the parameters and the optimal ranking model is built for the user. Experiments show that our approach is effective for the users to find what they want.  },
  keywords = {genetic algorithms, genetic programming},
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Beyer:2008:cec,
  author = "Hans-Georg Beyer and Steffen Finck",
  title = "On the Performance of Evolution Strategies on Noisy PDQFs: Progress Rate Analysis",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0133.pdf},
  url = {},
  size = {},
  abstract =	{This paper analyses the behaviour of the (&#956;/&#956;<sub>I</sub>,&#955;)-ES on a class of noisy positive definite quadratic forms (PDQFs). First the equations for the normalised progress rates are derived and then analysed for constant normalised noise strength and constant (non-normalised) noise strength. Since in the latter case the strategy is not able to reach the optimum, formulas for the final distances to the optimiser (steady state) are derived. The theoretical predictions are then compared with empirical results. In both noise cases the influence of the strategy parameters will be investigated. Further, the equipartition conjecture is used to provide an alternative derivation of the steady state distances in the case of vanishing mutation strength.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Garis:2008:cec,
  author = "Hugo de Garis and Tang Jian Yu and Huang Zhiyong and Bai Lu and Chen Cong and Guo Junfei and Tan Xianjin and Tian Hao and Tian Xiaohan and Xiong Ye and Yu Xiangqian and Huang Di",
  title = "A Four Year, 3 Million RMB Project to Build a 15,000 Evolved Neural Net Module Artificial Brain in China",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0136.pdf},
  url = {},
  size = {},
  abstract =	{The first author has recently received a 3 million RMB, 4 year grant to build China's first artificial brain, starting in 2008, that will consist of approximately 15,000 interconnected neural net modules, evolved one at a time in a special accelerator board [1] (which is 50 times faster than using an ordinary PC) to control the hundreds of behaviours of an autonomous robot. The approach taken in building this artificial brain is fast and cheap (e.g. 1500 for the FPGA board, 1000 for the robot, and 500 for the PC, a total of 3000), so we hope that other brain building groups around the world will copy this evolutionary engineering approach.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Tan:2008:cec,
  author = "Swee Chuan Tan and Kai Ming Ting and Shyh Wei Teng",
  title = "Issues of Grid-Cluster Retrievals in Swarm-Based Clustering",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0139.pdf},
  url = {},
  size = {},
  abstract =	{One common approach in swarm-based clustering is to use agents to create a set of clusters on a two-dimensional grid, and then use an existing clustering method to retrieve the clusters on the grid. The second step, which we call grid-cluster retrieval, is an essential step to obtain an explicit partitioning of data.

In this study, we highlight the issues in grid-cluster retrievals commonly neglected by researchers, and demonstrate the nontrivial difficulties involved. To tackle the issues, we then evaluate three methods: K-means, hierarchical clustering (Weighted Single-link) and density-based clustering (DBScan). Among the three methods, DBScan is the only method which has not been previously used for grid-cluster retrievals, yet it is shown to be the most suitable method in terms of effectiveness and efficiency.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Zhang3:2008:cec,
  author = "Lining Zhang and Maoguo Gong and Licheng Jiao  and Jie Yang  ",
  title = "Improved Clonal Selection Algorithm based on Baldwinian Learning",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0142.pdf},
  url = {},
  size = {},
  abstract =	{In this paper, based on Baldwin effect, an improved clonal selection algorithm, Baldwin Clonal Selection Algorithm, termed as BCSA, is proposed to deal with complex multimodal optimization problems. BCSA evolves and improves antibody population by three operations: clonal proliferation operation, Baldwinian learning operation and clonal selection operation. By introducing Baldwin effect, BCSA can make the most of experience of antibodies, accelerate the convergence, and obtain the global optimization quickly. In experiments, BCSA is tested on four types of functions and compared with the clonal selection algorithm and other optimization methods. Experimental results indicate that BCSA achieves a good performance, and is also an effective and robust technique for optimization.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Zhang4:2008:cec,
  author = "Lining Zhang and Maoguo Gong and Licheng Jiao and Jie Yang  ",
  title = "Optimal Approximation of Linear Systems by an Improved Clonal Selection Algorithm",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0143.pdf},
  url = {},
  size = {},
  abstract =	{Based on the theory of clonal selection in immunology, by introducing Baldwin effect, an improved clonal selection algorithm, termed as Baldwin Clonal Selection Algorithm (BCSA), is proposed to solve the optimal approximation of linear systems. For engineering computing, the novel algorithm adopts three operations to evolve and improve the population: clonal proliferation operation, Baldwinian learning operation and clonal selection operation. The experimental study on the optimal approximation of a stable linear system and an unstable one show that the approximate models searched by the new algorithm have better performance indices than those obtained by some existing algorithms including the differential evolution algorithm, multi-agent genetic algorithm and artificial immune response algorithm.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Yang4:2008:cec,
  author = "Jie Yang and Maoguo Gong and Licheng Jiao and Lining Zhang  ",
  title = "Improved Clonal Selection Algorithm Based on Lamarckian Local Search Technique",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0145.pdf},
  url = {},
  size = {},
  abstract =	{In this paper, we introduce Lamarckian learning theory into the Clonal Selection Algorithm and propose a sort of Lamarckian Clonal Selection Algorithm, termed as LCSA. The major aim is to use effectively the information of each individual to reinforce the exploitation with the help of Lamarckian local search. Recombination operator and tournament selection operator are incorporated into LCSA to further enhance the ability of global exploration. We compared LCSA with the Clonal Selection Algorithm(CSA) in solving twenty benchmark problems to test the performance of LCSA. The results demonstrate that LCSA is effective and efficient in solving numerical optimization problems.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Chang:2008:cec,
  author = "Pei-Chann Chang and Wei-Hsiu Huang and   Julie Yu-Chih Liu and Ching-Jung Ting",
  title = "Dynamic Diversity Control by Injecting Artificial Chromosomes for Solving TSP Problems",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0147.pdf},
  url = {},
  size = {},
  abstract =	{The applications of genetic algorithms (GAs) in solving combinatorial problems are frequently faced with a problem of early convergence and the evolutionary processes are often trapped in a local but not global optimum. This premature convergence occurs when the population of a genetic algorithm reaches a suboptimal state that the genetic operators can no longer produce offspring with a better performance than their parents. In the literature, plenty of work has been investigated to introduce new methods and operators in order to overcome this essential problem of genetic algorithms. As these methods and the belonging operators are rather problem specific in general. In this research, we take a different approach by observing the progress of the evolutionary process and when the diversity of the population dropping below a threshold level then artificial chromosomes with high diversity will be introduced to increase the average diversity level thus to ensure the process can jump out the local optimum. The proposed method is implemented independently of the problem characteristics and can be applied to improve the global convergence behavior of genetic algorithms. The experimental results using TSP instances show that the proposed approach is very effective in preventing the premature convergence when compared with the earlier approaches.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Qing:2008:cec,
  author = "Anyong Qing  ",
  title = "A Study on Base Vector for Differential Evolution",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0149.pdf},
  url = {},
  size = {},
  abstract =	{One of the keys leading to the success of differential evolution is its mechanism of differential mutation for generating mutant vectors. In the community of differential evolution, the mutation operator is usually marked as x/y where x indicates how the base vector is chosen and y (&#8805;  1) is the number of vector differences added to the base vector. It is noted that rand/1 has been the most widely used mutation operator. However, a comprehensive comparative parametric study on differential evolution shows that strategies applying random base vector are neither efficient nor robust.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Zhuang:2008:cec,
  author = "Tao Zhuang and Qiqiang Li and Qingqiang Guo and Xingshan Wang",
  title = "A Two-Stage Particle Swarm Optimizer",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0151.pdf},
  url = {},
  size = {},
  abstract =	{This paper presents a variant of particle swarm optimizers (PSOs), called the two-stage particle swarm optimizer (TSPSO). TSPSO performs a gross searching algorithm at the first stage, and switches to a fine-grained searching algorithm if it is stagnated at the first stage. A switching criterion was proposed, and a new fine-grained searching algorithm was devised to work at the second stage of TSPSO. For the first stage, Fully Informed PSO (FIPS) with U-square topology was adopted. At the second stage, the fine-grained searching algorithm has very good performance on complex multimodal functions such as Rastrigin and Schwefel functions. The switching behavior makes TSPSO adaptive to the problems to be solved. Experimental results show that TSPSO has very good performance on both unimodal and multimodal functions compared with six other variants of PSO. Especially on complex multimodal functions, TSPSO's performance is even better than the most state of art PSOs such as CLPS and CPSO-H.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Hingee:2008:cec,
  author = "Kassel Hingee and Marcus Hutter",
  title = "Equivalence of Probabilistic Tournament and Polynomial Ranking Selection",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0152.pdf},
  url = {},
  size = {},
  abstract =	{Crucial to an Evolutionary Algorithm's performance is its selection scheme. We mathematically investigate the relation between polynomial rank and probabilistic tournament methods which are (respectively) generalisations of the popular linear ranking and tournament selection schemes. We show that every probabilistic tournament is equivalent to a unique polynomial rank scheme. In fact, we derived explicit operators for translating between these two types of selection. Of particular importance is that most linear and most practical quadratic rank schemes are probabilistic tournaments.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Wang5:2008:cec,
  author = "Zhiwen Yu Dingwen Wang and Hau-San Wong",
  title = "Knowledge Learning based Evolutionary Algorithm for Unconstrained Optimization Problem",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0153.pdf},
  url = {},
  size = {},
  abstract =	{In this paper, we propose a new evolutionary algorithm called nearest neighbor evolutionary algorithm (NNE) to solve the unconstrained optimization problem. Specifically, NNE consists of two major steps: coarse nearest neighbor evolutionary and fine nearest neighbor evolutionary. The coarse nearest neighbor evolutionary step pays more attention to searching the optimal solutions in the global way, while the fine nearest neighbor evolutionary step focuses on searching the best solutions in the local way. NNE repeats two major steps until the terminate condition is reached. NNE not only adopts the elitist strategy and maintains the best individuals for the next generation, but also considers the knowledge obtained in the searching process. The experiments demonstrate that (1) NNE achieves good performance in most of numerical optimization problems; (2) NNE outperforms most of state-of-art evolutionary algorithms, such as traditional genetic algorithm (GA), the jumping gene genetic algorithm (JGGA).   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Luo:2008:cec,
  author = "Biao Luo and Jinhua Zheng",
  title = "A New Methodology for Searching Robust Pareto Optimal Solutions with MOEAs",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0156.pdf},
  url = {},
  size = {},
  abstract =	{It is of great importance for a solution with high robustness in the real application, not only with good quality. Searching for robust Pareto optimal solutions for multi-objective optimization problems (MOPs) is a challenge, no exception for multi-objective evolutionary algorithms (MOEAs). Recently, as one of the popular approach to search robust Pareto optimal solutions, ''effective objective function'' based MOEA (Eff-MOEA) can only find solutions which have average robustness and quality, but cannot find solutions which have the highest robustness and best quality. In this paper, we proposed a new methodology for robust Pareto optimal solutions and presented a novel MOEA named MOEA/R, which convert a multi-objective robust optimization problem (MROP) into a bi-objective optimization problem. Each of two objectives represents a sub-MOP, one of which optimizes solutions' quality and another optimizes solutions' robustness. Through the comparison and analysis between MOEA/R, Eff-MOEA and NSGA-II, the experimental results demonstrate that MOEA/R can acquire good purposes. The most important contribution of this paper is that MOEA/R explores a novel methodology for searching robust Pareto optimal solutions.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Liu3:2008:cec,
  author = "Bo Liu and Xuejun Zhang and Hannan Ma",
  title = "Hybrid Differential Evolution for Noisy Optimization",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0157.pdf},
  url = {},
  size = {},
  abstract =	{A robust hybrid algorithm named DEOSA for function optimization problems is investigated in this paper. In recent years, differential evolution (DE) has attracted wide research and effective applications in various fields. However, to the best of our knowledge, most of the available works did not consider noisy and uncertain environments in practical optimization problems. This paper focuses on a robust DE, which can adapt to noisy environment in real applications. By combining the advantages of DE algorithm, the optimal computing budget allocation (OCBA) technique and simulated annealing (SA) algorithm, a robust hybrid DE approach DEOSA is proposed. In DEOSA, the population-based search mechanism of DE is applied for well exploration and exploitation, and the OCBA technique is used to allocate limited sampling budgets to provide reliable evaluation and identification for good individuals. Meanwhile, SA is also applied in the hybrid approach to maintain the diversity of the population, in order to alleviate the negative influences on greedy selection mechanism of DE brought by the noises. DEOSA is tested by well-known benchmark problems with noise and the effect of noise magnitude is also investigated. The comparisons to several commonly used techniques for optimization in noisy environment are also carried out. The results and comparisons demonstrate the superiority of DEOSA.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Andras:2008:cec,
  author = "Peter Andras  ",
  title = "Uncertainty in Iterated Cooperation Games",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0158.pdf},
  url = {},
  size = {},
  abstract =	{The emergence and evolution of cooperation among selfish individuals is a key question of theoretical biology. Uncertainty of outcomes of interactions between individuals is an important determinant of cooperative behavior. Here we describe a model that allows the analysis of the effects of such uncertainty on the level of cooperation. We show that in iterated cooperation games the level of cooperation increases with the level of outcome uncertainty. We show that this is the case if the individuals communicate about their cooperation intentions and also if they do not communicate their intentions.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Zeng2:2008:cec,
  author = "Bin Zeng and Tao Hu and Jun Wei  ",
  title = "An Approach to Constructing Evolutionary Agent Structure for Workflow Management System Based on Simulation",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0159.pdf},
  url = {},
  size = {},
  abstract =	{Multi-agent Coordinate mechanism research has attracted increasing attention in recent years. Researches on the problem mainly focus on how to organize and coordinate relations between agents. The composition of different agents is an issue that must be faced by developers. This paper introduces an automatic agent combination method oriented to workflow which considers both task's dynamic workload and agent's evolving cognitive ability. It composes agent structure through three steps: 1)Clusters the tasks according to their resources requirements by using decision tree, which helps to define the corresponding agent set. 2)Calculates the ability and cost of agent executing workflow based on information about task workload and duration with uncertainty model. 3)Search for the optimal agents' composition with the objective to maximize the speed of workflow execution while balancing the workload among agents under the constraint of agent ability, workload threshold and execution cost based on performance analysis of simulation result. Experimental results show that this method has a good performance by identifying the optimal agent configuration to execute workflow scenario.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Jiang:2008:cec,
  author = "Dazhi Jiang and Zhijian Wu and Jun Zou and Ming Wei and Lishan Kang",
  title = "Algorithm Based on Heuristic Subspace Searching Strategy for Solving Investment Portfolio Optimization Problems",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0160.pdf},
  url = {},
  size = {},
  abstract =	{There exist many difficulties when investment portfolio problems based on Markowitz model are solved by using some traditional methods, such as Newton method, conjugate gradient method, etc. One of the difficulties is that Markowitz model has rigorous constraint conditions. Evolutionary Computation is a parallel global optimization algorithm with high efficiency and it has been widely used in portfolio investment field. A heuristic subspace searching algorithm is put forward in this paper for solving investment portfolio optimization problems based on Markowitz model. The experimental results indicate that this algorithm has an improved efficiency compared with traditional Evolutionary Computation.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Jiang2:2008:cec,
  author = "Dazhi Jiang and Zhijian Wu and Jun Zou and Jianwei Zhang and Lishan Kang",
  title = "Evolutionary Modeling Based on Overlap Reuse",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0161.pdf},
  url = {},
  size = {},
  abstract =	{Reuse (or reusability) plays an important role in the software engineering. The software reuse technique, considered as an effective approach to improve the productivity, can reduce the cost in software design and development. This paper introduces the concept of reuse in the software into the chromosome and presents an evolutionary modeling algorithm based on the overlapped reuse. Furthermore, a new Gene Reading &amp; Computing Machine is constructed for calculating the fitness of chromosome which has the characteristic of reusability. As a new kind of modeling algorithm, this is a new research way for evolutionary modeling.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Li4:2008:cec,
  author = "Miqing Li and Jinhua Zheng and Guixia Xiao",
  title = "An Efficient Multi-Objective Evolutionary Algorithm Based on Minimum Spanning Tree",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0163.pdf},
  url = {},
  size = {},
  abstract =	{Fitness assignment and external population maintenance are two important parts of multi-objective evolutionary algorithms. In this paper, we propose a new MOEA which uses the information of minimum spanning tree to assign fitness and maintain the external population. Moreover, a Minimum Spanning Tree Crowding Distance (MSTCD) is defined to estimate the density of solutions. From an extensive comparative study with three other MOEAs on a number of two and three objective test problems, it is observed that the proposed algorithm has good performance in convergence and distribution.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Li5:2008:cec,
  author = "Miqing Li and Jinhua Zheng and Guixia Xiao",
  title = "Uniformity Assessment for Evolutionary Multi-Objective Optimization",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0165.pdf},
  url = {},
  size = {},
  abstract =	{Uniformity assessment of approximations of the Pareto-optimal set is an important issue in comparing the performance of multi-objective evolutionary algorithms. Although a number of performance metrics existed, many are applicable to low objective problems (2-3 objectives). In addition, most of the existed metrics are only applied to the final non-dominated set. In this paper, we suggest a running metric which evaluates the uniformity of solutions at every generation of a MOEA run. In particular, this metric can compare the uniformity of population with different size in any number of objectives. With an agglomeration of generation-wise populations, the metric reveals the change of uniformity in a MOEA run or helps provide a comparative evaluation of two or more MOEAs.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Graaff:2008:cec,
  author = "A. J. Graaff and A. P. Engelbrecht",
  title = "Towards a Self Regulating Local Network Neighbourhood Artificial Immune System for Data Clustering",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0166.pdf},
  url = {},
  size = {},
  abstract =	{The theory of idiotopic lymphocyte networks in the natural immune system inspired the modelling of network based artificial immune systems (AIS). Many of these network based AIS models establish network links between the artificial lymphocytes (ALCs) whenever the measured Euclidean distance between the ALCs are below a certain network threshold. The linked ALCs represent an artificial lymphocyte network. Graaff and Engelbrecht introduced the Local Network Neighbourhood AIS (LNNAIS) [2]. The interpretation of the network theory is the main difference between LNNAIS and existing network based AIS models. The LNNAIS uses the concept of an artificial lymphocyte neighbourhood to determine network links between ALCs [2]. The purpose of this paper is to highlight the drawbacks of the proposed LNNAIS model and to address these drawbacks with some enhancements, improving LNNAIS towards a self regulating AIS.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Li6:2008:cec,
  author = "Kangshun Li and Weifeng Pan and Wensheng Zhang and Zhangxin Chen",
  title = "Automatic Modeling of a Novel Gene Expression Programming Based on Statistical Analysis and Critical Velocity",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0167.pdf},
  url = {},
  size = {},
  abstract =	{The basic principle of GEP is briefly introduced. And considering the defects of classic GEP such as lack of variety, the problem of convergence and blind searching without learning mechanism, a novel GEP based on statistical analysis and stagnancy velocity is proposed (called AMACGEP). It mainly has the following characteristics: First, improve the initial population by statistic analysis of repeated bodies. Second, introduce the concept of stagnancy velocity to adjust the searching space, evolution velocity, the diversity of individuals and the accuracy of prediction. Third, introduce dynamic mutation operator to improve the diversity of individuals and the velocity of convergence. Compared with other methods like traditional methods, methods of neural network, classic GEP and other improved GEPs in automatic modelling of complex function, the simulation results show that the AMACGEP set up by this paper is better.  },
  keywords = {genetic algorithms, genetic programming, gene expression programming},
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Fu:2008:cec,
  author = "Jian Fu and Qing Liu and Xinmin Zhou and Kui Xiang and Zhigang Zeng  ",
  title = "An Adaptive Variable Strategy Pareto Differential Evolution Algorithm for Multi-Objective Optimization",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0168.pdf},
  url = {},
  size = {},
  abstract =	{In the paper, we propose an adaptive variable strategy Pareto differential evolution algorithm for multi-objective optimization (AVSPDE). It is different from the general adaptive DE methods which are regulated by variable parameters and applied in single-objective area. Based on the real-time information from the tournament selection set (TSS), there are two DE variants to switch dynamically during the run, in which one aims at fast convergence and the other focus on the diverse spread. The theoretical analysis and the digital simulation show the presented method can achieved better performance.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Ashlock:2008:cec,
  author = "Daniel Ashlock and Taika {von Konigslow}",
  title = "Evolution of Artificial Ring Species",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0169.pdf},
  url = {},
  size = {},
  abstract =	{Biological ring species are a population surrounding a geographic obstruction such as a large lake or a mountain range. Adjacent sub-populations are mutually fertile, but fertility drops with distance. This study attempts to create examples of artificial ring species using evolutionary algorithms. ISAc lists, a representation with self-organised and potentially complex genetics, are used to evolve controllers for the Tartarus task. The breeding population of Tartarus controllers are arranged in a ring-shaped configuration with strictly local gene flow. Fertility is defined to be the probability that a child will have fitness at least that of its least fit parent. Fertility is found to drop steadily and significantly with distance around the ring in each of twelve replicates of the experiment. Comparison of fertility at various distances within a ring-shaped population is compared with sampled intra-population fertility. Some populations are found to have significantly higher than background fertility with other populations. This phenomena suggests the presence of aggressive genetics or dominant phenotype in which a creature has an enhanced probability of simply cloning its own phenotype during crossover. In addition to creating examples of artificial ring species this study also achieved a very high level of fitness with the Tartarus task. A comparison is made with another study that uses hybridisation to achieve record breaking Tartarus fitness.   },
  keywords = {genetic algorithms, genetic programming},
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Ashlock2:2008:cec,
  author = "Daniel A. Ashlock and Fatemeh Jafargholi",
  title = "Behavioral Regimes in the Evolution of Extremal Epidemic Graphs",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0170.pdf},
  url = {},
  size = {},
  abstract =	{Models of epidemic spread often incorporate contact networks along which the epidemic can spread. The character of the network can have a substantial impact on the course of the epidemic. In this study networks are optimized to yield longlasting epidemics. These networks represent an upper bound on one type of network behavior. The evolutionary algorithm used searches the space of networks with a specified degree sequence, with degrees representing the number of sexual partners of each member of the population. The representation used is a linear chromosome specifying a series of editing moves applied to an initial network. The initial network specifies the degree sequence of the searched networks implicitly and the editing moves preserve the degree sequence. The evolutionary algorithm uses a non-standard type of restart in which the currently best network in the population replaces the initial network. This restart operator is called a recentering operator. The recentering operator moves the evolving population to successively higher fitness portions of the network space. In this study the algorithm is applied to networks with average degree from 2.5 to 7. In low-degree networks, short epidemics result from failure of the disease to spread through the relatively sparse links of the network. In high-degree networks, short epidemics result from the rapid infection of the entire population. The evolutionary algorithm is able to optimize both high and low degree networks to significantly increase the epidemic duration.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Vergidis:2008:cec,
  author = "Kostas Vergidis and Ashutosh Tiwari",
  title = "Business Process Design and Attribute Optimization within an Evolutionary Framework",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0171.pdf},
  url = {},
  size = {},
  abstract =	{This paper discusses the problem of business process design and attribute optimization within a multiobjective evolutionary framework. Business process design and attribute optimization is considered as the problem of constructing feasible business process designs with optimum attribute values such as duration and cost. The feasibility of a process design is based on: (i)the process requirements such as the required input and the expected output resources and (ii)the connectivity of the participating tasks in the process design through their input and output resources. The proposed approach involves the application of the Evolutionary Multi- Objective Optimization Algorithm (EMOOA) Non-dominated Sorting Genetic Algorithm II (NSGA2) in an attempt to generate a series of diverse optimized business process designs for the same process requirements. The proposed optimization framework introduces a quantitative representation of business processes involving two matrices one for capturing the process design and one for calculating and evaluating the process attributes. It also introduces an algorithm that checks the feasibility of each candidate solution (i.e. process design). The results demonstrate that for a variety of experimental problems NSGA2 produces a satisfactory number of optimized design alternatives considering the problem complexity and high rate of infeasibility.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Meuth:2008:cec,
  author = "Ryan J. Meuth and Donald C. Wunsch II",
  title = "Divide and Conquer Evolutionary TSP Solution for Vehicle Path Planning",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0172.pdf},
  url = {},
  size = {},
  abstract =	{The problem of robotic area coverage is applicable to many domains, such as search, agriculture, cleaning, and machine tooling. The robotic area coverage task is concerned with moving a vehicle with an effector, or sensor, through the task space such that the sensor passes over every point in the space. For covering complex areas, back and forth paths are inadequate. This paper presents a real-time path planning architecture consisting of layers of a clustering method to divide and conquer the problem combined with a twolayered, global and local optimization method. This architecture is able to optimize the execution of a series of waypoints for a restricted mobility vehicle, a fixed wing airplane.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Lu:2008:cec,
  author = "Fuqiang Lu and Min Huang and Xingwei Wang",
  title = "PSO Based Stochastic Programming Model for Risk Management in Virtual Enterprise",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0176.pdf},
  url = {},
  size = {},
  abstract =	{Risk management in a Virtual Enterprise (VE) is an important issue due to its agility and diversity of its members and its distributed characteristics. In this paper, a stochastic programming model of risk management is proposed. More specifically, we consider about the stochastic characters of the risk in VE, and then we build a stochastic programming model to deal with the stochastic characters of the risk. In detail, this is a chance constraint programming model, One of the great advantages of this class of model is that it can exactlly describe the risk preference of the manager. In this model, the risk level of VE is obtained from a composite result of many risk factors. In order to reduce the risk level of VE, the manager has to select effective action for every risk factor. For each risk factor, there are several actions provided. Here we only select one action for a risk factor or do nothing with it. To solve this stochastic programming model, A particle swarm optimization (PSO) algorithm is designed. On the other hand, to deal with those stochastic variables, Monte Carlo simulation is combined with PSO algorithm. Finally, a numerical example is given to illustrate the effectiveness of the PSO algorithm and the result shows that the model is very useful for risk management in VE.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Cheung:2008:cec,
  author = "Mars Cheung and Stephen Johnson and David Hecht and Gary B. Fogel",
  title = "Quantitative Structure-Property Relationships for Drug Solubility Prediction Using Evolved Neural Networks",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0177.pdf},
  url = {},
  size = {},
  abstract =	{Preclinical in vivo studies of small molecule compound libraries can be enhanced using a model of specific quantitative structure-property relationships. This may include toxicological or solubility measures such as prediction of drug solubility in mixtures of polyethylene glycol and/or water. Here we examine the utility of both multiple linear regressions and evolved neural networks for the prediction of drug solubility in aqueous solution. Initial results suggest that modeling requires compound libraries with high similarity. Clustering approaches can be used to group compounds by similarity with models built for each cluster. Linear and nonlinear models can be used for modeling, however evolved neural networks can be used to simultaneously reduce the feature space as well as optimize models for solubility prediction. With these approaches it is also possible to identify ``human interpretable'' features from the best models that can be used by chemists during preclinical drug development.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Kimura:2008:cec,
  author = "S. Kimura and K. Matsumura  ",
  title = "Density Estimation using Crossover Kernels and its Application to a Real-coded Genetic Algorithm",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0183.pdf},
  url = {},
  size = {},
  abstract =	{Sakuma and Kobayashi have proposed a density estimation method that uses real-coded crossover operators. However, their method was used only to estimate normal distribution functions. In order to estimate more complicated PDFs, this study proposes a new density estimation method of using crossover operators. When we try to solve function optimization problems, on the other hand, real-coded genetic algorithms (GAs) show good performances if their crossover operators have an ability to estimate the PDF of the population well. Thus, this study then applies our density estimation method into a simple real-coded GA to improve its search performance. Finally, through numerical experiments, we verify the effectiveness of the proposed density estimation method.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Zhen-Zhao:2008:cec,
  author = "First Liu Zhen-Zhao and Second Liu Jie-Ping and Third Liu Yang",
  title = "Determination of Air Boarding Strategy Based on MINPL and Monte Carlo Simulation",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0184.pdf},
  url = {},
  size = {},
  abstract =	{With the increasing business in air travel area, reducing the plane's turnaround time is becoming more and more important. In this paper, it chooses the optimum boarding strategy to reduce the turnaround time. The MINPL model is for the small-size plane, with the boarding time mainly depending on seat interference and aisle interference. The GASimplex Algorithm (Genetic Algorithms mixed with Simplex method) is used to solve it. For the middle-size plane, a Monte Carlo Simulation model is designed which is based on probabilistic aspect. Then these two models are integrated to solve the large-plane problem. Based on these models, the paper uses Matlab 6.5 to do the calculation and found that the boarding strategy combined Reverse Pyramid with Rotation outperforms other strategies. Finally, it analyzes the strengths of the model.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Liao:2008:cec,
  author = "Huilian Liao and Zhen Ji and Q. H. Wu",
  title = "A Novel Genetic Particle-Pair Optimizer for Vector Quantization in Image Coding",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0185.pdf},
  url = {},
  size = {},
  abstract =	{This paper presents a novel Genetic Particle-Pair Optimiser (GPPO) for Vector Quantisation of image coding. GPPO only applies a particle-pair that consists of two particles, which contributes to the relief of huge computation load in most existing Vector Quantisation algorithms. GPPO combines the advantage both in Genetic Algorithms and Particle Swarm Optimization, due to the use of genetic operators and particle operators at each generation. Experimental results have demonstrated that the quality of the codebook design optimised by GPPO is better than that optimised respectively by Fuzzy K-means (FKM), Fuzzy Reinforcement Learning Vector Quantisation (FRLVQ), improved FRLVQ which uses Fuzzy Vector Quantization (FVQ) as post-process, called FRLVQFVQ, and Particle-Pair Optimiser (PPO). GPPO provides a satisfactory solution to vector quantisation, and shows a steady trend of improvement in the quality of codebook design. The dependence of the final codebook on the selection of the initial codebook is also reduced.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Li7:2008:cec,
  author = "Kangshun Li and Yang Xie and Wensheng Zhang and Zhangxin Chen",
  title = "A Novel Algorithm for Evolving Encryption Sequences Based on Particle Dynamics",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0186.pdf},
  url = {},
  size = {},
  abstract =	{In this paper a novel algorithm for evolving encryption sequences based on particle dynamics is presented to design encryption systems with high safety and efficiency. Because the algorithm based on particle dynamics is constructed by using the law of entropy increasing and the principle of energy minimum of particle systems, it has features of uniformity and diversity in particle distribution of particle systems. Therefore, the sequence encryption produced has high randomicity, more chaos and long periodicity. The experiments show that the sequence encryption by using this method has high randomicity, and the encryption system by using this method has better security and safety.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Chakraborty:2008:cec,
  author = "Jayasree Chakraborty and Amit Konar and  Uday K. Chakraborty and L. C. Jain",
  title = "Distributed Cooperative Multi-Robot Path Planning Using Differential Evolution",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0189.pdf},
  url = {},
  size = {},
  abstract =	{This paper provides an alternative approach to the co-operative multi-robot path planning problem using parallel differential evolution algorithms. Both centralized and distributed realizations for multi-robot path planning have been studied, and the performances of the methods have been compared with respect to a few pre-defined yardsticks. The distributed approach to this problem out-performs its centralized version for multi-robot planning. Relative performance of the distributed version of the differential evolution algorithm has been studied with varying numbers of robots and obstacles. The distributed version of the algorithm is also compared with a PSO-based realization, and the results are competitive.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Niu:2008:cec,
  author = "Li Niu and Jie Lu and Guangquan Zhang",
  title = "Improved Business Intelligence Analytics on Manager's Experience",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0190.pdf},
  url = {},
  size = {},
  abstract =	{Current business intelligence (BI) systems bring the manager with powerful data analysis functions. However the manager is either limited to predefined queries or feels lost within torrents of data. Thus, decision making is still a task with high cognitive load. BI analytics are based on queries into the data warehouse. Rather than predefining queries or less guided ad hoc analysis, we use the manager's experience, together with decision problem statement, business ontology and heuristics to automatically construct data retrieval queries. Our method is expected to provide the manager better decision support on cognitive orientation.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Gonzales:2008:cec,
  author = "Eloy Gonzales and Karla Taboada and Kaoru Shimada and Shingo Mabu and Kotaro Hirasawa",
  title = "Evaluating Class Association Rules using Genetic Relation Programming",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0191.pdf},
  url = {},
  size = {},
  abstract =	{The number of association rules generated during the data mining process is generally very large, that is, an association rule mining algorithm could generate thousands or millions of rules. However, only a small number of rules are likely to be of any interest to the domain expert analyzing the data, i.e., many of the rules are either irrelevant or obvious. Therefore, techniques for evaluating the relevance and usefulness of discovered patterns are required. The aim of this paper is to propose a new method for evaluating the relevance and usefulness of discovered association rules by reducing the number of rules extracted using an evolutionary method named Genetic Relation Programming (GRP). The algorithm evaluates the relationships between the rules at each generation using a specific measure of distance and gives the best set of rules at the final generation. The efficiency of the proposed method is compared with other conventional methods and it is clarified that the proposed method shows comparable accuracy with others.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Prime:2008:cec,
  author = "Ben Prime and Tim Hendtlass",
  title = "An Evolutionary Reincarnation Algorithm",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0192.pdf},
  url = {},
  size = {},
  abstract =	{As there is little or no experimental experience of reincarnation in the natural world, attempts to add a reincarnation metaphor to an evolutionary algorithm must of necessity proceed cautiously. In previous work the authors have established that the reintroduction of previously stored gene values into the population can have a noticeable effect on the progress of evolution, this paper now considers a range of options for deciding which gene values to store, which to return and which individuals in the current population should receive the returned gene values. Consistent experimental results on three well known functions allow a suggestion to be made of, if not the best choices, at least a good choice selection to use on initial experiments on other problems.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Dou:2008:cec,
  author = "Wenxiang Dou and Jinglu Hu and Kotaro Hirasawa and Gengfeng Wu",
  title = "Distributed Multi-Relational Data Mining Based on Genetic Algorithm",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0193.pdf},
  url = {},
  size = {},
  abstract =	{An efficient algorithm for mining important association rule from multi-relational database using distributed mining ideas. Most existing data mining approaches look for rules in a single data table. However, most databases are multi-relational. In this paper, we present a novel distributed data-mining method to mine important rules in multiple tables (relations) and combine the method with genetic algorithm to enhance the mining efficiency. Genetic algorithm is in charge of finding antecedent rules and aggregate of transaction set that produces the corresponding rule from the chief attributes. Apriori and statistic method is in charge of mining consequent rules from the rest relational attributes of other tables according to the corresponding transaction set producing the antecedent rule in a distributed way. Our method has several advantages over most exiting data mining approaches. First, it can process multi-relational database efficiently. Second, rules produced have finer pattern. Finally, we adopt a new concept of extended association rules that contain more import and underlying information.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Tian:2008:cec,
  author = "Jing Tian and Weiyu Yu and Shengli Xie  ",
  title = "An Ant Colony Optimization Algorithm For Image Edge Detection",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0194.pdf},
  url = {},
  size = {},
  abstract =	{Ant colony optimization (ACO) is an optimization algorithm inspired by the natural behavior of ant species that ants deposit pheromone on the ground for foraging. In this paper, ACO is introduced to tackle the image edge detection problem. The proposed ACO-based edge detection approach is able to establish a pheromone matrix that represents the edge information presented at each pixel position of the image, according to the movements of a number of ants which are dispatched to move on the image. Furthermore, the movements of these ants are driven by the local variation of the image's intensity values. Experimental results are provided to demonstrate the superior performance of the proposed approach.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Jorgensen:2008:cec,
  author = "Christopher Jorgensen and Garrison Greenwood and Peyman Arefi",
  title = "Practical Considerations for Implementing Intrinsic Fault Recovery in Embedded Systems",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0195.pdf},
  url = {},
  size = {},
  abstract =	{Evolvable hardware provides a viable fault recovery technique for embedded systems already deployed into an operational environment. Typically the fitness of each evolved configuration in such systems must be intrinsically determined because imprecise information about faults makes extrinsic methods impractical. 

Most work on intrinsic circuit evolution is conducted in laboratory environments where sophisticated measurement equipment is readily available and frequency domain analysis poses no real problems. In this paper we argue intrinsic fault recovery for embedded systems has to be done in the time domain. We report the results of several experiments conducted to identify potential problems with determining fitness in the time domain for embedded systems. We also discuss the limitations embedded systems impose on GAs used for evolvable hardware applications and suggest some possible solutions.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Montera:2008:cec,
  author = "Luciana Montera and Maria do Carmo Nicoletti and Flavio Henrique da Silva and Pablo Moscato",
  title = "An Effective Mutation-Based Measure for Evaluating the Suitability of Parental Sequences to Undergo DNA Shuffling Experiments",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0196.pdf},
  url = {},
  size = {},
  abstract =	{The DNA shuffling process has been successfully used in many experiments of Directed Molecular Evolution. In a shuffling experiment genes are recombined by an iterative procedure of PCR cycles aiming at obtaining new genes, hopefully with some of the original functions being improved. The optimizations of the parameters involved in the process as well as the characteristics of the parental sequences are of extreme importance to guarantee the success of a shuffling experiment. This paper proposes a new measure, based on the number of bases between existing mutations in the parental sequences, suitable for evaluating the suitability of two sequences to be submitted to a DNA shuffling experiment. In order to investigate the usefulness of the proposed mutation-based measure versus two commonly used measures, a family of 37 DNA gene sequences codifying for snake venom metallopeptidases was used for evaluation purposes using the three measures. The parental sequences identified by each of the three measures were validated by simulating the DNA shuffling process using the software eShuffle. The eShuffle results illustrate on the benefits of the mutationbased measure proposed in this paper.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Zhou2:2008:cec,
  author = "Jin Zhou and Lu Yu and Shingo Mabu and  Kaoru Shimada and Kotaro Hirasawa and Sandor Markon",
  title = "Double-Deck Elevator Systems Adaptive to Traffic Flows Using Genetic Network Programming",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0198.pdf},
  url = {},
  size = {},
  abstract =	{Double-deck elevator system (DDES) has been invented firstly as a solution to improve the transportation capacity of elevator group systems in the up-peak traffic pattern. The transportation capacity could be even doubled when DDES runs in a pure up-peak traffic pattern where two connected cages stop at every two floors in an elevator round trip. However, the specific features of DDES make the elevator system intractable when it runs in some other traffic patterns. Moreover, since almost all of the traffic flows vary continuously during a day, an optimised controller of DDES is required to adapt the varying traffic flow. In this paper, we have proposed a controller adaptive to traffic flows for DDES using Genetic Network Programming (GNP) based on our past studies in this field, where the effectiveness of DDES controller using GNP has been verified in three typical traffic patterns. A traffic flow judgement part was introduced into the GNP framework of DDES controller, and the different parts of GNP were expected to be functionally localised by the evolutionary process to make the appropriate cage assignment in different traffic flow patterns. Simulation results show that the proposed method outperforms a conventional approach and two heuristic approaches in a varying traffic flow during the work time of a typical office building.   },
  keywords = {genetic algorithms, genetic programming},
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Xu:2008:cec,
  author = "Weijun Xu and Yucheng Dong and Weilin Xiao and Jinhong Xu",
  title = "A Nonlinear Program Model to Obtain Consensus Priority Vector in the Analytic Hierarchy Process",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0200.pdf},
  url = {},
  size = {},
  abstract =	{In group decision making, because the decisionmakers usually represent different interest backgrounds, it is worth to study how to make the different decision makers coordinate and cooperate for aggregating group opinions. In this paper, based on the analytic hierarchy process, we propose a nonlinear program model to obtain consensus priority vector, and point that the model can make decision-makers reach consensus by improving compatibility of judgement matrices. Moreover, we use the genetic-simulated annealing algorithm to obtain its optimal solution. Finally, a numerical example is presented to illustrate the application of this method.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Xue:2008:cec,
  author = "Guixiang Xue and Zheng Zhao and Maode Ma and Tonghua Su and Shuang Liu",
  title = "Task Scheduling by Mean Field Annealing Algorithm in Grid Computing",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0202.pdf},
  url = {},
  size = {},
  abstract =	{Desirable goals for grid task scheduling algorithms would shorten average delay, maximize system use and fulfill user constraints. In this work, an agent-based grid management infrastructure coupled with Mean Field Annealing (MFA) scheduling algorithm has been proposed. An agent in grid uses a neural network algorithm to manage and schedule tasks. The Hopfield Neural Network is good at finding optimal solution with multi-constraints and can be fast to converge to the result. However, it is often trapped in a local minimum. Stochastic simulated annealing algorithm has an advantage in finding the optimal solution and escaping from the local minimum. Both significant characteristics of Hopfield neural network structure and stochastic simulated annealing algorithm are combined together to yield a mean field annealing scheme. A modified cooling procedure to accelerate reaching equilibrium for normalized mean field annealing has been applied to this scheme. The simulation results show that the scheduling algorithm of MFA works effectively.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Lu2:2008:cec,
  author = "Xingjia Lu and Yongsheng Ding and Kuangrong Hao",
  title = "Adaptive Design Optimization of Wireless Sensor Networks Using Artificial Immune Algorithms",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0204.pdf},
  url = {},
  size = {},
  abstract =	{The topology control is a very important issue in wireless sensor networks (WSNs). Many approaches have been proposed to carry out in this aspect, including modern heuristic approach. In this paper, the Topology Control based on Artificial Immune Algorithm (ToCAIA) is proposed to solute the energy-aware topology control for WSNs. ToCAIA is a heuristic algorithm, which is heuristic from the immune system of human. In ToCAIA, the antibody is the solution of the problem, and the antigen is the problem. ToCAIA could be used to solve the multi-objective minimum energy network connectivity (MENC) problem, and get the approximate solution. The experiment result shows that the topology control by using ToCAIA can be used for WSNs network optimization purposes.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Shi:2008:cec,
  author = "Guojun Shi and Qingsheng Ren",
  title = "Research on Compact Genetic Algorithm in Continuous Domain",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0205.pdf},
  url = {},
  size = {},
  abstract =	{Compact genetic algorithm (CGA) is a successful probability-based evolutionary algorithm which performs equivalent to the order-one behavior of the simple genetic algorithm (SGA) with uniform crossover. However, this equivalence only applies for binary encoded problems. To extend the basic concept of CGA to continuous domain, an improved CGA is proposed in this paper. We established a continuous CGA (cCGA) model by adopting two probability vectors to represent population. We study the update rules of the probability vectors and its initial value. In further we improve this cCGA by adopting elitism selection. We propose two kinds of elitism based cCGA by applying different elitism control policies. Theoretical analysis on elitism control is given and some useful results are concluded. The numerical experiment first gives a comparison between SGA and our cCGA in continuous domain and the results show the superiority and efficiency of cCGA. Comparison between elitism selection cCGA and non-elitism cCGA is also given to show the efficiency of elitism selection and the efficiency on elitism control.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Wang6:2008:cec,
  author = "Yu Wang and Bin Li",
  title = "Understand Behavior and Performance of Real Coded Optimization Algorithms via NK-linkage Model",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0206.pdf},
  url = {},
  size = {},
  abstract =	{Classical NK-landcape model was designed for analyzing optimization and evolution process in binary solution space, so it can not be used to analyze Real Coded Optimization Algorithms (RCOAs) directly, which work in continuous solution space directly. In this paper, the concept of NK-landscape model is extended to the continuous space, and a new NKlandscape model with continuous space is proposed. The new model is powerful and comprehensive with simple structure and flexible formula. Therefore, it can be used to construct test functions of various types of linkages for analyzing various performances of RCOAs. The feasibility of the proposed model is testified via experiments with 3 well-known RCOAs, (i.e. covariance matrix adapting evolutionary strategy (CMA-ES), differential evolution (DE), neighborhood search differential evolution (NSDE)). The results show that the new model can reveal the merits and demerits of RCOAs effectively.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Wang7:2008:cec,
  author = "Shaowei Wang and Xiaoyong Ji and Lishan Kang",
  title = "An Efficient Heuristic Method for Multiuser Detection in DS-CDMA Systems",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0207.pdf},
  url = {},
  size = {},
  abstract =	{Optimum multiuser detection (OMD) in direct-sequence code-division multiple access (DS-CDMA) communication systems is a combinatorial optimization problem and has been proven NP-complete. Many heuristics have been presented to solve this problem, but few of them consider the fitness landscape of OMD carefully. In this paper, we analyze the fitness landscape of OMD, including the neighborhood structure and the distribution of local optima. Numerical results give hints on how to design efficient heuristic algorithms for the problem. A meta-heuristic algorithm considering the analysis results is proposed. With a proper local search and a well-chosen perturbation strategy, the proposed algorithm can find the (near) optimal solution rapidly with lower computational complexity. Simulation results show it outperforms other heuristic multiuser detection algorithms when the number of users is large. In the condition of small number of users, it can achieve the bit error rate (BER) bound of OMD.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Zhan:2008:cec,
  author = "Song Zhan and Julian F. Miller and Andy M. Tyrrell",
  title = "An Evolutionary System using Development and Artificial Genetic Regulatory Networks",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0209.pdf},
  url = {},
  size = {},
  abstract =	{Biology presents incomparable, but desirable, characteristics compared to engineered systems. Inspired by biological development, we have devised a multi-layered design architecture that attempts to capture many of the favorable characteristics of biological mechanisms for application to design problems. In this paper we have identified and implemented essential features of Genetic Regulatory Networks (GRNs) and cell signaling so that our system exhibits self-organization which is reminiscent of aspects of biological systems.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Xu2:2008:cec,
  author = "Chunlin Xu and Xiufen Zou and Rongxiang Yuan and Chuansheng Wu",
  title = "Optimal Coordination of Protection Relays Using New Hybrid Evolutionary Algorithm",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0210.pdf},
  url = {},
  size = {},
  abstract =	{A reliable protection system is vital to power system. As the major equipment of protection system, protection relay plays a basilica role in power system. So searching for proper settings of relays to make them operate in a better way is significant. In this paper, a new optimization problem formulation is proposed to search the optimal relay setting of over current relays in power systems. Then, a new hybrid evolutionary algorithm based on tabu search (HEATS) is presented to solve this optimization problem, and results under different algorithm parameters are obtained. Finally, comparisons among HEATS, one of particle swarm optimizations(PSO) and test evolutionary algorithm(TEA) shown in other literatures are given. Simulation results show the formulation of protection relay setting is feasible and effective, and the proposed algorithm HEATS exhibits a good performance.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Huang3:2008:cec,
  author = "Qinghua Huang and Minhua Lu and Hong Yan",
  title = "An Evolutionary Algorithm for Discovering Biclusters in Gene Expression Data of Breast Cancer",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0212.pdf},
  url = {},
  size = {},
  abstract =	{The analysis of gene expression data of breast cancer is important for discovering the signatures that can classify different subtypes of tumors and predict prognosis. Biclustering algorithms have been proven to be able to group the genes with similar expression patterns under a number of samples and offer the capability to analyze the microarray data of cancer. In this study, we propose a new biclustering algorithm which uses an evolutionary search procedure. The algorithm is applied to the conditions to search for combinations of conditions for a potential bicluster. Preliminary results using synthetic and real yeast data sets demonstrate that our algorithm outperforms several existing ones. We have also applied the method to real microarray data sets of breast cancer, and successfully found several biclusters, which can be used as signatures for differentiating tumor types.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Yu2:2008:cec,
  author = "Yang Yu and Zhi-Hua Zhou",
  title = "On the Usefulness of Infeasible Solutions in Evolutionary Search: A Theoretical Study",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0214.pdf},
  url = {},
  size = {},
  abstract =	{Evolutionary algorithms (EAs) have been widely used in optimization, where infeasible solutions are often encountered. Some EAs regard infeasible solutions as useless individuals while some use infeasible solutions based on heuristic ideas. It is not clear yet that whether infeasible solutions are helpful or not in the evolutionary search. This paper theoretically analyzes that under what conditions infeasible solutions are beneficial. A sufficient condition and a necessary condition are derived and discussed. Then, the paper theoretically shows that the use of infeasible solutions could change the hardness of the task. For example, an EA-hard problem can be transformed to EA-easy by exploiting infeasible solutions.While, the conditions derived in the paper can be used to judge whether to use infeasible solutions or not.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Pang:2008:cec,
  author = "Wai-Man Pang and Tien-Tsin Wong and Pheng-Ann Heng",
  title = "Generating Massive High-Quality Random Numbers using GPU",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {841--847},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0215.pdf},
  url = {},
  doi = {doi:10.1109/MM.2008.31},
  size = {},
  abstract =	{Pseudo-random number generators (PRNG) have been intensively used in many stochastic algorithms in artificial intelligence, computer graphics and other scientific computing. However, the current commodity GPU design does not facilitate the efficient implementation of high-quality PRNGs that require high-precision integer arithmetics and bitwise operations. In this paper, we propose a framework to generate a high-quality PRNG shader for all kinds of GPUs. We adopt the cellular automata (CA) PRNG to facilitate high speed and parallel random number generation. The configuration of the CA PRNG is completed automatically by optimizing an objective function that accounts for quality of generated random sequences. To visually evaluate the result, we apply the best PRNG shader to photon mapping. Timing statistics show that our GPU parallelized PRNG is much faster than a pure CPU implementation.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Belkhelladi:2008:cec,
  author = "Kamel Belkhelladi and Pierre Chauvet and Arnaud Schaal",
  title = "An Agent Framework with an Efficient Information Exchange Model for Distributed Genetic Algorithms",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0216.pdf},
  url = {},
  size = {},
  abstract =	{Genetic Algorithms (GAs) are powerful search techniques that are used to solve difficult problems in many disciplines. Unfortunately, they can be very demanding in terms of computation load and memory. Parallel Genetic Algorithms (PGAs) are parallel implementations of GAs which can provide considerable gains in terms of performance and scalability. PGAs can easily be implemented on networks of heterogeneous computers or on parallel mainframes. },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Yang5:2008:cec,
  author = "Cheng-Hong Yang and Chang-Hsuan Ho and Li-Yeh Chuang",
  title = "Improved Tag SNP Selection Using Binary Particle Swarm Optimization",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0220.pdf},
  url = {},
  size = {},
  abstract =	{Single nucleotide polymorphisms (SNPs) hold much promise as a basis for disease-gene association. However, they are limited by the cost of genotyping the tremendous number of SNPs. It is therefore essential to select only informative subsets (tag SNPs) out of all SNPs. Several promising methods for tag SNP selection have been proposed, such as the haplotype block-based and block-free approaches. The block-free methods are preferred by some researchers because most of the block-based methods rely on strong assumptions, such as prior block-partitioning, bi-allelic SNPs, or a fixed number or locations for tagging SNPs. We employed the feature selection idea of binary particle swarm optimization (binary PSO) to find informative tag SNPs. This method is very efficient, as it does not rely on block partitioning of the genomic region. Using four public data sets, the method consistently identified tag SNPs with considerably better prediction ability than STAMPA. Moreover, this method retains its performance even when a very small number and 100percent prediction accuracy are used for the tag SNPs.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Lauri:2008:cec,
  author = "Fabrice Lauri and Abderrafi&#226;a Koukam  ",
  title = "A Two-Step Evolutionary and ACO Approach for Solving the Multi-Agent Patrolling Problem",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0223.pdf},
  url = {},
  size = {},
  abstract =	{Patrolling an environment involves a team of agents whose goal usually consists in continuously visiting its most relevant areas as frequently as possible. For such a task, agents have to coordinate their actions in order to achieve optimal performance. Current research that tackles this complex multi-agent problem usually defines the environment as a graph, so that a wide range of applications can be dealt with, from computer network management to computer games and vehicle routing. In this paper, we consider only the instances of the multi-agent patrolling problem where all the agents are located on the same starting node. These instances are often encountered in robotics applications, where e.g. drones start from the same area, disperse over it and finally patrol around distant locations. We introduce a new Ant Colony Optimization (ACO) algorithm that is combined with an Evolutionary Algorithm (EA) technique. The novel ACO algorithm uses several ant colonies that are engaged in a competition for finding out the best multi-agent patrolling strategy. The goal of the EA is to find the best set of distant nodes enabling each agent to disperse efficiently over the graph. Experimental results show that, irrespective of the number of the involved patrolling agents and for all the graphs evaluated, our two-step EA and ACO algorithm outperforms significantly and with efficiency the best techniques proposed in the literature since now.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Simons:2008:cec,
  author = "Christopher L. Simons and Ian C. Parmee",
  title = "User-Centered, Evolutionary Search in Conceptual Software Design",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0224.pdf},
  url = {},
  size = {},
  abstract =	{Although much evidence exists to suggest that conceptual software engineering design is a difficult task for software engineers to perform, current computationally intelligent tool support for software engineers is limited. While search-based approaches involving module clustering and refactoring have been proposed and show promise, such approaches are downstream in terms of the software development lifecycle - the designer must manually produce a design before search-based clustering and refactoring can take place. Interactive, user-centered search-based approaches, on the other hand, support the designer at the beginning of, and during, conceptual software design, and are investigated in this paper by means of a case study. Results show that interactive evolutionary search, supported by software agents, appears highly promising. As an open system, search is steered jointly by designer preferences and software agents. Directly traceable to the design problem domain, a mass of useful and interesting conceptual class designs are arrived at which may be visualized by the designer with quantitative measures of structural integrity such as design coupling and class cohesion. The conceptual class designs are found to be of equivalent or better coupling and cohesion when compared to a manual conceptual design of the case study, and by exploiting concurrent execution, the performance of the software agents is highly favorable.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Mukhopadhyay:2008:cec,
  author = "Anirban Mukhopadhyay and Sanghamitra Bandyopadhyay and Ujjwal Maulik",
  title = "Combining Multiobjective Fuzzy Clustering and Probabilistic ANN Classifier for Unsupervised Pattern Classification: Application to Satellite Image Segmentation",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0226.pdf},
  url = {},
  size = {},
  abstract =	{An important approach to unsupervised pixel classification in remote sensing satellite imagery is to use clustering in the spectral domain. In this article, a recently proposed multiobjective fuzzy clustering scheme has been combined with artificial neural networks (ANN) based probabilistic classifier to yield better performance. The multiobjective technique is first used to produce a set of non-dominated solutions. A part of these solutions having high confidence level are then used to train the ANN classifier. Finally the remaining solutions are classified using the trained classifier. The performance of this technique has been compared with that of some other wellknown algorithms for two artificial data sets and a IRS satellite image of the city of Calcutta.     },
  keywords = { Fuzzy clustering, ANN classifier, multiobjective optimization, Pareto-optimality, cluster validity measures, remote sensing satellite imagery. },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Chen3:2008:cec,
  author = "Yang Chen and Jinglu Hu and Kotaro Hirasawa and Songnian Yu",
  title = "Solving Deceptive Problems Using A Genetic Algorithm with Reserve Selection",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0227.pdf},
  url = {},
  size = {},
  abstract =	{Deceptive problems are a class of challenging problems for conventional genetic algorithms (GAs), which usually mislead the search to some local optima rather than the global optimum. This paper presents an improved genetic algorithm with reserve selection to solve deceptive problems. The concept ``potential'' of individuals is introduced as a new criterion for selecting individuals for reproduction, where some individuals with low fitness are also let survive only if they have high potentials. An operator called adaptation is further employed to release the potentials for approaching the global optimum. Case studies are done in two deceptive problems, demonstrating the effectiveness of the proposed algorithm.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Knabe:2008:cec,
  author = "Johannes F. Knabe and Chrystopher L. Nehaniv and Maria J. Schilstra  ",
  title = "Regulation of Gene Regulation - Smooth Binding with Dynamic Affinity Affects Evolvability",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0228.pdf},
  url = {},
  size = {},
  abstract =	{Understanding the evolvability of simple differentiating multicellular systems is a fundamental problem in the biology of genetic regulatory networks and in computational applications inspired by the metaphor of growing and developing networks of cells. We compare the evolvability of a static network model to a more realistic regulatory model with dynamic structure. In the former model, each regulatory protein-binding site is always influenced by exactly one gene product. In the latter model, binding is only more likely to occur the better the match between site and gene product is (smooth binding) and, in addition, affinity dynamically changes under the action of specificity factors during a cell's lifetime. On evolutionary timescales, this means that often the strength of influences between nodes is perturbed instead of direct changes being made to network connectivity. A main result is that for evolutionary search spaces of increasing sizes evolved performance drops much more strongly in the classical network model as compared to the smooth binding model. This effect was even greater in the case of using smooth binding together with specificity factors.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Wang8:2008:cec,
  author = "Jiahai Wang and Yunong Zhang and Yalan Zhou and Jian Yin",
  title = "Discrete Quantum-Behaved Particle Swarm Optimization Based on Estimation of Distribution for Combinatorial Optimization",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0230.pdf},
  url = {},
  size = {},
  abstract =	{Particle swarm optimization (PSO) is a population-based swarm intelligence algorithm. A quantumbehaved particle swarm optimization (QPSO) is also proposed by combining the classical PSO philosophy and quantum mechanics. These algorithms have been very successful in solving the global continuous optimization, but their applications to combinatorial optimization have been rather limited. Estimation of distribution algorithm (EDA) samples new solutions from a probability model which characterizes the distribution of promising solutions. This paper proposes a novel discrete QPSO based on EDA for the combinatorial optimization problem. The proposed algorithm combines global statistical information extracted by EDA with local information obtained by discrete QPSO to create promising solutions. To demonstrate the performance of the proposed algorithm, experiments are carried out on the unconstrained binary quadratic programming problem which numerous hard combinatorial optimization problems can be formulated as. The results show that the discrete QPSO based on EDA have superior performance to other algorithms.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Li8:2008:cec,
  author = "Kangshun Li and Jiusheng Liang and Wensheng Zhang and Feng Wang",
  title = "A New Method of Evolving Digital Circuit Based on Gene Expression Programming",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0231.pdf},
  url = {},
  size = {},
  abstract =	{Evolutionary Hardware (EHW) is a new focus in recent research work. The new method of design hardware is combined evolution algorithm with programmable logic device. Optimization digital circuit is a main domain of EHW. The algebra way and Karnaugh map way are the traditionary methods, but they will meet trouble with the large scale ones to get optimisation structure of circuit. This paper proposes a new method (GEP) to optimise the complex digital circuit and designs a new function fitness. The experiments demonstrate the GEP is not only fast convergence but also optimisation large circuit. It conquers the slow convergence even not convergence of the traditionary method. The GEP algorithm is simpler and more efficient than the traditional ones.},
  keywords = {genetic algorithms, genetic programming, gene expression programming},
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Zhou3:2008:cec,
  author = "Jianguo Zhou and Tao Bai",
  title = "Predicting Corporate Financial Distress using KPCA and GA-Based Support Vector Machine",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0232.pdf},
  url = {},
  size = {},
  abstract =	{In the analysis of predicting financial distress based on support vector machine (SVM), irrelevant or correlated features in the samples could spoil the performance of the SVM classifier, leading to decrease of prediction accuracy. On the other hand, the improper determining of two SVM parameters will cause either over-fitting or under-fitting of a SVM model. In order to solve the problems mentioned above, this paper used kernel principal component analysis (KPCA) as a preprocessor of SVM to extract the principal features of original data and employed the genetic algorithm (GA) to optimize the parameters of SVM. Additionally, the proposed GA-SVM model that can automatically determine the optimal parameters was tested on the prediction of financial distress of listed companies in China. Then, we compared the accuracies of the proposed GA-SVM model with those of other models of multivariate statistics (Fisher and Probit) and other artificial intelligence (BPN and fix-SVM). Especially, we adopted bootstrap technology to evaluate the reliability of validation. Experimental results showed that the GA-SVM model performed the best predictive accuracy and generalization, implying that the hybrid of GA with traditional SVM model can serve as a promising alternative for financial distress prediction.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Malan:2008:cec,
  author = "Katherine M. Malan and Andries P. Engelbrecht",
  title = "Algorithm Comparisons and the Significance of Population Size",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0233.pdf},
  url = {},
  size = {},
  abstract =	{In studies that compare the performance of population-based optimization algorithms, it is sometimes assumed that the comparison is valid as long as the number of function evaluations is equal, even if the population size differs. This paper shows that such comparisons are invalid. The performance of two algorithms: Differential Evolution (DE) and Global Best Particle Swarm Optimization (gbest PSO) are tested on standard benchmark problems with different numbers of individuals/particles (20, 50 and 100). It is shown that there are significance differences in the performance of the same algorithm with the same number of function evaluations, but with different numbers of individuals/particles. Comparisons of different algorithms should therefore always use the same population size for results to be valid.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Zhang5:2008:cec,
  author = "Xiangyin Zhang and Haibin Duan and Jiqiang Jin",
  title = "DEACO: Hybrid Ant Colony Optimization with Differential Evolution",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0234.pdf},
  url = {},
  size = {},
  abstract =	{Ant Colony Optimization (ACO) algorithm is a novel meta-heuristic algorithm for the  approximate solution of combinatorial optimization problems that has been inspired by the foraging behavior of real ant colonies. ACO has strong robustness and easy to combine with other methods in optimization, but it has the shortcomings of stagnation that limits the wide application to the various areas. In this paper, a hybrid ACO with Differential Evolution (DE) algorithm was proposed to overcome the above-mentioned limitations, and this algorithm was named DEACO. Considering the importance of ACO pheromone trail for ants exploring the candidate paths, DE was applied to optimize the pheromone trail in the basic ACO model. In this way, a reasonable pheromone trail between two neighboring cities can be formed, so as to lead the ants to find out the optimum tour. The proposed algorithm is tested with the Traveling Salesman Problem (TSP), and the experimental results demonstrate that the proposed DEACO is a feasible and effective ACO model in solving complex optimization problems.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Sheng:2008:cec,
  author = "Weiguo Sheng and Gareth Howells and Karl Harmer and Michael Fairhurst and Farzin Deravi",
  title = "A Genetic Algorithm for Fingerprint Matching based on an Integrated Measure",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0235.pdf},
  url = {},
  size = {},
  abstract =	{In this paper, we develop a fingerprint matching method which operates by first introducing an integrated measure, which combines two different matching criteria based on heterogeneous features. We then devise a genetically guided algorithm to optimise the integrated measure for simultaneous fingerprint alignment and verification. The proposed method is evaluated through experiments conducted on two public domain collections of fingerprint images and compared with related work, with very encouraging results.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Rakitianskaia:2008:cec,
  author = "Anna Rakitianskaia and Andries P. Engelbrecht",
  title = "Cooperative Charged Particle Swarm Optimiser",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0236.pdf},
  url = {},
  size = {},
  abstract =	{Most optimisation algorithms from the Computational Intelligence field assume that the search landscape is static. However, this assumption is not valid for many real-world problems. Therefore, there is a need for efficient optimisation algorithms that can track changing optima. A number of variants of Particle Swarm Optimisation (PSO) have been developed for dynamic environments. Recently, the cooperative PSO [1] has been shown to significantly improve performance of PSO in static environments, especially for highdimensional problems. This paper investigates the performance of a cooperative version of the charged PSO on a benchmark of dynamic optimisation problems. Empirical results show that the cooperative charged PSO is an excellent alternative to track dynamically changing optima.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Zhao:2008:cec,
  author = "Xinchao Zhao  ",
  title = "Convergent Analysis on Evolutionary Algorithm with Non-uniform Mutation",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0239.pdf},
  url = {},
  size = {},
  abstract =	{Evolutionary algorithm (EA) with non-uniform mutation has the merits of even "longer jumps" than Cauchy mutation at the early stage of the algorithm and much "finertunings" than Gaussian mutation operator at the later stage. Empirical comparisons with the recently proposed EAs show its excellence solution quality and reliability. One unified algorithmic framework with non-uniform mutation operator and its convergence analysis based on this algorithmic framework are provided in this paper. Two lemmas and two theorems are presented to show the relevant convergence properties of unimodal and multimodal functions.     },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Kelly:2008:cec,
  author = "Martin Kelly  ",
  title = "Decentralised Urban Traffic Control Using Genetic Algorithm and Cellular Automata",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0240.pdf},
  url = {},
  size = {},
  abstract =	{This paper describes a traffic control simulation based on exchange of messages between local intersections which incorporate dynamically assembled cellular automata. A genetic algorithm is employed to determine parameters governing the messaging and cellular behaviour. This paper reports both on the convergence of the genetic algorithm towards fittest solutions; and on the performance of the genetic algorithm plus cellular automata combination, under various configurations. },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Su:2008:cec,
  author = "Tonghua Su and Tianwen Zhang and Hujie Huang and Guixiang Xue and Zheng Zhao",
  title = "Transformation-Based Hierarchical Decision Rules using Genetic Algorithms and its Application to Handwriting Recognition Domain",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0241.pdf},
  url = {},
  size = {},
  abstract =	{This paper describes a new approach based on Transformation-Based Learning for extracting hierarchical decision rules. Genetic algorithms are adapted to establish the context environment for transformation operation and the transformation operation can lengthen the life cycle of "good" candidate rules. The experiments are conducted on iris, wine and glass datasets with a 10-fold cross validation setup. The results show that transformation operation can improve the precision of the classifier with a smaller number of rules and generations than hierarchical decision rules. The approach also works well in touching block extraction of Chinese handwritten text.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Duan:2008:cec,
  author = "Haibin Duan and Yaxiang Yu and Rui Zhou",
  title = "UCAV Path Planning Based on Ant Colony Optimization and Satisficing Decision Algorithm",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0243.pdf},
  url = {},
  size = {},
  abstract =	{Path planning of Uninhabited Combat Air Vehicle (UCAV) is a complicated global optimum problem. Ant Colony Optimization (ACO) algorithm was originally presented under the inspiration during collective behavior study results on real ant system, and it has strong robustness and easy to combine with other methods in optimization. In this paper, we propose a hybrid ACO with satisficing decision algorithm for solving the UCAV path planning in complicated combat field environments. When ant chooses the next node from the current candidate path nodes, the acceptance function and rejection function in satisficing decision are calculated. In this way, the efficiency of global optimization can be greatly improved. The detailed realization procedure for this hybrid approach is also presented. Series experimental comparison results show the proposed hybrid method is more effective and feasible in the UCAV path planning than the basic ACO model.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Chen4:2008:cec,
  author = "Dingjun Chen and Keith C. C. Chan and  Xindong Wu",
  title = "Gene Expression Analyses Using Genetic Algorithm based Hybrid Approaches",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0244.pdf},
  url = {},
  size = {},
  abstract =	{This paper presents two Genetic Algorithm (GA) based hybrid approaches for the prediction of tumor outcomes based on gene expression data. One approach is the hybrid GA and K-medoids for grouping genes based on the commonly used distance similarity. The goal of grouping genes here is to choose some top-ranked representatives from each cluster for gene dimensionality reduction. The second proposed approach is the hybrid GA and Support Vector Machines (SVM) for selecting marker genes and classifying tumor types or predicting treatment outcomes. These two hybrid approaches have been applied to public brain cancer datasets, and the experimental results are compared with those given in a 2001 paper published in the Nature. The final prediction accuracies are found to be superior both for tumor class prediction and treatment outcome prediction.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Dornberger:2008:cec,
  author = "Rolf Dornberger and Lukas Frey and Thomas Hanne",
  title = "Single and Multiobjective Optimization of the Train Staff Planning Problem Using Genetic Algorithms",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0245.pdf},
  url = {},
  size = {},
  abstract =	{We consider the problem of assigning train drivers to scheduled trains services, a combinatorial optimization problem which involves various hard and soft constraints. The problem is formulated as a single and a multiobjective optimization problem. A genetic algorithm is designed for solving it. As the problem is a real-life problem, various issues of application and use within a railway planning suite are discussed as well.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Yi:2008:cec,
  author = "Zhaoxiang Yi and Xiaodong Mu and Li Zhang and Peng Zhao  ",
  title = "A Matrix Negative Selection Algorithm for Anomaly Detection",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0247.pdf},
  url = {},
  size = {},
  abstract =	{This paper presents a matrix negative selection algorithm for anomaly detection. The proposed algorithm is a twofold improvement over conventional negative selection algorithms. In matrix representation, characteristics of the self set are emerged by multiple vectors to distinctly express the boundary of self and non-self. On the other hand, based on the matrix matching coefficient, separate match rules for generating detectors and monitoring anomaly are designed to avoid the sharp distinction caused by threshold. Results have demonstrated that the matrix negative selection algorithm is effective and reliable for anomaly detection and suitable for small sample problems of complex systems.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Wang9:2008:cec,
  author = "Jia Wang  ",
  title = "Evolutionary Game Analysis on Product Quality Management in the Automotive Supply Chain of China",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0248.pdf},
  url = {},
  size = {},
  abstract =	{Product quality management in the supply chain is a troublesome problem for auto industry of China. Based on Evolutionary Game Theory, the model of product quality management in the automotive supply chain of China is established and its dynamic evolutionary procedure is analyzed in this paper. The results shows that the quality-profit coefficient, the additional quality management cost of the high-quality strategy, the increment of the product quality due to the high-quality strategy and the distribution rate of the additional profits are the key factors that affect the system's evolution. Accordingly, some helpful ideas are proposed for product quality management in the automotive supply chain of China.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Gorissen:2008:cec,
  author = "Dirk Gorissen and Luciano De Tommasi and Jeroen Croon and Tom Dhaene",
  title = "Automatic Model Type Selection with Heterogeneous Evolution: An Application to RF Circuit Block Modeling",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0249.pdf},
  url = {},
  size = {},
  abstract =	{Many complex, real world phenomena are difficult to study directly using controlled experiments. Instead, the use of computer simulations has become commonplace as a cost effective alternative. However, regardless of Moore's law, performing high fidelity simulations still requires a great investment of time and money. Surrogate modeling (metamodeling) has become indispensable as an alternative solution for relieving this burden. Many surrogate model types exist (Support Vector Machines, Kriging, RBF models, Neural Networks, ...)  but no type is optimal in all circumstances. Nor is there any hard theory available that can help make this choice. The same is true for setting the surrogate model parameters (Bias - Variance trade-off). Traditionally, the solution to both problems has been a pragmatic one, guided by intuition, prior experience or simply available software packages. In this paper we present a more founded approach to these problems. We describe an adaptive surrogate modeling environment, driven by speciated evolution, to automatically determine the optimal model type and complexity. Its utility and performance is presented on a case study from electronics.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Friedrich:2008:cec,
  author = "Tobias Friedrich and Frank Neumann",
  title = "When to use Bit-Wise Neutrality",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0250.pdf},
  url = {},
  size = {},
  abstract =	{Representation techniques are important issues when designing successful evolutionary algorithms. Within this field the use of neutrality plays an important role. We examine the use of bit-wise neutrality introduced by Poli and L&#243;pez [9] from a theoretical point of view and show that this mechanism only enhances mutation-based evolutionary algorithms if not the same number of genotypic bits for each phenotypic bit is used. Using different numbers of genotypic bits for the bits in the phenome we point out by rigorous runtime analyses that it may reduce the optimization time significantly.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Wang10:2008:cec,
  author = "Lili Wang and Alioune Ngom and Robin Gras",
  title = "Non-Unique Oligonucleotide Microarray Probe Selection Method Based on Genetic Algorithms",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0252.pdf},
  url = {},
  size = {},
  abstract =	{In order to accurately measure the gene expression levels in microarray experiments, it is crucial to design unique, highly specific and sensitive oligonucleotide probes for the identification of biological agents such as genes in a sample. Unique probes are hard to obtain for closely related genes such as the known strains of HIV genes. The non-unique probe selection problem is to select a probe set that is able to uniquely identify targets, in a biological sample, while containing a minimal number of probe. This is a NP-hard problem and this paper contributes the first evolutionary method for finding near minimal non-unique probe sets. When used on benchmark data sets, our approach consistently performed better than three recently published methods. We also obtained results that are at least comparable to those of the current state-of-the-art heuristic.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Prabhu:2008:cec,
  author = "Raghavendra D. Prabhu",
  title = "{SOMGPU:} An Unsupervised Pattern Classifier on Graphical Processing Unit",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {1011--1018},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0253.pdf},
  url = {},
  doi = {doi:10.1109/CEC.2008.4630920},
  size = {},
  abstract =	{Graphical Processing Units (GPUs) have been, lately used for general purpose tasks owing to their implicit parallel nature. One such task is that of pattern classification. Highly parallel tasks like these suffer from performance loss owing to the sequential nature of Central Processing Unit (CPU). To match the image processing power of human brain even slightly, this problem beckons the use of enormous computational power and parallel environs of GPUs. Unless there is a task which can be parallelized to the required extent the gain obtained is lost owing to the overhead involved. Thus, it is equally important to understand some limitations of GPU before venturing in this direction and deal with it appropriately to obtain satisfactory results. Artificial Neural Networks (ANN) are found to be appropriate while dealing with pattern recognition problems. Kohonen's Self Organizing Map (SOM) has been used for classification out of other approaches for its implicit parallel nature, albeit with minor modifications to make it suit the parallel environment. nVIDIA GeForce 6150 Go with Microsoft Research Accelerator as the high level library has been chosen as the platform to provide this environment.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Ciftcioglu:2008:cec,
  author = "&#214;zer Ciftcioglu and Michael S. Bittermann",
  title = "Solution Diversity in Multi-Objective Optimization: A Study in Virtual Reality",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0254.pdf},
  url = {},
  size = {},
  abstract =	{Solution diversity in evolutionary multi-objective optimization is considered. Although the Pareto front is ubiquitously used for the multi-objective optimization, the method of formation of the Pareto front in the evolutionary process is important to ensure the diversity of the solutions so that they are desirably evenly distributed along the front. Conventionally this is an issue and in some cases this is compromised with sub-optimality or layered Pareto fronts. This issue is dealt with in this research and a novel method termed as relaxed dominance for design applications is presented. The method is implemented for a design process as a case study and the effectiveness of the method is demonstrated.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Wei2:2008:cec,
  author = "Shuang Wei and Henry Leung",
  title = "An Improved Genetic Algorithm for Pump Scheduling in Water injection systems for Oilfield",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0256.pdf},
  url = {},
  size = {},
  abstract =	{The complication of multiple reciprocating plunger pumps and centrifugal multilevel pumps connected in parallel in the pumping stations has been a challenge for decision makers to meet the demand of the efficiency and costeffective production. In this paper, an efficiency mathematical model and an effective pump scheduling problem based on Genetic Algorithm (GA) are proposed. The objective is to maximize the efficiency of pumps operation to balance the total water flow rate of the system within the constant-pressure constraint and constraints of optimal operation for pumps in the system. An improved GA with competition cross method is designed and applied to solve this problem. Computative simulations show that the proposed method is effective for pump scheduling in an oilfield water injection system.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Wu:2008:cec,
  author = "Q. H. Wu and Z. Lu and M. S. Li and T. Y. Ji  ",
  title = "Optimal Placement of FACTS Devices by A Group Search Optimizer with Multiple Producer",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0263.pdf},
  url = {},
  size = {},
  abstract =	{This paper presents a Group Search Optimizer with Multiple Producer (GSOMP) for reactive power dispatch incorporating with Flexible AC Transmission System (FACTS) devices, which is formulated as a nonlinear constrained multiobjective optimization problem. The optimal location of multitype FACTS devices and their control parameters are optimized by GSOMP to minimize the real power loss and also to improve voltage profile. The performance of GSOMP has been evaluated on the standard IEEE 14-bus and New England 39- bus test systems respectively. Simulation results show that the performance of the power systems is improved with multi-type FACTS devices optimally placed in the reactive power planning model.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Li9:2008:cec,
  author = "Li Li and Li Hong-Qi and Xie Shao-Long",
  title = "Particle Swarm Multi_optimizer for Locating all Local Solutions",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0265.pdf},
  url = {},
  size = {},
  abstract =	{In order to overcome the disadvantage that only one solution can be found in particle swarm optimization (PSO), a novel niche particle swarm Multi_optimizer (Multi_PSOer) which combines two strategies is devised in this paper. Firstly, Guaranteed Convergence PSO (GCPSO) is adopted to guarantee the algorithm can converge on a local point. Secondly, niche technique is used to ensure the algorithm is a global search algorithm. Different hills are looked as different niches. Particles are divided into different sub_swarms according to the Same_hill function. The function can judge whether particles are in the same hill through monitoring the change of particles' tangent. If the tangent values change from negative into positive, they are in different niches, otherwise they are in the same niche. Particle flies following the best one in the same hill with itself. Therefore each peak can be found in this way. It is necessary to know neither the niche radius nor other parameters at all. Numerical experiments show that the Multi_PSOer may, efficiently and reliably, obtain all local and global optima for multimodal optimization problems.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Ru:2008:cec,
  author = "Nie Ru and Yue Jianhua  ",
  title = "A GA and Particle Swarm Optimization Based Hybrid Algorithm",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0267.pdf},
  url = {},
  size = {},
  abstract =	{In this paper an improved particle swarm algorithm is presented firstly and then a hybrid method combining Genetic Algorithm(GA) and Particle Swarm Optimization(PSO) is proposed. This hybrid technique incorporates concepts from GA and PSO and creates individuals in a new generation not only by crossover and mutation operations as found in GA but also by mechanisms of PSO. It can solve the problem of local minimum of the particle swarm optimization and has higher efficiency of search. Simulation results show that the proposed method is effective for the optimization problems.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Ruan:2008:cec,
  author = "Xiaogang Ruan and Jinlian Wang and Hui Li and Xiaoming Li",
  title = "Study of Tumor Molecular Diagnosis Model Based on Artificial Neural Network with Gene Expression Profile",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0268.pdf},
  url = {},
  size = {},
  abstract =	{We introduce a method for modeling cancer diagnosis at the molecular level using a Chinese microarray gastric cancer dataset. The method combines an artificial neural network with a decision tree that is intended to precede standard techniques, such as classification, and enhance their performance and ability to detect cancer genes. First, we used the relief algorithm to select the featured genes that could unravel cancer characteristics out of high dimensional data. Then, an artificial neural network was employed to find the biomarker subsets with the best classification performance for distinguishing cancerous tissues and their counterparts. Next a decision tree expression was used to extract rules subsets from these biomarker sets. Rules induced from the best performance decision tree, in which the branches denote the level of gene expression, were interpreted as a diagnostic model by using previous biological knowledge. Finally, we obtained a gastric cancer diagnosis model for Chinese patients. The results show that using the Chinese gastric biomarker genes with the diagnostic model provides more instruction in biological experiments and clinical diagnosis reference than previous methods.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Xu3:2008:cec,
  author = "Xing Xu and Yuanxiang Li and Shenlin Fang and Yu Wu and  Feng Wang  ",
  title = "A Novel Differential Evolution Scheme Combined with Particle Swarm Intelligence",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0269.pdf},
  url = {},
  size = {},
  abstract =	{Differential evolution (DE) and particle swarm optimization (PSO) are the evolutionary computation paradigms, and both have shown superior performance on complex nonlinear function optimization problems. This paper detects the underlying relationship between them and then qualitatively proves that the two heuristic approaches from different theoretical background are consistent in form. Within the general perspective, the PSO can be regarded as a kind of DE. Inspired by this, a novel variant of DE mixed with particle swarm intelligence (DE-SI) is presented. Comparison experiments involving ten test functions well studied in the evolutionary optimization literature are used to highlight some performance differences between the DE-SI, two versions of DE and two PSO variants. The results from our study show that DE-SI keeps the most rapid convergence rate of all techniques and obtains the global optima for most benchmark problems.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Shi2:2008:cec,
  author = "Yuhui Shi and Russell C. Eberhart",
  title = "Population Diversity of Particle Swarms",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0270.pdf},
  url = {},
  size = {},
  abstract =	{In the field of evolutionary computation, an important attribute of a population is diversity. This paper proposes a method for measuring the diversity of a particle swarm optimization population. It involves the measurement of position and velocity attributes of the particles that comprise the population. The proposed method is computationally straightforward and is adaptable to other evolutionary algorithms.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Ono:2008:cec,
  author = "Satoshi Ono and Kensuke Morinaga and  Shigeru Nakayama",
  title = "Two-Dimensional Barcode Decoration Based on Real-Coded Genetic Algorithm",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0271.pdf},
  url = {},
  size = {},
  abstract =	{This paper proposes a system for decorating 2-dimensional barcode with some illustrations inside the code without detracting machine-readability and stored information. The proposed system formulates the task of finding appropriate positions, scales, and angles of illustrations, and solves the task by using real-coded genetic algorithm. The proposed system also uses multiple barcode decoder with the aim of improving decode feasibility of the decorated barcode. Experiments have shown that the proposed system can decorate barcodes with three illustrations, and that using more than one decoder can improve decoding feasibility of the decorated barcodes.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Xiaodong:2008:cec,
  author = "Duan Xiaodong and Wang Cunrui and Liu Xiangdong and Lin Yanping",
  title = "Web Community Detection Model using Particle Swarm Optimization",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0274.pdf},
  url = {},
  size = {},
  abstract =	{Web community detection is one of the important ways to enhance retrieval quality of web search engine. How to design one highly effective algorithm to partition network community with few domain knowledge is the key to network community detection. Traditional algorithms, such as Wu-Huberman algorithm, need priori information to detect community, the Radichi algorithm relies on the triangle number in the network, the Extremal Optimization Algorithm proposed by Duch J. is extremely sensitive to the initial solution, easy to fall into the local optimum. This article proposes a new model based on particle swarm optimization to detect network community, and with different scale network chart, Zachary, Krebs and dolphins network architecture to test the algorithm, the experimental results indicate this model can effectively find web communities of network structure without any domain information.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Chang2:2008:cec,
  author = "Weng-Long Chang and Ting-Ting Ren and Jun Luo and Mang Feng and Minyi Guo",
  title = "Quantum Algorithms for Bio-molecular Solutions to the Satisfiability Problem on a Quantum Computer",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0275.pdf},
  url = {},
  size = {},
  abstract =	{We demonstrate that the logic computation performed by the DNA-based algorithm for solving general cases of the satisfiability problem can be implemented by our proposed quantum algorithm on the quantum machine proposed by Deutsch. Moreover, we also prove that the logic computation by the bio-molecular operations proposed by Adleman can be implemented by quantum gates (for example, the Hadamard gate, NOT, CNOT, and CCNOT) on the quantum machine. Furthermore, those NP-complete problems solved on a bio-molecular computer are also solvable on a quantum computer. To test our theory, we carry out a three-qubit NMR experiment for solving the simplest satisfiability problem.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Yamamoto:2008:cec,
  author = "Yu Yamamoto and Akira Notsu and Hidetomo Ichihashi and Katsuhiro Honda",
  title = "Agent-Based Social Simulation Based on Cognitive Economic Efficiency",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0276.pdf},
  url = {},
  size = {},
  abstract =	{We propose a model based on cognitive economic efficiency that can be set more than three parameters which express the relationships in each actors cognitive image by using the eigenvalue of an adjacency-matrix that represents an actor's cognitive image in social groups. Moreover, we studied how the groups can be formed when reaching a balance state under some conditions between agents involved in communications.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Bansal:2008:cec,
  author = "Richa Bansal and Kamal Srivastava and Shweta and Kirti Varshney and Nidhi Sharma",
  title = "An Evolutionary Algorithm for the 2-Page Crossing Number Problem",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0279.pdf},
  url = {},
  size = {},
  abstract =	{ Many real-life scheduling, routing and location problems can be formulated as combinatorial optimization problems whose goal is to find a linear layout of an input graph in such a way that the number of edge crossings is minimized. The minimization of edge crossings in a book drawing of a graph is one of the important goals for a linear VLSI design. In this paper, we propose an evolutionary algorithm for crossing number minimization in the 2-page book drawing of graphs in which the initial population is generated by depth first search method with edge length strategy for dividing the edges into two pages. An important feature of the evolutionary process of this algorithm is that it improves the number of crossings by exploring various depth first search trees of the graph instead of applying the usual crossover operator. Minor disturbances are created by the mutation operator. The experiments done on benchmark graphs and some standard graphs show that the algorithm achieves the crossing numbers that are either optimal or known to be the best so far, in much lesser time as compared with the existing techniques.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Bailong:2008:cec,
  author = "Liu Bailong and Zhang Rubo and Shi Changting",
  title = "Response Threshold Model of Aggregation in a Swarm: A Theoretical and Simulative Comparison",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0281.pdf},
  url = {},
  size = {},
  abstract =	{Swarm Intelligence(SI) which is inspired by social animals has been paid more and more attention. It always appeals to the collective behaviors observed in social animals. Aiming at the feature and factors in self-organization of SI system, the aggregation behavior is studied. Firstly the response threshold model of the system is built according to the rules in aggregation. Then the stability of the steady-state solutions of the model is analyzed and the bifurcation of the steady-state solution is obtained. Finally, the effects of the parameter are analyzed based on the theory model. And the Monte Carlo simulations which give certain differences against theory results are also analyzed. All of the theoretical and simulative results show that the aggregation behavior is impacted by the relationship between the swarm size and the response threshold and sensitivity significantly. It is also proved that complex behavior emerges from local interaction of individuals. The work of this paper gives the mechanism in the emergent complex pattern of self-organized aggregation and the factors which affect the system evolution.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Yang6:2008:cec,
  author = "Zhenyu Yang and Ke Tang and Xin Yao",
  title = "Self-Adaptive Differential Evolution with Neighborhood Search",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0282.pdf},
  url = {},
  size = {},
  abstract =	{In this paper we investigate several self-adaptive mechanisms to improve our previous work on NSDE [1], which is a recent DE variant for numerical optimization. The selfadaptive methods originate from another DE variant, SaDE [2], but are remarkably modified and extended to fit our NSDE. And thus a Self-adaptive NSDE (SaNSDE) is proposed to improve NSDE's performance. Three self-adaptive mechanisms are used in SaNSDE: self-adaptation for two candidate mutation strategies, self-adaptations for controlling scale factor F and crossover rate CR, respectively. Experimental studies are carried out on a broad range of different benchmark functions, and the proposed SaNSDE has shown significant superiority over NSDE.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Yan:2008:cec,
  author = "Yunyi Yan and Baolong Guo",
  title = "Particle Swarm Optimization Inspired by $r$- and $K$-Selection in Ecology",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0286.pdf},
  url = {},
  size = {},
  abstract =	{An optimization technique named  r/KPSO (Particle Swarm Optimization withr- and K-selection) was developed in this paper. In Ecology, two evolutionary "strategies" are termed, r-selection for those species that breed many "cheap" offspring and live in unstable environments and K-selection for those species that produce few "expensive" offspring and live in stable environments. r-selection can be characterized as: quantitative, little parent care, large growth rate and rapid development and K-selection as: qualitative, much parent care, small growth rate and slow development. r/KPSO selects r- and K-selection to produce the progenies in the iterative procedure according to the concerned particle's fitness value. K-selection is performed for those particles (K-subswarm called in this paper) in high fitness, and K-subswarm only can produce few progenies but the progenies are nurtured delicately with much parent care. On the other hand, r-selection is performed for those particles (r-subswarm called) in relatively low fitness. And with little parent care, r-subswarm can produce a large number of progenies, the progenies have to compete with the r-subswarm for survival according to fitness and only the best ones can survive. In r/KPSO, the particles performed r-selection mainly explore the search space as possible as they can to find more potential solutions in large speed, and those particles performed K-selection keep the current optimum solutions and exploit the space as they can to find more ideal solutions. Combined the advantages of r-selection and K-selection, r/KPSO can converge in higher speed and higher precision.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Chen5:2008:cec,
  author = "Guo Chen and Zhao Yang Dong",
  title = "On the Weak Ergodicity of the Markov Chain Associated with a Chaotic Simulated Annealing Algorithm",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0287.pdf},
  url = {},
  size = {},
  abstract =	{Chaotic simulated annealing (CSA) is a relatively new heuristic optimization technique and has been widely applied to optimization problems because of its simplicity and capability of finding fairly good solutions rapidly. However, currently only experimental results are used for verifying its superiority. In this paper, a new of chaotic simulated annealing method (CSA) is introduced and then a mathematic proof is given. It shows that the Markov Chain associated with the algorithm is weakly ergodic, which guarantees that the asymptotic behavior of the algorithm is independent of initial states. Furthermore, the theoretical analysis of the proposed CSA is very important to understand the essential features which make the algorithm work well.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Olorunda:2008:cec,
  author = "Olusegun Olorunda and Andries P. Engelbrecht",
  title = "Measuring Exploration/Exploitation in Particle Swarms Using Swarm Diversity",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0288.pdf},
  url = {},
  size = {},
  abstract =	{An important factor contributing to the success of particle swarm optimization (PSO) is the balance between exploration and exploitation of the swarm. Exploration is typically preferred at the initial stages of the search but is required to gradually give way to exploitation of promising solutions as the search progresses. The  diversity  of a particle swarm optimization algorithm can be defined, simply, as the degree of dispersion of the particles in the swarm. This dispersion could be defined around some center-point or not. It could also be defined based on the positions of the particles or on their velocities.This paper takes a look at some of the different definitions of swarm diversity with the intention of determining their usefulness in quantifying swarm exploration/exploitation. This work is intended to lay the foundations for the development of a suitable means to quantify the rate of change from exploration to exploitation of a PSO, i.e. the rate of change of diversity.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Haider:2008:cec,
  author = "Sajjad Haider and Alexander H. Levis  ",
  title = "Finding Effective Courses of Action Using Particle Swarm Optimization",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0289.pdf},
  url = {},
  size = {},
  abstract =	{The paper applies particle swarm optimization (PSO) technique to identify effective courses of action (COAs) in a dynamic uncertain situation. The uncertain situation is modeled using Timed Influence Nets (TINs), an instance of Dynamic Bayesian Networks. The TIN-based framework aids a system analyst in connecting a set of actionable events and a set of desired effects through chains of cause and effect relationships. The purpose of building these TIN models is to analyze several courses of action (COAs) and identify the ones that maximize the likelihood of achieving the desired effect(s). The paper attempts to automate this identification process of the best COA. It does so by exploring the solution space, consisting of potential courses of action, using PSO. The paper also compares the performance of PSO with that of an evolutionary algorithm (EA). The results suggest there is not a significant difference between the performances of the two techniques but PSO takes less time compared to EA.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Yu3:2008:cec,
  author = "Xin Yu and Ke Tang and Xin Yao",
  title = "An Immigrants Scheme Based on Environmental Information for Genetic Algorithms in Changing Environments",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0290.pdf},
  url = {},
  size = {},
  abstract =	{Addressing dynamic optimization problems (DOPs) has been a challenging task for the genetic algorithm (GA) community. One approach is to maintain the diversity of the population via introducing immigrants. This paper intensively examines several design decisions when employing immigrants schemes, and from these observations an environmental information-based immigrants scheme is derived for GAs to deal with DOPs. In the scheme, the environmental information (e.g., the allele distribution over the population in this paper) from previous generation is used to create immigrants to replace the worst individuals in the current population. In this way, the introduced immigrants are more adapted to the changing environment. A hybrid scheme combining immigrants based on current environmental information and its complementation is also proposed in this paper to address different degrees of changes. Experimental results validate the efficacy of the proposed environmental information-based and hybrid environmental information-based immigrants schemes.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Wang11:2008:cec,
  author = "Zai Wang and Ke Tang and Xin Yao",
  title = "A Multi-Objective Approach to Testing Resource Allocation in Modular Software Systems",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0291.pdf},
  url = {},
  size = {},
  abstract =	{Nowadays, as the software systems become increasingly large and complex, the problem of allocating the limited testing-resource during the testing phase has become more and more difficult. In this paper, we propose to solve the testing-resource allocation problem (TRAP) using multi-objective evolutionary algorithms. Specifically, we formulate TRAP as two multi-objective problems. First, we consider the reliability of the system and the testing cost as two objectives. In the second formulation, the total testing-resource consumed is also taken into account as the third goal. Two multi-objective evolutionary algorithms, Non-dominated Sorting Genetic Algorithm II (NSGA2) and Multi-Objective Differential Evolution Algorithms (MODE), are applied to solve the TRAP in the two scenarios. This is the first time that the TRAP is explicitly formulated and solved by multi-objective evolutionary approaches. Advantages of our approaches over the state-of-the-art single-objective approaches are demonstrated on two parallel-series modular software models.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Grobler:2008:cec,
  author = "Jacomine Grobler and Andries P. Engelbrecht and V. S. S. Yadavalli",
  title = "Multi-Objective DE and PSO Strategies for Production Scheduling",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0292.pdf},
  url = {},
  size = {},
  abstract =	{This paper investigates the application of alternative multi-objective optimization (MOO) strategies to a complex scheduling problem. Two vector evaluated algorithms, namely the vector evaluated particle swarm optimization (VEPSO) algorithm as well as the vector evaluated differential evolution (VEDE) algorithm is compared to a differential evolutionbased modified goal programming approach. This paper is considered significant since no other reference to the application of vector evaluated algorithms in a scheduling environment could be found. Algorithm performance is evaluated on real customer data and meaningful conclusions are drawn with respect to the application of MOO algorithms in a multiple machine multi-objective scheduling environment.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Mahajan:2008:cec,
  author = "Anjali Mahajan and M. S. Ali",
  title = "Hybrid Evolutionary Algorithm for Graph Colouring Register Allocation",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0293.pdf},
  url = {},
  size = {},
  abstract =	{Memory or registers are used to store the results of computation of a program. As compared to memory, accessing a register is much faster, but they are scarce resources and have to be used very efficiently. If the register set is not sufficient to hold all program variables, certain values have to be stored in memory and so-called spill code has to be inserted. The optimization goal is to hold as many live variables as possible in registers in order to avoid expensive memory accesses. We present a new hybrid evolutionary algorithm (HEA) for graph colouring register allocation problem for embedded systems. We have used MachineSUIF [19] compiler research framework for implementing our algorithm.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Sun:2008:cec,
  author = "Tsung-Ying Sun and Chan-Cheng Liu and Tsung-Ying Tsai and Sheng-Ta Hsieh",
  title = "Adequate Determination of a Band of Wavelet Threshold for Noise Cancellation Using Particle Swarm Optimization",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0295.pdf},
  url = {},
  size = {},
  abstract =	{Noise reduction problem is addressed by this study. Recently, wavelet thresholding has become popular and has gotten much attention among a number of de-noisy approaches. The most of threshold determination are developed from universal method proposed by Donoho. But, some shortcomings of the determination are caused from several incorrectly estimated factors and the lack of adaptability for whole frequency. By the reason, this paper replaces a universal threshold by multi-thresholds for matching the coefficients of each wavelet segment, and then the band of threshold will be fined by particle swarm optimization (PSO). Because original signals and noise are mutually independent, an objective function of PSO is created to evaluate the second order correlation and high order correlation. In order to confirm the validity and efficiency of the proposed algorithm, several simulations which include four benchmarks with high or low noise degree are designed. Moreover, the performance of proposed algorithm will have compared with that of other existing algorithms.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Meignan:2008:cec,
  author = "David Meignan and Jean-Charles Creput and  Abderrafi&#226;a Koukam",
  title = "A Coalition-Based Metaheuristic for the Vehicle Routing Problem",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0297.pdf},
  url = {},
  size = {},
  abstract =	{This paper presents a population based Metaheuristic adopting the metaphor of social autonomous agents. In this context, agents cooperate and self-adapt in order to collectively solve a given optimization problem. From an evolutionary computation point of view, mechanisms driving the search consist of combining intensification operators and diversification operators, such as local search and mutation or recombination. The multiagent paradigm mainly focuses on the adaptive capabilities of individual agents evolving in a context of decentralized control and asynchronous communication. In the proposed metaheuristic, the agent's behavior is guided by a decision process for the operators' choice which is dynamically adapted during the search using reinforcement learning and mimetism learning between agents. The approach is called Coalition-Based Metaheuristic (CBM) to refer to the strong autonomy conferred to the agents. This approach is applied to the Vehicle Routing Problem to emphasize the performance of learning and cooperation mechanisms.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Sun2:2008:cec,
  author = "Tsung-Ying Sun and Chih-Li Huo and Shang-Jeng Tsai and Chan-Cheng Liu",
  title = "Optimal UAV Flight Path Planning Using Skeletonization and Particle Swarm Optimizer",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0298.pdf},
  url = {},
  size = {},
  abstract =	{The purpose of this paper is to search the best flight route efficiently for Unmanned Aerial Vehicle (UAV) in the 3-dimention complicated topography. The proposed method for the best flight route is mainly using evolutionary algorithm, and give the proper initial population of evolutionary algorithm through skeletonization, efficient pre-processing procedure.In order to provide a smooth flight route for UAV, this paper adopts B-spline Curve method. Several control points of B-spline Curve method must be determined to generate flight route. The best control points can be calculated by Particle Swarm Optimizer (PSO). In this paper, the initial population of PSO is provided by skeletonization. The skeletonization of pre-processing procedure mainly includes two parts: one is Skeletonization and the other is candidate path searching. The purpose of pre-processing procedure is to reduce computation time, to prevent search the best solutions aimless, and execute evolutionary process efficiently. This paper uses Matlab as the experiment environment. The results of the experiments present the proposed method can provide the best flight route for UAV efficiently.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Hu:2008:cec,
  author = "Zhong-Bo Hu and Qing-Hua Su and Sheng-Wu Xiong and Fu-Gao Hu",
  title = "Self-Adaptive Hybrid Differential Evolution with Simulated Annealing Algorithm for Numerical Optimization",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0305.pdf},
  url = {},
  size = {},
  abstract =	{A self-adaptive hybrid differential evolution with simulated annealing algorithm, termed SaDESA, is proposed. In the novel SaDESA, the choice of learning strategy and several critical control parameters are not required to be pre-specified. During evolution, the suitable learning strategy and parameters setting are gradually self-adapted according to the learning experience. The performance of the SaDESA is evaluated on the set of 25 benchmark functions provided by CEC2005 special session on real parameter optimization. Comparative study exposes the SaDESA algorithm as a competitive algorithm for a global optimization.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Chaharsooghi:2008:cec,
  author = "S. K. Chaharsooghi and Amir H. Meimand Kermani",
  title = "An Intelligent Multi-Colony Multi-Objective Ant Colony Optimization (ACO) for the 0-1 Knapsack Problem",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0309.pdf},
  url = {},
  size = {},
  abstract =	{The knapsack problem is a famous optimization problem. Even the single objective case has been proven to be NP-hard the multi-objective is harder than the single objective case. This paper presents the modified ant colony optimization (ACO) algorithm for solving knapsack multi-objective problem to achieve the best layer of non-dominated solution. We also proposed a new pheromone updating rule for multi-objective case which can increase the learning of algorithm and consequently increase effectiveness. Finally, the computational result of proposed algorithm is compared with the NSGA II which outperforms most of the multi-objective ant colony optimization algorithm which are reviewed in this paper.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Suwannik:2008:cec,
  author = "Worasait Suwannik and Prabhas Chongstitvatana",
  title = "Solving One-Billion-Bit Noisy OneMax Problem using Estimation Distribution Algorithm with Arithmetic Coding",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0310.pdf},
  url = {},
  size = {},
  abstract =	{This paper presents an algorithm which combines Estimation Distribution Algorithm with a chromosome compression scheme to solve large scale Noisy OneMax problem. The search space reduction resulted from chromosome compression enables the algorithm to solve a one-billion-bit problem. Arithmetic Coding represents a compressed binary string with two real numbers. Using this representation, a model of highly fit individuals can be constructed. This model can be used to evolve the solution in the manner of Estimation Distribution Algorithm. The experimental result shows that the algorithm can solve billion-bit Noisy OneMax problem in about 34 hours using a normal PC-class computer.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Thiruvady:2008:cec,
  author = "Dhananjay R. Thiruvady and Bernd Meyer and Andreas T. Ernst",
  title = "Strip Packing with Hybrid ACO: Placement Order is Learnable",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0312.pdf},
  url = {},
  size = {},
  abstract =	{This paper investigates the use of hybrid metaheuristics based on Ant Colony Optimization (ACO) for the strip packing problem. Here, a fixed set of rectangular items of fixed sizes have to be placed on a strip of fixed width and infinite height without overlaps and with the objective to minimize the height used. We analyze a commonly used basic placement heuristic (BLF) by itself and in a number of hybrid combinations with ACO. We compare versions that learn item order only, item rotation only, both independently, and rotations conditionally upon placement order. Our analysis shows that integrating a learning meta-heuristic provides a significant performance advantage over using the basic placement heuristic by itself. The experiments confirm that even just learning a placement order alone can provide significant performance improvements. Interestingly, learning item rotations provides at best a marginal advantage. The best hybrid algorithm presented in this paper significantly outperforms previously reported strip packing meta-heuristics.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Wang12:2008:cec,
  author = "Dazhi Wang and Chun-Ho Wu and Andrew Ip and Dingwei Wang and Yang Yan",
  title = "Parallel Multi-Population Particle Swarm Optimization Algorithm for the Uncapacitated Facility Location Problem using OpenMP",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0313.pdf},
  url = {},
  size = {},
  abstract =	{Parallel multi-population Particle Swarm Optimi- zation (PSO) Algorithm using OpenMP is presented for the Uncapacitated Facility Location (UFL) problem. The parallel algorithm performed asynchronously by dividing the whole particle swarm into several sub-swarms and updated the particle velocity with a variety of local optima. Each sub-swarm changes its best position so far of to its neighbor swarm after certain generations. The parallel multi-population PSO (PMPSO) algorithm is applied to several benchmark suits collected from OR-library. And the results are presented and compared to the result of serial execution multi-population PSO. It is conducted that the parallel multi-population PSO is time saving, especially for large scale problem and generated more robust results.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Ra:2008:cec,
  author = "Syungkwon Ra and Galam Park and ChangHwan Kim and Bum-Jae You",
  title = "PCA-Based Genetic Operator for Evolving Movements of Humanoid Robot",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0317.pdf},
  url = {},
  size = {},
  abstract =	{This paper proposes a new genetic operator in order to evolve the humanoid movements, which is composed of principal component analysis (PCA) and descent-based local optimization with respect to robot dynamics. The aim of the evolution is to let humanoid robots generate human-like and energy-efficient motions in real-time. We first capture human motions and build a set of movement primitives. The set is then evolved to the optimal movement primitives for the specific robot, which contain its dynamic characteristics, by using an evolutionary algorithm with the proposed genetic operator. Finally, the humanoid robot can generate arbitrary motions in real-time through the mathematical interpolation of the movement primitives in the evolved set. The evolved set of movement primitives endows the humanoid robot with natural motions which require minimal torque. This technique gives a systematic methodology for a humanoid robot to learn natural motions from human considering dynamics of the robot. The feasibility of our genetic operator is investigated by simulation experiments in regard to catching a ball that a man throws of the humanoid robot.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Supudomchok:2008:cec,
  author = "S. Supudomchok and N. Chaiyaratana and C. Phalakornkule",
  title = "Co-Operative Co-Evolutionary Approach for Flux Balance in Bacillus Subtilis",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0318.pdf},
  url = {},
  size = {},
  abstract =	{Flux balance analysis of the metabolic network of Bacillus subtilis was employed to investigate flux distribution with maximizing ATP and ATP per sum of all flux values. The first objective function, which is to maximize ATP, is a conventional linear objective function and is performed with a hill-climbing algorithm. The second, which is to maximize ATP per sum of all flux values, is a non-linear objective function and is performed with a co-operative co-evolutionary genetic algorithm (CCGA). The effects of co-substrate supplementation; i.e. serine, cysteine, aspartate and threonine, are investigated. Employing two different objective functions predicts different effect of substrate supplementation. The optimization results according to the first objective function suggest that no improvement can be gained by substrate supplementation, while those according to the second objective function suggest that the introduction of each alternative substrate can lead to an improvement in ATP production. Exploration of alternative objective functions by CCGA is illustrated to generate more flux scenarios.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Mase:2008:cec,
  author = "Motohiro Mase and Seiji Yamada and Katsumi Nitta",
  title = "Extracting Topic Maps from Web Pages by Web Link Structure and Content",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0320.pdf},
  url = {},
  size = {},
  abstract =	{We propose a framework to extract topic maps from a set of Web pages. We use the clustering method with the Web pages and extract the topic map prototypes. We introduced the following two points to the existing clustering method: The first is merging only the linked Web pages, thus extracting the underlying relationships between the topics. The second is introducing weighting based on the similarity from the contents of the Web pages and relevance between topics of pages. The relevance is based on the types of links with directories in the Web sites structure and the distance between the directories in which the pages are located. We generate the topic map prototypes by assuming that the clusters are the topics, the edges are the associations, and the Web pages related to the topics are the occurrences from the results of the clustering. Finally, users complete the prototype by labeling the topics and associations and removing the unnecessary items. We incrementally use a user's evaluation of the topic maps to judge whether a Web page is unnecessary or necessary and then reduce the number of unnecessary pages. We use the relevance feedback along with a Support Vector machine (SVM) to judge the Web pages. For this paper, at the first step, we mounted the proposed clustering method and conducted experiments to evaluate the effectiveness of extracting topic map prototypes. We eventually discussed the effectiveness of our two additional points by evaluating the extracted topic map prototypes.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Huang4:2008:cec,
  author = "Kuang Yu Huang and Chuen-Jiuan Jane and   Ting-Cheng Chang",
  title = "A RS Model for Stock Market Forecasting and Portfolio Selection Allied with Weight Clustering and Grey System Theories",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0322.pdf},
  url = {},
  size = {},
  abstract =	{In this study, the weight clustering model which consists of GM(1,N) with K-means Clustering is combined with Grey Systems theory and Rough Set (RS) theory to create an automatic stock market forecasting and portfolio selection mechanism. In our proposed approach, financial data are collected every quarter and are inputted to an GM(1,1) predicting model to forecast the future trends of the collected data over the next quarter. Next, the forecasted data of financial statement is transformed into financial ratios using a GM(1,N) model and clustered by using a K-means clustering algorithm, and then supplied to a RS classified module which selects appropriate investment stocks by adopting a set of decision-making rules. Finally, a grey relational analysis technique is applied to specify an appropriate weighting of the selected stocks to maximize the portfolio's rate of return. The validity of our proposed approach is demonstrated to use the electronic stock data extracted from the financial database maintained by the Taiwan Economic Journal (TEJ). The portfolio's results derived by using our proposed weight clustering model are compared with those portfolio's results of a conventionally clustering method. It is found that our proposed method yielded a greater average annual rate of return (23.42percent) on the selected stocks from 2004 to 2006 in Taiwan stock market.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Singh:2008:cec,
  author = "Chingtham Tejbanta Singh and Ujjwal Maulik",
  title = "A Framework for an Artificial Immunity and Speech Based Navigation for Mobile Robots",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0325.pdf},
  url = {},
  size = {},
  abstract =	{In recent years speech recognition technology and immunity based algorithms have made an impact in various areas and are deployed for a wide range of applications. This paper describes a learning process of a mobile robot which takes speech input as commands and performs some navigation task through a distinct man-machine interaction with the application of the learning based on the Artificial Immune System. For this purpose a 4-channel radio controlled Wheelbot and Microsoft's Speech SDK for speech recognition is employed. The speech recognition system is trained to recognize defined commands and the robot has been designed to navigate based on the instruction through the Speech Commands. The position of the obstacles are learnt and avoided by the help of immune algorithm.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Huang5:2008:cec,
  author = "Haoming Huang and Michel Pasquier and Chai Quek  ",
  title = "Application of a Hierarchical Coevolutionary Fuzzy System for Financial Prediction and Trading",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0326.pdf},
  url = {},
  size = {},
  abstract =	{In this paper, the application of a hierarchical coevolutionary fuzzy system called HiCEFS for predicting financial time series is investigated. A novel financial trading system using the HiCEFS as a predictive model and employing a prudent trading strategy based on the price percentage oscillator (PPO) is proposed. In order to construct an accurate predictive model, a form of generic membership function named Irregular Shaped Membership Function (ISMF) is employed and a hierarchical coevolutionary genetic algorithm (HCGA) is adopted to automatically derive the ISMFs for each input variable in HiCEFS. With the accurate prediction from HiCEFS and a prudent trading strategy, the proposed financial trading system outperforms the simple buy-and-hold strategy, the trading system without prediction and the trading system with other predictive models (EFuNN, DENFIS and RSPOP) on real-world financial data.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Dong:2008:cec,
  author = "Weishan Dong and Xin Yao",
  title = "NichingEDA: Using the Diversity Inside a Population of EDAs for Continuous Optimization",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0327.pdf},
  url = {},
  size = {},
  abstract =	{Since the Estimation of Distribution Algorithms (EDAs) have been introduced, several single model based EDAs and mixture model based EDAs have been developed. Take Gaussian models as an example, EDAs based on single Gaussian distribution have good performance on solving simple unimodal functions and multimodal functions whose landscape has an obvious trend towards the global optimum. But they have difficulties in solving multimodal functions with irregular landscapes, such as wide basins, flat plateaus and deep valleys. Gaussian mixture model based EDAs have been developed to remedy this disadvantage of single Gaussian based EDAs. A general framework NichingEDA is presented in this paper from a new perspective to boost single model based EDAs' performance. Through adopting a niching method and recombination operators in a population of EDAs, NichingEDA significantly boosts the traditional single model based EDAs' performance by making use of the diversity inside the EDA population on hard problems without estimating a precise distribution. Our experimental studies have shown that NichingEDA is very effective for some hard global optimization problems, although its scalability to high dimensional functions needs improving. Analyses and discussions are presented to explain why NichingEDA performed well/poorly on certain benchmark functions.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Burgos-Artizzu:2008:cec,
  author = "Xavier P. Burgos-Artizzu and Angela Ribeiro and  Alberto Tellaeche and Gonzalo Pajares",
  title = "Optimisation of Natural Images Processing Using Different Evolutionary Algorithms",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0329.pdf},
  url = {},
  size = {},
  abstract =	{The development of image processing methods to discriminate between weed, crop and soil is an important step for Precision Agriculture, the main goal of which is the sitespecific management of crops. The main challenge in terms of image analysis is to achieve an appropriate discrimination in outdoor field images under varying conditions of lighting, soil background texture and crop damage. This work presents several developed computer-vision-based methods for the estimation of percentages of weed, crop and soil in digital images of a crop field. These methods are interchangeable among them, having each one of them a set of input parameters that need to be adjusted differently for each image. Two different evolutionary methods (standard genetic algorithm and NSGAII) have been used to adjust these parameters and find the best method combinations. The proposed approach can reach a correlation with real data of up to 97percent for a set of images acquired from different fields and under different conditions.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Majhi2:2008:cec,
  author = "Ritanjali Majhi and G. Panda and G. Sahoo and Abhishek Panda and Arvind Choubey",
  title = "Prediction of S&#38;P 500 and DJIA Stock Indices Using Particle Swarm Optimization Technique",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0330.pdf},
  url = {},
  size = {},
  abstract =	{The present paper introduces the Particle Swarm Optimization (PSO) technique to develop an efficient forecasting model for prediction of various stock indices. The connecting weights of the adaptive linear combiner based model are optimized by the PSO so that its mean square error (MSE) is minimized. The short and long term prediction performance of the model is evaluated with test data and the results obtained are compared with those obtained from the multilayer perceptron (MLP) based model. It is in general observed that the proposed model is computationally more efficient, prediction wise more accurate and takes less training time compared to the standard MLP based model.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Sheta:2008:cec,
  author = "Alaa Sheta and David Rine and Aladdin Ayesh",
  title = "Development of Software Effort and Schedule Estimation Models Using Soft Computing Techniques",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0334.pdf},
  url = {},
  size = {},
  abstract =	{Accurate estimation of the software effort and schedule affects the budget computation. Bidding for contracts depends mainly on the estimated cost. Inaccurate estimates will lead to failure of making a profit, increased probability of project incompletion and delay of the project delivery date. In this paper, we explore the use of Soft Computing Techniques to build a suitable model structure to use improved estimations of software effort for NASA software projects. In doing so, we plan to use Particle Swarm Optimization (PSO) to tune the parameters of the famous COnstructive COst MOdel (COCOMO). We plan also to explore the advantages of Fuzzy Logic to build a set of linear models over the domain of possible software Line Of Code (LOC). The performance of the developed model was evaluated using NASA software projects data set [1]. A comparison between COCOMO tuned-PSO, Fuzzy Logic (FL), Halstead, Walston-Felix, Bailey-Basili and Doty models were provided.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Li10:2008:cec,
  author = "Ya-Liang Li and Fei Ding and Yu-Xuan Wang",
  title = "Iterated Function System Based Adaptive Differential Evolution Algorithm",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0335.pdf},
  url = {},
  size = {},
  abstract =	{In this paper, we propose a new adaptive Differential Evolution algorithm, in which a simple mechanism based on Iterated Function System is applied to the control parameters F and CR. The performance is reported on a set of benchmark functions, which shows that our algorithm is better than, or at least comparable to the standard DE algorithm and the other adaptive versions of DE algorithm.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Hu2:2008:cec,
  author = "Rong Hu and Ling Wang and Bin Qian and Fu-zhuo Huang",
  title = "Differential Evolution Method for Stochastic Flow Shop Scheduling with Limited Buffers",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0336.pdf},
  url = {},
  size = {},
  abstract =	{The flow shop scheduling problem (FSSP) with limited buffers constraint is a typical NP-hard combinatorial optimization problem and represents an important area in production scheduling. In this paper, a class of differential evolution (DE) method with the optimal computing budget allocation (OCBA) technique and hypothesis test (HT), namely OHTDE, is proposed for the stochastic flow shop scheduling with limited buffers between consecutive machines to minimize the maximum completion time (i.e., makespan). In the OHTDE, the population-based search mechanism of DE and a special crossover are applied for well exploration and exploitation, and the OCBA technique is used to allocate limited sampling budgets to provide reliable evaluation and identification for good individuals. Meanwhile, HT is also applied to perform a statistical comparison to avoid some repeated search to some extent. The results and comparisons demonstrate the superiority of OHTDE in terms of effectiveness and robustness.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Li11:2008:cec,
  author = "M. S. Li and T. Y. Ji and Z. Lu and Henry Wu",
  title = "Optimal Harmonic Estimation Using Dynamic Bacterial Swarming Algorithm",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0337.pdf},
  url = {},
  size = {},
  abstract =	{This paper presents a Dynamic Bacterial Swarming Algorithm (DBSA) for harmonic estimation in dynamic environment. DBSA is designed from a dynamic searching framework that combines the underlying mechanisms of bacterial chemotaxis, quorum sensing and environment adaptation. The harmonic estimation process uses DBSA to estimate the phases of the harmonics, alongside a Least Square (LS) method to estimate the amplitudes. A cost function is given as an error between the original signal and the reconstructed signal.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Ashlock3:2008:cec,
  author = "Daniel Ashlock and Elizabeth Warner",
  title = "The Geometry of Tartarus Fitness Cases",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0339.pdf},
  url = {},
  size = {},
  abstract =	{Tartarus is a standard AI task for grid robots in which boxes must be moved to the walls of a virtual world. There are 320,320 fitness cases for the standard Tartarus task of which 297,040 are valid according to the original statement of the problem. This paper studies different schemes for allocating fitness trials for Tartarus using an agent-based metric on the fitness cases to aid in the design process. This agent-based metric is a tool that permits exploration of the geometry of the space of fitness cases. The information gained from this exploration demonstrates why a scheme designed to yield a superior set of training cases in fact yielded an inferior one. The information gained also suggests a new scheme for allocating fitness trials that decreases the number of trials required to achieve a given fitness of the best agent. This scheme achieves similar fitness to a standard evolutionary algorithm using fewer fitness cases. The space of fitness cases for Tartarus is found, relative to the agent-based metric, to form a hollow sphere with a nonuniform distribution of the fitness cases within the space. The tools developed in this study include a generalisable technique for placing an agent-based metric space structure on the fitness cases of any problem that has multiple fitness cases. This metric space structure can be used to better understand the distribution of fitness cases and so design more effective evolutionary algorithms.   },
  keywords = {genetic algorithms, genetic programming},
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Leong:2008:cec,
  author = "Wen-Fung Leong and Gary G. Yen",
  title = "Impact of Tuning Parameters on Dynamic Swarms in PSO-Based Multiobjective Optimization",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0340.pdf},
  url = {},
  size = {},
  abstract =	{In this paper, the improvement of two design components (swarm growing strategy and objective space compression and expansion strategy) from the existing multiple swarms MOPSO, namely DSMOPSO, is presented. In addition, sensitivity analysis is conducted to study the impact of the five tuning parameters on its performance through two performance metrics. Simulation results show the improved design is robust with respect to the tuning parameters.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Daneshyari:2008:cec,
  author = "Moayed Daneshyari and Gary G. Yen",
  title = "Cultural MOPSO: A Cultural Framework to Adapt Parameters of Multiobjective Particle Swarm Optimization",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0341.pdf},
  url = {},
  size = {},
  abstract =	{Multiobjective particle swarm optimization algorithms (MOPSO) have been widely used to solve multiobjective optimization problems. Most of MOPSOs use fixed momentum and acceleration for all particles throughout the evolutionary process. In this paper, we introduce a cultural framework to adapt the flight parameters of the MOPSO namely momentum, personal, and global acceleration for each individual particle based upon the various types of knowledge in belief space, specifically situational knowledge, normative knowledge, and topographical knowledge. Movement of the particles using the adapted parameters helps the MOPSO to perform efficiently and effectively in multiobjective optimization.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Valdez:2008:cec,
  author = "Fevrier Valdez and Patricia Melin and Oscar Castillo and Oscar Montiel",
  title = "A New Evolutionary Method with a Hybrid Approach Combining Particle Swarm Optimization and Genetic Algorithms Using Fuzzy Logic for Decision Making",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0342.pdf},
  url = {},
  size = {},
  abstract =	{We describe in this paper a new hybrid approach for mathematical function optimization combining Particle Swarm Optimization (PSO) and Genetic Algorithms (GAs) using Fuzzy Logic to integrate the results. The new evolutionary method combines the advantages of PSO and GA to give us an improved PSO+GA hybrid method. Fuzzy Logic is used to combine the results of the PSO and GA in the best way possible. The new hybrid PSO+GA approach is compared with the PSO and GA methods with a set of benchmark mathematical functions. The new hybrid PSO+GA method is shown to be superior than the individual evolutionary methods.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Ara&#250;jo:2008:cec,
  author = "Ricardo de A. Ara&#250;jo and Aranildo R. L. J&#250;nior and  Tiago A. E. Ferreira",
  title = "Morphological-Rank-Linear Time-Lag Added Evolutionary Forecasting Method for Financial Time Series Forecasting",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0344.pdf},
  url = {},
  size = {},
  abstract =	{This paper proposes the Morphological-Rank-Linear Time-lag Added Evolutionary Forecasting (MRLTAEF) method for financial time series forecasting, which performs an evolutionary search for the minimum number of relevant time lags necessary to efficiently represent complex time series. It consists of an intelligent hybrid model composed of a Morphological-Rank-Linear (MRL) filter combined with a Modified Genetic Algorithm (MGA) which employs optimal genetic operators in order to accelerate its search convergence. The MGA searches for the particular time lags capable of a fine tuned characterization of the time series and estimates the initial (sub-optimal) parameters of the MRL filter - the mixing parameter (<font face="symbol">&#x3BB;</font>), the rank (r), the coefficients of the linear Finite Impulse Response (FIR) filter (<u>b</u>) and the coefficients of the Morphological-Rank (MR) filter (<u>a</u>). Thus, each individual of the MGA population is trained by the averaged Least Mean Squares (LMS) algorithm to further improve the parameters of the MRL filter supplied by the MGA. Initially, the proposed MRLTAEF method chooses the most tuned prediction model for time series representation, thus it performs a behavioral statistical test in the attempt to adjust forecasting time phase distortions that appear in financial time series. Experiments are conducted with the proposed MRLTAEF method using three real world financial time series according to a group of relevant performance metrics and the results are compared to MultiLayer Perceptron (MLP) networks, MRL filters and the previously introduced Time-delay Added Evolutionary Forecasting (TAEF) method.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Ara&#250;jo2:2008:cec,
  author = "Ricardo de A. Ara&#250;jo and Aranildo R. L. J&#250;nior and Tiago A. E. Ferreira",
  title = "A Quantum-Inspired Intelligent Hybrid Method for Stock Market Forecasting",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0345.pdf},
  url = {},
  size = {},
  abstract =	{This work introduces a Quantum-Inspired Intelligent Hybrid (QIIH) method for stock market forecasting. It performs a quantum-inspired evolutionary search for the minimum necessary dimension (time lags) embedded in the problem for determining the characteristic phase space that generates the financial time series phenomenon. The proposed QIIH method consists of a quantum-inspired intelligent hybrid model composed of an Artificial Neural Network (ANN) with a Modified Quantum-Inspired Evolutionary Algorithm (MQIEA), which is able to evolve the complete network architecture and parameters (pruning process), its training algorithm (used to further improve the ANN parameters supplied by the MQIEA) and the particular time lags capable of a fine tuned time series characterization. Initially, the proposed QIIH method chooses the most fitted forecasting model, thus it performs a behavioral statistical test in the attempt to adjust forecasting time phase distortions that appear in financial time series. Furthermore, an experimental analysis is conducted with the proposed QIIH method using three real world stock market time series, and the achieved results are discussed and compared, according to a group of relevant performance metrics, to results found withMultiLayer Perceptron (MLP) networks and the previously introduced Time-delay Added Evolutionary Forecasting (TAEF) method.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Chiang:2008:cec,
  author = "Cheng-Hsiung Chiang  ",
  title = "A Symbolic Controller Based Intelligent Control System with Quantum Particle Swarm Optimization Based Hybrid Genetic Algorithm",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0346.pdf},
  url = {},
  size = {},
  abstract =	{In this paper, a new symbolic controller based intelligent control system is proposed, namely qSyICS, which consists of a symbolic controller, a percepter, and a qAdaptor. The symbolic controller is made up of a number of symbolic rules. The percepter is to detect the control efficiency. Once the sensory information is inefficient or inadaptable, the qAdaptor will be activated; otherwise, the symbolic controller will keep on the controlling assignments. The qAdaptor consisted of the exploration process and symbolic rule generator is firstly to explore the new control actions, and then transforms them into new symbolic rules to update the rule base. The improved hybrid genetic algorithm is proposed to implement the exploration process for searching new actions, namely qHGA. A quantum behavior inspired particle swarm optimization that has the variable-length particles with discrete encoding is proposed to generate the partial initial population of qHGA. An application of robotic path planning is applied to demonstrate the proposed method through comparing with other methods. The simulation results showed that the proposed approach is more efficient than the other approaches.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Sim:2008:cec,
  author = "Kwang Mong Sim  ",
  title = "An Evolutionary Approach for P-S-Optimizing Negotiation",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0348.pdf},
  url = {},
  size = {},
  abstract =	{This work devises an approach for co-evolving negotiation strategies of agents that have different preference criteria such as optimizing price and optimizing negotiation speed. Whereas many works on e-commerce negotiation define utility functions in terms of price only, this work defines a utility function in terms of both price and negotiation speed. Different emphases on these two criteria can be modeled by placing different weights on them. Hence, in this work, negotiation agents can be price-optimizing, speed optimizing, and P-S-optimizing. Additionally, this work is one of the earliest works that adopt an Estimation Distribution Algorithm (EDA) for finding best response strategies for negotiation agents with different preference criteria. Empirical results show that the EDA can evolve price-optimizing, speedoptimizing, and P-S-optimizing strategies that generally achieve high utilities for negotiation agents. Furthermore, empirical results show that the EDA can evolve to a near optimal strategy for price-optimizing negotiation agents.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Merelo-Guerv&#243;s:2008:cec,
  author = "Juan Julian Merelo-Guerv&#243;s and Pedro A. Castillo and JLJ Laredo and A. Mora Garc&#237;a and A. Prieto  ",
  title = "Asynchronous Distributed Genetic Algorithms with Javascript and JSON",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0357.pdf},
  url = {},
  size = {},
  abstract =	{In a connected world, spare CPU cycles are up for grabs, if you only make its obtention easy enough. In this paper we present a distributed evolutionary computation system that uses the computational capabilities of the ubiquituous web browser. Asynchronous Javascript and JSON (Javascript Object Notation, a serialization protocol) allows anybody with a web browser (that is, mostly everybody connected to the Internet) to participate in a genetic algorithm experiment with little effort, or none at all. Since, in this case, computing becomes a social activity and is inherently impredictable, in this paper we will explore the performance of this kind of virtual computer by solving simple problems such as the Royal Road function and analyzing how many machines and evaluations it yields. We will also examine possible performance bottlenecks and how to solve them, and, finally, issue some advice on how to set up this kind of experiments to maximize turnout and, thus, performance. The experiments show that we we can obtain high, and to a certain point, reliable performance from volunteer computing based on AJAJ, with speedups of up to several (averaged) machines.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Maeda:2008:cec,
  author = "Yutaka Maeda and Naoto Matsushita  ",
  title = "Combination of Particle Swarm Optimization and Simultaneous Perturbation",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0362.pdf},
  url = {},
  size = {},
  abstract =	{In this paper, we propose some different optimization schemes which are combinations of the particle swarm optimization and the simultaneous perturbation optimization method. The proposed schemes can use local information of an objective function and global shape of the function at the same time. These characteristics are from the simultaneous perturbation optimization method and the particle swarm optimization. The schemes have good properties of global search and efficient local search capability. Moreover, the schemes themselves are very simple and easy to implement. These methods only require values of the function similar to the original particle swarm optimization and the simultaneous perturbation method. The proposed schemes are investigated using some test function to know convergence properties such as convergence rate or convergence speed.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Haasdijk:2008:cec,
  author = "E. Haasdijk and P. Vogt and A. E. Eiben",
  title = "Social Learning in Population-Based Adaptive Systems",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0363.pdf},
  url = {},
  size = {},
  abstract =	{The subject of the present investigation is Population-based Adaptive Systems (PAS), as implemented in the NEW TIES platform. In many existing PASs two adaptation mechanisms are combined, (non-Lamarckian) evolution and individual learning, inevitably raising the issue of `forgetful populations': individually learnt knowledge disappears when the individual that learnt it dies. We propose social learning by explicit knowledge transfer to overcome this problem. Our mechanism is based on (1) direct communication among agents in the population, (2) messages carrying rules that the sender agent uses in its controller, and (3) the ability of the recipient agent to incorporate foreign rules into its controller. Thus, knowledge can be disseminated and multiplied within the same generation, making the population a knowledge reservoir for individually acquired knowledge. We present an initial assessment of this idea and show that this social mechanism is capable of efficiently distributing knowledge and improving the performance of the population.   },
  keywords = {genetic algorithms, genetic programming},
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Fu2:2008:cec,
  author = "Ali Fu and Xiujuan Lei and Xiao Xiao",
  title = "The Aircraft Departure Scheduling Based on Particle Swarm Optimization Combined with Simulated Annealing Algorithm",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0364.pdf},
  url = {},
  size = {},
  abstract =	{Particle swarm optimization combined with simulated annealing algorithm (PSOCSA) was an improved particle swarm optimization algorithm which introduced the simulated annealing (SA) strategy in particle swarm optimization (PSO). It was proposed to solve a mathematical model which is built for aircraft departure sequencing problem in this paper. The correlative implementation techniques and detailed design process of the algorithm were presented. Then the simulation is performed to solve a representative problem using PSOCSA, PSO, and SA. The comparison showed that the PSOCSA algorithm was rational and feasible and more easily converge to the global optimal solution of aircraft departure sequencing problem. Method described in this paper will curtail the consumption of aircraft departure effectively, so it is worth researching it further in the field of airport operations and air traffic control.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Lei:2008:cec,
  author = "Xiujuan Lei and Ali Fu and Zhongke Shi",
  title = "The Aircraft Departure Scheduling Based on Second-Order Oscillating Particle Swarm Optimization Algorithm",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0365.pdf},
  url = {},
  size = {},
  abstract =	{The second-order oscillating particle swarm optimization (SO-PSO) algorithm, which introduced the second-order oscillating evolutionary equation to the evolutionary equation of PSO, could adjust the particles' global and local search capability and avoid the local optimization. It was proposed to solve a mathematical model which was built for aircraft departure sequencing problem in this paper. The correlative implementation techniques and detailed design process of the algorithm were presented. Then the simulation was performed to solve this sequencing problem using the SO-PSO algorithm. The results showed that the global optimal solution was obtained, so the SO-PSO algorithm was rational and feasible and curtailed the consumption of aircraft departure effectively.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Liu4:2008:cec,
  author = "Xiaojie Liu  ",
  title = "An Immune Method for Network Security Risk Evaluation",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0366.pdf},
  url = {},
  size = {},
  abstract =	{An immune method for real-time computer network security risk evaluation is proposed. The concepts of self, nonself, antigen and immunocyte of computer immune system are defined. The dynamic model of self, marrow model, clone selection, learning scheme, life span of immunocyte are built. A computational model of risk evaluation based on the antibody concentration of memory immunocytes for network security is thus presented. The experiment shows that this method has the features of quantitative calculation and real-time processing ability.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Rahnamayan:2008:cec,
  author = "Shahryar Rahnamayan and Hamid Reza Tizhoosh",
  title = "Image Thresholding Using Micro Opposition-Based Differential Evolution (Micro-ODE)",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0368.pdf},
  url = {},
  size = {},
  abstract =	{Image thresholding is a challenging task in image processing field. Many efforts have already been made to propose universal, robust methods to handle a wide range of images. Previously by the same authors, an optimization-based thresholding approach was introduced. According to the proposed approach, Differential Evolution (DE) algorithm, minimizes dissimilarity between the input grey-level image and the bi-level (thresholded) image. In the current paper, micro Opposition-Based Differential Evolution (micro-ODE), DE with very small population size and opposition-based population initialization, has been proposed. Then, it is compared with a well-known thresholding method, Kittler algorithm and also with its non-opposition-based version (micro-DE). In overall, the proposed approach outperforms Kittler method over 16 challenging test images. Furthermore, the results confirm that the micro-ODE is faster than micro-DE because of embedding the opposition-based population initialization.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Zhuang2:2008:cec,
  author = "Ruixin Zhuang and Bin Hu and Zhongxing Ye",
  title = "Minimal Spanning Tree for Shanghai-Shenzhen 300 Stock Index",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0370.pdf},
  url = {},
  size = {},
  abstract =	{This paper uses the ultrametric clustering which is based on time series of stock prices. By calculating the ultrametric distance-matrix of the stocks in Shanghai-Shenzhen 300 Index, a minimum spanning tree (MST) which has certain topological meaning can be drawn. And this MST explains the physical correlation between the topological structure and economic classification of the stocks trading on markets.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Kaylani:2008:cec,
  author = "A. Kaylani and M. Georgiopoulos and M. Mollaghasemi and G. C. Anagnostopoulos  ",
  title = "MO-GART: Multiobjective Genetic ART Architectures",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0372.pdf},
  url = {},
  size = {},
  abstract =	{In this work we present, for the first time, the evolution of ART Neural Network architectures (classifiers) using a multiobjective optimization approach. In particular, we propose the use of a multiobjective evolutionary approach to evolve simultaneously the weights, as well as the topology of three well-known ART architectures; Fuzzy ARTMAP (FAM), Ellipsoidal ARTMAP (EAM) and Gaussian ARTMAP (GAM). We refer to the resulting architectures as MO-GFAM, MOGEAM, or MO-GGAM, and collectively as MO-GART. The major advantage of MO-GART is that it produces a number of solutions for the classification problem at hand that have different levels of merit (accuracy on unseen data (generalization) and size (number of categories created)). MO-GART is shown to be more elegant (does not require user intervention to define the network parameters), more effective (of better accuracy and smaller size), and more efficient (faster to produce the solution networks) than other ART neural network architectures that have appeared in the literature.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Chang3:2008:cec,
  author = "Pei Chann Chang and Shih Hsin Chen and Qingfu Zhang and Jun Lin Lin",
  title = "MOEA/D for Flowshop Scheduling Problems",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0373.pdf},
  url = {},
  size = {},
  abstract =	{Many multiobjective evolutionary algorithms are based Pareto domination, among them NSGA II and SPEA 2 are two very popular ones. MOEA/D is a very recent multiobjective evolutionary algorithm using decomposition. In this paper, we implement MOEA/D for multi-objective flowshop scheduling problems. We study the replacement strategy of neighboring solutions, the determination of the reference point, and compare different decomposition methods. Experimental results demonstrate that MOEA/D outperforms NSGA II and SPEA 2 significantly for the 2-objective and 3-objective benchmark flowshop-scheduling instances.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Dasgupta:2008:cec,
  author = "Sambarta Dasgupta and Arijit Biswas and Swagatam Das and Ajith Abraham",
  title = "The Population Dynamics of Differential Evolution: A Mathematical Model",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0375.pdf},
  url = {},
  size = {},
  abstract =	{Differential Evolution (DE) is well known as a simple and efficient algorithm for global optimization over continuous spaces. This article provides a simple mathematical model of the underlying evolutionary dynamics of a one-dimensional DE. The model relates the search process of DE with the classical gradient descent search and also analyzes the convergence behavior of a DE population, very near to optima.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Zhou4:2008:cec,
  author = "Aimin Zhou and Qingfu Zhang and Yaochu Jin and Bernhard Sendhoff",
  title = "Combination of EDA and DE for Continuous Biobjective Optimization",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0376.pdf},
  url = {},
  size = {},
  abstract =	{The Pareto front (Pareto set) of a continuous optimization problem with m objectives is a (m<font face="symbol"> -</font>1) dimensional piecewise continuous manifold in the objective space (the decision space) under some mild conditions. Based on this regularity property in the objective space, we have recently developed several multiobjective estimation of distribution algorithms (EDAs). However, this property has not been used in the decision space. Using the regularity property in both the objective and decision space, this paper proposes a simple EDA for multiobjective optimization. Since the location information has not efficiently used in EDAs, a combination of EDA and differential evolution (DE) is suggested for improving the algorithmic performance. The hybrid method and the pure EDA method proposed in this paper, and a DE based method are compared on several test instances. Experimental results have shown that the algorithm with the proposed strategy is very promising.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Bernardino:2008:cec,
  author = "H. S. Bernardino and H. J. C. Barbosa and  A. C. C. Lemonge and L. G. Fonseca",
  title = "A New Hybrid AIS-GA for Constrained Optimization Problems in Mechanical Engineering",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0377.pdf},
  url = {},
  size = {},
  abstract =	{A genetic algorithm (GA) is hybridized with an artificial immune system (AIS) as an alternative to tackle constrained optimization problems in engineering. The AIS is inspired in the clonal selection principle and is embedded into a standard GA search engine in order to help move the population into the feasible region. The procedure is applied to mechanical engineering problems available in the literature and compared to other alternative techniques. },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Angira:2008:cec,
  author = "Rakesh Angira and Alladwar Santosh",
  title = "A Modified Trigonometric Differential Evolution Algorithm for Optimization of Dynamic Systems",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0378.pdf},
  url = {},
  size = {},
  abstract =	{Differential Evolution (DE) is a novel evolutionary algorithm capable of handling non-differentiable, nonlinear and multimodal objective functions. Previous studies have shown that DE is an efficient, effective and robust evolutionary optimization method. Still it takes large computational time for solving the computationally expensive objective functions (for example optimization problems in the areas of computational mechanics, computational fluid dynamics, optimal control etc.) And therefore, an attempt to speed up DE is considered necessary. This paper deals with application and evaluation of a modified version of Trigonometric Differential Evolution (TDE) algorithm. The modification in TDE algorithm is made to further enhance its convergence speed. Further the Modified Trigonometric Differential Evolution (MTDE) algorithm is applied and evaluated for solving dynamic optimization problems encountered in chemical engineering. The performance of MTDE algorithm is compared with that of TDE and original DE algorithms. Results indicate that the MTDE algorithm is efficient and significantly faster than TDE and DE algorithms.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Zhou5:2008:cec,
  author = "Shang-Ming Zhou and Robert I. John and Xiao-Ying Wang and Jonathan M. Garibaldi and Ian O. Ellis",
  title = "Compact Fuzzy Rules Induction and Feature Extraction Using SVM with Particle Swarms for Breast Cancer Treatments",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0379.pdf},
  url = {},
  size = {},
  abstract =	{Developing a treatment plan for breast cancer patient is a very complex process. In this paper, we propose a scheme of inducing fuzzy rules that characterise breast caner treatment knowledge from data. These fuzzy rules can augment the human experts in the process of medical diagnosis to select optimal treatment for patients. The proposed machine learning scheme applies the particle swarm optimisation technique (PSO) to the construction of an optimal support vector machine (SVM) model for the sake of inducing accurate and parsimonious fuzzy rules and simultaneously reducing input space dimensions, in which a new fittness function that regularises the importance ranks of features with misclassification rate is suggested. The SVM-based fuzzy classifier evades the curse of dimensionality in high-dimensional breast cancer data space in the sense that the number of support vectors, which equals the number of induced fuzzy rules, is not related to the dimensionality. The experiments have shown that not only the classification performance achieved by the proposed fuzzy classifier outperforms the ones achieved by other methods in the literature, but also the input space dimension has been reduced greatly.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Abraham:2008:cec,
  author = "Ajith Abraham and Arijit Biswas and Sambarta Dasgupta and Swagatam Das",
  title = "Analysis of Reproduction Operator in Bacterial Foraging Optimization Algorithm",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0380.pdf},
  url = {},
  size = {},
  abstract =	{One of the major driving forces of Bacterial Foraging Optimization Algorithm (BFOA) is the reproduction phenomenon of virtual bacteria each of which models one trial solution of the optimization problem. During reproduction, the least healthier bacteria (with a lower accumulated value of the objective function in one chemotactic lifetime) die and the other healthier bacteria each split into two, which then starts exploring the search place from the same location. This keeps the population size constant in BFOA. The phenomenon has a direct analogy with the selection mechanism of classical evolutionary algorithms. In this article, we provide a simple mathematical analysis of the effect of reproduction on bacterial dynamics. Our analysis reveals that the reproduction event contributes to the quick convergence of the bacterial population near optima.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Mitavskiy:2008:cec,
  author = "Boris Mitavskiy and Jonathan Rowe and  Chris Cannings",
  title = "Preliminary Theoretical Analysis of a Local Search Algorithm to Optimize Network Communication Subject to Preserving the Total Number of Links",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0382.pdf},
  url = {},
  size = {},
  abstract =	{A variety of phenomena such as world wide web, social or business interactions are modeled by various kinds of networks (such as the scale free or preferential attachment networks). However, due to the model-specific requirements one may want to rewire the network to optimize the communication among the various nodes while not overloading the number of channels (i.e. preserving the number edges). In the current paper we present a formal framework for this problem and a simple heuristic local search algorithm to cope with it. We estimate the expected single-step improvement of our algorithm, establish the ergodicity of the algorithm (i.e. that the algorithm never gets stuck at a local optima) with probability 1) and we also present a few initial empirical results for the scale free networks.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Wen:2008:cec,
  author = "Jianping Wen and Xiaolan Wu and Kuo Jiang and Binggang Cao",
  title = "Particle Swarm Algorithm Based on Normal Cloud",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0383.pdf},
  url = {},
  size = {},
  abstract =	{This paper presents a novel parameter automation strategy for the particle swarm optimization algorithm; the normal cloud model is used to improve the performance of the particle swarm optimization algorithm. First, the normal cloud model is used to initialize the population; particles are no longer uniformly distributed throughout the search space. Second, one and the same normal cloud is used to nonlinearly, dynamically adjust inertia weight and update two random numbers in velocity update equation. Therefore, three components in the velocity update equation do interact in the PSO search process, which maintains the diversity of the population, provides balance between the global and local search abilities and makes the convergence faster. Experimental results are provided to show that the improved particle swarm optimization algorithm can successfully locate all optima on a small set of benchmark functions. A comparison of the improve algorithm with the standard particle swarm optimization algorithm is also made.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Chen6:2008:cec,
  author = "H. N. Chen and Y. L. Zhu and K. Y. Hu and T. Ku",
  title = "Global Optimization Based on Hierarchical Coevolution Model",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0384.pdf},
  url = {},
  size = {},
  abstract =	{This paper presents a novel optimization algorithm that we call the particle swarms swarm optimizer (PS<sup>2</sup>O), which based on a hierarchical coevolution model (HCO model) of symbiotic species. HCO model introduced a number of M  species each possesses a number of N  individuals to represent the ``biological community''. Both the heterogeneous coevolution and the homogeneous coevolution aspects are simulated in this model to maintain the community biodiversity. This strategy enable the symbiotic species find the optima faster and discourage premature convergence effectively. The experiments compare the performance of PS<sup>2</sup>O with the canonical PSO, the fully informed particle swarm (FIPS), the unified particle swarm (UPSO) and the Fitness-Distance-Ratio based PSO (FDR-PSO) on a set of 6 benchmark functions. The simulation results show the PS<sup>2</sup>O algorithm markedly outperforms the four mentioned algorithms on all benchmark functions and has the potential to solve the complex problems with high dimensionality.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Tenne:2008:cec,
  author = "Yoel Tenne and S. W. Armfield",
  title = "Metamodel Accuracy Assessment in Evolutionary Optimization",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0385.pdf},
  url = {},
  size = {},
  abstract =	{Evolutionary optimization of expensive functions typically uses a metamodel, i.e. a computationally cheaper but inaccurate approximation of the objective function. The success of the optimization search depends on the accuracy of the metamodel hence an integral part of the metamodelling framework is assessing the metamodel accuracy. In this paper we survey a range of accuracy assessment methods such as methods requiring additional sites, hypothesis testing and minimum lossfunction methods. We describe two numerical experiments: the first benchmarks different accuracy assessment methods from which it follows the most accurate methods are LOOCV and the 0.632 bootstrap estimator followed by the 10-CV and lastly the holdout method. The second experiment studies the effect of two different accuracy assessment methods on the performance of a typical metamodel-assisted EA, from which it follows the accuracy assessment method has significant effect on the obtained optimum and hence should be chosen corresponding to the objective function features and dimension.We also discuss several issues related to the performance of accuracy assessment methods in practice.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Xiao:2008:cec,
  author = "Jing Xiao and YuPing Yan and Ying Lin and Ling Yuan and Jun Zhang",
  title = "A Quantum-inspired Genetic Algorithm for Data Clustering",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0388.pdf},
  url = {},
  size = {},
  abstract =	{The conventional K-Means clustering algorithm must know the number of clusters in advance and the clustering result is sensitive to the selection of the initial cluster centroids. The sensitivity may make the algorithm converge to the local optima. This paper proposes an improved K-Means clustering algorithm based on Quantum-inspired Genetic Algorithm (KMQGA). In KMQGA, Q-bit based representation is employed for exploration and exploitation in discrete 0-1 hyperspace by using rotation operation of quantum gate as well as three genetic algorithm operations (Selection, Crossover and Mutation) of Q-bit. Without knowing the exact number of clusters beforehand, the KMQGA can get the optimal number of clusters as well as providing the optimal cluster centroids after several iterations of the four operations (Selection, Crossover, Mutation, and Rotation). The simulated datasets and the real datasets are used to validate KMQGA and to compare KMQGA with an improved K-Means clustering algorithm based on the famous Variable string length Genetic Algorithm (KMVGA) respectively. The experimental results show that KMQGA is promising and the effectiveness and the search quality of KMQGA is better than those of KMVGA.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Oiso:2008:cec,
  author = "Masashi Oiso and Yoshiyuki Matsumura and Kazuhiro Ohkura and Noriyuki Fujimoto and Yoshiki Matsuura",
  title = "Application of Grid Task Scheduling Algorithm R3Q to Evolutionary Multi-Robotics Problem",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0389.pdf},
  url = {},
  size = {},
  abstract =	{A computational method for the implementation of an evolutionary multi-robotics (EMR) problem in grid computing environments is discussed. Due to the synchronization requirements of Evolutionary Algorithms (EAs), when the EMR problem is deployed in the grid environment there is a higher waiting time overhead because of medium-grained tasks. The round-robin replication remote work queue (R3Q) is adopted to reduce both the synchronous waiting time and communication time. In the current research, the performance of the grid scheduling is evaluated using uniform computational granularity despite that many problems such as EMR have nonuniform computational granularity. Therefore, in order to evaluate R3Q on nonuniform computational granularity, the cooperative object pushing EMR problem is adopted; and R3Q is compared with grid scheduling algorithms Work Queue (WQ), and list scheduling with round-robin order replication (RR). Our results show that R3Q is effective for tasks which have nonuniform computational granularity.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Li12:2008:cec,
  author = "Lily D. Li and Xiaodong Li and Xinghuo Yu",
  title = "A Multi-Objective Constraint-Handling Method with PSO Algorithm for Constrained Engineering Optimization Problems",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0390.pdf},
  url = {},
  size = {},
  abstract =	{This paper presents a multi-objective constraint handling method incorporating the Particle Swarm Optimization (PSO) algorithm. The proposed approach adopts a concept of Pareto domination from multi-objective optimization, and uses a few selection rules to determine particles' behaviors to guide the search direction. A goaloriented programming concept is adopted to improve efficiency. Diversity is maintained by perturbing particles with a small probability. The simulation results on the three engineering benchmark problems demonstrate the proposed approach is highly competitive.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Maulik:2008:cec,
  author = "Ujjwal Maulik and Anirban Mukhopadhyay and Sanghamitra Bandyopadhyay and Xuegong Zhang",
  title = "Multiobjective Fuzzy Biclustering in Microarray Data: Method and a New Performance Measure",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0392.pdf},
  url = {},
  size = {},
  abstract =	{Objective of any biclustering algorithm in microarray data is to discover a subset of genes that are expressed similarly in a subset of conditions. The boundaries of biclusters usually overlap as genes and conditions may belong to different biclusters with different membership degrees. Hence the notion of fuzzy sets is useful for discovering such overlapping biclusters. In this article an attempt has been made to develop a multiobjective genetic algorithm based approach for probabilistic fuzzy biclustering that minimizes the residual and maximizes cluster size and expression profile variance. A novel variable string length encoding has been proposed in this regard that encodes multiple biclusters in a single string. Also a new performance measure that reflects how a bicluster is statistically distinguished from the background is proposed. Performance of the proposed algorithm has been compared with some well known biclustering algorithms.      Keywords: Fuzzy biclustering, fuzzy K-medoids, mean squared residue, expression profile variance, multiobjective genetic algorithm, variable string length, statistical difference from background. },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Lei2:2008:cec,
  author = "Bin Lei and Wenfeng Li and Fan Zhang",
  title = "Stable Flocking Algorithm for Multi-Robot Systems Formation Control",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0394.pdf},
  url = {},
  size = {},
  abstract =	{The problem of multiple robots system formation using a distributed control method is studied in this paper. The main idea of this paper is that uses swarm flocking control algorithm to implement the ``biods'' model of Reynolds among multi-robots. With the help of graph theory, we propose a provably-stable flocking control law, which ensures that the internal group formation is stabilized to a desired shape, while all the robots' velocities and directions converge to the same. Player/Stage simulation results show that the proposed method can be efficiently applied to multiple robots formation control. With the characteristic of Player/Stage, the algorithm in this paper can be implemented on the real robots with so few or no changes.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Liu5:2008:cec,
  author = "Li Liu and Wenbo Xu",
  title = "A Cooperative Artificial Immune Network with Particle Swarm Behavior for Multimodal Function Optimization",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0395.pdf},
  url = {},
  size = {},
  abstract =	{Artificial immune network has been receiving particular attention over the last few years. Recent researches have revealed that, without stimulation and cooperation of network cells, lots of redundant explorations waste "resources", which affects searching ability and searching speed. In this paper, a cooperative artificial immune network denoted CoAIN is devised for multimodal function optimization. To explore and exploit searching space efficiently and effectively, the interactions within the network are not only suppression but also cooperation. Network cells cooperate with particle swarm behavior making use of the best position encountered by itself and its neighbor. Numeric benchmark functions were used to assess the performance of CoAIN compared with opt-aiNet, BCA, hybrid GA, and PSO algorithms.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Coensel:2008:cec,
  author = "Bert De Coensel and Dick Botteldooren and Kenny Debacq and Mats E. Nilsson and Birgitta Berglund",
  title = "Clustering Outdoor Soundscapes Using Fuzzy Ants",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0399.pdf},
  url = {},
  size = {},
  abstract =	{A classification algorithm for environmental sound recordings or ``soundscapes'' is outlined. An ant clustering approach is proposed, in which the behavior of the ants is governed by fuzzy rules. These rules are optimized by a genetic algorithm specially designed in order to achieve the optimal set of homogeneous clusters. Soundscape similarity is expressed as fuzzy resemblance of the shape of the sound pressure level histogram, the frequency spectrum and the spectrum of temporal fluctuations. These represent the loudness, the spectral and the temporal content of the soundscapes. Compared to traditional clustering methods, the advantages of this approach are that no a priori information is needed, such as the desired number of clusters, and that a flexible set of soundscape measures can be used. The clustering algorithm was applied to a set of 1116 acoustic measurements in 16 urban parks of Stockholm. The resulting clusters were validated against visitor's perceptual measurements of soundscape quality.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Oliveto:2008:cec,
  author = "Pietro S. Oliveto and Jun He and Xin Yao",
  title = "Analysis of Population-Based Evolutionary Algorithms for the Vertex Cover Problem",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0400.pdf},
  url = {},
  size = {},
  abstract =	{Recently it has been proved that the (1+1)-EA produces poor worst-case approximations for the vertex cover problem. In this paper the result is extended to the (1+<font face="symbol" >&#x3BB;</font>)-EA by proving that, given a polynomial time, the algorithm can only find poor covers for an instance class of bipartite graphs. Although the generalisation of the result to the (<font face="symbol">u</font>+1)-EA is more difficult, hints are given in this paper to show that this algorithm may get stuck on the local optimum of bipartite graphs as well because of premature convergence. However a simple diversity maintenance mechanism can be introduced into the EA for optimising the bipartite instance class effectively. It is proved that the diversity mechanism combined with one point crossover can change the runtime for some instance classes from exponential to polynomial in the number of nodes of the graph.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Neoh:2008:cec,
  author = "Siew Chin. Neoh and Arjuna. Marzuki and Norhashimah. Morad and Chee Peng. Lim and Zalina. Abdul Aziz  ",
  title = "An Interactive Genetic Algorithm Approach to MMIC Low Noise Amplifier Design Using A Layered Encoding Structure",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0401.pdf},
  url = {},
  size = {},
  abstract =	{In this paper, an interactive genetic algorithm (IGA) approach is developed to optimize design variables for a monolithic microwave integrated circuit (MMIC) low noise amplifier. A layered encoding structure is employed to the problem representation in genetic algorithm to allow human intervention in the circuit design variable tuning process. The MMIC amplifier design is synthesized using the Agilent Advance Design System (ADS), and the IGA is proposed to tune the design variables in order to meet multiple constraints and objectives such as noise figure, current and simulated power gain. The developed IGA is compared with other optimization techniques from ADS. The results showed that the IGA performs better in achieving most of the involved objectives.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Wang13:2008:cec,
  author = "Zhaohua Wang and Jianhua Yin and Weimin Ma",
  title = "A Reverse Logistics Optimization Model for Hazardous Waste in the Perspective of Fuzzy Multi-Objective Programming Theory",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0403.pdf},
  url = {},
  size = {},
  abstract =	{Combining with the characteristic of hazardous waste, this paper develops a multi-objective mathematic model for the location of treatment sites and transfer sites for hazardous wastes. Based on the fuzzy satisfactory levels of objectives, it proposes a two-phase fuzzy algorithm. Through solving the model, it conducts an analysis on the locations and numbers of these sites and how to assign the generation sites to transfer sites. Therefore, a reverse network for hazardous waste is constructed. Finally, it takes Tianjin Economic-technological Develop Area (TEDA) in Tianjin city in China as a case to prove the availability of the fuzzy model.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Mart&#237;nez-Estudillo:2008:cec,
  author = "F. J. Mart&#237;nez-Estudillo and P. A. Gutierrez and C. Hervas and J. C. Fernandez",
  title = "Evolutionary Learning by a Sensitivity-Accuracy Approach for Multi-Class Problems",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0404.pdf},
  url = {},
  size = {},
  abstract =	{Performance evaluation is decisive when improving classifiers. Accuracy alone is insufficient because it cannot capture the myriad of contributing factors differentiating the performances of two different classifiers and approaches based on a multi-objective perspective are hindered by the growing of the Pareto optimal front as the number of classes increases. This paper proposes a new approach to deal with multi-class problems based on the accuracy (C) and minimum sensitivity (S) given by the lowest percentage of examples correctly predicted to belong to each class. From this perspective, we compare different fitness functions (accuracy, C, entropy, E, sensitivity, S, and area, A) in an evolutionary scheme. We also present a two stage evolutionary algorithm with two sequential fitness functions, the entropy for the first step and the area for the second step. This methodology is applied to solve six benchmark classification problems. The two-stage approach obtains promising results and achieves a high classification rate level in the global dataset with an acceptable level of accuracy for each class.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Ding2:2008:cec,
  author = "Nan Ding and Shude Zhou and Hao Zhang and Zengqi Sun",
  title = "Marginal Probability Distribution Estimation in Characteristic Space of Covariance-Matrix",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0406.pdf},
  url = {},
  size = {},
  abstract =	{Marginal probability distribution has been widely used as the probabilistic model in EDAs because of its simplicity and efficiency. However, the obvious shortcoming of the kind of EDAs lies in its incapability of taking the correlation between variables into account. This paper tries to solve the problem from the point view of space transformation. As we know, it seems a default rule that the probabilistic model is usually constructed directly from the selected samples in the space defined by the problem. In the algorithm CM-MEDA, instead, we first transform the sampled data from the initial coordinate space into the characteristic space of covariance-matrix and then the marginal probabilistic model is constructed in the new space. We find that the marginal probabilistic model in the new space can capture the variable linkages in the initial space quite well. The relationship of CM-MEDA with Covariance-Matrix estimation and principal component analysis is also analyzed in this paper. We implement CM-MEDA in continuous domain based on both Gaussian and histogram models. The experimental results verify the effectiveness of our idea.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Cheng4:2008:cec,
  author = "Hsueh-Chien Cheng and Tsung-Che Chiang and Li-Chen Fu  ",
  title = "Multiobjective Permutation Flowshop Scheduling by an Adaptive Genetic Local Search Algorithm",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0407.pdf},
  url = {},
  size = {},
  abstract =	{The multiobjective flowshop problem with makespan and total flow time as objectives is addressed. A genetic local search algorithm is proposed with the ability to allocate the computational resources through the dynamic population size and local search intensity. The proposed method is compared with existing algorithms for flowshop scheduling with a public benchmark problem set. The experimental results show that the proposed method is capable of discovering solutions with better quality and diversity. The proposed method yields the best known nondominated solutions for the commonly studied permutation flowshop benchmarks, and the set of best known solutions is useful for the evaluation of performance of future studies.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Jr.:2008:cec,
  author = "Maury M. Gouv&#234;a Jr. and Aluizio F. R. Ara&#250;jo",
  title = "Population Dynamics Model for Gene Frequency Prediction in Evolutionary Algorithms",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0408.pdf},
  url = {},
  size = {},
  abstract =	{The performance of evolutionary algorithms (EAs) may be enhanced whether the choice of some parameters, as mutation rate and crossover method, is made appropriately. Several methods to adjust those parameters have been developed in order to enhance EAs performance. For this reason, it is important to understand EA dynamics. This paper presents a new population dynamics model to describe and predict the diversity at one generation. The formulation is based on the selection probability density function of each individual. The proposed population dynamics is modeled for an infinite population with generational evolution method. The model was tested in several case studies of different population sizes. The results suggest that the prediction error decreases with the population size increasement.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Chen7:2008:cec,
  author = "Zhenfeng Chen and Yanru Zhong and Jie Li",
  title = "Parameter Identification of Induction Motors Using Ant Colony Optimization",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0409.pdf},
  url = {},
  size = {},
  abstract =	{In this paper, the Ant Colony Optimization (ACO) is introduced and applied to the parameter identification of an induction motor for vector control. The error between the actual stator current output of an induction motor and the stator current output of the model is used as the criterion to correct the model parameters, so as to identify all the parameters of an induction motor. Digital simulations are conducted on speed-varying operation with no load. The ACO is compared with the genetic algorithm (GA) and adaptive genetic algorithm (AGA). Consequently, the ACO is shown to acquire more precise parameter values and need much less computing time than the GA and AGA. },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Hernandez-D&#237;az:2008:cec,
  author = "Alfredo G. Hernandez-D&#237;az and Carlos A. Coello Coello and Fatima Perez and Julian Molina",
  title = "Seeding the Initial Population of a Multi-Objective Evolutionary Algorithm using Gradient-Based Information",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0410.pdf},
  url = {},
  size = {},
  abstract =	{In the field of single-objective optimization, hybrid variants of gradient-based methods and evolutionary algorithms have been shown to perform better than an evolutionary method by itself. This same idea has been recently used in Evolutionary Multiobjective Optimization (EMO), obtaining also very promising results. In most cases, gradient information is used along the whole process, which involves a high computational cost, mainly related to the computation of the step lengths required. In contrast, in this paper we propose the use of gradient information only at the beginning of the search process. We will show that this sort of scheme maintains results of good quality while considerably decreasing the computational cost. In our work, we adopt a steepest descent method to generate some nondominated points which are then used to seed the initial population of a multi-objective evolutionary algorithm (MOEA), which will spread them along the Pareto front. The MOEA adopted in our case is the NSGA-II, which is representative of the state-of-the-art in the area. To validate our proposal, we adopt box-constrained continuous problems (the ZDT test suite). The gradients required are approximated using quadratic regressions. Our proposed approach performs a total of 2000 objective function evaluations, which is much lower than the number of evaluations normally adopted with the ZDT test suite in the specialized literature. Our results are compared with respect to the ``pure'' NSGA-II (i.e., without using gradient-based information) so that the potential benefit of these initial solutions fed into the population can be properly assessed.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Ashlock4:2008:cec,
  author = "Wendy Ashlock  ",
  title = "Evolving Diverse Populations of Prisoner's Dilemma Strategies",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0411.pdf},
  url = {},
  size = {},
  abstract =	{It is common for evolved populations of Iterated Prisoner's Dilemma to become homogenous with most of the strategies either identical or similar to each other. As fitness is usually based on play with other members of the population, this favors the evolution of strategies which score well when playing themselves or close mutants of themselves. Also, populations tend to change considerably over time. New strategies arise and take over. A population consisting entirely of a highly cooperative strategy like tit-for-tat can become a population consisting entirely of a highly uncooperative strategy like always-defect. This study uses an experimental setup which incorporates geography in an attempt to evolve a diversity of coexisting strategies. The resulting populations are analyzed using Prisoner's Dilemma fingerprints and found to be both diverse and ``stable'' in the sense that they remain highly cooperative over time. },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Bui:2008:cec,
  author = "Lam T. Bui and James M. Whitacre and  Hussein A. Abbass",
  title = "Performance Analysis of Elitism in Multi-Objective Ant Colony Optimization Algorithms",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0412.pdf},
  url = {},
  size = {},
  abstract =	{This paper investigates the effect of elitism on multi-objective ant colony optimization algorithms (MACOs). We use a straightforward and systematic approach in this investigation with elitism implemented through the use of local, global, and mixed non-dominated solutions. Experimental work is conducted using a suite of multi-objective traveling salesman problems (mTSP), each with two objectives. The experimental results indicate that elitism is essential to the success of MACOs in solving multi-objective optimization problems. Further, global elitism is shown to play a particularly important role in refining the pheromone information for MACOs during the search process.Inspired by these results, we also propose an adaptation strategy to control the effect of elitism. With this strategy, the solutions most recently added to the global non-dominated archive are given a higher priority in defining the pheromone information. The obtained results on the tested mTSPs indicate improved performance in the elitist MACO when using the adaptive strategy compared to the original version.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Mason:2008:cec,
  author = "Jonathan Mason and Ronaldo Menezes",
  title = "Autonomous Algorithms for Terrain Coverage Metrics, Classification and Evaluation",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0414.pdf},
  url = {},
  size = {},
  abstract =	{Terrain coverage algorithms are quite common in the computer science literature and for a good reason: they are able to deal with a diverse set of problems we face. From Web crawling to automated harvesting, from spell checking to area reconnaissance by Unmanned Aerial Vehicles (UAVs), a good terrain coverage algorithm lies at the core of a successful approach to these and other problems. Despite the popularity of terrain coverage, none of the works in the field addresses the important issue of classification and evaluation of these algorithms. It is easy to think that all algorithms (since they are all called terrain coverage) deal with the same problem but this is a fallacy that this paper tries to correct. This paper presents a summary of many algorithms in the field, classifies them based on their goals, introduces metrics to evaluate them, and finally performs the evaluation.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Jassbi:2008:cec,
  author = "J. Jassbi and S. Khanmohammadi and H. Kharrati",
  title = "A New Hybrid Method for Determination of Fuzzy Rules and Membership Functions",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0416.pdf},
  url = {},
  size = {},
  abstract =	{Fuzzy Logic Controllers are applied to various industrial and non-linear systems, however, their control rules and membership functions are usually obtained by timeconsuming trial and error procedure. This paper presents a hybrid method for determining the fuzzy rules and membership functions simultaneously. The optimization process consists of a Genetic Algorithm (GA) which determines the rule base, and an Extended Kalman Filter (EKF) approach for tuning the parameters of membership functions. The procedure discussed in this study is illustrated on a simple automotive cruise control problem. By comparing nominal and optimized fuzzy controllers, we demonstrate that the hybrid algorithm, as a combination of genetic algorithm and extended Kalman filter, can be an effective tool for improving the performance of a fuzzy controller. In other words, the fuzzy controller thus designed can implement simpler in the real world applications, by using a few fuzzy variables.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Singh2:2008:cec,
  author = "Hemant Kumar Singh and Amitay Isaacs and Tapabrata Ray and Warren Smith",
  title = "A Simulated Annealing Algorithm for Constrained Multi-Objective Optimization",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0417.pdf},
  url = {},
  size = {},
  abstract =	{In this paper, we introduce a simulated annealing algorithm for constrained Multi-Objective Optimization (MOO). When searching in the feasible region, the algorithm behaves like recently proposed Archived Multi-Objective Simulated Annealing (AMOSA) algorithm [1], whereas when operating in the infeasible region, it tries to minimize constraint violation by moving along Approximate Descent Direction (ADD) [2]. An Archive of non-dominated solutions found during the search is maintained. The acceptance probability of a new point is determined by its feasibility status, and its domination status as compared to the current point and the points in the Archive. We report the performance of the proposed algorithm on a set of seven constrained bi-objective test problems (CTP2 to CTP8), which have been known to pose difficulties to existing multi-objective algorithms. A comparative study of current algorithm with the widely used multi-objective evolutionary algorithm NSGA-II has been included.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Yang7:2008:cec,
  author = "Zhenyu Yang and Ke Tang and Xin Yao",
  title = "Multilevel Cooperative Coevolution for Large Scale Optimization",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0418.pdf},
  url = {},
  size = {},
  abstract =	{In this paper, we propose a multilevel cooperative coevolution (MLCC) framework for large scale optimization problems. The motivation is to improve our previous work on grouping based cooperative coevolution (EACC-G) [1], which has a hard-to-determine parameter, group size, in tackling problem decomposition. The problem decomposer takes group size as parameter to divide the objective vector into low dimensional subcomponents with a random grouping strategy. In the MLCC, a set of problem decomposers is constructed based on the random grouping strategy with different group sizes. The evolution process is divided into a number of cycles, and at the start of each cycle MLCC uses a self-adapted mechanism to select a decomposer according to its historical performance. Since different group sizes capture different interaction levels between the original objective variables, MLCC is able to selfadapt among different levels. The efficacy of the proposed MLCC is evaluated on the set of benchmark functions provided by CEC'2008 special session [2].   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Chiong:2008:cec,
  author = "Raymond Chiong and Yang Yaw Chang and Pui Ching Chai and Ai Leong Wong",
  title = "A Selective Mutation Based Evolutionary Programming for Solving Cutting Stock Problem without Contiguity",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0420.pdf},
  url = {},
  size = {},
  abstract =	{The Cutting Stock Problem (CSP) is a combinatorial optimisation problem that involves cutting large stock sheets into smaller pieces. It has attracted vast attention along the years due to its applicability in many industries ranging from steel, glass, wood, plastic to paper manufacturing. A good solution to CSP is thus important as a mean to increase efficiency in these industrial sectors. In this paper, we present a selective mutation based evolutionary programming (SMBEP) for solving CSP without contiguity. We conduct experiments with our novel SMBEP on the benchmark problems of CSP. We show that the performance of our approach is slightly better than the previous results.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Gong2:2008:cec,
  author = "Dunwei Gong and Jie Yuan and Xiaoping Ma  ",
  title = "Interactive Genetic Algorithms with Large Population Size",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0421.pdf},
  url = {},
  size = {},
  abstract =	{Interactive genetic algorithms (IGAs) are effective methods to solve an optimization problem with implicit indices. Whereas it requires direct evaluation of user for each individual and the fact limits the population size for user fatigue problem. While, in general to solve many problems with genetic algorithm, it is desirable to maintain the population size as large as possible. To break the restriction of population size and not increasing the number of individuals being evaluated by user we propose an interactive genetic algorithm with large population size in this paper. The algorithm divides the whole population into several clusters, the maximum number of which changes along with the evolution. User only assigns one representative individual's fitness for each cluster and expresses it with an accurate number. The fitness of other individuals are estimated according to the representative's fitness directly, and are expressed with some intervals, which can maintain the large population size with less number of individuals being evaluated by user. In addition we choose appropriate individuals and crossover point to perform crossover operator. This algorithm is applied in a fashion evolutionary design system and compared it with the above interactive genetic algorithm with small population size, the results effectively validate that the proposed algorithm has good performance in alleviating user fatigue and looking for ``the most satisfactory suits.''    },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Gong3:2008:cec,
  author = "Tao Gong and Andrea Puglisi and Vittorio Loreto and William S.-Y. Wang",
  title = "Conventionalization of Linguistic Categories Under Simple Communicative Constraints",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0424.pdf},
  url = {},
  size = {},
  abstract =	{The language game approach is widely adopted to study conventionalization of linguistic knowledge. Most of contemporary models concentrate on the dynamics of language games in random or predefined social structures, but neglect the role of communicative constraints. This paper adopts one form of language games, the category game, to discuss whether some simple distance-related communicative constraint may affect the conventionalization of linguistic categories. By comparing the simulation results with those based on another form of language games, the naming game, we point out some essential differences between these two games which cause their distinct performances under the same communicative constraint. This study fills the gap between the dynamics of language games in random structures and that in complex networks, and suggests that internal properties of language games may reversely influence communicative constraints and social structures.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Parsopoulos:2008:cec,
  author = "K. E. Parsopoulos and V. C. Georgopoulos and  M. N. Vrahatis",
  title = "A Technique for the Visualization of Population-Based Algorithms",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0425.pdf},
  url = {},
  size = {},
  abstract =	{A technique for the visualization of stochastic population-based algorithms in multidimensional problems with known global minimizers is proposed. The technique employs projections of the populations in the 2-dimensional vector space spanned by the two extremal eigenvectors of the Hessian matrix of the objective function at a global minimizer. This space condenses information regarding the shape of the objective function around the given minimizer. The proposed approach can provide intuition regarding the behavior of the algorithm in unknown high-dimensional problems. It also provides an alternative visualization framework for problems of any dimension, which alleviates drawbacks of the most popular projection methods. The proposed technique is illustrated for three well-known population-based algorithms, namely, Differential Evolution, Covariance Matrix Adaptation Evolution Strategies and Particle Swarm Optimization, on three test problems of different dimensionality.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Han2:2008:cec,
  author = "Kyu Y. Han and Brian A. Lail and Fredric M. Ham",
  title = "Low-Profile Twist Reflector Design Using Genetic Algorithms",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0426.pdf},
  url = {},
  size = {},
  abstract =	{A novel low-profile twist reflector is genetically engineered. For a linear polarized incident wave, a perpendicular linear polarized wave can be achieved upon reflection. Unlike the typical twist reflector, which requires at least a quarter-wavelength space between the top metal strip and main reflector, the proposed structure is built on a 0.08<font face="symbol">&#x3BB;</font> thick substrate, still enhancing about 20percent bandwidth. Finally, there is good agreement between the measured and the simulated results promising that the twist reflector can be used to alter the antenna polarization response.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Weis:2008:cec,
  author = "Gerhard Weis and Andrew Lewis and Marcus Randall and Amir Galehdar and David Thiel",
  title = "Local Search for Ant Colony System to Improve the Efficiency of Small Meander Line RFID Antennas",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0427.pdf},
  url = {},
  size = {},
  abstract =	{The efficient design of meander line antennas for RFID devices is a significant real-world problem. Traditional manual tuning of antenna designs is becoming impractical for larger problems. Thus the use of automated techniques, in the form of combinatorial search algorithms, is a necessity. Ant colony system (ACS) is a very efficient meta-heuristic that is commonly used to solve path construction problems. Apart from its own native search capacity, ACS can be dramatically improved by combining it with local search strategies. As shown in this paper, applying local search as a form of structure refinement to RFID meander line antennas delivers effective antenna structures. In particular, we use the operator known as backbite, that has had previous application in the construction of self-avoiding walks and compact polymer chains. Moreover, we apply it in a novel, hierarchical manner that allows for good sampling of the local search space. Its use represents a significant improvement on results obtained previously.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Chuang:2008:cec,
  author = "Cheng-Long Chuang and Chung-Ming Chen and Grace S. Shieh and Joe-Air Jiang",
  title = "A Fuzzy Logic Approach to Infer Transcriptional Regulatory Network in  Saccharomyces Cerevisiae Using Promoter Site Prediction and Gene Expression Pattern Recognition",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0428.pdf},
  url = {},
  size = {},
  abstract =	{ A fuzzy logic approach, called FuzzyTRN, to infer transcriptional regulatory networks (TRN) in Saccharomyces cerevisiae is proposed. FuzzyTRN predicts potential regulators and their target genes using sequences analysis on transcription factor binding sites (TFBS) of transcriptional factors (TF) and promoter region of target genes. Those potential regulators and target genes are used to form vertices in the TRN. Furthermore, multiple sets of microarray gene expression data (MGED) are used by FuzzyTRN to predict links in the TRN. FuzzyTRN predicts transcriptional interactions by recognizing expression patterns of genes. In this study, a number of confirmed genetic interactions are used to train FuzzyTRN. 112 indirect genetic interactions that were confirmed by quantitative real-time polymerase chain reaction (qRT-PCR) experiments, and 259 and 86 direct genetic interactions that were collected by TRANSFAC database and literature surveying, were used as training set in this work. A simulation that encompasses 170 TFs and 40 target genes has been conducted and checked against YEASTRACT database to evaluate the performance of the proposed algorithm.    },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Wei3:2008:cec,
  author = "Ming Wei and Yuanxiang Li and Dazhi Jiang and  Yangfan He and Xingyan Huang and Xing Xu",
  title = "A New Evolutionary Algorithm based on Quantum Statistical Mechanics",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0429.pdf},
  url = {},
  size = {},
  abstract =	{A new Evolutionary Algorithm based on quantum statistical mechanics (QSEA) is raised in this paper. In the algorithm, the whole evolutionary system is treated as a quantum statistical system, where quantum coding is adopted to express chromosomes, and superposition of quantum bits is used to simulate the linear superposition state of the system. Quantum system entropy and statistical energy have been defined by analogy with corresponding concepts in quantum statistical mechanics. And the competition between quantum statistical energy and entropy of the system is used to simulate the conflict between 'selection pressure' and 'diversity of population', which helps the algorithm to keep a delicate balance between these two issues,and obtain optimal solution rapidly. Numerical experiments show that this new algorithm has high efficiency and strong ability to get global optimal solution.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Santos:2008:cec,
  author = "Wellington P. dos Santos and Ricardo E. de Souza and Pl&#237;nio B. Santos Filho and Francisco M. de Assis",
  title = "A Dialectical Approach for Classification of DW-MR Alzheimer's Images",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0430.pdf},
  url = {},
  size = {},
  abstract =	{Multispectral image analysis is a relatively promising field of research with applications in several areas, such as medical imaging and satellite monitoring. However, a considerable number of current methods of analysis are based on parametric statistics. Alternatively, some methods in Computational Intelligence are inspired by biology and other sciences. Here we claim that Philosophy can be also considered as a source of inspiration. This work proposes the Objective Dialectical Method (ODM), which is a computational intelligent method for classification based on the Philosophy of Praxis. Here, ODM is instrumental in assembling evolvable mathematical tools to analyze multispectral images. In the case study described in this paper, such multispectral images are composed of diffusionweighted (DW) magnetic resonance (MR) images. The results are compared to ground-truth images produced by polynomial networks using a morphological similarity index.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Liu6:2008:cec,
  author = "Pang-Kai Liu and Chiou-Hwa Yuh and Feng-Sheng Wang",
  title = "Inference of Genetic Regulatory Networks Using S-System and Hybrid Differential Evolution",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0431.pdf},
  url = {},
  size = {},
  abstract =	{The inference of genetic regulatory networks from time-course data is one of the main challenges in systems biology. The ultimate goal of inferred model is to obtain the expressions quantitatively comprehending every detail and principle of biological systems. This study introduces a multiobjective optimization approach to infer a realizable S-system structure for genetic regulatory networks. The work of inference is to minimize simultaneously the concentration error, slope error and interaction measure in order to find a suitable S-system model structure and its corresponding model parameters. Hybrid differential evolution is applied to solve the <font face="symbol">e</font>-constrained problem, which is converted from the multiobjective optimization problem, for minimizing the interaction measure with subject to the expectation constraints for the concentration and slope error criteria. This approach could avoid assigning a suitable penalty weight for sum of magnitude of kinetic orders for the penalty problem in order to prune the model structure.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Boddhu:2008:cec,
  author = "Sanjay K. Boddhu and John C. Gallagher",
  title = "Evolved Neuromorphic Flight Control for a Flapping-Wing Mechanical Insect Model",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0432.pdf},
  url = {},
  size = {},
  abstract =	{This paper examines the feasibility of evolving analog neuromorphic devices to control flight in a realistic flapping-wing mechanical insect model. It will summarize relevant prior results in controlling a legged robot and explain why these results are relevant to the problem of winged flight. Following, it will present the outcomes of experiments to evolve flight controllers and discuss the implications of those results and possible future work.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Cheng5:2008:cec,
  author = "Wang Cheng and Zeng Maimai and Li Jian",
  title = "Solving Traveling Salesman Problems with Time Windows by Genetic Particle Swarm Optimization",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0433.pdf},
  url = {},
  size = {},
  abstract =	{The genetic particle swarm optimization (GPSO) was derived from the original particle swarm optimization (PSO), which is incorporated with the genetic reproduction mechanisms, namely crossover and mutation. To solve traveling salesman problems (TSP), a modified genetic particle swarm optimization (MGPSO) was introduced, where the new solution was generated with local best and individual best solutions with crossover and mutation operators. MGPSO was implemented to the well-known TSP and by comparison with the results of the original PSO, MGPSO has provided much better performance. Furthermore, MGPSO was employed to solve TSP with time windows, where besides minimizing the route, the truck were required to arrive at specifically during a time window, which made the TSP to be a constrained combinatorial optimization. To solve the constraints, the stochastic ranking algorithm was introduced. The approach was experimented with the well-known TSP case. The simulation results have shown its robust and consistent effectiveness.    },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Taboada:2008:cec,
  author = "Karla Taboada and Eloy Gonzales and Kaoru Shimada and Kotaro Hirasawa",
  title = "Genetic Network Programming Based Data Mining Method for Extracting Fuzzy Association Rules",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0434.pdf},
  url = {},
  size = {},
  abstract =	{In this paper, a new data mining algorithm is proposed to enhance the capability of exploring interesting knowledge from databases with continuous values. The algorithm integrates Fuzzy Set Theory and ``Genetic Network Programming (GNP)'' to find interesting fuzzy association rules from given transaction data. GNP is a novel evolutionary Optimization technique, which uses directed graph structures as gene instead of strings (Genetic Algorithms) or trees (Genetic Programming), contributing to creating quite compact programs and implicitly memorising past action sequences. We adopt the Fuzzy Set Theory to mine associate rules that can be expressed in linguistic terms, which are more natural and understandable for human beings. The proposed method can measure the significance of the extracted association rules using support, confidence and <font face="symbol">x</font><sup>2</sup> test, and obtains a sufficient number of important association rules in a short time. Experiments conducted on real world databases are also made to verify the performances of the proposed method.  },
  keywords = {genetic algorithms, genetic programming},
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Liu7:2008:cec,
  author = "Wen-jie Liu and Han-wu Chen and Zhi-qiang Li and Fang-ying Xiao",
  title = "Efficient Quantum Secure Direct Communication with Authentication",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0435.pdf},
  url = {},
  size = {},
  abstract =	{Two protocols of quantum direct communication with authentication [Phys. Rev. A 73, 042305(2006)], proposed by Lee, Lim and Yang, recently were indicated to be insecure against the authenticator Trent's attacks [Phys. Rev. A 75, 026301(2007)]. In this paper, two novel efficient protocols of quantum direct communication with authentication are presented by using four kinds of Pauli operations (I,<font face="symbol">&#963;</font><sub>x</sub>,i&#963;<font face="symbol"></font><sub>y</sub>,<font face="symbol">&#963;</font><sub>z</sub>). These new protocols can transmit two bits message every GHZ state, instead of one bit in the aforementioned protocols. Analysis shows that they are secure against the inner participant's attacks (such as Trent's attacks) as well as the outer Eve's attacks. Finally, we generalize them to multiparty quantum direction communication.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Zhou6:2008:cec,
  author = "Xiuling Zhou and Ning Mao and Chengyi Sun and Wenjuan Li  ",
  title = "An Improved CHSO Algorithm for Multi-Objective Optimization Problem",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0436.pdf},
  url = {},
  size = {},
  abstract =	{The CHSO algorithm is a fast algorithm for computing the contribution of a point to the hypervolume of the whole set directly. In this paper an improved CHSO is described. And it is explained by theory why not only the points in the first nondominated front, but also the points in the second nondominated front which are dominated only by one of points in the first nondominated front are considered in CHSO while the points in the first nondominated front are considered in HSO. It is shown by experiment that improved CHSO outperforms basic CHSO with reduction running time of approximately 50percent. So improved CHSO can enable hypervolume to be used as diversity or selection mechanism more efficiently.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Hsieh:2008:cec,
  author = "Sheng-Ta Hsieh and Tsung-Ying Sun and Chan-Cheng Liu and Shang-Jeng Tsai",
  title = "Solving Large Scale Global Optimization Using Improved Particle Swarm Optimizer",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0439.pdf},
  url = {},
  size = {},
  abstract =	{As more and more real-world optimization problems become increasingly complex, algorithms with more capable optimizations are also increasing in demand. For solving large scale global optimization problems, this paper presents a variation on the traditional PSO algorithm, called the Efficient Population Use Strategy for Particle Swarm Optimizer (EPUS-PSO). This is achieved by using variable particles in swarms to enhance the searching ability and drive particles more efficiently. Moreover, sharing principals are constructed to stop particles from falling into the local minimum and make the global optimal solution easier found by particles. Experiments were conducted on 7 CEC 2008 test functions to present solution searching ability of the proposed method.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Victoire:2008:cec,
  author = "T. Aruldoss Albert Victoire and P. N. Suganthan",
  title = "Differential Evolution and Evolutionary Programming for Solving Non-Convex Economic Dispatch Problems",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0440.pdf},
  url = {},
  size = {},
  abstract =	{This article presents a novel and effective algorithm for solving the non-convex economic dispatch problems (EDP) by integrating the Differential Evolution (DE) and Evolutionary Programming (EP) techniques. This Hybrid DE-EP based economic dispatch (DE-EPBED) algorithm uses the strengths of both the techniques to explore the search space to find the best solution. This algorithm incorporates the non-convexity of the EDP while formulating the evaluation function and constraints. To validate the feasibility and effectiveness of the presented algorithm, experiments were carried out on five different test systems. It is demonstrated that, the Hybrid DE-EP algorithm for non-convex EDPs generates quality solutions quickly and reliably.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Lim:2008:cec,
  author = "Yow Tzu Lim and Pau Chen Cheng and John Andrew Clark and Pankaj Rohatgi",
  title = "Policy Evolution with Genetic Programming: A Comparison of Three Approaches",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0442.pdf},
  url = {},
  size = {},
  abstract =	{In the early days a policy was a set of simple rules with a clear intuitive motivation that could be formalised to good effect. However the world is now much more complex. Subtle risk decisions may often need to be made and people are not always adept at expressing rationale for what they do. Previous research has demonstrated that Genetic Programming can be used to infer statements of policies from examples of decisions made [1]. This allows a policy that may not formally have been documented to be discovered automatically, or an underlying set of requirements to be extracted by interpreting user decisions to posed ``what if'' scenarios. This study compares the performance of three different approaches in using Genetic Programming to infer security policies from decision examples made, namely symbolic regression, IF-THEN rules inference and fuzzy membership functions inference. The fuzzy membership functions inference approach is found to have the best performance in terms of accuracy. Also, the fuzzification and de-fuzzification methods are found to be strongly correlated; incompatibility between them can have strong negative impact to the performance.   },
  keywords = {genetic algorithms, genetic programming},
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Zhang6:2008:cec,
  author = "Kelvin Xi Zhang and B. F. Francis Ouellette",
  title = "A New Approach to Predict Interactions Between Integral Membrane Proteins in Yeast",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0443.pdf},
  url = {},
  size = {},
  abstract =	{Protein-protein interactions (PPIs) play an extremely important role in performing a variety of biological functions. The interactomes of several model organisms including budding yeast Saccharomyces cerevisiae have recently been studied using experimental techniques such as the yeast two-hybrid assay. However, these techniques are generally biased against integral membrane proteins due to their intrinsic limitations. Given the fact that the interactions between integral membrane proteins cover a large fraction of the whole interactome, we report a study of predicting interactions between integral membrane proteins in yeast by a quantitative model. We integrate protein-protein interaction and domain-domain interaction (DDI) data from disparate sources and apply a log likelihood scoring method on all putative integral membrane proteins in yeast to predict their interactions based on a cut-off threshold. We show that our approach improves on other predictive approaches when tested on a ``gold-standard'' data set and achieves 74.6percent true positive rate at the expense of 0.43percent false positive rate. Furthermore, we find that two integral membrane proteins are more likely to interact with each other if they share more common interaction partners. This study allows us to reach a more extensive understanding of the yeast integral membrane proteins from a network view, which also complements the previous prediction approaches based on the genomic context.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Al-Hammadi:2008:cec,
  author = "Yousof Al-Hammadi and Uwe Aickelin and Julie Greensmith",
  title = "DCA for Bot Detection",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0444.pdf},
  url = {},
  size = {},
  abstract =	{Ensuring the security of computers is a nontrivial task, with many techniques used by malicious users to compromise these systems. In recent years a new threat has emerged in the form of networks of hijacked zombie machines used to perform complex distributed attacks such as denial of service and to obtain sensitive data such as password information. These zombie machines are said to be infected with a 'bot' - a malicious piece of software which is installed on a host machine and is controlled by a remote attacker, termed the 'botmaster of a botnet'. In this work, we use the biologically inspired Dendritic Cell Algorithm (DCA) to detect the existence of a single bot on a compromised host machine. The DCA is an immune-inspired algorithm based on an abstract model of the behaviour of the dendritic cells of the human body. The basis of anomaly detection performed by the DCA is facilitated using the correlation of behavioural attributes such as keylogging and packet flooding behaviour. The results of the application of the DCA to the detection of a single bot show that the algorithm is a successful technique for the detection of such malicious software without responding to normally running programs.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Esmin:2008:cec,
  author = "A. A. A. Esmin and D. L. Pereira and F. P. A. de Ara&#250;jo",
  title = "Study of Different Approach to Clustering Data by Using the Particle Swarm Optimization Algorithm",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0446.pdf},
  url = {},
  size = {},
  abstract =	{This paper proposes two new data clustering approaches using the Particle Swarm Optimization Algorithm (PSO). It is shown how the PSO can be used to find centroids of a user specified number of clusters. The proposed approaches are an attempt to improve the Merwe and Engelbrecht method using different fitness functions and considering the situation where data is uniformly distributed. The data clustering PSO algorithm, using the original and proposed fitness functions is evaluated on well known data sets. Notable improvements on the results were achieved by the modifications, this shows the potential of the PSO, not only on data clustering but also on the several areas it can be applied.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Tin&#243;s:2008:cec,
  author = "Renato Tin&#243;s and Shengxiang Yang",
  title = "Evolutionary Programming with  q-Gaussian Mutation for Dynamic Optimization Problems",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0447.pdf},
  url = {},
  size = {},
  abstract =	{The use of evolutionary programming algorithms with self-adaptation of the mutation distribution for dynamic optimization problems is investigated in this paper. In the proposed method, the q -Gaussian distribution is employed to generate new candidate solutions by mutation. A real parameter q, which defines the shape of the distribution, is encoded in the chromosome of individuals and is allowed to evolve. Algorithms with self-adapted mutation generated from isotropic and anisotropic distributions are presented. In the experimental study, the q -Gaussian mutation is compared to Gaussian and Cauchy mutation on three dynamic optimization problems.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Sato:2008:cec,
  author = "Yuji Sato and Ryosuke Suzuki and Yosuke Akatsuka",
  title = "Formation Dependency in Event-Driven Hybrid Learning Classifier Systems for Soccer Video Games",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0449.pdf},
  url = {},
  size = {},
  abstract =	{In this paper, we discuss dependencies on player formation when using a classifier system in a decision algorithm for agents in a soccer game. Our aim is to respond to the changing environment of video gaming that has resulted from the growth of the Internet, and to provide bug-free programs in a short time. We have already proposed a bucket brigade algorithm and a procedure for choosing what to learn depending on the frequency of events with the aim of facilitating real-time learning while a game is in progress. We have also proposed a hybrid system configuration that combines existing algorithm strategies with a classifier system, and we have reported on the effectiveness of this hybrid system. In this paper, we pit players in several different formations against each other and show that the proposed system is able to learn regardless of the differences in formation. We also show that by performing simulations ahead of time, it is possible to investigate formations that will be effective against an opponent's formation. Finally, by investigating changes in frequency and success rates for each type of play due to changes in formation, we show that it is possible to acquire a team strategy for the current formation through learning.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Ren:2008:cec,
  author = "Zhigang Ren and Zuren Feng and Liangjun Ke and Hong Chang",
  title = "A Fast and Efficient Ant Colony Optimization Approach for the Set Covering Problem",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0450.pdf},
  url = {},
  size = {},
  abstract =	{In this paper, we present an ant colony optimization (ACO) approach to solve the set covering problem. A constraint-oriented solution construction method is proposed. The main difference between it and the existing method is that, while adding a column to the current partial solution, it randomly selects an uncovered row and only considers the columns covering the row, but not all the unselected columns as candidate solution components. This decreases the number of candidate solution components and therefore accelerates the run speed of the algorithm. Moreover, a simple but effective local search procedure, which aims at eliminating redundant columns and replacing some columns with more effective ones, is developed to improve the quality of solutions constructed by ants while keeping their feasibility. The proposed algorithm has been tested on a number of benchmark instances. Computational results indicate that it is capable of producing high quality solutions and performs better than the existing ACO-based algorithms.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Yao:2008:cec,
  author = "Chen Yao and Huo Jia-Zhen and Li Hu  ",
  title = "Optimal Model, Algorism and Decision Support System of Bulk Ship Loading Problem",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0451.pdf},
  url = {},
  size = {},
  abstract =	{Ship loading planning Problem is a kind of 3/V/D/R combinational optimization problem. In this paper, a new mathematical 0-1 optimal model is proposed for the loading multi-sized bulk cargo to ship, a hybrid genetic algorithm is constructed to find a satisfying solution and a Decision Support System for solving a real-world problem is designed, developed and implemented. The system has successfully improved the rented ship's loading efficiency of a Chinese steel plant in Shanghai. The model and hybrid genetic algorithm will serve as references for the other problems in loading area.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Ji2:2008:cec,
  author = "Zhen Ji and Jiarui Zhou and Huilian Liao and Q. H. Wu",
  title = "Requantization Codebook Design Using Particle-Pair Optimizer",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0452.pdf},
  url = {},
  size = {},
  abstract =	{A new algorithm of optimal requantization codebook design using particle-pair optimizer (PPO) is proposed to provide an effective way for image transmission over multispeed communication system with minimal transmission delay. PPO is used for optimal codebook design in the first and second quantization respectively. In the second quantization, global distortion is used as the fitness value instead of second quantization distortion. Simulation results demonstrated that the proposed algorithm is able to achieve higher PSNR value with less transmission delay in comparison with conventional codebook optimization strategies.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Li13:2008:cec,
  author = "Cuimin Li and Tomoyuki Hiroyasu and Mitsunori Miki  ",
  title = "Mesh Dependency of Stress-Based Crossover for Structural Topology Optimization",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0453.pdf},
  url = {},
  size = {},
  abstract =	{This paper presents a genetic algorithm (GA) with a stress-based crossover (SX) operator to obtain a solution without ``checkerboard'' patterns for multi-constrained topology optimization problems. SX is based on the element stress. On one hand, smaller mesh size is required to improve the accuracy of structure analysis results. On the other hand, the computation cost of genetic algorithms for structural topology optimization problems (STOPs) increases with a more detailed mesh. Therefore, it is necessary to discuss the mesh dependency of SX for STOPs. Here, the mesh dependency of SX has been investigated through experiments with four different sized meshes. Furthermore, a comparison of evolutionary structural optimization (ESO) and SX is discussed.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Numnark:2008:cec,
  author = "Somrak Numnark and Worasait Suwannik",
  title = "Improving the Performance of LZWGA by Using a New Mutation Method",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0454.pdf},
  url = {},
  size = {},
  abstract =	{LZW encoding in Genetic Algorithm (LZWGA) encodes a chromosome in a format that can be decompressed by Lempel-Ziv-Welch (LZW) algorithm. This encoding reduces the size of the chromosome and enabled the algorithm to solve a very large problem. This paper proposes a novel mutation in LZWGA. The result shows that the new method can solve OneMax and Trap problem 46.3percent faster. Moreover, this method can reduce the size of the compressed chromosome by 54.8percent.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Long:2008:cec,
  author = "Fei Long and Fuchun Sun and Fengge Wu",
  title = "A QoS Routing Based on Heuristic Algorithm for Double-Layered Satellite Networks",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0455.pdf},
  url = {},
  size = {},
  abstract =	{Double-Layered Satellite Networks (DLSNs) that consist of low earth orbit (LEO) and medium earth orbit (MEO) satellites are becoming increasingly important since they have higher coverage and better service than single-layered satellite networks. One of the challenges in DLSNs is the development of specialized and efficient routing algorithms. In this paper, virtual topology grouping strategy is improved, and a routing scheme based on heuristic algorithm is proposed to satisfy the QoS requirements of the applications. Three typical heuristic algorithms-Ant Colony Algorithm, Taboo Search Algorithm and Genetic Algorithm are used in the routing scheme for avoiding package loss and link congestion. Simulation results show that heuristic routing algorithm can provide more QoS guarantees than shortest path first (SPF) algorithm on package loss probability and link congestion.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Tai:2008:cec,
  author = "K. Tai and N. F. Wang and Y. W. Yang",
  title = "Target Geometry Matching Problem with Conflicting Objectives for Multiobjective Topology Design Optimization Using GA",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0456.pdf},
  url = {},
  size = {},
  abstract =	{Genetic algorithms (GA) do have some advantages over gradient-based methods for solving topology design optimization problems. However, their success depends largely on the geometric representation used. In this work, an enhanced morphological representation of geometry is applied and evaluated to be efficient and effective in producing good results via a target matching problem: a simulated topology and shape design optimization problem where a `target' geometry set is first predefined as the Pareto optimal solutions and a multiobjective optimization problem formulated such that the design solutions will evolve and converge towards the target geometry set. As the objectives (and constraints) are conflicting, the problem is challenging and an adaptive constraint strategy is also incorporated in the GA to improve convergence towards the true Pareto front.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Chow:2008:cec,
  author = "Chi Kin Chow and Shiu Yin Yuen",
  title = "A Non-Revisiting Particle Swarm Optimization",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0457.pdf},
  url = {},
  size = {},
  abstract =	{In this article, a non-revisiting particle swarm optimization (NrPSO) is proposed. NrPSO is an integration of the non-revisiting scheme and a standard particle swarm optimization (PSO). It guarantees that all updated positions are not evaluated before. This property leads to two advantages:(1) it undisputedly reduces the computation cost on evaluating a time consuming and expensive objective function and;(2) It helps prevent premature convergence. The non-revisiting scheme acts as a self-adaptive mutation. Particles genericly switch between local search and global search. In addition, since the adaptive mutation scheme of NrPSO involves no parameter, comparing with other variants of PSO which involve at least two performance sensitive parameters, the performance of NrPSO is more reliable. The simulation results show that NrPSO outperforms four variants of PSOs on optimizing both uni-modal and multi-modal functions with dimensions up to 40. We also illustrate that the overhead and archive size of NrPSO are insignificant. Thus NrPSO is practical for real world applications. In addition, it is shown that the performance of NrPSO is insensitive to the specific chosen values of parameters.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Chow2:2008:cec,
  author = "Chi Kin Chow and Shiu Yin Yuen",
  title = "A Non-Revisiting Particle Swarm Optimization",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0458.pdf},
  url = {},
  size = {},
  abstract =	{In this article, a non-revisiting particle swarm optimization (NrPSO) is proposed. NrPSO is an integration of the non-revisiting scheme and a standard particle swarm optimization (PSO). It guarantees that all updated positions are not evaluated before. This property leads to two advantages: (1) it undisputedly reduces the computation cost on evaluating a time consuming and expensive objective function and; (2) It helps prevent premature convergence. The non-revisiting scheme acts as a self-adaptive mutation. Particles genericly switch between local search and global search. In addition, since the adaptive mutation scheme of NrPSO involves no parameter, comparing with other variants of PSO which involve at least two performance sensitive parameters, the performance of NrPSO is more reliable. The simulation results show that NrPSO outperforms four variants of PSOs on optimizing both uni-modal and multi-modal functions with dimensions up to 40. We also illustrate that the overhead and archive size of NrPSO are insignificant. Thus NrPSO is practical for real world applications. In addition, it is shown that the performance of NrPSO is insensitive to the specific chosen values of parameters.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Durillo:2008:cec,
  author = "Juan J. Durillo and Antonio J. Nebro and Carlos A. Coello Coello and Enrique Alba",
  title = "A Comparative Study of the Effect of Parameter Scalability in Multi-Objective Metaheuristics",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0460.pdf},
  url = {},
  size = {},
  abstract =	{Some real-world optimization problems have hundreds or even thousands of decision variables. However, the effect that the scalability of parameters has in modern multiobjective metaheuristic algorithms has not been properly studied (the current benchmarks are normally adopted with ten to thirty decision variables). In this paper, we adopt a benchmark of parameter-wise scalable problems (the ZDT test problems) and analyze the behavior of six multi-objective metaheuristics on these test problems when using a number of decision variables that goes from 8 up to 2048. The computational effort required by each algorithm in order to reach the true Pareto front is also analyzed. Our study concludes that a particle swarm algorithm provides the best overall performance, although it has difficulties in multifrontal problems.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Lin:2008:cec,
  author = "Zhiyong Lin and Zhifeng Hao and Xiaowei Yang",
  title = "Evolutionary Support Center Machine",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0461.pdf},
  url = {},
  size = {},
  abstract =	{Support Vector Machines (SVMs) are powerful tools in machine learning community, but it is not easy to select suitable parameters for them. And, very often SVMs show slow speeds in test phase due to their large number of support vectors. To remedy SVMs deficiencies, we propose a novel SVM-like method, which is called evolutionary support center machine (ESCM) in this paper. The key idea behind ESCM is to apply evolutionary algorithm to construct the separation hyperplane with the similar form to those constructed by SVMs in an incremental way. ESCM can not only optimize the support centers and tune the kernel parameters adaptively, but also control the number of support centers appropriately. Numerical experiments on several UCI benchmarks verify the efficiency of ESCM.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Zhang7:2008:cec,
  author = "Cheng Zhang and Suling Jia and Fajie Wei",
  title = "Artificial Ant Colony Foraging Simulation and Emergent Property Analysis",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0462.pdf},
  url = {},
  size = {},
  abstract =	{Based on Wilensky's ant colony foraging model and considering additional nature rules including multi-species, competition and evolution mechanism, the model called AntcolonySim was promoted. Simulations based on AntcolonySim under different conditions were carried out as well as emergent properties including foraging predominance and competition predominance were analyzed quantificationally. AntcolonySim has simple rules which can simulate nature ant colony foraging realistically. The model is also a reference for discussing emergent behaviors in complex systems by bottom interactional agents. },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Hasan:2008:cec,
  author = "S. M. Kamrul Hasan and Ruhul Sarker and  David Cornforth",
  title = "GA with Priority Rules for Solving Job-Shop Scheduling Problems",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0464.pdf},
  url = {},
  size = {},
  abstract =	{The Job-Shop Scheduling Problem (JSSP) is considered as one of the difficult combinatorial optimization problems and treated as a member of NP-complete problem class. In this paper, we consider JSSPs with an objective of minimizing makespan while satisfying a number of hard constraints. First, we develop a genetic algorithm (GA) based approach for solving JSSPs. We then introduce a number of priority rules such as partial reordering, gap reduction and restricted swapping to improve the performance of the GA. We run the GA incorporating these rules in a number of different ways. We solve 40 benchmark problems and compared their results with that of a number of well-known algorithms. We obtain optimal solutions for 27 problems, and the overall performance of our algorithms is quite encouraging.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Harding:2008:cec,
  author = "Simon Harding",
  title = "Evolution of Image Filters on Graphics Processor Units Using Cartesian Genetic Programming",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0465.pdf},
  url = {},
  size = {},
  abstract =	{Graphics processor units are fast, inexpensive parallel computing devices. Recently there has been great interest in harnessing this power for various types of scientific computation, including genetic programming. In previous work, we have shown that using the graphics processor provides dramatic speed improvements over a standard CPU in the context of fitness evaluation. In this work, we use Cartesian Genetic Programming to generate shader programs that implement image filter operations. Using the GPU, we can rapidly apply these programs to each pixel in an image and evaluate the performance of a given filter. We show that we can successfully evolve noise removal filters that produce better image quality than a standard median filter.   },
  keywords = {genetic algorithms, genetic programming, Cartesian Genetic Programming},
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Atashpaz-Gargari:2008:cec,
  author = "Esmaeil Atashpaz-Gargari and Farzad Hashemzadeh and Caro Lucas",
  title = "Designing MIMO PIID Controller Using Colonial Competitive Algorithm: Applied to Distillation Column Process",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0466.pdf},
  url = {},
  size = {},
  abstract =	{In this paper, a colonial competitive algorithm is applied to the problem of designing a multivariable PID controller. The goal is to design a controller to decouple the controlled process, and to track the desired inputs by outputs of the process as much as possible. The method is used to design a multi variable controller for a typical distillation column process. Also a GA and an analytical method are used to design the controller parameters. Comparison results among these methods show that the controller obtained by colonial competitive algorithm has better performance than the others.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Komatsu:2008:cec,
  author = "Takanori Komatsu and Seiji Yamada",
  title = "How Does Appearance of Agents Affect how People Interpret the Agents' Attitudes -Experimental Investigation on Expressing the Same Information from Agents Having Different Appearance",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0467.pdf},
  url = {},
  size = {},
  abstract =	{An experimental investigation of how the appearance of agents affects interpretations people make of the agents' attitudes is described. We conducted a psychological experiment where participants were presented artificial sounds that can make people estimate specific agents' primitive attitudes from three kinds of agents, e.g., Mindstorms robot, AIBO robot, and a normal laptop PC. Specifically, the participants were asked to select the appropriate attitude based on the sounds expressed by these three agents. The results showed that the participants had higher correct interpretation rates when a PC presented the sounds, while they had lower rates when Mindstorms and AIBO robots presented the sounds, even though these agents expressed information that was completely the same.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Castillo:2008:cec,
  author = "P. A. Castillo and J. J. Merelo and M. Moreto and F. J. Cazorla and M. Valero and A. M. Mora and  S. A. McKee",
  title = "Evolutionary System for Prediction and Optimization of Hardware Architecture Performance",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0468.pdf},
  url = {},
  size = {},
  abstract =	{The design of computer architectures is a very complex problem. The multiple parameters make the number of possible combinations extremely high.Many researchers have used simulation, although it is a slow solution since evaluating a single point of the search space can take hours. In this work we propose using evolutionary multilayer perceptron (MLP) to compute the performance of an architecture parameter settings. Instead of exploring the search space, simulating many configurations, our method randomly selects some architecture configurations; those are simulated to obtain their performance, and then an artificial neural network is trained to predict the remaining configurations performance. Results obtained show a high accuracy of the estimations using a simple method to select the configurations we have to simulate to optimize the MLP. In order to explore the search space, we have designed a genetic algorithm that uses the MLP as fitness function to find the niche where the best architecture configurations (those with higher performance) are located. Our models need only a small fraction of the design space, obtaining small errors and reducing required simulation by two orders of magnitude.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Tseng:2008:cec,
  author = "Vincent S. Tseng and Chun-Hao Chen and Pai-Chieh Huang and Tzung-Pei Hong",
  title = "A Cluster-Based Genetic Approach for Segmentation of Time Series and Pattern Discovery",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0470.pdf},
  url = {},
  size = {},
  abstract =	{In the past, we proposed a time series segmentation approach by combining the clustering technique, the discrete wavelet transformation and the genetic algorithm to automatically find segments and patterns from a time series. In this paper, we propose an enhanced approach to solve the problems that may occur during the evolution process. Two factors, namely the density factor and the distortion factor, are used to solve them. The distortion factor is used to avoid the distortion of the segments and the density factor is used to avoid generation of meaningless patterns. The fitness value of a chromosome is then evaluated by the distances of segments and these two factors. Experimental results on a financial dataset also show the effectiveness of the proposed approach.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Aguilar:2008:cec,
  author = "Jose Aguilar and Lu&#237;s Hernandez and Anny Olivar",
  title = "Design and Implementation of a Patterns Recognition System for Analysis of Biological Liquids",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0471.pdf},
  url = {},
  size = {},
  abstract =	{Given the great amount of data that are generated of the experiments that are made to analyze information of the extracted chemical fluids from the brain of a rodent, arises the necessity to design and to implement data mining systems to process this data. In this work is proposed a Fuzzy System for the Analysis of Biological Liquids (FSABL) that allows to analyze and to process the data, and this way, to know a series of disorders products of alterations, storage, and liberation of the Neurotransmitters. The FSABL is constructed under the paradigm of the Classifier Systems. Our system has been tested to determine the variation of the glutamate Neurotransmitter in the cerebral tonsil of the rats. It discovers and evaluates new rules, and it generates new solutions associated to clinical disorders.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Wu2:2008:cec,
  author = "Zhifeng Wu and Houkuan Huang and Xiong Zhang and  Bei Yang and Hongbin Dong",
  title = "Adaptive Equalization Using Differential Evolution",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0472.pdf},
  url = {},
  size = {},
  abstract =	{Adaptive equalization technology requires a long training sequence to update the parameters of the taps by gradient descent method step by step. In order to decrease the number of training sequence, this paper proposes an improved version of the classical differential evolution algorithm for adaptive equalizer to estimate the parameters, in which two trial vectors are created by crossover operator. The modified algorithm speeds up the convergence rate and improves the convergence precision through the evolution of multi-generation in the situation of a short training set. Compared with the traditional least mean squares (LMS) algorithm and the classical differential evolution (CDE) algorithm, the modified algorithm can switch to data transmission mode from the training mode much earlier; at the same time improve the efficiency of the transmission greatly. The simulation results have confirmed that the proposed algorithm achieves the faster convergence rate, the lower misadjustment and the less symbol error rate than the LMS algorithm and CDE algortihm in 4-PAM and 16-QAM signal systems.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Lukasiewycz:2008:cec,
  author = "Martin Lukasiewycz and Michael Gla&#223; and J&#252;rgen Teich",
  title = "A Feasibility-Preserving Local Search Operator for Constrained Discrete Optimization Problems",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0474.pdf},
  url = {},
  size = {},
  abstract =	{Meta-heuristic optimization approaches are commonly applied to many discrete optimization problems. Many of these optimization approaches are based on a local search operator like, e.g., the mutate or neighbor operator that are used in Evolution Strategies or Simulated Annealing, respectively. However, the straightforward implementations of these operators tend to deliver infeasible solutions in constrained optimization problems leading to a poor convergence. In this paper, a novel scheme for a local search operator for discrete constrained optimization problems is presented. By using a sophisticated methodology incorporating a backtracking-based ILP solver, the local search operator preserves the feasibility also on hard constrained problems. In detail, an implementation of the local serach operator as a feasibility-preserving mutate and neighbor operator is presented. To validate the usability of this approach, scalable discrete constrained testcases are introduced that allow to calculate the expected number of feasible solutions. Thus, the hardness of the testcases can be quantified. Hence, a sound comparison of different optimization methodologies is presented.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Wang14:2008:cec,
  author = "Feng Wang and Yuanxiang Li and Kangshun Li and Zhiyi Lin",
  title = "A New Circuit Representation Method for Analog Circuit Design Automation",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0475.pdf},
  url = {},
  size = {},
  abstract =	{The Analog circuits are very important in many high-speed applications such as communications. Since the size of analog circuit is becoming larger and more complex, the design is becoming more and more difficult. This paper proposes a new circuit representation method based on a two layer evolutionary scheme with Genetic Programming (TLGP), which uses a divide-and-conquer approach to evolve the analog circuits. This representation has the desirable property which is more helpful to generate expectant circuit graphs. And it is capable of generating various kinds of circuits by evolving the circuits with dynamical size, circuit topology, and component values. The experimental results on the designs of the voltage amplifier and the low-pass filter show that this method is efficient.   },
  keywords = {genetic algorithms, genetic programming},
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Mostaghim:2008:cec,
  author = "Sanaz Mostaghim and J&#252;rgen Branke and Andrew Lewis and Hartmut Schmeck",
  title = "Parallel Multi-Objective Optimization using Master-Slave Model on Heterogeneous Resources",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0476.pdf},
  url = {},
  size = {},
  abstract =	{In this paper, we study parallelization of multiobjective optimization algorithms on a set of heterogeneous resources based on the Master-Slave model. The Master-Slave model is known to be the simplest parallelization paradigm, where a master processor sends function evaluations to several slave processors. The critical issue when using the standard methods on heterogeneous resources is that in every iteration of the optimization, the master processor has to wait for all of the computing resources (including the slow ones) to deliver the evaluations. In this paper, we study a new algorithm where all of the available computing resources are efficiently used to perform the multi-objective optimization task independent of the speed (fast or slow) of the computing processors. For this we propose a hybrid method using Multi-objective Particle Swarm optimization and Binary search methods. The new algorithm has been tested on a scenario containing heterogeneous resources and the results show that not only does the new algorithm perform well for parallel resources, but also when compared to a normal serial run on one computer.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Khanmohammadi:2008:cec,
  author = "S. Khanmohammadi and G. Alizadeh and J. Jassbi and M. Pourmahmood",
  title = "A New Artificial Intelligence Approach for 2D Path Planning for Underwater Vehicles Avoiding Static and Energized Obstacles",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0477.pdf},
  url = {},
  size = {},
  abstract =	{Optimal trajectories in energetic environment for underwater vehicles can be computed using a numerical solution of the optimal control problem (OCP). An underwater vehicle is modeled with the six dimensional nonlinear and coupled equations of motion, controlled by DC motors in all degrees of freedom. An energy performance index that should be minimized may be considered. This leads to a Two Point Boundary Value Problem (TPBVP). The resulting TPBVP is generally solved using iterative methods. In this paper, the applications of two different intelligent algorithms are briefly studied and compared versus the generally acceptable conjugate gradient penalty (CGP) method for the OCP. Genetic algorithm (GA) and particle swarm optimization (PSO) methods are applied to solve OCP. Two approaches for performance index minimization, using GA and PSO, are proposed. CGP method is used to solve the TPBVP, by applying Euler-Lagrange equation. The simulation results show that the trajectories obtained by the intelligent methods were better than that of conjugate gradient penalty. After analyzing the simple path planning problem, the problem energetic environments consisting some energy sources is propounded. The optimal paths are found using GA and PSO algorithms. The problem of collision avoidance in an energetic environment is solved and energy avoidance paths are computed.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Watchareeruetai:2008:cec,
  author = "Ukrit Watchareeruetai and Yoshinori Takeuchi and  Tetsuya Matsumoto and  Noboru Ohnishi",
  title = "Transformation of Redundant Representations of Linear Genetic Programming into Canonical Forms for Efficient Extraction of Image Features",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0478.pdf},
  url = {},
  size = {},
  abstract =	{Recently, evolutionary computation (EC) has been adopted to search for effective feature extraction programs for given image recognition problems. For this approach, feature extraction programs are constructed from a set of primitive operations (POs), which are usually general image processing and pattern recognition operations. In this paper, we focus on an approach based on a variation of linear genetic programming (LGP). We describe the causes of redundancies in LGP based representation, and propose a transformation that converts the redundant LGP representation into a canonical form, in which all redundancies are removed. Based on this transformation, we present a way to reduce computation time, i.e., the evolutionary search that avoids executions of redundant individuals. Experimental results demonstrate a success in computation time reduction; around 7-62percent of total compuation time can be reduced. Also, we have experimented with an evolutionary search that prohibits existence of redundant individuals. When selection pressure is high enough, its search performance is better than that of conventional evolutionary search.   },
  keywords = {genetic algorithms, genetic programming},
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Sharma:2008:cec,
  author = "Deepak Sharma and Kalyanmoy Deb and N. N. Kishore",
  title = "Towards Generating Diverse Topologies of Path Tracing Compliant Mechanisms Using A Local Search Based Multi-Objective Genetic Algorithm Procedure",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0479.pdf},
  url = {},
  size = {},
  abstract =	{A new bi-objective optimization problem is formulated for generating the diverse topologies of compliant mechanisms tracing a user-defined path. Motivation behind the present study is to generate the compliant mechanisms which perform the same task of tracing a prescribed trajectory near minimum-weight solution. Therefore, the constraint are imposed at each precision point representing a prescribed path for accomplishing the tracing task. An additional constraint on stress is also included for the feasible designs. The study starts with a single objective analysis of minimum-weight of compliant mechanism and the obtained topology is referred as the reference design. Thereafter, a bi-objective optimization problem is solved by considering the objectives as minimization of weight of structure and maximization of diversity of structure with respect to the reference design. Here, the diversity is evaluated by finding the dissimilarity in the bit value at each gene position of the binary strings of the reference design and a structure evolved from the GA population.A local search based multi-objective genetic algorithm (MOGA) optimization procedure is used in which the NSGAII is used as a global search and optimization algorithm. A parallel computing is employed in the study for evaluating non-linear geometric FE analysis and also for the NSGA-II operations. After the NSGA-II run, a few solutions are selected from the non-dominated front and the local search is applied on them. With the help of a given optimization procedure, compliant mechanism designs tracing curvilinear and straight line trajectories are evolved and presented in the study. In both examples, compliant mechanisms are designed to have any arbitrary support and loading regions.    },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Kwong:2008:cec,
  author = "K. M. Kwong and James N. K. Liu and P. W. Chan and Raymond Lee",
  title = "Using LIDAR Doppler Velocity Data and Chaotic Oscillatory-Based Neural Network for the Forecast of Meso-Scale Wind Field",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0481.pdf},
  url = {},
  size = {},
  abstract =	{Current research based on various approaches including the use of numerical prediction models, statistical models and machine learning models have provided some encouraging results in the area of long-term weather forecasting. But at the level of meso-scale and even micro-scale severe weather phenomena (involving very short-term chaotic perturbations) such as turbulence and wind shear phenomena, these approaches have not been so successful. This paper focuses on the use of chaotic oscillatory-based neural networks for the study of a meso-scale weather phenomenon, namely, wind shear, a challenging and complex meteorological phenomena which has a vital impact on aviation safety. Using LIDAR data collected at the Hong Kong International Airport via the Hong Kong Observatory, we are able to forecast the Doppler velocities with reasonable accuracy and validate our prediction model. Preliminary results are promising and provide room for further research into its potential for application in aviation forecasting.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Wu3:2008:cec,
  author = "Ling Wu and Hang-Yu Wang and Fa-Xing Lu and Peifa Jia",
  title = "An Anytime Algorithm Based on Modified GA for Dynamic Weapon-Target Allocation Problem",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0482.pdf},
  url = {},
  size = {},
  abstract =	{An anytime algorithm based on modified genetic algorithm (GA) for dynamic WTA problem, subject to temporal constraints, is developed in the paper. In the algorithm the weapons are assigned to targets one by one before the deadline of each target comes. After a target is assigned with some weapon, the target is replaced by a new one in all chromosomes in the population while the optimization process will not undergo any restart. The algorithm has three main advantages: (1) a new target can be dynamically accommodated in the allocation process without losing previous optimizing information, (2) the quality of the pairing decisions may be improved in the evolving process with a prolonged computation time, and (3) it optimally deploys weapons to targets where a weapon can be assigned to more than one target asynchronously without missing any deadline of the targets, under the precondition that the weapon can be allocated to only one target at one time. The feasibility and the validity of the modified GA are verified in simulations.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Xin:2008:cec,
  author = "Chun-lin Xin and Wei-min Ma and Bin Liu",
  title = "Online Quantity Flexibility Contract Model and its Competitive Analysis",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0483.pdf},
  url = {},
  size = {},
  abstract =	{The literatures related to online Quantity Flexibility contract model (or various applications) is quite extensive. The common denominator of all previous theoretical work on the subject is based on the traditional ``average case analysis''. In other word, analyses are typically made under the assumption that the market demand function follows a particular stochastic process that may or may not be known to the online player. But in some situation this leads to the very difficult questions as to how the distribution was selected and what evidence suggests that this distribution is either typical or representative. In this paper we use the competitive ratio optimality criterion to restudy this model and some interesting results are obtained. We present a QF strategy and get the optimal competitive ratio.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Brest:2008:cec,
  author = "Janez Brest and Ale&#353; Zamuda and Borko Bo&#353;kovi&#263; and Mirjam Sepesy Maus&#353;ec and Viljem 	&#353;umer  ",
  title = "High-Dimensional Real-Parameter Optimization using Self-Adaptive Differential Evolution Algorithm with Population Size Reduction",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0484.pdf},
  url = {},
  size = {},
  abstract =	{In this paper we investigate a Self-Adaptive Differential Evolution algorithm (jDEdynNP-F) where F and CR control parameters are self-adapted and a population size reduction method is used. Additionally the proposed jDEdynNPF algorithm uses a mechanism for sign changing of F control parameter with some probability based on the fitness values of randomly chosen vectors, which are multiplied by the F control parameter (scaling factor) in the mutation operation of DE algorithm. The performance of the jDEdynNP-F algorithm is evaluated on the set of 7 benchmark functions provided for the CEC'2008 special session on high-dimensional real-parameter optimization.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Ast:2008:cec,
  author = "Jelmer van Ast and Robert Babu&#353;ka and Bart De Schutter",
  title = "Ant Colony Optimization for Optimal Control",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0485.pdf},
  url = {},
  size = {},
  abstract =	{Ant Colony Optimization (ACO) has proven to be a very powerful optimization heuristic for Combinatorial Optimization Problems (COPs). It has been demonstrated to work well when applied to various NP-complete problems, such as the traveling salesman problem. In this paper, an ACO approach to optimal control is proposed. This approach requires that a continuous-time, continuous-state model of the system, together with a finite action set, is formulated as a discrete, nondeterministic automaton. The control problem is then translated into a stochastic COP. This method is applied to the time-optimal swing-up and stabilization of a pendulum.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Chen8:2008:cec,
  author = "Jun Ying Chen and Zheng Qin and Ji Jia",
  title = "A PSO-Based Subtractive Clustering Technique for Designing RBF Neural Networks",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0487.pdf},
  url = {},
  size = {},
  abstract =	{When designing radial basis function neural networks, the central task is to set parameters of radial basis functions. In this paper, subtractive clustering is improved by particle swarm optimization (PSO) to automatically select the number and locations of radial basis functions. Subtractive clustering is used to find center prototypes and then PSO fines their locations iteratively. Comparative experiments were executed between subtractive clustering and PSO-based subtractive clustering proposed in this paper for designing RBF neural networks on several datasets. The experimental results suggest that the PSO-based subtractive clustering algorithm can be successfully applied to design RBF neural networks with competitive classification accuracy and small number of radial basis functions. The RBF neural networks evolved by PSO-based subtractive clustering have stronger generalization ability than the ones evolved by subtractive clustering.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Qiu:2008:cec,
  author = "Xuan Qiu and Shenshan Qiu",
  title = "Convergence Analysis of the Brain-State-in-a-Box(BSB) Model with Delay",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0488.pdf},
  url = {},
  size = {},
  abstract =	{In this paper, theoretical analysis proves the convergence properties of the Brain-state-in-a-Box (BSB) models with delay. We propose a convergence theorem of the BSB with delay, generalized the BSB without delay, while all previous studies on this model without delay assumed that symmetric and quasi-symmetric. We have performed a detailed convergence analysis of this network and found convergence theorem under proper assumptions of the weight matrices of this network: ones is non-symmetric and the other is row diagonal dominant. Meanwhile, the updating process is presented by a newly given updating rule. Theoretical analysis demonstrates that the BSB with delay performs much better than the original one in updating to an equilibrium point, and its updating rate is four times higher than that of the original BSB.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Zhang8:2008:cec,
  author = "Junqi Zhang and Zhongmin Xiao and Ying Tan and Xingui He  ",
  title = "Hybrid Particle Swarm Optimizer with Advance and Retreat Strategy and Clonal Mechanism for Global Numerical Optimization",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0489.pdf},
  url = {},
  size = {},
  abstract =	{A novel particle swarm optimization algorithm based on advance and retreat strategy and clone mechanism(ARC-PSO) is proposed in this paper. It is well known that the advance-and-retreat strategy is a simple and effective method of one-dimensional search. We use the advance-and-retreat strategy to endow the clones with faster speed to find nearby local basins before next clonal operation. Furthermore, in the next clonal operation, the search space is enlarged greatly and the diversity of clones is increased. When the fitness value turns better after last ``flying'', the cloned particle advances. On the contrary, the cloned particle retreats then searches in the reverse direction of the last ''flying'' with a small step-size of the previous velocity. Thus, the swarm has strong optimization ability. Comparisons among the proposed ARC-PSO, the conventional standard particle swarm optimization(SPSO) and the pure clone particle swarm optimization(CPSO) on thirteen benchmark test functions are presented in this paper. Experimental results show that the proposed ARC-PSO is capable of speeding up the evolution process significantly and improving the performance of global optimizer greatly.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Meshoul:2008:cec,
  author = "S. Meshoul and M. Batouche",
  title = "Aligning Images with Multiple Objectives",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0491.pdf},
  url = {},
  size = {},
  abstract =	{Most high level interpretation tasks in image analysis rely on image registration (alignment) process. Basically, image registration consists in finding the geometric transformation that best aligns two or several images. In this paper, we focus on mono-modality image alignment. The core task to do in this case is to put into correspondence two sets of data points assuming the presence of noise and outliers. The novelty of the proposed method consists in the fact that we cast the problem as a multi-objective optimization task for which a quantum evolutionary algorithm is defined to carry out the optimization process. The advantage of such process is to get at the end of the process, a set of solutions from which the best alignment is derived using mutual information measure. Experiments show that good and promising results have been obtained.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Griffioen:2008:cec,
  author = "A. R. Griffioen and S. K. Smit and A. E. Eiben",
  title = "Learning Benefits Evolution if Sex Gives Pleasure",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0492.pdf},
  url = {http://www.cs.vu.nl/~gusz/papers/2008-CEC-Griffioen-Smit-Eiben.pdf},
  size = {},
  abstract =	{In this paper the effects of individual learning on an evolving population of situated agents are investigated. We work with a novel type of system where agents can decide autonomously (by their controllers) if/when they reproduce and the bias in the agent controllers for the mating action is adaptable by individual learning. Our experiments show that in such a system reinforcement learning with the straightforward rewards system based on energy makes the agents lose their interest in mating. In other words, we see that learning frustrates evolution, killing the whole population on the long run. This effect can be counteracted by introducing a specially designated positive mating reward, pretty much like an orgasm in Nature.With this twist individual learning becomes a positive force. It can make the otherwise disappearing population viable by keeping agents alive that did not yet learn the task at hand. This hiding effect proves positive for it provides a smooth road for the population to adapt and learn the task with a lower risk of extinction.   },
  keywords = {genetic algorithms, genetic programming},
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Chen9:2008:cec,
  author = "Chuanliang Chen and Rongfang Bie and Ping Guo",
  title = "Combining LPP with PCA for Microarray Data Clustering",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0493.pdf},
  url = {},
  size = {},
  abstract =	{DNA Microarray technique has produced large amount of gene expression data. To analyze these data, many excellent machine learning techniques have been proposed in recent related work. In this paper, we try to perform the clustering of microarray data by combining the recently proposed Locality Preserving Projection (LPP) method with PCA, i.e. PCA-LPP. The comparison between PCA and PCA-LPP is performed based on two clustering algorithms, K-means and agglomerative hierarchical clustering. As we already known, clustering with the components extracted by PCA instead of the original variables does improve cluster quality. Moreover, our empirical study shows that by using LPP to perform further process the dimensions of components extracted by PCA can be further reduced and the quality of the clusters can be improved greatly meanwhile. Particularly, the first few components obtained by PCA-LPP capture more information of the cluster structure than those of PCA.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Chang4:2008:cec,
  author = "Chia-Hung Chang and Bor-Sen Chen and  Yung-Jen Chuang",
  title = "Robust Model Matching Control of Immune Systems Under Environmental Disturbances: Fuzzy Dynamic Game Approach",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0494.pdf},
  url = {},
  size = {},
  abstract =	{A robust model matching control of immune response is proposed for therapeutic enhancement to match a prescribed immune response under uncertain initial states and environmental disturbances, including continuous intrusion of exogenous pathogens. The worst-case effect of all possible environmental disturbances and uncertain initial states on the matching for a desired immune response is minimized for the enhanced immune system, i.e. a robust control is designed to track a prescribed immune model response from the minimax matching perspective. This minimax matching problem could be transformed to an equivalent dynamic game problem. The exogenous pathogen and environmental disturbances are considered as a player to maximize (worsen) the matching error when the therapeutic control agents are considered as another player to minimize the matching error. Since the innate immune system is highly nonlinear, it is not easy to solve the robust model matching control problem by the nonlinear dynamic game method directly. A fuzzy model is proposed to interpolate several linearized immune systems at different operation points to approximate the innate immune system via smooth fuzzy membership functions. With the help of fuzzy approximation method, the minimax matching control problem of immune systems could be easily solved by the proposed fuzzy dynamic game method via the linear matrix inequality (LMI) technique with the help of Robust Control Toolbox in Matlab. Finally, an in silico example is given to illustrate the design procedure and to confirm the efficiency and efficacy of the proposed method.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Yang8:2008:cec,
  author = "Cheng-San Yang and Li-Yeh Chuang and Chao-Hsuan Ke and Cheng-Hong Yang",
  title = "Boolean Binary Particle Swarm Optimization for Feature Selection",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0495.pdf},
  url = {},
  size = {},
  abstract =	{Feature selection is the process of choosing a subset of features from an original set. This subset should be necessary, reasonably represent the original data, and useful for identification classification. The task of feature selection is to search for an optimal solution in a- usually large- search space. However, if the search space too large, difficulties can occur during the search process, often resulting in a considerable increase in computational time. A particle swarm optimization algorithm (PSO) is a relatively new evolutionary computation technique, which has previously been used to implement feature selection. However, particle swarm optimization, like other evolutionary algorithms, tends to converge at a local optimum early. In this paper, we introduce a Boolean function which improves on the disadvantages of standard particle swarm optimization and use it to implement a feature selection for six microarray data sets. The experimental results show that the proposed method selects a smaller number of feature subsets and obtains better classification accuracy than standard PSO.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Ostaszewski:2008:cec,
  author = "Marek Ostaszewski and Pascal Bouvry and Franciszek Seredynski",
  title = "An Approach to Intrusion Detection by Means of Idiotypic Networks Paradigm",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0498.pdf},
  url = {},
  size = {},
  abstract =	{In this paper we present a novel intrusion detection architecture based on Idiotypic Network Theory (INIDS), that aims at dealing with large scale network attacks featuring variable properties, like Denial of Service (DoS). The proposed architecture performs dynamic and adaptive clustering of the network traffic for taking fast and effective countermeasures against such high-volume attacks. INIDS is evaluated on the MIT'99 dataset and outperforms previous approaches for DoS detection applied to this set.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Kotecha:2008:cec,
  author = "Ketan Kotecha and Apurva Shah",
  title = "Adaptive Scheduling Algorithm for Real-Time Operating System",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0499.pdf},
  url = {},
  size = {},
  abstract =	{EDF (Earliest Deadline First) has been proved to be optimal scheduling algorithm for single processor realtime operating systems when the systems are preemptive and underloaded. The limitation of this algorithm is, its performance decreases exponentially when system becomes slightly overloaded. Authors have already proved ability of ACO (Ant Colony Optimization) based scheduling algorithm for real-time operating system which is optimal during underloaded condition and it gives outstanding results in overloaded condition. The limitation of this algorithm is, it takes more time for execution compared to EDF algorithm.

In this paper, an adaptive scheduling algorithm is proposed which is combination of both of these algorithms. Basically the new algorithm uses EDF algorithm but when the system becomes overloaded, it will switch to ACO based scheduling algorithm. Again, when the overload disappears, the system will switch to EDF algorithm. Therefore, the proposed algorithm takes the advantages of both algorithms and overcomes the limitations of each other.

The proposed algorithm along with EDF algorithm and ACO based scheduling algorithm, is simulated for real-time system and the results are obtained. The performance is measured in terms of Success Ratio and Effective CPU Use. Execution Time taken by each scheduling algorithm is also measured. From analysis and experiments it reveals that the proposed algorithm is fast as well as very efficient in both underloaded and overloaded conditions.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Caponio:2008:cec,
  author = "Andrea Caponio and Ferrante Neri and Giuseppe L. Cascella and Nadia Salvatore",
  title = "Application of Memetic Differential Evolution Frameworks to PMSM Drive Design",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0500.pdf},
  url = {},
  size = {},
  abstract =	{This paper proposes the application of Memetic Algorithms employing Differential Evolution as an evolutionary framework in order to achieve optimal design of the control system for a permanent-magnet synchronous motor. Two Memetic Differential Evolution frameworks have been considered in this paper and their performance has been compared to a standard Differential Evolution, a standard Genetic Algorithm and a Memetic Algorithm presented in literature for solving the same problem. All the algorithms have been tested on a simulation of the whole system (control system and plant) using a model obtained through identification tests. Numerical results show that the Memetic Differential Evolution frameworks seem to be very promising in terms of convergence speed and has fairly good performance in terms of final solution detected for the realworld problem under examination. In particular, it should be remarked that the employment of a meta-heuristic local search component during the early stages of the evolution seems to be very beneficial in terms of algorithmic efficiency.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Oliveira:2008:cec,
  author = "Tatyana B. S. de Oliveira and Liang Zhao and Katti Faceli and Andre C. P. L. F. de Carvalho",
  title = "Data Clustering Based on Complex Network Community Detection",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0501.pdf},
  url = {},
  size = {},
  abstract =	{Data clustering is an important technique to extract and understand relevant information in large data sets. In this paper, a clustering algorithm based on graph theoretic models and community detection in complex networks is proposed. Two steps are involved in this processing: The first step is to represent input data as a network and the second one is to partition the network into subnetworks producing data clusters. In the network partition stage, each node has a randomly assigned initial angle and it is gradually updated according to its neighbors angle agreement. Finally, a stable state is reached and nodes belonging to the same cluster have similar angles. This process is repeated, each time a cluster is chosen and results in an hierarchical divisive clustering. Simulation results show two main advantages of the algorithm: the ability to detect clusters in different shapes, densities and sizes and the ability to generate clusters with different refinement degrees. Besides of these, the proposed algorithm presents high robustness and efficiency in clustering.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Shir:2008:cec,
  author = "Ofer M. Shir and Thomas B&#228;ck and Herschel Rabitz and Marc J. J. Vrakking",
  title = "On the Evolution of Laser Pulses under a Dynamic Quantum Control Environment",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0502.pdf},
  url = {},
  size = {},
  abstract =	{This paper introduces the optimization of a Quantum Control application, the so-called molecular alignment problem, subject to a dynamic environment. Given the relative simplicity of optimized pulse-shapes in the low-intensity variant of the problem, versus the high complexity of the optimized pulse-shapes in the high-intensity case, a dynamic-intensity environment is simulated in a noise-free calculation.

Specific Evolution Strategies, natural candidates for optimization in dynamic environments, are applied to this task. The calculations reveal the evolution of the pulse-shapes and their underlying evolving structures, that allow a complete physical interpretation. The combination of an optimization in a dynamic environment with the examination of the intermediate optimized solutions offers a sharper physics view of the problem, and accomplishes a fruitful interdisciplinary study.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Neri:2008:cec,
  author = "Ferrante Neri and Ville Tirronen",
  title = "On Memetic Differential Evolution Frameworks: A Study of Advantages and Limitations in Hybridization",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0505.pdf},
  url = {},
  size = {},
  abstract =	{This paper aims to study the benefits and limitations in the hybridization of the Differential Evolution with local search algorithms. In order to perform this study, the performance of three Memetic Algorithms employing a Differential Evolution as an evolutionary framework and several local search algorithms adaptively coordinated by means of a fitness diversity logic have been analyzed. The performance of a standard Differential Evolution whose parameter setting has been executed only after fine tuning has also been taken into account in the comparison. The comparative analysis has been performed on a set of various test functions. Numerical results show that the Memetic Algorithms without any extensive parameter tuning are still competitive with the finely tuned plain Differential Evolution.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Woldemariam:2008:cec,
  author = "Kumlachew M. Woldemariam and Gary G. Yen",
  title = "Vaccine Enhanced Artificial Immune System for Multimodal Function Optimization",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0506.pdf},
  url = {},
  size = {},
  abstract =	{This paper proposes the use of vaccine to promote exploration in the search space for solving multimodal function optimization problems using artificial immune system. In this method, first we divide the decision space into equal subspaces. Vaccine is then extracted randomly from each subspace. A few of these antigens are then injected into the algorithm to enhance the exploration of global and local optima. The vaccine is introduced in the form of suppressed antibodies. The goal of this process is to allocate the available antibodies at unexplored areas. Using this biologically motivated notion we design the vaccine enhanced artificial immune system for multimodal function optimization.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Saffari:2008:cec,
  author = "A. Saeed Saffari and R. Mohammad and T. Akbarzadeh and Mahmoud Naghibzadeh",
  title = "A Novel Approach to Distributed Routing by Super-AntNet",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0508.pdf},
  url = {},
  size = {},
  abstract =	{Various forms of swarm intelligence are inspired by social behavior of insects that live collectively. AntNet is a form of such social algorithms, but it has a scalability problem with growing network size. If every node sends only one ant to each destination node and there are N nodes in the network, the total number of ants that are sent is N(N<font face="symbol">-</font>1). In addition with increasing overhead for large networks, most of the ants are often lost for distant destinations. Furthermore, due to long travel times, ants that do arrive may carry outdated information. In this paper, a novel hierarchical algorithm is proposed to resolve this scalability problem of AntNet. The proposed Super-AntNet divides a large scale network into several small networks that are chosen based their internal traffic patterns. A separate ant colony is then assigned to each of these networks. A Super-Ant Colony is then responsible to coordinate data routing among the colonies. Performance of Super-AntNet is compared with those of standard AntNet as well as two other conventional routing algorithms such as Distance Vector (DV) and Link State (LS) in terms of end-toend delay, throughput, packet loss ratio, increased overhead, as well as jitter. Application to a 16-node network indicates the superiority of the proposed algorithm.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Peng:2008:cec,
  author = "Chen Peng and Meng Anbo and Zhao Chunhua",
  title = "Particle Swarm Optimization in Multi-Agent System for the Intelligent Generation of Test Papers",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0509.pdf},
  url = {},
  size = {},
  abstract =	{Agent-oriented design is one of the most active areas in the field of deployment of web-based distance education, and test is a popular measurement tool of learners' knowledge in order to verify the learner's level of understanding and select corresponding educational strategy. In this paper, an innovative approach to seamless integration of the particle swarm optimization (PSO) and multi-agent system (MAS) is proposed. In order to generate a test paper automatically, a modified genetic particle swarm optimization (GPSO) is presented, in which the values of parameters will be decreased linearly with the number of iterations for improving the late convergence rate. For the implementation of GPSO based on multi-agent system, a core agents TPAgent(TPA) is provided to undertake the operations of GPSO and will control the evolution operations of each generation of population. To keep communication between different nodes at a minimum cost, fitness evaluation tasks are implemented by the TPAgents at local nodes, only the local minimum fitness and the corresponding best particle are sent to center node so as to get the global best particle in the parallel computing environment. For avoiding the prematurity, the global best particle will be dispatched to remote node randomly. Based on the JADE, a prototype system is setup, and the simulation results show that the proposed approach is feasible and robust.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Lewis:2008:cec,
  author = "Andrew Lewis and David Ireland",
  title = "Automated Solution Selection in Multi-Objective Optimisation",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0510.pdf},
  url = {},
  size = {},
  abstract =	{This paper proposes an approach to the solution of multi-objective optimisation problems that delivers a single, preferred solution. A conventional, population-based, multiobjective optimisation method is used to provide a set of solutions approximating the Pareto front. As the set of solutions evolves, an approximation to the Pareto front is derived using a Kriging method. This approximate surface is traversed using a single objective optimisation method, driven by a simple, aggregated objective function that expresses design preferences. The approach is demonstrated using a combination of multi-objective particle swarm optimisation (MOPSO) and the Simplex method of Nelder and Mead, applied to several, standard, multi-objective test problems. Good, compromise solutions meeting user-defined design preferences are delivered without manual intervention.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Vitela:2008:cec,
  author = "J. E. Vitela and O. Casta&#241;os",
  title = "A Real-Coded Niching Memetic Algorithm for Continuous Multimodal Function Optimization",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0511.pdf},
  url = {},
  size = {},
  abstract =	{In this work we extend the sequential niching technique of Beasley et at. for multiple optimal determination, incorporating a local search to improve accuracy. In the proposed method a sequence of GA runs make use of a derating function and of niching and clearing techniques to promote the occupation of different niches in the function to be optimized. The algorithm searches the solution space eliminating from the fitness landscape previously located peaks forcing the individuals to converge into unoccupied niches. Unlike other algorithms the efficiency of this sequential niching memetic algorithm (SNMA) is not highly sensitive to the niche radius. Performance measurements with standard test functions used by other researchers, show that the SNMA proposed outperforms other algorithms in accurately locating all optima, both global and local, in the search space.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Katada:2008:cec,
  author = "Yoshiaki Katada and Jun Nakazawa  ",
  title = "Investigation of Simply Coded Evolutionary Artificial Neural Networks on Robot Control Problems",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0512.pdf},
  url = {},
  size = {},
  abstract =	{One of the advantages of evolutionary robotics over other approaches in embodied cognitive science would be its parallel population search. Due to the population search, it takes a long time to evaluate all robot in a real environment. Thus, such techniques as to shorten the time are required for real robots to evolve in a real environment. This paper proposes to use simply coded evolutionary artificial neural networks for robot control to make genetic search space as small as possible and investigates the performance of them using simulated robots. Two types of genetic algorithm (GAs) are employed, one is the standard GA and the other is an extended GA, to achieve higher final fitnesses as well as achieve high fitnesses faster. The results suggest the benefits of the proposed method.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Shum:2008:cec,
  author = "Dennis T. F. Shum and Raymond S. T. Lee and  James N. K. Liu",
  title = "Chaotic Weatherman; the Design and Implementation of a Chaotic Weather Prediction System",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0513.pdf},
  url = {},
  size = {},
  abstract =	{Chaos Theory describes systems that are extremely sensitive to initial conditions. Assuming that weather is a chaotic phenomenon, chaos theory may provide suitable weather prediction models. The study describes chaos theory-based weather prediction model for forecasting short range severe rainstorms in Hong Kong. The model uses a Lee oscillator, which is based on chaos theory, as a transfer function in a Multi-Layer Perceptron model. The proposed model was compared with three non-linear time series models, RBF, SVM and MLP, and was more accurate by 4.518percent, 4.902percent and 3.791percent respectively.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Yu4:2008:cec,
  author = "L. Yu and P. N. Suganthan",
  title = "Empirical Comparison of Niching Methods on Hybrid Composition Functions",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0514.pdf},
  url = {},
  size = {},
  abstract =	{In this paper, we compare the performance of three popular niching genetic algorithms namely deterministic crowding, restricted tournament selection, and clearing by a set of hybrid composition test functions originally proposed for the special session on real parameter optimization at CEC 2005. The number of function evaluations is used as the main control parameter for an unbiased comparison instead of using the generation count as done frequently in the previous comparative studies. Results are given in tables and graphs to show the searching ability, accuracy, and computation time requirement of each method.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Li14:2008:cec,
  author = "Zhiqiang Li and Hanwu Chen and Baowen Xu and  Wenjie Liu and Xiaoyu Song and Xilin Xue",
  title = "Fast Algorithm for 4-qubit Reversible Logic Circuits Synthesis",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0515.pdf},
  url = {},
  size = {},
  abstract =	{Owing to the exponential nature of the memory or run-time complexity, many existing methods can only synthesize 3-qubit circuits, however, [14] can achieve 12 steps for the CNP (Controlled-Not gate, NOT gate and Peres gate) library in 4-qubit circuit synthesis with mini-length by using an enhanced bi-directional synthesis approach. We mainly absorb the ideas of our 3-qubit synthesis algorithms based on Hash table and present a novel and efficient algorithm which can construct almost all optimal 4-qubit reversible logic circuits with various types of gates and mini-length cost based on constructing the shortest coding and the specific topological compression, whose lossless compression ratios of the space of n-qubit circuits is near 2&#215;n!. Our algorithm has created all 3120218828 optimal 4-qubit circuits whose length is less than 9 for the CNT(Toffoli gate) library, and it can quickly achieve 16 steps through cascading created circuits. To the best of our knowledge, there are no other algorithms to achieve the contribution.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Ghandar:2008:cec,
  author = "Adam Ghandar and Zbigniew Michalewicz and Thuy-Duong T&#244; and Ralf Zurbruegg",
  title = "The Performance of an Adaptive Portfolio Management System",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0516.pdf},
  url = {},
  size = {},
  abstract =	{This paper describes the operation and performance of a computational intelligence rulebase system that manages a portfolio of stocks according to investment objectives. We present an overview of several improvements to the system presented in previous papers and provide detailed results from applying the system in representative scenarios toward determining the robustness of the approach.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Yuen:2008:cec,
  author = "Shiu Yin Yuen and Chi Kin Chow",
  title = "Applying Non-Revisiting Genetic Algorithm to Traveling Salesman Problem",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0518.pdf},
  url = {},
  size = {},
  abstract =	{In [1], we propose non-revisiting genetic algorithm (NrGA) and apply it to a set of bench mark real valued test functions. NrGA has the advantage that it is non-revisiting, i.e. a visited point will not be visited again. This provides an automatic mechanism for diversity maintenance which does not suffer from premature convergence. Another advantage is that it supports a parameter-less adaptive mutation mechanism. In this paper, we show how NrGA can be adapted to a real world combinatorial optimization problem - the famous traveling salesman problem (TSP). Comparison with genetic algorithm (GA) (with revisits and standard mutation) is made. It is shown that NrGA gives superior performance compared to GA. Moreover, it gives the same stable performance using different types of mutation operators. Moreover, turning off GA's mutation operator but only use the NrGA inherent parameter-less adaptive mutation gives the best performance.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Fukunaga:2008:cec,
  author = "Alex S. Fukunaga  ",
  title = "A New Grouping Genetic Algorithm for the Multiple Knapsack Problem",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0519.pdf},
  url = {},
  size = {},
  abstract =	{The Multiple Knapsack Problem (MKP) is the problem of assigning (packing) objects of various weights and values (profits) to a set of containers (bins) of various capacities, in order to maximize the total profit of the items assigned to the containers. We propose a new genetic algorithm for the MKP which searches a space of undominated candidate solutions. We compare the new algorithm to previous heuristics for the MKP, as well as alternative evolutionary algorithms, and show experimentally that our new algorithm yields the best performance on difficult instances where item weights and profits are highly correlated.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Rungrattanaubol:2008:cec,
  author = "Jaratsri Rungrattanaubol and Pensiri Tongpadungrod",
  title = "Sensing Positions Optimisation of a Distributive Tactile Sensor Using Principal Component Analysis",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0520.pdf},
  url = {},
  size = {},
  abstract =	{This paper describes a method to optimise sensing positions of a two-dimensional distributive tactile sensor for determining an applied load position from surface deflections. The distributive approach relies on coupling between sensing positions that capture a pattern of response to contacting load. The paper describes an experimental arrangement and the corresponding mathematical model that incorporates surface's response induced by a contact. The size of the experimental rig is 250 mm  	&#215; 340 mm. The determination of an applied load position is completed through a back propagation neural network as an interpretation algorithm using surface deflections as input data. The average Euclidean error using 16 inputs from measurement was approximately 23 mm when sensing positions were at an equal pitch. Optimisation was achieved using principal component analysis as a tool to evaluate the performance. The number of inputs was simulated surface deflection at 4-16 positions. It was found that the number of sensing elements converged accordingly to the number of principal components (eigenvalues) used in optimisation. In terms of performance, the errors ranged from approximately 23.9<font face="symbol">-</font>15.8 mm and 20.5<font face="symbol">-</font>14.0 mm when inputs were mathematically derived from 4-16 non-optimised and optimised sensing positions respectively. Optimisation was an effective method to enhance the accuracy in determining an applied load position, in particular with a smaller number of sensing elements.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Tan2:2008:cec,
  author = "C. H. Tan and J. H. Ang and K. C. Tan and A. Tay  ",
  title = "Online Adaptive Controller for Simulated Car Racing",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0521.pdf},
  url = {},
  size = {},
  abstract =	{An adaptive game AI has the potential of tailoring a uniquely entertaining and meaningful game experience to a specific player. An online adaptive AI should be able to profile its opponent efficiently during the early phase of the game and adapts its own playing style to the level of the player so that the player feels entertained playing against it. This paper presents an online adaptive algorithm that uses ideas from evolutionary computation to match the skill level of the opponent during the game. The proposed algorithms demonstrated using a car racing simulator is capable of matching its opponents in terms of both mean score and winning percentages.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Foo:2008:cec,
  author = "Cherhan Foo and Michael Kirley",
  title = "An Analysis of the Effects of Clustering in Graph-Based Evolutionary Algorithms",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0523.pdf},
  url = {},
  size = {},
  abstract =	{Recently, there has been increased interest in combining work from the complex networks domain with evolutionary computation to solve challenging search and optimization problems. Typically, individuals in the evolving population occupy a node in a graph (or network) and are only allowed to mate with individuals within their local neighbourhood. The use of specific graph topologies have been shown to alter the population dynamics, which in turn impacts on the ability of the algorithm to find (near)-optimal solutions for a given problem. In this paper, we continue this line of research. Here, we have analyzed the impact of clustering on the performance of graph-based evolutionary models. We have constructed a range of alternative graphs to act as scaffolding for the evolving population by systematically rewiring some of the edges/links in a regular lattice. Significantly, we have kept the mean node degree constant in all graphs. Two different problems defined on a binary string with regulated levels of epistasis;- the NK Landscape problem and the hierarchical if and only if (H-IFF) problem;- were used to examine the efficacy of our model. Simulation results show that the clustering coefficient of the underlying graph has a significant impact on the ability of a graph-based evolutionary algorithm to solve a given  problem.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Onoda:2008:cec,
  author = "Takashi Onoda and Norihiko Ito and Yamasaki Hironobu",
  title = "Unusual Condition Monitoring based on Support Vector Machines for Hydroelectric Power Plants",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0524.pdf},
  url = {},
  size = {},
  abstract =	{Kyushu Electric Power Co., Inc. collects different sensor data and weather information to maintain the safety of hydroelectric power plants while the plants are running. It is very rare to occur trouble condition in equipment of hydroelectric power plants. And in order to collect the trouble condition data, it is hard to construct experimental power generation plant and hydroelectric power plant. In this situation, we have to find trouble condition sign. In this paper, we consider that the rise inclination of unusual condition data gives trouble condition sign. This paper shows results of detecting unusual condition data of bearing vibration from the collected different sensor data and weather information by using one class support vector machine and analyzing the trend of generating unusual condition data by using a support vector machine. The result shows that our approach may be useful for unusual condition data detection in bearing vibration and maintaining hydroelectric power plants.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Ghosh:2008:cec,
  author = "Madhumala Ghosh and Amit Konar and L. C. Jain and Uday K. Chakraborty",
  title = "Behavioral Analysis of Co-operative/Competitive Antibody Dynamics",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0528.pdf},
  url = {},
  size = {},
  abstract =	{The paper presents an analysis of chaos, limit cycles and stability in the antigen-antibody interactive dynamics. Both co-operation and competition of antibodies are considered in the dynamics. The classical approach of Lyapunov has been employed here for the stability analysis of the dynamics. Computer simulations have been undertaken to support the results of the analysis. Both temporal behaviors of the antibodies and their phase portraits have been given to study their chaotic, limit cyclic and stable behavior. Results of stability analysis of the dynamics have been applied in a garbage cleaning problem by a mobile robot.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Talukder:2008:cec,
  author = "A. K. M. Khaled Ahsan Talukder and Michael Kirley",
  title = "A Pareto Following Variation Operator For Evolutionary Dynamic Multi-objective Optimization",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0529.pdf},
  url = {},
  size = {},
  abstract =	{Tracking the Pareto-front in a dynamic multiobjective optimization problem (MOP) is a challenging task. Evolutionary algorithms are a representative meta-heuristic capable of meeting this challenge. Typically, the stochastic variation operators used in an evolutionary algorithm work in decision (or design) variable space, thus there are no guarantees that the new individuals produced are non-dominated and/or are unique in the population. In this paper, we introduce a novel variation operator that manipulates the values in both objective space and design variable space in such a way that it can avoid re-exploration of dominated solutions. The proposed operator, inspired by the theory of dynamic system identification, is based on integral transformation. Here, we approximate the next expected Pareto-front, and from this expected front, we generate corresponding correct decision variables. We show empirically that our algorithm can approximate the Pareto-optimal set for given static benchmark MOP's and that it can track changes in the Pareto-front for particular dynamic MOP's.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Gao2:2008:cec,
  author = "Yanping Gao and Hong Yu and Xinzhong Cui and Yi Xie",
  title = "Implementation of a New Algorithm for the Various Pattern and Language in the Workflow Management System",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0532.pdf},
  url = {},
  size = {},
  abstract =	{Workflow Management Systems (WfMS) allow organizations to streamline and automate business processes and reengineer their structure. One important requirement for this type of system is the management and computation of the Quality of Service (QoS) of processes and workflows. Currently, a range of Web processes and workflow languages exist. Each language can be characterized by the set of patterns they support. Developing and implementing a suitable and generic algorithm to compute the QoS of processes that have been designed using different languages is a difficult task. This is because some patterns are specific to particular process languages and new patterns may be introduced in future versions of a language. In this paper, we describe an adaptive algorithm implemented to cope with these two problems. The algorithm is called adaptive since it can be dynamically changed as the patterns of a process language also change.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Deb:2008:cec,
  author = "Kalyanmoy Deb and Karthik Sindhya",
  title = "Deciphering Innovative Principles for Optimal Electric Brushless D.C. Permanent Magnet Motor Design",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0534.pdf},
  url = {},
  size = {},
  abstract =	{This paper shows how a routine design optimization task can be enhanced to decipher important and innovative design principles which shall provide far-reaching knowledge about the problem at hand. Although the  	&#8216;innovization' task for this purpose was proposed by the first author elsewhere, the application to a brushless D.C. permanent magnet motor design is the first real application of the innovization concept to a discrete optimization problem. The model for cost and peak-torque objectives and associated constraints are borrowed from an existing study. The extent of knowledge gained in designing high-performing yet low-cost motors achieved in this study is phenomenal and should motivate other practitioners to pursue similar studies in other design and optimization related activities.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Kaji:2008:cec,
  author = "Hirotaka Kaji and Kokolo Ikeda and Hajime Kita",
  title = "Acceleration of Parametric Multi-Objective Optimization by an Initialization Technique for Multi-Objective Evolutionary Algorithms",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0535.pdf},
  url = {},
  size = {},
  abstract =	{Most real world problems can be formulated as Multi-objective Optimization Problems (MOPs) because they have various competing objectives. Engine calibration, which is the tuning process of controller parameters in automotive engine development, is such a problem. In the engine calibration, a set of MOPs depending on plural operating conditions such as engine speed have to be optimized one at a time. In this paper, such a problem composed by MOPs parameterized by condition variables as subproblems is called Parametric MOP (PMOP). We can solve the PMOP by applying Multi-Objective Evolutionary Algorithms (MOEAs) to each MOP separately. However, the calculation cost of PMOP becomes quite expensive in real world applications. To accelerate the evolutionary multiobjective optimization of PMOPs, we propose an initialization method of MOEAs for PMOPs. This method uses an interpolation of plural Pareto approximation populations of different conditions obtained in the past for an initial population of succeeding MOPs. The effectiveness of the proposed method is demonstrated through a numerical experiment.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Misra:2008:cec,
  author = "B. B. Misra and S. Dehuri and P. K. Dash and G. Panda",
  title = "Reduced Polynomial Neural Swarm Net for Classification Task in Data Mining",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0536.pdf},
  url = {},
  size = {},
  abstract =	{In this paper, we proposed a reduced polynomial neural swarm net (RPNSN) for the task of classification. Classification task is one of the most studied tasks of data mining. In solving classification task of data mining, the classical algorithm such as Polynomial Neural Network (PNN) takes large computation time because the network grows over the training period (i.e. the partial descriptions (PDs) in each layer grows in successive generations). Unlike PNN our proposed network needs to generate the partial description for a single layer. Particle swarm optimization (PSO) technique is used to select a relevant set of PDs as well as features, which are then fed to the output layer of our proposed net which contain only one neuron. The selection mechanism used here is a kind of wrapper approach. Performance of this model is compared with the results obtained from PNN. Simulation result shows that the performance of RPNSN is encouraging for harnessing its power in data mining area and also better in terms of processing time than the PNN model.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Takahama:2008:cec,
  author = "Tetsuyuki Takahama and Setsuko Sakai",
  title = "Reducing Function Evaluations in Differential Evolution using Rough Approximation-Based Comparison",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0537.pdf},
  url = {},
  size = {},
  abstract =	{In this study, we propose to use a rough approximation model, which is an approximation model with low accuracy and without learning process, to reduce the number of function evaluations effectively. Although the approximation errors between the true function values and the approximation values estimated by the rough approximation model are not small, the rough model can estimate the order relation of two points with fair accuracy. In order to use this nature of the rough model, we propose estimated comparison which omits the function evaluations when the result of comparison can be judged by approximation values. The advantage of the estimated comparison method is shown by comparing the results obtained by Differential Evolution (DE) and DE with estimated comparison method in various types of benchmark functions.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Takama:2008:cec,
  author = "Yasufumi Takama and Hiroki Namba and Yoshihiro Iwase and Yuki Muto",
  title = "Application of TV Program Recommendation to Communication Support between Human and Partner Robot",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0539.pdf},
  url = {},
  size = {},
  abstract =	{This paper studies the communication support between human and partner robot based on TV program recommendation. It is expected in near future that various robots designed for supporting humans will come into our daily lives. In order for such robots to coexist with humans, those should be recognized as ``partners'' for us. Therefore, studying communication between humans and robots is very important. In this paper, we focus on the communication under the situation of watching TV, because TV is bound up with the ordinary, natural rhythms of our daily life. If a robot can estimate our interests/preference by observing our watching behavior and have conversation about the topics we might be interested in, we could recognize the robot as a partner. This paper applies TV program recommendation to communication support between human and partner robot. First, the paper proposes a method for generating user profile for TV program recommendation based on fuzzy inference. The method does not estimate user's interest in a TV program only from its watching time as most of existing methods do, but also from user's utterances by applying sentiment analysis. Experiments are performed with test subjects, and the results show the proposed method can generate a user profile that can reflect user's interests. The paper also introduces a prototype system for studying communication support between human and partner robot under TV watching environment, in which the proposed profile generation method is implemented. The system architecture as well as how it works for TV program recommendation is described.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(He2:2008:cec,
  author = "Huimin He and Haiyan Du and Yongjin Liu and Fangping Li and Yi Xie",
  title = "New Coding Method to Reduce the Database Size and Algorithm with Significant Efficiency in Association Rules",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0540.pdf},
  url = {},
  size = {},
  abstract =	{The problem of discovery association rules in large databases is considered. An encoding method for converting large databases to small one is proposed. Significant efficiency is obtained by applying some modified known algorithm on our proposed database layout. In addition, a new algorithm based on the proposed encoding method is introduced. Using some properties of numbers our database converts itemset to numerical domain. Our implementation indicates that the proposed layout made the size of database significantly smaller. Also the time to find association rules is reduced.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Jeurissen:2008:cec,
  author = "Roland Jeurissen and Jan van den Berg",
  title = "Optimized Index Tracking using a Hybrid Genetic Algorithm",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0543.pdf},
  url = {},
  size = {},
  abstract =	{Assuming the market is efficient, an obvious portfolio management strategy is passive where the challenge is to track a certain benchmark like a stock index such that equal returns and risks are achieved. A tracking portfolio consists of a (usually small) weighted subset of stock funds. The weights are supposed to be positive here which means that short selling is not allowed. We investigate an approach for tracking the Dutch AEX index where an optimal tracking portfolio is determined. The optimal weights of a portfolio are found by minimizing the tracking error for a set of historical returns and covariances. The overall optimal portfolio is found using a hybrid genetic algorithm where each chromosome represents a specific subset of the stocks from the index, the fitness function of each chromosome corresponds to the minimized tracking error achievable with that subset, and the optimal portfolio is the tracking portfolio with highest fitness achievable. We show the experimental setup and the simulation results, including the out-of-sample performance of the optimal tracking portfolio. The hybrid genetic algorithms used appear to be robust in finding the optimal tracking portfolio and the performance of this portfolio on the out-of-sample data set is approximately four times better than that of randomly selected portfolios with optimized stock weights. By choosing a dedicated crossover operator, the hybrid genetic algorithm appears to find the optimal tracking portfolio using, on average, less than 23 generations only.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Yu5:2008:cec,
  author = "Zhiwen Yu and Dingwen Wang and Hau-San Wong",
  title = "Nearest Neighbor Evolutionary Algorithm for Constrained Optimization Problem",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0544.pdf},
  url = {},
  size = {},
  abstract =	{Although there exist a lot of approaches to solve constrained optimization problem, few of them makes use of the knowledge obtained in the searching process. In the paper, a new algorithm called nearest neighbor evolutionary algorithm (NNE) is proposed to solve the constrained optimization problem. NNE not only performs global search and local search in the searching process, but also considers the knowledge obtained in the searching process. NNE also avail itself of the elitist strategy and keeps the best individuals for the next generation. The results in the experiments show that NNE not only achieves good performance in a lot of constrained optimization problems, but also outperforms most of state-ofart approaches in most of constrained optimization problems, such as ASCHEA and SEMS.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Hu3:2008:cec,
  author = "Laihong Hu and Fuchun Sun and Hualong Xu and Huaping Liu and Fengge Wu",
  title = "On-Orbit Long-Range Maneuver Transfer via EDAs",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0546.pdf},
  url = {},
  size = {},
  abstract =	{Long-range maneuver transfer consumes the most fuel and time of the rendezvous process, and it is a multivariable, multi-extremum optimization problem, which is difficult to solve using traditional optimization algorithms. This paper researched the mathematical model of long-range maneuver transfer of spacecraft with impulse thrust, and optimized the parameters of orbit transfer based on a class of novel stochastic optimization algorithms, estimation of distribution algorithms (EDAs) with minimum fuel-time consumption being the optimization objective, and compared with Genetic Algorithms (GAs). Simulation results showed that EDAs were effective method for solving long-range maneuver transfer.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Lee:2008:cec,
  author = "Ki-Baek Lee and Jong-Hwan Kim",
  title = "Mass-Spring-Damper Motion Dynamics-Based Particle Swarm Optimization",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0547.pdf},
  url = {},
  size = {},
  abstract =	{Mass-spring-damper motion dynamics-based particle swarm optimization (MMD-PSO) is a novel optimization paradigm based on motion dynamic model which consists of mass, spring and damper. In MMD-PSO some particles, which are located fitter places than other particles, drop their anchor and connect springs and dampers between the anchors and all the particles. These connections influence the movements of the particles so as to proceed to fitter places attracted by the anchors. To demonstrate the effectiveness of MMD-PSO, several experiments are carried out on numerical optimization problems with complex test functions. The results show that proposed MMD-PSO is more powerful than original PSO and PSO mass-spring analogy in terms of robustness and convergence speed with no tuning parameters.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Park:2008:cec,
  author = "Hyungmin Park and Jong-Hwan Kim",
  title = "Potential and Dynamics-Based Particle Swarm Optimization",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0548.pdf},
  url = {},
  size = {},
  abstract =	{The Particle Swarm Optimization (PSO) algorithm is a robust stochastic evolutionary computation technique based on the movement and intelligence of swarms. This paper proposes a novel PSO algorithm, based on the potential field and the motion dynamics model. It is assumed that particles form potential fields and each particle has its own mass. The potential filed and mass are modeled by the particles' fitness value. By using these fitness based models, the proposed algorithm performs well, in particular, in avoiding the local minima compare to the original PSO. The proposed PDPSO successfully solves minimization problems of complex test functions.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Khatir:2008:cec,
  author = "Mehrdad Khatir and Amir Hossein Jahangir and Hamid Beigy",
  title = "Investigating the Baldwin Effect on Cartesian Genetic Programming Efficiency",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0549.pdf},
  url = {},
  size = {},
  abstract =	{Cartesian Genetic Programming (CGP) has an unusual genotype representation which makes it more efficient than Genetic programming (GP) in digital circuit design problem. However, to the best of our knowledge, all methods used in evolutionary design of digital circuits deal with rugged, complex search space, which results in long running time to obtain successful evolution. Therefore, employing a method to guide evolution in these spaces can facilitate achieving more reasonable results. It has been claimed that a two-step evolutionary scenario caused by benefit and cost of learning called Baldwin effect can guide evolution in the biology and artificial life. Therefore, we have been motivated to examine this effect on CGP. We observe using this scenario the success rate and evolution time of CGP improves dramatically especially when size of chromosomes increases.   },
  keywords = {genetic algorithms, genetic programming, Cartesian Genetic Programming, Baldwin Effect, Phenotypic Plasticity, Digital Circuit, Reinforcement Learning.},
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Wang15:2008:cec,
  author = "Feng Wang and Yuanxiang Li and Li Liang and Kangshun Li",
  title = "Triangular Arbitrage in Foreign Exchange Rate Forecasting Markets",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0550.pdf},
  url = {},
  size = {},
  abstract =	{The non-existence of triangular arbitrage in an efficient foreign exchange markets is widely believed. In this paper, we deploy a forecasting model to predict foreign exchange rates and apply the triangular arbitrage model to evaluate the possibility of an arbitrage opportunity. Surprisingly, we substantiate the existence of triangular arbitrage opportunities in the exchange rate forecasting market even with transaction costs. This also implies the inefficiency of the market and potential market threats of profit-seeking investors. In our experiments, Neural Network based model with back-propagation (BP-NN) is used for exchange rate forecasting.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(MacNish:2008:cec,
  author = "Cara MacNish and Xin Yao",
  title = "Direction Matters in High-Dimensional Optimisation",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0552.pdf},
  url = {},
  size = {},
  abstract =	{Directional biases are evident in many benchmarking problems for real-valued global optimisation, as well as many of the evolutionary and allied algorithms that have been proposed for solving them. It has been shown that directional biases make some kinds of problems easier to solve for similarly biased algorithms, which can give a misleading view of algorithm performance.

In this paper we study the effects of directional bias for highdimensional optimisation problems. We show that the impact of directional bias is magnified as dimension increases, and can in some cases lead to differences in performance of many orders of magnitude.

We present a new version of the classical evolutionary programming algorithm, which we call unbiased evolutionary programming (UEP), and show that it has markedly improved performance for high-dimensional optimisation.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Xu4:2008:cec,
  author = "Yiliang Xu and Meng Hiot Lim and Yew-Soon Ong",
  title = "Automatic Configuration of Metaheuristic Algorithms for Complex Combinatorial Optimization Problems",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0554.pdf},
  url = {},
  size = {},
  abstract =	{We report our work on the algorithmic development of an evolutionary methodology for automatic configuration of metaheuristic algorithms for solving complex combinatorial Optimization problems.We term it Automatic Configuration Engine for Metaheuristics (ACEM). We first propose a novel Left Variation s- Right Property (LVRP) tree structure to manage various metaheuristic procedures and properties. With LVRP tree, feasible configurations of metaheuristics can be easily specified. An evolutionary learning algorithm is then proposed to evolve the internal context of the trees based on pre-selected training set. Guided by a user-defined satisfaction function of the candidate algorithms, it converges to the optimal or a very good algorithm. The experimental comparison with two recent state-of-the-art algorithms for solving the quadratic assignment problem (QAP) shows that ACEM produces an hybrid-genetic algorithm with human-competitive or even better performance.   },
  keywords = {genetic algorithms, genetic programming},
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Lin2:2008:cec,
  author = "Ying Lin and Jun Zhang and Lu-kai Lan  ",
  title = "A Contour Method in Population-Based Stochastic Algorithms",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0555.pdf},
  url = {},
  size = {},
  abstract =	{Inspired by the contours in topography, this paper proposes a contour method for the population-based stochastic algorithms to solve the problems with continuous variables. Relying on the existed population, the contour method explores the landscape of the fitness function in the search space, which leads to effective speculation about the positions of the potential optima. The contour method is embedded into every generation of the simple genetic algorithm (SGA) for efficiency examination. The genetic algorithm with the contour method is first realized in a two-dimensional space, where the contours in topography can be directly used. Then the proposed contour method is modified to adapt high dimensional space. Numerical optimization experiments are carried out on ten benchmark functions of two and thirty dimensions. Results show that the genetic algorithm with the contour method can outperform the SGA in both solution quality and convergence speed.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Chen10:2008:cec,
  author = "Angela H. L. Chen and Chiuh-Cheng Chyu",
  title = "A Memetic Algorithm for Maximizing Net Present Value in Resource-Constrained Project Scheduling Problem",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0557.pdf},
  url = {},
  size = {},
  abstract =	{In this study, we develop a model that considers monetary issues in resource-constrained environments, and involves scheduling project activities to maximize net present value. This problem is recognized as the ''resource-constrained project scheduling problem with discounted cash flows (RCPSPDCF),'' which is strongly NP-hard. All resources considered are both types of renewable and nonrenewable; the duration of each activity depends on the amount of resources allocated to its execution. Efforts are made by considering a two-stage method applying mode selection rules at the first stage and the memetic algorithm at the second stage. Results are shown in a comparative study which demonstrates the effectiveness of using memetic algorithm in maximizing project net present value; as well as, a combination of mode selection rules which provide a high probability of giving the best solution.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Jiang3:2008:cec,
  author = "He Jiang and Jifeng Xuan and Xianchao Zhang",
  title = "An Approximate Muscle Guided Global Optimization Algorithm for the Three-Index Assignment Problem",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0558.pdf},
  url = {},
  size = {},
  abstract =	{The Three-Index Assignment Problem (AP3) is a famous NP-hard problem with wide applications. Since it's intractable, many heuristics have been proposed to obtain near optimal solutions in reasonable time. In this paper, a new meta-heuristic was proposed for solving the AP3. Firstly, we introduced the conception of muscle (the union of optimal solutions) and proved that it is intractable to obtain the muscle under the assumption that P&#8800; NP. Moreover, we showed that the whole muscle can be approximated by the union of local optimal solutions. Therefore, the Approximate Muscle guided Global Optimization (AMGO) is proposed to solve the AP3. AMGO employs a global optimization strategy to search in a search space reduced by the approximate muscle, which is constructed by a multi-restart scheme. During the global optimization procedure, the running time can be dramatically saved by detecting feasible solutions and extracting poor partial solutions. Extensive experimental results on the standard AP3 benchmark indicated that the new algorithm outperforms the state-of-the-art heuristics in terms of solution quality. Work of this paper not only provides a new meta-heuristic for NP-hard problems, but shows that global optimization can provide promising results in reasonable time, by restricting it to a fairly reduced search space.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Li15:2008:cec,
  author = "Gang Li and Tak-Ming Chan and Kwong-Sak Leung and Kin-Hong Lee",
  title = "An Estimation of Distribution Algorithm for Motif Discovery",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0559.pdf},
  url = {},
  size = {},
  abstract =	{The problem of Transcription Factor Binding Sites identification or motif discovery is to identify the motif binding sites in the cis-regulatory regions of DNA sequences. The biological experiments are expensive and the problem is NP-hard computationally. We have proposed Estimation of Distribution Algorithm for Motif Discovery (EDAMD). We use Bayesian analysis to derive the fitness function to measure the posterior probability of a set of motif instances, which can be used to handle a variable number of motif instances in the sequences. EDAMD adopts a Gaussian distribution to model the distribution of the sets of motif instances, which is capable of capturing the bivariate correlation among the positions of motif instances. When a new Position Frequency Matrix (PFM) is generated from the Gaussian distribution, a new set of motif instances is identified based on the PFM via the Greedy Refinement operation. At the end of a generation, the Gaussian distribution is updated with the sets of motif instances. Since Greedy Refinement assumes a single motif instance on a sequence, a Post Processing operation based on the fitness function is used to find more motif instances after the evolution. The experiments have verified that EDAMD is comparable to or better than GAME and GALF on the real problems tested in this paper. },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Ishibuchi:2008:cec,
  author = "Hisao Ishibuchi and Noritaka Tsukamoto and Yusuke Nojima",
  title = "Evolutionary Many-Objective Optimization: A Short Review",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0560.pdf},
  url = {},
  size = {},
  abstract =	{Whereas evolutionary multiobjective optimization (EMO) algorithms have successfully been used in a wide range of real-world application tasks, difficulties in their scalability to many-objective problems have also been reported. In this paper, first we demonstrate those difficulties through computational experiments. Then we review some approaches proposed in the literature for the scalability improvement of EMO algorithms. Finally we suggest future research directions in evolutionary many-objective optimization.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Shaobin:2008:cec,
  author = "Zhan Shaobin and Chen Shengbo and Bao Yunfei",
  title = "Building Grid Service on Atmospheric Radiative Transfer Simulation of Remote Sensing Data",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0561.pdf},
  url = {},
  size = {},
  abstract =	{The radiance leaving the earth-atmosphere system which can be sensed by a satellite borne radiometer is the sum of radiation emission from the earth surface and each atmospheric level that are transmitted to the top of the atmosphere. It can be separated from the radiance at the top the atmospheric level measured by radiometer. However, it is very difficult to measure the atmospheric radiance, especially the synchronous measurement with the satellite. Thus some atmospheric radiative transfer models (ARTM) have been developed to provide many options for modeling atmospheric radiation transport, the newly atmospheric ARTM, MODTRAN, will be researched after the atmospheric radiative transfer is described. And the simulation procedures and the applications to atmospheric transmittance, retrieval of atmospheric elements, and surface parameters, will also be presented. At the same time, the powerful computing resource was required which the urgent requirement of disposal plentiful data. When test area of atmospheric radiative transfer extends the state, even all of country, the computing capability in single computer fall short of demand. So we introduce the conception- ''Grid Service'', it can solve the problem commendably.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Lin3:2008:cec,
  author = "Ying Lin and Jian Huang and Jun Zhang",
  title = "New Evaluation Criteria for the Convergence of Continuous Evolutionary Algorithms",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0562.pdf},
  url = {},
  size = {},
  abstract =	{The first hitting time (FHT) plays an important role in convergence evaluation for evolutionary algorithms. However, the current criteria of the FHT are mostly under a hypothesis that never has been testified: the FHT subjects to the normal distribution. Aiming at more convincible evaluations, this paper investigates the distribution of the FHT through a goodness-of-fit test and discovers an unexpected result. Based on this result, this paper proposes a new set of criteria, which uses two types of relative frequency histograms. This paper validates the proposed criteria on the optimization problem of benchmark functions by the standard genetic algorithm (SGA) and the particle swarm optimization (PSO). The experiments show that the proposed criteria are effective to evaluate the convergent speed and the convergent stability of the evolutionary algorithms.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Zhang9:2008:cec,
  author = "Jun Zhang and Ying Lin  ",
  title = "A Particle Swarm Optimizer with Lifespan for Global Optimization on Multimodal Functions",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0563.pdf},
  url = {},
  size = {},
  abstract =	{The particle swarm optimizer (PSO) is a popular computing technique of swarm intelligence, known for its fast convergence speed and easy implementation. All the particles in the traditional PSO must learn from the best-so-far solution, which makes the best solution the leader of the swarm. This paper proposes a variation of the traditional PSO, named the PSO with lifespan (LS-PSO), in which the lifespan of the leader is adjusted according to its power of leading the swarm towards better solutions. When the lifespan is exhausted, a new solution is produced and it will conditionally replace the original leader depending on its leading power. Experiments on six benchmark multimodal functions show that the proposed algorithm can significantly improve the performance of the traditional PSO.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Wang16:2008:cec,
  author = "Luyi Wang and Hiroyuki Ishida and Tomoyuki Hiroyasu and Mitsunori Miki",
  title = "Examination of Multi-Objective Optimization Method for Global Search Using DIRECT and GA",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0566.pdf},
  url = {},
  size = {},
  abstract =	{A number of multi-objective genetic algorithms (MOGAs) have been developed to obtain Pareto optimal solu- tions for multi-objective optimization problems. However, as these methods involve probabilistic algorithms, there is no guarantee that the global search will be conducted in the design variable space. In such cases, there are unsearched areas in the design variable space, and the obtained Pareto solutions may not be truly optimal. In this paper, we propose an optimization method called NSDIRECT-GA to conduct a global search over the design variable space as much as possible, which improves the reliability of the obtained Pareto solutions. The effectiveness of NSDIRECT-GA was examined through numerical experiments. NSDIRECT-GA can obtain not only Pareto solutions, but also grasp the landscape of the search space, which results in higher reliability of the obtained solutions compared to MOGAs.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Huang6:2008:cec,
  author = "Tao Huang and Jian Huang and Jun Zhang",
  title = "An Orthogonal Local Search Genetic Algorithm for the Design and Optimization of Power Electronic Circuits",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0567.pdf},
  url = {},
  size = {},
  abstract =	{In this paper, an orthogonal local search genetic algorithm (OLSGA) is proposed for the design and optimization of power electronic circuits. The genetic algorithm is accelerated with a fast local search operator that automatically adjusts the search direction and the step size. An experimental design method called orthogonal design is used to determine the most promising direction of the potential region in the local search. In each generation, the step size is adaptively expanded or shrunk according to whether there is a newly improvement in the given local region. As a result, with proper direction and step size, the local search operator is able to stride forward and provide better exploitation ability to speed up the convergence rate of the genetic algorithm. The proposed method is applied to design and optimize a buck regulator. The results in comparison with other published results indicate that our proposed algorithm is effective and efficient.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Zhong:2008:cec,
  author = "Wen-Liang Zhong and Jian Huang and Jun Zhang",
  title = "A Novel Particle Swarm Optimization for the Steiner Tree Problem in Graphs",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0568.pdf},
  url = {},
  size = {},
  abstract =	{The Steiner tree problem (STP) in graphs is a special but essential case of multiple destination routing (MDR) problems, which focuses on finding a minimal spanning tree (MST) that connecting the source and destinations. It has been proved to be an NP-hard problem. Particle swarm optimization (PSO) is an important swarm intelligent algorithm with fast convergence speed and easy implementation. In this paper, a novel discrete PSO for the STP (DPSO-STP), with the concept that the particle is guided by social and self cognition, is proposed. Different from the standard PSO, the DPSO-STP includes four parts: (1) two preprocessing operations are introduced, which are to construct a complete graph and to calculate each node's total distance from itself to the source and destination nodes; (2) the position of a particle is represented as a binary string, where 1 stands for the selected nodes and 0 denotes the opposite; (3) several novel update operations, including new mutation factor c<sub>3</sub>, are adopted for the binary string; (4) when generating a MST from a binary string, a modified Prim's algorithm and a trimming strategy are employed. The experiments based on the benchmarks from category B, C of STP in the OR-library have been carried out to demonstrate the effectiveness of the proposed algorithm. Compared with traditional heuristic algorithms, such as shortest path heuristic (SPH), average distance heuristic (ADH), etc., the DPSO obtains more promising results. And it also performs better than the other iteration based algorithm, with much less computation. The discussion to extend the algorithm to other MDR problems is also given.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Kattan:2008:cec,
  author = "Ahmad Kattan and Riccardo Poli",
  title = "Evolutionary Lossless Compression with GP-ZIP",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0569.pdf},
  url = {},
  size = {},
  abstract =	{In this paper we propose a new approach for applying Genetic Programming to loss-less data compression based on combining well-known lossless compression algorithms. The file to be compressed is divided into chunks of a predefined length, and GP is asked to find the best possible compression algorithm for each chunk in such a way to minimise the total length of the compressed file. This technique is referred to as ''GP-zip''. The compression algorithms available to GP-zip (its function set) are: Arithmetic coding (AC), Lempel-Ziv-Welch (LZW), Unbounded Prediction by Partial Matching (PPMD), Run Length Encoding (RLE), and Boolean Minimisation. In addition, two transformation techniques are available: Burrows-Wheeler Transformation (BWT) and Move to Front (MTF). In experimentation with this technique, we show that when the file to be compressed is composed of heterogeneous data fragments (as is the case, for example, in archive files), GP-zip is capable of achieving compression ratios that are superior to those obtained with well-known compression algorithms.  },
  keywords = {genetic algorithms, genetic programming},
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Osmera:2008:cec,
  author = "Pavel Osmera and Ondrej Popelka and Petr Pivonka",
  title = "Two Level Parallel Grammatical Evolution",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0570.pdf},
  url = {},
  size = {},
  abstract =	{This paper describes a Two Level Parallel Grammatical Evolution (TLPGE) that can evolve complete programs using a variable length linear genome to govern the mapping of a Backus Naur Form grammar definition. To increase the efficiency of Grammatical Evolution (GE) the influence of backward processing was tested and a second level with differential evolution was added. The significance of backward coding (BC) and the comparison with standard coding of GEs is presented. The new method is based on parallel grammatical evolution (PGE) with a backward processing algorithm, which is further extended with a differential evolution algorithm. Thus a two-level optimisation method was formed in attempt to take advantage of the benefits of both original methods and avoid their difficulties. Both methods used are discussed and the architecture of their combination is described. Also application is discussed and results on a real-word application are described.  },
  keywords = {genetic algorithms, genetic programming, grammatical evolution},
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Scriven:2008:cec,
  author = "Ian Scriven and David Ireland and Andrew Lewis and Sanaz Mostaghim and J&#252;rgen Branke",
  title = "Asynchronous Multiple Objective Particle Swarm Optimisation in Unreliable Distributed Environments",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0571.pdf},
  url = {},
  size = {},
  abstract =	{This paper examines the performance characteristics of both asynchronous and synchronous parallel particle swarm optimisation algorithms in heterogeneous, fault-prone environments. Algorithm convergence is measured as a function of both iterations completed and time elapsed, allowing the two particle update mechanisms to be comprehensively evaluated and compared in such an environment. Asynchronous particle updates are shown to negatively impact the convergence speed in regards to iterations completed, however the increased parallel efficiency of the asynchronous model appears to counter this performance reduction, ensuring the asynchronous update mechanism performs comparably to the synchronous mechanism in fault-free environments. When faults are introduced, the synchronous update method is shown to suffer significant performance drops, suggesting that at least partly asynchronous algorithms should be used in real-world environments where faults can regularly occur.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Lu3:2008:cec,
  author = "Xiaojun Lu and Guowu Yang and Jianping Li and  Xiaoyu Song and William N. N. Hung",
  title = "The Probability Logics for Nanoscale Inverters Cascade",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0572.pdf},
  url = {},
  size = {},
  abstract =	{Device failure is an important consideration in nano-scale design. This paper presents a probabilistic logic model to compute the probability distribution of the nano gate states. The characterization is based on markov random field and statistical physics. The basic logic gates are probabilistically characterized. The effectiveness of the method is demonstrated by an inverter and the inverter casecade. Our analysis shows that the device probability distribution highly depends on the system structures and other performance parameters.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Li16:2008:cec,
  author = "Yamin Li and Jinru Ma and Qiuxia Zhao",
  title = "Two Improvements in Genetic Programming for Image Classification",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0573.pdf},
  url = {},
  size = {},
  abstract =	{A new classification algorithm for multi-image classification in genetic programming (GP) is introduced, which is the centred dynamic class boundary determination with quick-decreasing power value of arithmetic progression. In the classifier learning process using GP for multi-image classification, different sets of power values are tested to achieve a more suitable range of margin values for the improvement of the accuracy of the classifiers. In the second development, the program size is introduced into the fitness function to control the size of program growth during the evolutionary learning process. The approach is examined on a Chinese character image data set and a grass leaves data set, both of which have four or more classes. The experimental results show that while dealing with complicated problems of multi-image classification, the new approach can be used for more accurate classification and work better than the previous algorithms of either static or dynamic class boundary determination. With the fitness function, the size of the programs in the population can be controlled effectively and shortened considerably during evolution. Thus, the readability of the programs could be seemingly improved.  },
  keywords = {genetic algorithms, genetic programming},
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Albrecht:2008:cec,
  author = "Andreas A. Albrecht and Peter C. R. Lane and  Kathleen Steinhofel",
  title = "Combinatorial Landscape Analysis for {\it k}-SAT Instances",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0574.pdf},
  url = {},
  size = {},
  abstract =	{Over the past ten years, methods from statistical physics have provided a deeper inside into the average complexity of hard combinatorial problems, culminating in a rigorous proof for the asymptotic behaviour of the k-SAT phase transition threshold by Achlioptas and Peres in 2004. On the other hand, when dealing with individual instances of hard problems, gathering information about specific properties of instances in a pre-processing phase might be helpful for an appropriate adjustment of local search-based procedures. In the present paper, we address both issues in the context of landscapes induced by k-SAT instances: Firstly, we use a sampling method devised by Garnier and Kallel in 2002 for approximations of the number of local maxima in landscapes generated by individual k-SAT instances and a simple neighbourhood relation. The objective function is given by the number of satisfied clauses. Secondly, we outline a method for obtaining upper bounds for the average number of local maxima in k-SAT instances which indicates some kind of phase transition for the neighbourhood-specific ratio m/n = &#920;(2<sup>k</sup>/k).   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Piccand:2008:cec,
  author = "Sebastien Piccand and Michael O'Neill and Jacqueline Walker",
  title = "On the Scalability of Particle Swarm Optimisation",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0577.pdf},
  url = {},
  size = {},
  abstract =	{Particle swarm has proven to be competitive to other evolutionary algorithms in the field of optimization, and in many cases enables a faster convergence to the ideal solution. However, like any optimization algorithm it seems to have difficulties handling optimization problems of high dimension. Here we first show that dimensionality is really a problem for the classical particle swarm algorithms. We then show that increasing the swarm size can be necessary to handle problem of high dimensions but is not enough. We also show that the issue of scalability occurs more quickly on some functions.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Zhang10:2008:cec,
  author = "Xiao-hang Zhang and Jun Wu and Xue-cheng Yang and Ting-jie Lu",
  title = "Extracting Meaningful Patterns for Time Series Classification",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0578.pdf},
  url = {},
  size = {},
  abstract =	{An import area in machine learning is multivariate time series classification. In this paper we present a novel algorithm which extracts some meaningful patterns from time series data and then uses traditional machine learning algorithm to create classifier. During the stage of pattern extraction, the Gini function is used to evaluate the patterns and the starting position and the length of each pattern are automatically determined. We also apply sampling method to reduce the search space and improve efficiency. The common datasets are used to check our algorithm which is compared with the na&#239;ve algorithms. The results show that a lot of improvement can be gained in terms of interpretability, simplicity of the model and also in terms of accuracy.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Liu8:2008:cec,
  author = "Julie Yu-Chih Liu and Pei-Chann Chang",
  title = "Constraints for Data Operations in Extended Possibility-Based Databases",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0579.pdf},
  url = {},
  size = {},
  abstract =	{This paper considers the data operation for multidatabases in an extended possibility-based data model. Owing to the complexity of the data model considered, inconsistent redundancy of tuples may occur when database relations being operated are associated with different resemblance relations on a given domain. This work first demonstrates the inconsistency problem, and then presents the notions of consistency constraints for multi-database design. Under the constraints, the extended possibility-based databases using different resemblance relations can preserve consistent redundancy.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Tsai:2008:cec,
  author = "Sientang Tsai and Wei-Yeh Chen and Rumin Yang",
  title = "Molecular Solutions for the Set-Partition Problem on DNA-Based Computing",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0581.pdf},
  url = {},
  size = {},
  abstract =	{Suppose that a finite set S has q elements, and each element in S is a positive integer. The set-partition problem is to determine whether there is a subset T &#8838; S such that &#931;<sub>x &#8712; T</sub>x = &#8721;<sub>x &#8712;  	&#932;</sub>x, where  	&#932; = {x<font face="symbol">|</font>x &#8712; S and x &#8713; T}. This paper shows that biological operations can be applied to solve the set-partition problem. In order to perform this goal, we offer two DNA-based algorithms, an unsigned parallel adder and a parallel Exclusive-OR (XOR) operation, that formally verify our designed molecular solutions for solving the set-partition problem.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Godley:2008:cec,
  author = "Paul Godley and Julie Cowie and David Cairns and  John McCall and Catherine Howie",
  title = "Optimisation of Cancer Chemotherapy Schedules Using Directed Intervention Crossover Approaches",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0582.pdf},
  url = {},
  size = {},
  abstract =	{This paper describes two directed intervention crossover approaches that are applied to the problem of deriving optimal cancer chemotherapy treatment schedules. Unlike traditional uniform crossover (UC), both the calculated expanding bin (CalEB) method and targeted intervention with stochastic selection (TInSSel) approaches actively choose an intervention level and spread based on the fitness of the parents selected for crossover. Our results indicate that these approaches lead to significant improvements over UC when applied to cancer chemotherapy scheduling.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Passow:2008:cec,
  author = "Benjamin N. Passow and Mario Gongora and  Simon Coupland and Adrian A. Hopgood",
  title = "Real-Time Evolution of an Embedded Controller for an Autonomous Helicopter",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0584.pdf},
  url = {},
  size = {},
  abstract =	{In this paper we evolve the parameters of a proportional, integral, and derivative (PID) controller for an unstable, complex and nonlinear system. The individuals of the applied genetic algorithm (GA) are evaluated on the actual system rather than on a simulation of it, thus avoiding the ''reality gap''. This makes implicit a formal model identification for the implementation of a simulator. This also calls for the GA to be approached in an unusual way, where we need to consider new aspects not normally present in the usual situations using an unnaturally consistent simulator for fitness evaluation. Although elitism is used in the GAs, no monotonic increase in fitness is exhibited by the algorithm. Instead, we show that the GA's individuals converge towards more robust solutions.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Wang17:2008:cec,
  author = "D. Wang and G. S. Ng and C. Quek  ",
  title = "A Novel Hybrid Intelligent System: Genetic Algorithm and Rough Set Incorporated Neural Fuzzy Inference System",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0585.pdf},
  url = {},
  size = {},
  abstract =	{This paper proposes a novel hybrid intelligent system denoted as genetic algorithm and rough set incorporated neural fuzzy inference system (GARSINFIS). Its network structure dynamically changes along with the evolving genetic algorithm based rough set clustering (GARSC) technique. When input data set is applied, only the most essential information is retained in the clustering result, as knowledge reduction is done using rough set approximations and the most optimal solution is selected by genetic algorithm. The system not only obtains promising accuracy but also possesses a great level of interpretability to meet the increasing need of understanding the inference process. In terms of TSK type of fuzzy inference system, better structural interpretability is typically manifested as employing less number of input features, less number of rules, less number of fuzzy membership functions in each feature, and less complex rules in both antecedent and consequent parts. Extensive simulations on various data sets were conducted, and the performance of GARSINFIS was benchmarked against other well established neural and neural-fuzzy systems. Experimental results have shown that GARSINFIS performs well in both accuracy and interpretability.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Akhtar:2008:cec,
  author = "Junaid Akhtar and Mian M. Awais and Basit B. Koshul",
  title = "Evolutionary Algorithms Based on Non-Darwinian Theories of Evolution",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0586.pdf},
  url = {},
  size = {},
  abstract =	{One name that comes to mind in connection with the word evolution is Darwin. One evolutionist however, who is rarely talked about, especially in the Artificial Intelligence community, is Peirce. The Darwinian model is based on the concepts of absolute chance, mechanistic laws, and inexplicable interaction between the two. In contrast, Peirce's framework posits a dynamic interaction between possibility, necessity and regularity to describe the process of evolution. The theory of evolution proposed by Peirce is superior to the one proposed by Darwin because it is more general and it has greater explanatory power. Peirce's insights are significant enough to be used to improve the existing evolutionary algorithms. It was observed during our literature review that almost all evolutionary algorithms are fundamentally based on Darwinian principles of evolution. The present paper highlights the differences between Darwinian and Peircian evolutionary theories and provides the theoretical foundation for developing a novel Peirce based Evolutionary Algorithm. Preliminary experiments have been conducted and results seem very promising.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Ramstein:2008:cec,
  author = "Gerard Ramstein and Nicolas Beaume and Yannick Jacques",
  title = "A Grammatical Swarm for Protein Classification",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0587.pdf},
  url = {},
  size = {},
  abstract =	{We present a Grammatical Swarm (GS) for the Optimization of an aggregation operator. This combines the results of several classifiers into a unique score, producing an optimal ranking of the individuals. We apply our method to the identification of new members of a protein family. Support Vector Machine and Naive Bayes classifiers exploit complementary features to compute probability estimates. A great advantage of the GS is that it produces an understandable algorithm revealing the interest of the classifiers. Due to the large volume of candidate sequences, ranking quality is of crucial importance. Consequently, our fitness criterion is based on the Area Under the ROC Curve rather than on classification error rate. We discuss the performances obtained for a particular family, the cytokines and show that this technique is an efficient means of ranking the protein sequences.  },
  keywords = {genetic algorithms, genetic programming, grammatical evolution},
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Samways:2008:cec,
  author = "Neale Samways and Yaochu Jin and Xin Yao and Bernhard Sendhoff",
  title = "Toward a Gene Regulatory Network Model for Evolving Chemotaxis Behavior",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0588.pdf},
  url = {},
  size = {},
  abstract =	{Inspired from bacteria, a gene regulatory network model for signal transduction is presented in this paper. After describing experiments on stabilizing the population size for sustained open-ended evolution, we examine the ability of the model to evolve gradient-following behavior resembling bacterial chemotaxis. Under the conditions defined in this paper, an overwhelming chemotaxis behavior does not seem to emerge. Further experimentation suggests that chemotaxis is selectively favored, however, it is shown that the gradient information, which is critical for evolving chemotaxis, is heavily degraded under the current regime. It is hypothesized that lack of consistent gradient information results in the selection of nonchemotaxis behavior. Future work on revising the model as well as the environmental setups is discussed.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Smit:2008:cec,
  author = "S. K. Smit and A. R. Griffioen and M. C. Schut",
  title = "A Controller Architecture for the Evolution of State-Persistent Controllers: Behaviour Oriented Decision Tree (BODT)",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0589.pdf},
  url = {},
  size = {},
  abstract =	{In this paper we present a new controller architecture. A central design choice is that the controller can be easily modified or changed by evolution. Our aim is to initially endow the agents with as little knowledge as possible and to let them evolve their controllers autonomously. One particular aspect of the controller that we will investigate in this research is the evolution of state persistent controllers. With this is meant controllers that that can carry out multiple tasks. Without state persistence, agents may suffer from so-called ``unfocused attention'': the case where an agent is caught in the middle between tasks and interchangeably executes these partially, but can and will never fully commit to either one and therefore never accomplish any. We will present the state-persistent controller architecture and demonstrate this property in an experiment.  },
  keywords = {genetic algorithms, genetic programming},
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Kuyucu:2008:cec,
  author = "T&#252;ze Kuyucu and Martin Trefzer and Andrew Greensted and Julian Miller and Andy Tyrrell",
  title = "Fitness Functions for the Unconstrained Evolution of Digital Circuits",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0590.pdf},
  url = {},
  size = {},
  abstract =	{This work is part of a project that aims to develop and operate integrated evolvable hardware systems using unconstrained evolution. Experiments are carried out on an evolvable hardware platform featuring both combinatorial and registered logic as well as sequential feedback loops. In order to be able to accurately assess the transient output of the system and at the same time speed up evolution, new fitness evaluation methods are introduced. These bitwise and hierarchical fitness evaluation methods are adapted and further developed specifically for hardware implementation. It is shown that the newly developed approaches are particularly powerful in coping with two important issues: computational ambiguities, which generally occur when evaluating binary strings, and transient effects resulting from measuring hardware output. On two combinatorial problems it is shown that the new fitness functions improve the performance of evolution and allow stable solutions to be found more reliably. The experiments are carried out with a recently developed hardware platform called reconfigurable integrated system array (RISA).  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Ni:2008:cec,
  author = "Bing Ni and M. H. Wong and K. S. Leung  ",
  title = "N-SAMSAM : A Simple and Faster Algorithm for Solving Approximate Matching in DNA Sequences",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0591.pdf},
  url = {},
  size = {},
  abstract =	{This work proposes a novel algorithm to do approximate matching in a database consisting of multiple sequences. We apply Agrep algorithm in an indexing structure, the r-cut numerical substring array (r-NSA). The structure basically indexes all the substrings of length r. The advantage of using the r-NSA is two-fold: (1) The space requirement of the r-NSA is much smaller than that of the other existing indexing structures, such as the generalized suffix tree. (2) We propose an algorithm to apply Agrep in the r-NSA, in which the substrings are processed sequentially. Since the common substrings are processed only once, the cost of our algorithm is smaller than that of the full scanning search by Agrep. Consequently, the matching time of our algorithm is also reduced. We design experiments to validate and compare the performance of our algorithm against the full scanning search by Agrep. We define the speed-up of our algorithm as the time required by the full scanning search by Agrep over that of our algorithm. We use eight sets of real DNA sequences in our experiments, and the results show that our algorithm achieves significant speed-up. We also investigate the speed-up of difference data sets, and analyze their differences in detail.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Johnson:2008:cec,
  author = "Colin G. Johnson  ",
  title = "Multi-Level Neutrality in Optimization",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0592.pdf},
  url = {},
  size = {},
  abstract =	{This paper explores the idea of neutrality in heuristic optimization algorithms. In particular, the effect of having multiple levels of neutrality in representations is explored. Two experiments using a fitness-adaptive walk algorithm are carried out: the first is concerned with function optimization with Random Boolean Networks, the second with a tunable neutral mapping applied to the hierarchical if-and-only-if function. In both of these cases it is shown that a two-level neutral mapping can be found that performs better than both nonneutral mappings and mappings with a single level of neutrality.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Laredo:2008:cec,
  author = "J. L. J. Laredo and P. A. Castillo and A. M. Mora and  J. J. Merelo",
  title = "Exploring Population Structures for Locally Concurrent and Massively Parallel Evolutionary Algorithms",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0594.pdf},
  url = {},
  size = {},
  abstract =	{In this paper we present the Gossip-based Evolvable Agent Model (GossEvAg) within the context of parallel fine-grained Evolutionary Algorithms (EAs). It extends the Cellular Evolutionary Algorithm (CEA) definition with two novel features designed to work on Peer-to-Peer (P2P) networks: every individual is self-scheduled in a single thread and dynamically self-organizes its neighbourhood via newscasting, a gossip protocol.

As a consequence of such multi-threading model, each Evolvable Agent (EvAg) updates asynchronously its state at random depending on the underlying platform scheduler.

In order to assess the effects of asynchrony and the gossip protocol, we perform an experimental evaluation of the model for a set of discrete optimization problems. As a baseline for comparison we use two canonical genetic algorithms (GA): A steady-state GA (ssGA) and a generational GA (gGA). We also test two more topologies for the EvAg, a complete graph topology which allows panmixia and a Watts-Strogatz topology which has shown good theoretical and empirical results in related papers.

We found that leaving the management of the EvAg to the underlying platform scheduler has an interesting emerging feature: the model is able to scale seamlessly in desktop computers without any effort from the practitioner.We measure how the algorithm speed scales by conducting the experiments in a Single and a Dual-Core Processor architectures.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Tasgetiren:2008:cec,
  author = "Fatih Tasgetiren and Quan-Ke Pan and Yun-Chia Liang",
  title = "A Discrete Differential Evolution Algorithm for Single Machine Total Weighted Tardiness Problem with Sequence Dependent Setup Times",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0595.pdf},
  url = {},
  size = {},
  abstract =	{In this paper, a discrete differential evolution algorithm with the reference local search is presented to solve the single machine total weighted tardiness problem with sequence dependent setup times. In addition, To facilitate the greedy job insertion into a partial solution, newly designed speed-up methods are presented for the insertion move as a further and novel contribution to the single machine tardiness related scheduling with sequence dependent setup times literature. To evaluate its performance, the discrete differential evolution algorithm is tested on a set of benchmark instances from the literature. Through the analyses of experimental results, highly effective performance of the discrete differential evolution algorithm is shown against the best known solutions from the literature, especially, against the very recent newly designed particle swarm optimization algorithm and ant colony algorithm of Anghinolfi &amp; Paolucci [European Journal of Operational Research 2007; doi: 10.1016/j.ejor.2007.10.044, Available Online] and Anghinolfi &amp; Paolucci [to appear in the International Journal of Operations Research 2007], respectively. Ultimately, 46 out of 120 aggregated best known solutions so far in the literature are further improved.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Brownlee:2008:cec,
  author = "Alexander E. I. Brownlee and John A. W. McCall and Qingfu Zhang and Deryck F. Brown",
  title = "Approaches to Selection and their Effect on Fitness Modelling in an Estimation of Distribution Algorithm",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0596.pdf},
  url = {},
  size = {},
  abstract =	{Selection is one of the defining characteristics of an evolutionary algorithm, yet inherent in the selection process is the loss of some information from a population. Poor solutions may provide information about how to bias the search toward good solutions. Many Estimation of Distribution Algorithms (EDAs) use truncation selection which discards all solutions below a certain fitness, thus losing this information. Our previous work on Distribution Estimation using Markov networks (DEUM) has described an EDA which constructs a model of the fitness function; a unique feature of this approach is that because selective pressure is built into the model itself selection becomes optional. This paper outlines a series of experiments which make use of this property to examine the effects of selection on the population. We look at the impact of selecting only highly fit solutions, only poor solutions, selecting a mixture of highly fit and poor solutions, and abandoning selection altogether. We show that in some circumstances, particularly where some information about the problem is already known, selection of the fittest only is suboptimal.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Patricio:2008:cec,
  author = "Miguel A. Patricio and J. Garc&#237;a and A. Berlanga and Jose M. Molina",
  title = "Solving Video-Association Problem with Explicit Evaluation of Hypothesis Using EDAs",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0598.pdf},
  url = {},
  size = {},
  abstract =	{In this work the data association problem in visual tracking is formulated as a combinatorial hypotheses search with a heuristic evaluation function taking into account structural and specific information such as distance, shape, colour, etc. In order to guarantee real time performance, the search process has a time limit to explore alternative solutions. This time defines the upper bound of the number of evaluations depending on the efficiency of the search algorithm. Estimation Distribution Algorithms (EDA) is proposed as an efficient Evolutionary Computation technique to search in this hypothesis space. Then, an exhaustive comparison of the performance of alternative algorithms is carried out considering complex representative situations in real video sets.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Ashlock5:2008:cec,
  author = "Daniel A. Ashlock and Kenneth M. Bryden and Steven Corns",
  title = "Small Population Effects and Hybridization",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0599.pdf},
  url = {},
  size = {},
  abstract =	{This paper examines the confluence of two lines of research that seek to improve the performance of evolutionary computation systems through management of information flow. The first is hybridisation; the second is using small population effects. Hybridisation consists of restarting evolutionary algorithms with copies of bestof- population individuals drawn from many populations. Small population effects occur when an evolutionary algorithm's performance, either speed or probability of premature convergence, is improved by use of a very small population. This paper presents a structure for evolutionary computation called a blender which performs hybridisation of many small populations. The blender algorithm is tested on the PORS and Tartarus tasks. Substantial and significant effects result from varying the size of the small populations used and from varying the frequency with which hybridisation is performed. The major effect results from changing the frequency of hybridization; the impact of population size is more modest. The parameter settings which yield best performance of the blender algorithm are remarkably consistent across all seven sets of experiments performed. Blender performance is found to be superior to other algorithms for six cases of the PORS problem. For Tartarus, blender performs well, but not as well as the previous hybridization experiments that motivated its development.  },
  keywords = {genetic algorithms, genetic programming},
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Gallagher:2008:cec,
  author = "John C. Gallagher and Kshitij S. Deshpande and Mitch Wolff",
  title = "An Adaptive Neuromorphic Chip for Augmentative Control of Air Breathing Jet Turbine Engines",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0602.pdf},
  url = {},
  size = {},
  abstract =	{Continuous Time Recurrent Neural Network Evolvable Hardware (CTRNN-EH) has been proposed as an enabling control technology for electromechanical devices. In addition to being able to learn control laws tabula rasa, CTRNNs can learn how to augment existing, trusted, controllers to add new capabilities without breaking existing operation. The ability to augment would be most useful in situations in which significant patching of existing controllers is needed to address contingencies not seen at design time and in which traditional design processes might be too slow to deliver quickly. In this paper, we will discuss the use of CTRNN-EH to augment a standard FADEC controller for an air-breathing jet turbine engine. We will show how we were able to extend the FADEC to properly control thrust under unusual loading conditions that were not considered at design time. Following, we will discuss future applications.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Dorronsoro:2008:cec,
  author = "Bernabe Dorronsoro and Enrique Alba and  Gabriel Luque and Pascal Bouvry",
  title = "A Self-Adaptive Cellular Memetic Algorithm for the DNA Fragment Assembly Problem",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0604.pdf},
  url = {},
  size = {},
  abstract =	{The DNA fragment assembly problem is to reconstruct a DNA chain from multiple fragments that have previously been sequenced in a laboratory. This is a critical step in any genomic project, since the resulting chains are the basis of all the work. Therefore, the quality of these chains is a prime importance to the correct development of the project. The methods typically applied to this problem usually encounter difficulties on large instances, so more efficient techniques are necessary. In this context, this work proposes a new method combining a general purpose metaheuristic (an advanced cellular genetic algorithm which automatically regulates the intensity of the search) with a local search method specifically designed for this problem (PALS). This local search method (recently published) finds very accurate solutions in very short times. As a result, our proposal is a very accurate and efficient hybrid technique clearly outperforming the other existing ones.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Tayarani:2008:cec,
  author = "N. M. H. Tayarani and T. M. R. Akbarzadeh",
  title = "Magnetic Optimization Algorithms a New Synthesis",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0605.pdf},
  url = {},
  size = {},
  abstract =	{A novel optimization algorithm is proposed here that is inspired by the principles of magnetic field theory. In the proposed Magnetic Optimization Algorithm (MOA) the possible solutions are magnetic particles scattered in the search space. Each magnetic particle has a measure of mass and magnetic field according to its fitness. The fitter magnetic particles are those with higher magnetic field and higher mass. These particles are located in a lattice-like environment and apply a force of attraction to their neighbors. The proposed cellular structure allows a better exploitation of local neighborhoods before they move towards the global best, hence it increases population diversity. Experimental results on 14 numerical benchmark functions show that MOA in some benchmark functions can work better than GA and PSO.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Tayarani2:2008:cec,
  author = "N. M. H. Tayarani and T. M. R. Akbarzadeh",
  title = "A Cellular Structure and Diversity Preserving Operator in Quantum Evolutionary Algorithms",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0606.pdf},
  url = {},
  size = {},
  abstract =	{A Diversity Preserving Cellular Quantum Evolutionary Algorithm (DPCQEA) is proposed in which the quantum individuals are located in a specific topology and interact only with their neighbors. The proposed cellular structure aims to provide a better exploitation of local neighborhoods before moving towards a global best, hence it increases population diversity. This paper also proposes a new operator for diversity preservation in the population. In standard QEA the diversity in the population decreases across the generations. Decreasing the diversity of the population decreases the exploration performance of the algorithm and causes possible algorithm trapping in the local optima. In the proposed algorithm, only the fittest of converged q-individuals from among similar individuals are preserved, while others are reinitialized. A criterion is then proposed to measure convergence and similarity among individuals. Experimental results on Knapsack Problem, Trap Problem as well as 14 Numerical benchmark functions show that DPCQEA consistently exceeds the performance of QEA.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Morales-Reyes:2008:cec,
  author = "Alicia Morales-Reyes and Evangelos F. Stefatos and  Ahmet T. Erdogan and Tughrul Arslan",
  title = "Fault Tolerant Cellular Genetic Algorithm",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0607.pdf},
  url = {},
  size = {},
  abstract =	{This paper presents a cellular Genetic Algorithm (cGA) which aims at realizing a fault tolerant platform based on the inherent ability of cGAs to deal with Single Hard Errors (SHE) that could permanently affect the operation of a system. To attain this objective it is indispensable to control the parameters of the cGA which directly affect the efficiency and accuracy of its search process. Among the overall set of parameters, the migration rate and frequency, the grid size, and the shape and size of local neighbourhoods have a remarkable effect on the cGA performance. By appropriately controlling these parameters, the complex search space (presenting multi-peak fitness-function) associated with the practical case study of the investigation herein presented, is conveniently explored in terms of efficiency and efficacy. Initially, fitness score registers have been identified as critical for proper system's operation. In case, SHEs occur at these registers, the algorithm will ignore possible good solutions and rapidly spread bad individuals. Experiments results show the faults effects regarding convergence time, search rate and results accuracy, as well as the cGA improvement on faulty scenarios when migration is applied following different selection and replacement criteria or increasing selection intensity through different local neighbourhoods configurations.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Wong:2008:cec,
  author = "Phillip Wong and Mengjie Zhang",
  title = "SCHEME: Caching Subtrees in Genetic Programming",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0608.pdf},
  url = {},
  size = {},
  abstract =	{This paper introduces SCHEME (Sub-tree Caching using a Hashing for Equivalence MEthod), a method of caching program subtrees while taking into consideration algebraic equivalences between these programs. By using hashing in order to estimate algebraic equivalence between subtrees, we develop a hash table based caching mechanism which is easily integrated with the standard GP system. Experiments are performed on two regression and four classification tasks of varying difficulty. The results suggest that using SCHEME significantly reduces the number of node evaluations performed during the GP runs, which in turn leads to a faster GP training process.  },
  keywords = {genetic algorithms, genetic programming},
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Epitropakis:2008:cec,
  author = "M. G. Epitropakis and V. P. Plagianakos and M. N. Vrahatis",
  title = "Balancing the Exploration and Exploitation Capabilities of the Differential Evolution Algorithm",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0609.pdf},
  url = {},
  size = {},
  abstract =	{The hybridisation and composition of different Evolutionary Algorithms to improve the quality of the solutions and to accelerate execution is a common research practice. In this paper we propose a hybrid approach that combines Differential Evolution mutation operators in an attempt to balance their exploration and exploitation capabilities. Additionally, a self-balancing hybrid mutation operator is presented, which favours the exploration of the search space during the first phase of the Optimization, while later opts for the exploitation to aid convergence to the optimum. Extensive experimental results indicate that the proposed approaches effectively enhance DE's ability to accurately locate solutions in the search space.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Vergidis2:2008:cec,
  author = "Theodoros Vergidis and Kostas Vergidis and Ashutosh Tiwari",
  title = "The Evaluation Line: A Posteriori Preference Articulation Approach",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0610.pdf},
  url = {},
  size = {},
  abstract =	{This paper presents the concept of the evaluation line - a posteriori preference articulation approach for evaluating Pareto-optimal solutions using high level preference criteria. The evaluation line is an approach based on analytical geometry. It is sketched using a weighted function that prioritizes the different objectives by assigning unique weights. It then evaluates each Pareto-optimal solution based on its point-line distance from the line. Based on its function, the evaluation line leans appropriately to demonstrate preference towards one or more objectives and its function is extended to the n-objective space. The evaluation line is compared with the classical weighted sum approach. The comparison demonstrates the relativity of the two approaches but also highlights the strength of the evaluation line in the cases of nonconvex Pareto-optimal fronts.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Stefatos:2008:cec,
  author = "Evangelos F. Stefatos and Tughrul Arslan and Alister Hamilton",
  title = "Evolutionary Techniques for Precise and Real-Time Implementation of Low-Power FIR Filters",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0611.pdf},
  url = {},
  size = {},
  abstract =	{This paper presents an evolutionary based reconfigurable framework that aims at implementing and reconfiguring precise and low-power FIR filters within short amount of time. Five evolutionary techniques are evaluated for their efficiency to drive the evolution of FIR filters upon the same custom reconfigurable hardware substrate. From a hardware perspective, our architecture composes a novel topology that achieves hardware economy and does not introduce hardware dependencies between different coefficients within the targeted coefficient-set. Three novel evolutionary techniques are proposed that guarantee accurate, prompt and low-power implementation of FIR filters. Each evolutionary technique mainly emphasises on one or two out of the three investigated parameters (accuracy, power-consumption and real-time adaptation) and hence the designer can select one of these techniques, based on the nature and the needs of the targeted application.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Graff:2008:cec,
  author = "Daniel Graff and Ronaldo Menezes and Robert Tolksdorf",
  title = "On the Performance of Swarm-Based Tuple Organization in Linda Systems",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0612.pdf},
  url = {},
  size = {},
  abstract =	{Coordination systems have been gaining popularity since the early 80s with the introduction of the Linda coordination model. Soon after its introduction researchers and practitioners alike started to realize that coordination is ubiquitous to any distributed systems but unfortunately partially responsible for the inefficiency found in these systems-coordination deals with costly issues such as process communication and synchronization. In the beginning of this decade, researchers looked for alternatives for implementing more efficient means of coordination; they turned to Swarm Intelligence; the first of these approaches was called Swarmlinda. Performance of Linda-based coordination systems is directly related to the issue of system entropy (tuple organization). Swarmlinda approaches for tuple organization are investigated in this paper using a simulator. After a careful study on the performance of Swarmlinda, we introduce modifications on the algorithm so as to achieve better entropic levels.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Someya:2008:cec,
  author = "Hiroshi Someya  ",
  title = "Theoretical Parameter Value for Appropriate Population Variance of the Distribution of Children in Real-coded GA",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0613.pdf},
  url = {},
  size = {},
  abstract =	{The purposes of this paper are to discuss theoretical parameter value for crossover operators in real-coded GAs (RCGAs) and to bridge the gap between earlier related studies. Crossover operator in RCGAs has at least one parameter that forms its probability distribution function. The appropriateness of the value for this parameter affects optimization performance of RCGA. To obtain suitable parameter value, some manners have been reported. However, they have confused us by their several differences, such as the reference point often used as the first choice to be tuned. This paper has theoretically introduced a constraint that explains that these manners are essentially identical. Parameter values determined under this constraint have been empirically confirmed that they satisfy requirements of the manners. Experiments on several test functions have supported that such parameter values are suitable for the reference point.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Almosallam:2008:cec,
  author = "Ibrahim A. Almosallam and Yi Shang",
  title = "A New Adaptive Framework for Collaborative Filtering Prediction",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0615.pdf},
  url = {},
  size = {},
  abstract =	{Collaborative filtering is one of the most successful techniques for recommendation systems and has been used in many commercial services provided by major companies including Amazon, TiVo and Netflix. In this paper we focus on memory-based collaborative filtering (CF). Existing CF techniques work well on dense data but poorly on sparse data. To address this weakness, we propose to use z-scores instead of explicit ratings and introduce a mechanism that adaptively combines global statistics with item-based values based on data density level. We present a new adaptive framework that encapsulates various CF algorithms and the relationships among them. An adaptive CF predictor is developed that can self adapt from user-based to item-based to hybrid methods based on the amount of available ratings. Our experimental results show that the new predictor consistently obtained more accurate predictions than existing CF methods, with the most significant improvement on sparse data sets. When applied to the Netflix Challenge data set, our method performed better than existing CF and singular value decomposition (SVD) methods and achieved 4.67percent improvement over Netflix's system.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Wijesinghe:2008:cec,
  author = "Gayan Wijesinghe and Shahrul Badariah Mat Sah and Vic Ciesielski",
  title = "Grid vs. Arbitrary Placement of Tiles for Generating Animated Photomosaics",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0617.pdf},
  url = {},
  size = {},
  abstract =	{A traditional photo-mosaic is a still image where a larger picture is created by selectively arranging small picture tiles on a blank, gridded canvas. We show interesting and engaging animations can be generated from an evolutionary search for the final photomosaic image. We then investigate two different tile placement strategies for generating the animations. In the first strategy tiles can only be placed in fixed cells in a 2 dimensional grid and it is not possible for tiles to overlap. This strategy is implemented with a genetic algorithm. In the second strategy, which is implemented using genetic programming, the tiles can be placed in any position and at an arbitrary rotation. It is possible for one tile to be placed on top of another so a method for dealing with overlap is needed. We have investigated three methods for dealing with overlap. The second strategy generates more engaging animations but at considerably increased computational cost. We conclude that evolutionary search can be used to produce very engaging animations in which a target image gradually emerges from an initial random collection of tiles.},
  keywords = {genetic algorithms, genetic programming},
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Pinto:2008:cec,
  author = "Pedro C. Pinto and Andreas N&#228;gele and Math&#228;us Dejori and Thomas A. Runkler and Jo&#227;o M. C. Sousa",
  title = "Learning of Bayesian Networks by a Local Discovery Ant Colony Algorithm",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0618.pdf},
  url = {},
  size = {},
  abstract =	{Bayesian networks (BNs) are knowledge representation tools capable of representing dependence or independence relationships among random variables that compose a problem domain. Bayesian networks learned from data sets are receiving increasing attention within the community of researchers of uncertainty in artificial intelligence, due to their capacity to provide good inference models and to discover the structure of complex domains. One approach to learning BNs from data is to use a scoring metric to evaluate the fitness of any given candidate network for the database, and apply an optimization procedure to explore the set of candidate networks. Among the most frequently used optimization methods for this purpose is greedy search, either deterministic or stochastic.   This article proposes a hybrid Bayesian network learning algorithm MMACO, based on the local discovery algorithm Max-Min Parents and Children (MMPC) and ant colony optimization (ACO). MMPC is used to construct the skeleton of the Bayesian network and then ACO is used to orientate its edges, thus returning the final structure. We apply MMACO (Max-Min ACO) to several sets of benchmark networks and show that it outperforms greedy search (GS) and simulated annealing (SA) algorithms. },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Yang9:2008:cec,
  author = "Erfu Yang and Nick H. Barton and Tughrul Arslan and Ahmet T. Erdogan",
  title = "A Novel Shifting Balance Theory-Based Approach to Optimization of an Energy-Constrained Modulation Scheme for Wireless Sensor Networks",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0619.pdf},
  url = {},
  size = {},
  abstract =	{This paper presents a new approach to optimization of an energy-constrained modulation scheme for wireless sensor networks by taking advantage of a novel bio-inspired optimization algorithm. The algorithm is inspired by Wright's shifting balance theory (SBT) of evolution in population genetics. The total energy consumption of an energy-constrained modulation scheme is minimized by using the new SBT-based optimization algorithm. The results obtained by this new algorithm are compared with other popular optimization algorithms. Numerical experiments are performed to demonstrate that the SBT-based algorithm could be used as an efficient optimizer for solving the optimization problems arising from currently emerging energy-efficient wireless sensor networks.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Tapia:2008:cec,
  author = "Jose Juan Tapia and Edgar E. Vallejo",
  title = "A Clustering Genetic Algorithm for Inferring Protein-Protein Functional Interactions from Phylogenetic Profiles",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0620.pdf},
  url = {},
  size = {},
  abstract =	{This paper explores the capabilities of genetic algorithms for clustering genomic data. We conducted a series of computational experiments on reconstructing proteinprotein interactions from phylogenetic profiles. We validated the proposed model using experimentally confirmed functional associations and known bacterial operons. Experimental results demonstrated that clustering genetic algorithms produce competitive results with respect to traditional clustering methods.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Li17:2008:cec,
  author = "Rui Li and Michael T. M. Emmerich and Jeroen Eggermont and Ernst G. P. Bovenkamp and Jouke Dijkstra and Johan H. C. Reiber",
  title = "Metamodel-Assisted Mixed Integer Evolution Strategies and Their Application to Intravascular Ultrasound Image Analysis",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0623.pdf},
  url = {},
  size = {},
  abstract =	{This paper discusses mixed integer evolution strategies (MIES) assisted by metamodels based on radial basis function networks (RBFN). The goal is to make MIES more suitable for optimization with time consuming evaluation functions.

A novelty of the presented research is that RBFN are studied for metamodeling in heterogeneous (mixed-integer) parameter spaces. A heterogeneous metric (HEOM) is adopted that is in conformity with the design of the MIES. In addition, crossvalidation based optimization techniques are suggested for adjusting hyper-parameters of the model and avoid singularities. Empirical studies on prediction of random sets indicate good prediction capabilities of the proposed RBFN for functional landscapes of moderate dimension/smoothness. The influence of the training set size as well as of the dimension on computational complexity and accuracy of the RBFN is investigated.

 In the metamodel-assisted MIES, a RBFN metamodel is build and updated after each generation. The metamodel is used for selecting a small subset of offspring individuals from a bigger set of variations and thereby increase the number of promising solutions in the offspring population. The algorithm is designed in a way that in case of failure of the metamodel (e.g. ''random'' predictions) the metamodel-assisted MIES behaves like a standard MIES.

 Experimental results, both on artificial test problems and a real world application, namely the optimization of feature detectors in ultrasound images, indicate a clear acceleration that can be achieved by using heterogeneous RBFN.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Pietro:2008:cec,
  author = "Anthony Di Pietro and Luigi Barone and Lyndon While",
  title = "On the Behaviour of Evolutionary Strategies for Problems with Varying Noise Strength",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0624.pdf},
  url = {},
  size = {},
  abstract =	{For many real-world applications of evolutionary computation, the fitness function is obscured by random noise which may vary throughout the search space. Previously, we presented algorithms that were significantly better than naive resampling, but found (perhaps counter-intuitively) that for some problems it is better to use a higher resampling rate where the noise strength is lower and vice versa. This paper analyses why this is the case, and explores how the evolutionary process works differently on these problems. We show why it is often the case that using a high resampling rate in high noise regions is ineffective and it is instead better to use these samples in low noise regions. We conclude that when applying a basic evolutionary strategy to this class of problems, it is only better to use higher resampling rates where the noise strength is higher if it is too difficult to reach a good solution without searching in or through the high noise regions.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Isaacs:2008:cec,
  author = "Amitay Isaacs and Tapabrata Ray and Warren Smith",
  title = "Blessings of Maintaining Infeasible Solutions for Constrained Multi-Objective Optimization Problems",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0625.pdf},
  url = {},
  size = {},
  abstract =	{The most common approach to handling constraints in a constrained optimization problem has been the use of penalty functions. In recent years non-dominance based ranking methods have been applied for an efficient handling of constraints. These techniques favor the feasible solutions over the infeasible solutions, thus guiding the search through the feasible space. Usually the optimal solutions of the constrained optimization problems are spread along the constraint boundary. In this paper we propose a constraint handling method that maintains infeasible solutions in the population to aid the search of the optimal solutions through the infeasible space. The constraint handling method is implemented in Constraint Handling Evolutionary Algorithm (CHEA), which is the modified Non-dominated Sorting Genetic Algorithm II (NSGAII) [1]. The original constrained minimization problem with k objectives is reformulated as an unconstrained minimization problem with k+1 objectives, where an additional objective function is the number of constraint violations. In CHEA, the infeasible solutions are ranked higher than the feasible solutions, thereby focusing the search for the optimal solutions near the constraint boundaries through infeasible region. CHEA simultaneously obtains the solutions to the constrained as well as the unconstrained optimization problem. The performance of CHEA is compared with NSGA-II on the set of CTP test problems. For a fixed number of function evaluations, CHEA converges to the Pareto optimal solutions much faster than NSGA-II. It is observed that retaining even a small number of infeasible solutions in the population, CHEA is able to prevent the search from prematurely converging to a sub-optimal Pareto front.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Huang7:2008:cec,
  author = "Hui-Ling Huang and Kuan-Wei Chen and Shinn-Jang Ho and Shinn-Ying Ho",
  title = "Inferring S-system Models of Genetic Networks from a Time-Series Real Data Set of Gene Expression Profiles",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0628.pdf},
  url = {},
  size = {},
  abstract =	{It is desirable to infer cellular dynamic regulation networks from gene expression profiles to discover more delicate and substantial functions in molecular biology, biochemistry, bioengineering, and pharmaceutics. The S-system model is suitable to characterize biochemical network systems and capable of analyzing the regulatory system dynamics. To cope with the problem ``multiplicity of solutions'', a sufficient amount of data sets of time-series gene expression profiles were often used. An efficient newly-developed method iTEA was proposed to effectively obtain S-system models from a large number (e.g., 15) of simulated data sets with/without noise. In this study, we propose an extended optimization method (named iTEAP) based on iTEA to infer the S-system models of genetic networks from a time-series real data set of gene expression profiles (using SOS DNA microarray data in E.coli as an example). The algorithm iTEAP generated additionally multiple data sets of gene expression profiles by perturbing the given data set. The results reveal that (1) iTEAP can obtain S-system models with high-quality profiles to best fit the observed profiles; (2) the performance of using multiple data sets is better than that of using a single data set in terms of solution quality, and 3) the effectiveness of iTEAP using a single data set is close to that of iTEA using two real data sets.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Zhang11:2008:cec,
  author = "Jingqiao Zhang and Viswanath Avasarala and  Arthur C. Sanderson and Tracy Mullen",
  title = "Differential Evolution for Discrete Optimization: An Experimental Study on Combinatorial Auction Problems",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0629.pdf},
  url = {},
  size = {},
  abstract =	{Differential evolution (DE) mutates solution vectors by the weighted difference of other vectors using arithmetic operations. As these operations cannot be directly extended to discrete combinatorial space, DE algorithms have been traditionally applied to optimization problems where the search space is continuous. In this paper, we use JADE, a self-adaptive DE algorithm, for winner determination in Combinatorial Auctions (CAs) where users place bids on combinations of items. To adapt JADE to discrete optimization, we use a rank-based representation schema that produces only feasible solutions and a regeneration operation that constricts the problem search space. It is shown that JADE compares favorably to a local stochastic search algorithm, Casanova, and a genetic algorithm based approach, SGA.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Zhang12:2008:cec,
  author = "Jingqiao Zhang and Arthur C. Sanderson",
  title = "Self-Adaptive Multi-Objective Differential Evolution with Direction Information Provided by Archived Inferior Solutions",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0630.pdf},
  url = {},
  size = {},
  abstract =	{We propose a new self-adaptive differential evolution algorithm for multi-objective optimization problems. To address the challenges in multi-objective optimization, we introduce an archive to store recently explored inferior solutions whose difference with the current population is used as direction information about the optimum, and also consider a fairness measure in calculating crowding distances to prefer the solutions whose distances to nearest neighbors are large and close to be uniform. As a result, the obtained solutions can spread well over the computed non-dominated front and the front can be moved fast toward the Pareto-optimal front. In addition, the control parameters of the algorithm are adjusted in a self-adaptive manner, avoiding parameter tuning for problems of different characteristics. The proposed algorithm, named JADE2, achieves better or at least competitive results compared to NSGA-II and GDE3 for a set of twenty-two benchmark problems.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Neshatian:2008:cec,
  author = "Kourosh Neshatian and Mengjie Zhang",
  title = "Genetic Programming for Performance Improvement and Dimensionality Reduction of Classification Problems",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0631.pdf},
  url = {},
  size = {},
  abstract =	{In this paper, Genetic programming (GP) is used to construct a new set of high level features based on the original attributes of a classification problem with the goal of improving the classification performance and reducing the dimensionality. A non-wrapper approach is taken and a new fitness function is proposed based on the Renyi entropy. The GP system uses a variable terminal pool which is constructed by the class-wise orthogonal transformations of the original features. The performance measure is classification accuracy on 12 benchmark problems using constructed features in a decision tree classifier. The performance over difficult problems has been improved by constructing features for compound classes. This approach is compared with the principle component analysis (PCA) method and the results show that the new approach outperforms the PCA method on most of the problems in terms of classification performance and dimensionality reduction.  },
  keywords = {genetic algorithms, genetic programming},
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Larson:2008:cec,
  author = "Eric C. Larson and Gary G. Yen",
  title = "Facial Feature Analysis in Dynamic Bandwidth Environments: A Genetic Approach",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0633.pdf},
  url = {},
  size = {},
  abstract =	{Facial feature tracking for model-based coding has evolved over the past decades. Of particular interest is its application in very low bit rate coding in which optimization is used to analyze head and shoulder sequences. We present the results of a computational experiment in which we apply a combination of non-dominated sorting genetic algorithm (NSGAII) and a deterministic search to find optimal facial animation parameters at many bandwidths, simultaneously. As objective functions are concerned, peak signal-to-noise ratio is chosen to be maximized while the total number of facial animation parameters is chosen to be minimized. Particularly, the algorithm is tested for efficiency and reliability. The results show that the overall methodology works effectively, but that a better error assessment function is needed.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Au:2008:cec,
  author = "Chun-Kit Au and Ho-Fung Leung",
  title = "On the Behavior of Cooperative Coevolution in Dynamic Environments",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0634.pdf},
  url = {},
  size = {},
  abstract =	{This paper investigates the behavior of cooperative coevolutionary algorithms (CCEAs) under dynamic environments. The backgroud of dynamic optimization and the approaches used in evolutionary algorithms (EAs) to address dynamic environments are first briefly reviewed. Two common approaches, including hypermutations and random immigrants, are incorporated into CCEAs to solve two dynamic problems: one moving peak problem and two moving peaks problem. The performance on these two problems under different change severities and different change periods are empirically compared with those of the EA counterparts. Experimental results indicate that using cooperative coevolutionary approach can generally provide a better performance than the EA conterparts. In particular, CCEA with the use of random immigrants consistently outperforms other algorithms we study. The reasons behind these observations are analyzed by studying the best-of-generation fitness against generations and the trajectories of best-of-generation individuals when tracking the moving optima in the search space.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Soliman:2008:cec,
  author = "Omar S. Soliman and Lam T. Bui",
  title = "A Self-Adaptive Strategy for Controlling Parameters in Differential Evolution",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0635.pdf},
  url = {},
  size = {},
  abstract =	{The Differential Evolution (DE) is a stochastic population-based search method for global optimization over continuous spaces. This paper presents an efficient strategy for self-adapting control parameters in Differential Evolution to solve real-parameter optimization problems. The proposed strategy introduces an adaptive mechanism at the individual level based on Cauchy distribution(CD) where the step length and crossover rate are self-adapted during the evolution process. This strategy is to use attractive features of CD, which has thick tails that enable it to generate considerable changes more frequently and to escape a local optima for multi-modal optimization problems. Detailed performance comparisons of a DE using the proposed strategy on wide range of fifteen standard benchmark test problems are carried out. The obtained results showed that the performance of the DE had been improved with the proposed self-adaptive strategy.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Mart&#237;:2008:cec,
  author = "Luis Mart&#237; and Jes&#250;s Garc&#237;a and Antonio Berlanga and  Jose M. Molina  ",
  title = "Model-Building Algorithms for Multiobjective EDAs: Directions for Improvement",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0637.pdf},
  url = {},
  size = {},
  abstract =	{In order to comprehend the advantages and shortcomings of each model-building algorithm they should be tested under similar conditions and isolated from the MOEDA it takes part of. In this work we will assess some of the main machine learning algorithms used or suitable for model-building in a controlled environment and under equal conditions. They are analyzed in terms of solution accuracy and computational complexity. To the best of our knowledge a study like this has not been put forward before and it is essential for the understanding of the nature of the model-building problem of MOEDAs and how they should be improved to achieve a quantum leap in their problem solving capacity.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Matsui:2008:cec,
  author = "Shouichi Matsui and Seiji Yamada",
  title = "A Genetic Algorithm for Optimizing Hierarchical Menus",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0639.pdf},
  url = {},
  size = {},
  abstract =	{Hierarchical menus are widely used as a standard user interface in modern applications that use GUIs. The performance of the menu depends on many factors: structure, layout, colours and so on. There has been extensive research on novel menus, but there has been little work on improving performance by optimizing the menu's structure. This paper proposes algorithms based on the genetic algorithm (GA) and the simulated annealing (SA) for optimizing the performance of menus. The algorithms aim to minimize the average selection time of menu items by considering the user's pointer movement and search/decision time. We will show the results on a static hierarchical menu of a cellular phone as an example where a small screen and limited input device are assumed. We will also show performance comparison of GA-based algorithm and the SA-based one by using wide variety of the useage patterns.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Xie:2008:cec,
  author = "Huayang Xie and Mengjie Zhang and Peter Andreae",
  title = "An Analysis of the Distribution of Swapped Subtree Sizes in Tree-based Genetic Programming",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0641.pdf},
  url = {},
  size = {},
  abstract =	{This paper analyses the distribution of swapped subtree sizes involved in crossover events in approximations of an optimal crossover operator that allows the root node to be crossed over. The goal is to examine how the offspring search space can be effectively reduced for given parents. It concludes that good crossover events have a strong preference for the roots of the parent programs and for nodes with small sub-trees. This paper also quantifies the ability of crossover to optimise offspring fitness, and concludes that this ability is far below what was expected.  },
  keywords = {genetic algorithms, genetic programming},
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Aghdam:2008:cec,
  author = "Mehdi Hosseinzadeh Aghdam and Nasser Ghasem-Aghaee and Mohammad Ehsan Basiri",
  title = "Application of Ant Colony Optimization for Feature Selection in Text Categorization",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0642.pdf},
  url = {},
  size = {},
  abstract =	{Feature selection is commonly used to reduce dimensionality of datasets with tens or hundreds of thousands of features. A major problem of text categorisation is the high dimensionality of the feature space; therefore, feature selection is the most important step in text categorisation. This paper presents a novel feature selection algorithm that is based on ant colony Optimization. Ant colony Optimization algorithm is inspired by observation on real ants in their search for the shortest paths to food sources. Proposed algorithm is easily implemented and because of use of a simple classifier in that, its computational complexity is very low. The performance of proposed algorithm is compared to the performance of information gain and CHI algorithms on the task of feature selection in Reuters-21578 dataset. Simulation results on Reuters-21578 dataset show the superiority of the proposed algorithm.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Wu4:2008:cec,
  author = "Xiaolan Wu and Binggang Cao and Jianping Wen and Zhanbin Wang  ",
  title = "Application of Particle Swarm Optimization for Component Sizes in Parallel Hybrid Electric Vehicles",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0643.pdf},
  url = {},
  size = {},
  abstract =	{This paper describes an approach for the optimization of parallel Hybrid Electric Vehicle (HEV) component sizing using Particle Swarm Optimization (PSO) algorithm. In this study, the fitness function is defined to minimize the vehicle engine fuel consumption (FC) and emissions. The driving performance requirements are then considered as constraints. Finally, the optimization process is performed over the test procure TEST_CYCLE_HYWT, in which a vehicle model named ADVISOR is used as the analysis tool. The results from the computer simulation show the effectiveness of the approach and reduction in FC, emissions while ensuring that the vehicle performance is not sacrificed  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Du:2008:cec,
  author = "Zhihua Du and Yiwei Wang and Zhen Ji",
  title = "Gene Clustering Using an Evolutionary Algorithm",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0644.pdf},
  url = {},
  size = {},
  abstract =	{Microarray technology enables the study of measuring gene expression levels for thousands of genes simultaneously. Cluster analysis of gene expression profiles has been applied for analyzing the function of gene because co-expressed genes are likely to share the same biological function. K-MEANS is one of well-known clustering methods. However, it requires a precise estimation of number of clusters and it has to assign all the genes into clusters. Other main problems are sensitive to the selection of an initial clustering and easily becoming trapped in a local minimum. We present a new clustering method for microarray gene data, called ppoCluster. It has two steps: (1) Estimate the number of clusters (2) Take sub-clusters resulting from the first step as input, and bridge a variation of traditional Particle Swarm Optimization (PSO) algorithm into K-MEANS for particles perform a parallel search for an optimal clustering. Our results indicate that ppoCluster is generally more accurate than K-MEANS and FKM. It also has better robustness for it is less sensitive to the initial randomly selected cluster centroids. And it outperforms comparable methods with fast convergence rate and low computation load.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Xu5:2008:cec,
  author = "Jinhong Xu and Weijun Xu and Jinling Li and Yucheng Dong",
  title = "Competitive Algorithms About Online Reverse Auctions",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0645.pdf},
  url = {},
  size = {},
  abstract =	{Similar to the concept of on-line auctions presented by Ron Lavi and Noam Nisan [3], this paper discusses pricing algorithms for on-line reverse auction which bidders arrive one by one and on-line buyer must be required to make a decision immediately about each bid as it is received. For online buyer in a reverse auction, we propose on-line mean pricing algorithm and on-line randomized pricing algorithm, and then prove that the two algorithms are competitive and incentive compatible. Moreover, as the bid prices concentrated in a small domain, by competitive analysis for the two algorithms, we find their merits which can avoid the results of purchasing failure or more cost caused by reservation price algorithm. Finally, an example is obtained to illustrate their application.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Lee2:2008:cec,
  author = "Chi-Ho Lee and Ye-Hoon Kim and Jong-Hwan Kim",
  title = "Multiobjective Evolutionary Algorithm Reinforcing Specific Objective",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0646.pdf},
  url = {},
  size = {},
  abstract =	{This paper proposes a multiobjective evolutionary algorithm (MOEA) for the problem with many objectives, where each objective is more strengthened. In the real world applications, satisfying as many objectives as possible somewhat at the same time can be less preferred than optimizing each specific objective individually. To solve this kind of problems, this paper proposes the complement of (1-k) dominance and the pruning method considering objective deviation to get a set of nondominated solutions with specifically optimized objectives. Promoting the specificity of objective improves the optimization performance on problems with many objectives. In experimental results, proposed algorithm shows improved performance compared with the state-of-the-art MOEAs such as SPEA, SPEA2 and NSGA2. The performance is measured in terms of the solution set coverage and the closeness to the true Pareto front. Also, diversity metric is applied to verify the spread of nondominated set.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Li18:2008:cec,
  author = "Boyang Li and Yew-Soon Ong and Minh Nghia Le and Chi Keong Goh",
  title = "Memetic Gradient Search",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0648.pdf},
  url = {},
  size = {},
  abstract =	{This paper reviews the different gradient-based schemes and the sources of gradient, their availability, precision and computational complexity, and explores the benefits of using gradient information within a memetic framework in the context of continuous parameter optimization, which is labeled here as Memetic Gradient Search. In particular, we considered a quasi-Newton method with analytical gradient and finite differencing, as well as simultaneous perturbation stochastic approximation, used as the local searches. Empirical study on the impact of using gradient information showed that Memetic Gradient Search outperformed the traditional GA and analytical, precise gradient brings considerable benefit to gradient-based local search (LS) schemes. Though gradient-based searches can sometimes get trapped in local optima, memetic gradient searches were still able to converge faster than the conventional GA.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Alfaro-Cid:2008:cec,
  author = "E. Alfaro-Cid and P. A. Castillo and A. Esparcia and  K. Sharman and J. J. Merelo and A. Prieto and  J. L. J. Laredo",
  title = "Comparing Multiobjective Evolutionary Ensembles for Minimizing Type I and II Errors for Bankruptcy Prediction",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0649.pdf},
  url = {},
  size = {},
  abstract =	{In many real world applications type I (false positive) and type II (false negative) errors have to be dealt with separately, which is a complex problem since an attempt to minimise one of them usually makes the other grow. In fact, a type of error can be more important than the other, and a trade-off that minimises the most important error type must be reached. In the case of the bankruptcy prediction problem the error type II is of greater importance, being unable to identify that a company is at risk causes problems to creditors and slows down the taking of measures that may solve the problem. Despite the importance of type II errors, most bankruptcy prediction methods take into account only the global classification error. In this paper we propose and compare two methods to optimise both error types in classification: artificial neural networks and function trees ensembles created through multiobjective Optimization. Since the multiobjective Optimization process produces a set of equally optimal results (Pareto front) the classification of the test patterns in both cases is based on the non-dominated solutions acting as an ensemble. The experiments prove that, although the best classification rates are obtained using the artificial neural network, the multiobjective genetic programming model is able to generate comparable results in the form of an analytical function.  },
  keywords = {genetic algorithms, genetic programming},
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Sathe:2008:cec,
  author = "Madan Sathe and G&#252;nter Rudolph and Kalyanmoy Deb",
  title = "Design and Validation of a Hybrid Interactive Reference Point Method for Multi-Objective Optimization",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0650.pdf},
  url = {},
  size = {},
  abstract =	{This paper offers a classification of the main representatives of interactive classical and evolutionary methods. After a crossfertilization of these two fields a new hybrid interactive reference point method is designed. The method combines the reference point idea with the relative speed of a (1+1)- EA and is implemented with a graphical user interface. Finally, it is validated on two well-known real-world test problems.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Greeff:2008:cec,
  author = "Marde Greeff and Andries P. Engelbrecht",
  title = "Solving Dynamic Multi-Objective Problems with Vector Evaluated Particle Swarm Optimisation",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0651.pdf},
  url = {},
  size = {},
  abstract =	{Many optimisation problems are multi-objective and change dynamically. Many methods use a weighted average approach to the multiple objectives. This paper introduces the usage of the vector evaluated particle swarm optimiser (VEPSO) to solve dynamic multi-objective optimisation problems. Every objective is solved by one swarm and the swarms share knowledge amongst each other about the objective that it is solving. Not much work has been done on using this approach in dynamic environments. This paper discusses this approach as well as the effect of the population size and the response methods to a detected change on the performance of the algorithm. The results showed that more non-dominated solutions, as well as more uniformly distributed solutions, are found when all swarms are re-intialised when a change is detected, instead of only the swarm(s) optimising the specific objective function(s) that has changed. Furthermore, an increase in population size results in a higher number of non-dominated solutions found, but can lead to solutions that are less uniformly distributed.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Scriven2:2008:cec,
  author = "Ian Scriven and Andrew Lewis and David Ireland and Junwei Lu",
  title = "Decentralised Distributed Multiple Objective Particle Swarm Optimisation Using Peer-to-Peer Networks",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0652.pdf},
  url = {},
  size = {},
  abstract =	{This paper describes a distributed particle swarm optimisation algorithm (PSO) based on peer-to-peer computer networks. A number of modifications are made to the more traditional synchronous PSO algorithm to allow for fully decentralised, scalable and fault-tolerent operation. The modified algorithm uses staggered propagation of objective-space knowledge between sub-swarms to eliminate the need for a centralised data store. Analytical test functions are used to examine the performance of the proposed algorithm and its variations in comparison with a basic synchronous PSO implementation. The results clearly show the feasibility of decentralised particle swarm optimisation.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Mohemmed:2008:cec,
  author = "Ammar W. Mohemmed and Mengjie Zhang  ",
  title = "Evaluation of Particle Swarm Optimization Based Centroid Classifier with Different Distance Metrics",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0653.pdf},
  url = {},
  size = {},
  abstract =	{The Nearest Centroid Classifier (NCC) is based on finding the arithmetic means of the classes from the training instances and unseen-class instances are classified by measuring the distance to these means. It may work well if the classes are well separated which is not the case for many practical datasets. In this paper, particle swarm optimization (PSO) is used to find the centroids under an objective function to minimize the error of classification. Three different measures are investigated namely the Euclidean distance, the Mahalanobis distance and a Weighted distance to represent the distance function. The performance is tested on eight practical datasets. Simulation results show that the PSO based centroid classifier improves the classification results especially for datasets that the basic NCC does not handle well.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Luo2:2008:cec,
  author = "Wenjian Luo and Peng Guo and Xufa Wang",
  title = "On Convergence of Evolutionary Negative Selection Algorithms for Anomaly Detection",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0654.pdf},
  url = {},
  size = {},
  abstract =	{Evolutionary Negative Selection Algorithms (ENSAs) are proposed by combining negative selection model and evolutionary operators. In this paper, the convergence of ENSAs with two different mutation operators is analyzed. The first mutation operator is that only one bit of a detector is selected and flipped with a high probability. The second mutation operator is that every bit of a detector has a positive probability to be flipped. The analysis results show that the ENSAs with different mutation operators have different convergent properties. Especially, the shape of the self set will affect the convergence of ENSAs with the first mutation operator.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Zhang13:2008:cec,
  author = "Min Zhang and Wenjian Luo and Xingxin Pei and Xufa Wang",
  title = "The Self-Adaption Strategy for Parameter &#949; in &#949;-MOEA",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0655.pdf},
  url = {},
  size = {},
  abstract =	{A novel self-adaption strategy for the parameter &#949; in &#949;-MOEA is proposed in this paper based on the analyses of the relationship between the value of &#949;and the maximum number of non-dominated solutions. Then this novel strategy is applied in &#949;-MOEA and tested on 10 common benchmark functions. The experimental results demonstrate that even if without the good initial value for the parameter &#949;, &#949;-MOEA with this self-adaption strategy (named Algorithm 1) is able to approximately obtain the expected number of non-dominated solutions, which are very close to and uniformly distributed on the Pareto-optimal front. Furthermore, the genetic drift phenomenon in Algorithm 1 is discussed. Two cases of genetic drift are pointed out, and one case can be fixed up by a simple approach proposed in this paper.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Srivastava:2008:cec,
  author = "Kamal Srivastava and Reeti Sharma",
  title = "A Hybrid Simulated Annealing Algorithm for the Bipartite Crossing Number Minimization Problem",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0657.pdf},
  url = {},
  size = {},
  abstract =	{The bipartite crossing number of a bipartite graph is the minimum number of crossings of edges when the partitions are placed on two parallel lines and edges are drawn as straight line segments between the lines. In this paper, a simulated annealing algorithm is designed which exploits the relationship between the linear arrangement problem and the Bipartite Crossing Number Minimization Problem. The initial ordering of the vertices is provided by the spectral sequencing technique. Extensive tests on several benchmark graphs show that in a majority of the cases there is a considerable improvement in the crossing number when compared with the best known results.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Pan:2008:cec,
  author = "Quan-Ke Pan and Fatih Tasgetiren and Yun-Chia Liang and P. N. Suganthan",
  title = "Upper Bounds on Taillard's Benchmark Suite for the No-Wait Flowshop Scheduling Problem with Makespan Criterion",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0659.pdf},
  url = {},
  size = {},
  abstract =	{In this paper, the discrete particle swarm optimization (DPSO) algorithm is employed to solve the no-wait flowshop scheduling problem with the makespan criterion for Taillard's benchmark suite [1]. As known, there exist 31 benchmark instances provided by Carlier [2], Heller [3], and Revees [4] for the makespan criterion. However, these benchmarks are relatively small in size and easy to be solved even by a simple descent algorithm. Since there is a lack of a sound benchmark suite for the no-wait flowshop scheduling problem with the makespan criterion, the DPSO algorithm presented by the authors [5] is applied to the 110 benchmark instances of Taillard by treating them as the no-wait flowshop problem instances with the makespan criterion. The DPSO algorithm is hybridized with the variable neighborhood descent (VND) algorithm to further improve the solution quality. Ultimately, we carried out extensive runs and provide the upper bounds for the future researchers to test their algorithms.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Huynh:2008:cec,
  author = "Hieu Trung Huynh and Yonggwan Won",
  title = "Hematocrit Estimation from Compact Single Hidden Layer Feedforward Neural Networks Trained by Evolutionary Algorithm",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0661.pdf},
  url = {},
  size = {},
  abstract =	{Hematocrit is expressed as the percentage of red blood cells in the whole blood; it is the most highly influencing factor for measuring glucose in the whole blood by handheld devices. This paper presents hematocrit estimation from transduced current curves by using single hidden layer feedforward neural networks (SLFNs). These transduced current curves are produced by glucose-oxidase reaction in electrochemical biosensors which is used in glucose measurements. Points of the current curve sampled at frequency of 10Hz are used as the input features for the networks. Applying neural networks to hematocrit estimation has also proposed in our previous works. However, in this paper, the SLFN is trained by evolutionary least-squares extreme learning machine (ELS-ELM) algorithm in which the input weights and hidden layer biases are determined based on the differential evolution (DE). Experimental results show that the accuracy of hematocrit estimation on ELS-ELM can be improved, from which it can be used to reduce the dependency of hematocrit in measurement of glucose values by handheld devices.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Yan2:2008:cec,
  author = "Yang Yan and Hongfeng Wang and Dingwei Wang and Shengxiang Yang and Dazhi Wang",
  title = "A Multi-Agent Based Evolutionary Algorithm in Non-stationary Environments",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0662.pdf},
  url = {},
  size = {},
  abstract =	{In this paper, a multi-agent based evolutionary algorithm (MAEA) is introduced to solve dynamic optimization problems. The agents simulate living organism features and co-evolve to find optimum. All agents live in a lattice like environment, where each agent is fixed on a lattice point. In order to increase the energy, agents can compete with their neighbors and can also acquire knowledge based on statistic information. In order to maintain the diversity of the population, the random immigrants and adaptive primal dual mapping schemes are used. Simulation experiments on a set of dynamic benchmark problems show that MAEA can obtain a better performance in non-stationary environments in comparison with several peer genetic algorithms.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Biermann:2008:cec,
  author = "D. Biermann and K. Weinert and T. Wagner",
  title = "Model-Based Optimization Revisited: Towards Real-World Processes",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0663.pdf},
  url = {},
  size = {},
  abstract =	{The application of empirically determined surrogate models provides a standard solution to expensive optimization problems. Over the last decades several variants based on DACE (Design and Analysis of Computer Experiments) have provided excellent optimization results in cases where only a few evaluations could be made. In this paper these approaches are revisited with respect to their applicability in the optimization of production processes, which are in general multiobjective and allow no exact evaluations. The comparison to standard methods of experimental design shows significant improvements with respect to prediction quality and accuracy in detecting the optimum even if the experimental outcomes are highly distorted by noise. The universally assumed sensitivity of DACE models to nondeterministic data can therefore be refuted. Additionally, a practical example points out the potential of applying EC-methods to production processes by means of these models.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Smart:2008:cec,
  author = "Will Smart and  Mengjie Zhang",
  title = "Empirical Analysis of Schemata in Genetic Programming using Maximal Schemata and MSG",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0665.pdf},
  url = {},
  size = {},
  abstract =	{Plenteous research studies schemata in Genetic Programming (GP), though little of it is been empirical, due to the vast numbers of typical schemata in even small populations. In this research, we define maximal schemata, and extend our Trips algorithm to the more general Max-Schema-Growth (MSG) algorithm, applicable to a wider range of schema forms (Trips only handles standard fragment schemata). We present MSG specialised to work with unordered-fragments schemata (tree-fragments with unordered functions), and compare the number of maximal schemata found of these two forms. For most maximal fragments, another maximal fragment was also found that differed only by the orders of function node arguments. We conclude that maximal unordered-fragments may represent a greater range of common patterns between programs than standard maximal fragments, though the greater reach comes at a price with a severe increase in the time taken by the algorithm.  },
  keywords = {genetic algorithms, genetic programming},
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Wu5:2008:cec,
  author = "Yanghui Wu and John McCall and Paul Godley and Alexander Brownlee and Julie Cowie",
  title = "Bio-Control in Mushroom Farming Using a Markov Network EDA",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0666.pdf},
  url = {},
  size = {},
  abstract =	{In this paper we present an application of an Estimation of Distribution Algorithm (EDA) that uses a Markov network probabilistic model. The application is to the problem of bio-control in mushroom farming, a domain which admits bang-bang-control solutions. The problem is multiobjective and uses a weighted fitness function. Previous work on this problem has applied genetic algorithms (GA) with directed intervention crossover schemes aimed at effective biocontrol at an efficient level of intervention. Here we compare these approaches with the EDA Distribution Estimation Using Markov networks (DEUM<sub>d</sub>). DEUM<sub>d</sub> constructs a probabilistic model using Markov networks. Our experiments compare the quality of solutions produced by DEUM<sub>d</sub> with the GA approaches and also reveal interesting differences in the search dynamics that have implications for algorithm design.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Chicano:2008:cec,
  author = "Francisco Chicano and Enrique Alba",
  title = "Finding Liveness Errors with ACO",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0668.pdf},
  url = {},
  size = {},
  abstract =	{Model Checking is a well-known and fully automatic technique for checking software properties, usually given as temporal logic formulae on the program variables. Most of model checkers found in the literature use exact deterministic algorithms to check the properties. These algorithms usually require huge amounts of memory if the checked model is large. We propose here the use of an algorithm based on ACOhg, a new kind of Ant Colony Optimization model, to search for liveness property violations in concurrent systems. This algorithm has been previously applied to the search for safety errors with very good results and we apply it here for the first time to liveness errors. The results state that our algorithmic proposal, called ACOhg-live, is able to obtain very short error trails in faulty concurrent systems using a low amount of resources, outperforming by far the results of Nested-DFS, the traditional algorithm used for this task in the model checking community and implemented in most of the explicit state model checkers. This fact makes ACOhg-live a very suitable algorithm for finding liveness errors in large faulty concurrent systems, in which traditional techniques fail because of the model size.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Johansson:2008:cec,
  author = "Ulf Johansson and Rikard Konig and Tuve Lofstrom and Lars Niklasson",
  title = "Increasing Rule Extraction Accuracy by Post-Processing GP Trees",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0669.pdf},
  url = {},
  size = {},
  abstract =	{Genetic programming (GP), is a very general and efficient technique, often capable of outperforming more specialised techniques on a variety of tasks. In this paper, we suggest a straightforward novel algorithm for post-processing of GP classification trees. The algorithm iteratively, one node at a time, searches for possible modifications that would result in higher accuracy. More specifically, the algorithm for each split evaluates every possible constant value and chooses the best. With this design, the post-processing algorithm can only increase training accuracy, never decrease it. In this study, we apply the suggested algorithm to GP trees, extracted from neural network ensembles. Experimentation, using 22 UCI datasets, shows that the post-processing results in higher test set accuracies on a large majority of datasets. As a matter of fact, for two setups of three evaluated, the increase in accuracy is statistically significant.  },
  keywords = {genetic algorithms, genetic programming},
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Pant:2008:cec,
  author = "Millie Pant and Radha Thangaraj and Crina Grosan and Ajith Abraham",
  title = "Improved Particle Swarm Optimization with Low-Discrepancy Sequences",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0670.pdf},
  url = {},
  size = {},
  abstract =	{Quasirandom or low discrepancy sequences, such as the Van der Corput, Sobol, Faure, Halton (named after their inventors) etc. are less random than a pseudorandom number sequences, but are more useful for computational methods which depend on the generation of random numbers. Some of these tasks involve approximation of integrals in higher dimensions, simulation and global optimization. Sobol, Faure and Halton sequences have already been used [7, 8, 9, 10] for initializing the swarm in a PSO. This paper investigates the effect of initiating the swarm with another classical low discrepancy sequence called Vander Corput sequence for solving global optimization problems in large dimension search spaces. The proposed algorithm called VC-PSO and another PSO using Sobol sequence (SO-PSO) are tested on standard benchmark problems and the results are compared with the Basic Particle Swarm Optimization (BPSO) which follows the uniform distribution for initializing the swarm. The simulation results show that a significant improvement can be made in the performance of BPSO, by simply changing the distribution of random numbers to quasi random sequence as the proposed VC-PSO and SO-PSO algorithms outperform the BPSO algorithm by noticeable percentage, particularly for problems with large search space dimensions.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Lee3:2008:cec,
  author = "Dong-Hyun Lee and Jong-Hwan Kim",
  title = "Evolutionary Personalized Robotic Doll: GomDoll",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0671.pdf},
  url = {},
  size = {},
  abstract =	{Genetic robot is one of artificial creatures and has its own genome in which each chromosome consists of many genes that contribute to defining its personality. By using the concept of genetic robot, this paper proposes personalized robotic doll by applying evolutionary process to generate unique propensity, defined by its genome. A genome population is evolved such that it customizes the genome satisfying a propensity desired by user based on Big Five personality dimensions. Robotic doll has emotion and motivation to reflect its internal state and to provide human friendly interaction. To demonstrate the effectiveness of this scheme, a bear-like robotic doll, GomDoll, is developed and the evolved genome is implanted to it to see its manner of internal and external responses to stimuli.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Lee4:2008:cec,
  author = "Donghoon Lee and Kunsu Kim and Tae Bok Yoon and Jee-Hyong Lee",
  title = "Design of Web Page Evaluation System Using Ajax and Neural Networks",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0672.pdf},
  url = {},
  size = {},
  abstract =	{Web page evaluation is an important issue in the Internet. The page view count is a widely used criterion for the web page evaluation because of its easiness. But, the evaluation methods based on the page view count cannot reflect whether the web page content corresponds with users' needs because users click a page after looking at only the title or the small part of the page. If the page content does not satisfy a user, the user generally does not spend much time nor take any actions to look at the page so therefore we developed an Ajax Log System. Using this system, we collect users' visiting time and action on web pages such as clicks, scrolling, etc. Users are not interrupted while Ajax works. But the collected data are continuous values. We cannot determine adaptive criteria to each user data. To solve this problem, the evaluation module of the system is based on the neural network. The system with neural network learns users' action pattern while reading useful web pages and evaluates the usefulness of web pages from users' actions. Our system can more accurately find pages which satisfy users than a search engine.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Li19:2008:cec,
  author = "Huan Li and Beibei Huang and Jinhu L&#252;",
  title = "Dynamical Evolution Analysis of the Object-Oriented Software Systems",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0673.pdf},
  url = {},
  size = {},
  abstract =	{Software evolution and update play a vital role in software engineering. It has many advantages, such as improving the efficiency of programming, reducing the cost of maintenance and promoting the development of software systems. This paper further analyzes the evolution and update processes of three typical kinds of real-world object-oriented software systems by using the tools of complex networks. It discovers some underlying dynamical evolution characteristics and rules of the object-oriented software systems. These results are very useful for the design and development of the objectoriented software systems.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Vo&#223;:2008:cec,
  author = "Thomas Vo&#223; and Nicola Beume and G&#252;nter Rudolph and Christian Igel",
  title = "Scalarization Versus Indicator-Based Selection in Multi-Objective CMA Evolution Strategies",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0675.pdf},
  url = {},
  size = {},
  abstract =	{While scalarization approaches to multicriteria optimization become infeasible in the case of many objectives, for few objectives the benefits of population-based methods compared to a set of independent single-objective optimization trials on scalarized functions are not obvious.

The multi-objective covariance matrix adaptation evolution strategy (MO-CMA-ES) is a powerful algorithm for real-valued multi-criteria optimization. This population-based approach combines mutation and strategy adaptation from the elitist CMA-ES with multi-objective selection.

We empirically compare the steady-state MO-CMA-ES with different scalarization algorithms, in which the elitist CMA-ES is used as single-objective optimizer. Although only bicriteria benchmark problems are considered, the MO-CMA-ES performs best in the overall comparison. However, if the scalarized problems have a structure that can easily be exploited by the CMA-ES and that is less apparent in the vector-valued fitness function, the CMA-ES with scalarization outperforms the population-based approach.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Carvalho:2008:cec,
  author = "Danilo F. Carvalho and Carmelo J. A. Bastos-Filho",
  title = "Clan Particle Swarm Optimization",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0677.pdf},
  url = {},
  size = {},
  abstract =	{Particle Swarm Optimization (PSO) has been used to solve many different types of optimization problems. By applying PSO to problems where the feasible solutions are too much difficult to find, new ways of solving the problems are required. Many variations on the basic PSO form have been explored, targeting the velocity update equation. Other approaches attempt to change the structure of the swarm. In this paper a Clan PSO topology is proposed for improving the PSO degree of convergence focusing on the distribution of the particles in the search space. A comparison with star, ring, and Four Clusters topologies was performed. Our simulation results have shown that the proposed topology achieves better degrees of convergence than the cluster-based one.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Tseng2:2008:cec,
  author = "Lin-Yu Tseng and Chun Chen",
  title = "Multiple Trajectory Search for Large Scale Global Optimization",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0678.pdf},
  url = {},
  size = {},
  abstract =	{In this paper, the multiple trajectory search (MTS) is presented for large scale global optimization. The MTS uses multiple agents to search the solution space concurrently. Each agent does an iterated local search using one of three candidate local search methods. By choosing a local search method that best fits the landscape of a solution's neighborhood, an agent may find its way to a local optimum or the global optimum. We applied the MTS to the seven benchmark problems designed for the CEC 2008 Special Session and Competition on Large Scale Global Optimization.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Liu9:2008:cec,
  author = "Wudong Liu and Qingfu Zhang and Edward Tsang and Botond Virginas",
  title = "Tchebycheff Approximation in Gaussian Process Model Composition for Multi-Objective Expensive Black Box",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0679.pdf},
  url = {},
  size = {},
  abstract =	{Black-box expensive function is ubiquitous in real world problems. Much research has been done on scalar objective optimization for such problems with great success. Comparatively, very little work has been done in multi-objective optimization. In many cases, it is not straightforward to convert methods from scalar objective optimization to multi-objective optimization due to the complexities incurred by Pareto domination.},
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Miconi:2008:cec,
  author = "Thomas Miconi  ",
  title = "Evosphere: Evolutionary Dynamics in a Population of Fighting Virtual Creatures",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0681.pdf},
  url = {},
  size = {},
  abstract =	{It is often suggested that traditional models of artificial evolution, based on explicit, human-defined fitness functions, are fundamentally more restricted and less creative than natural evolution, in which no such constraint exists. After a discussion and refinement of this statement, we suggest a classification of evolutionary systems according to their evolutionary ``creativity''. We describe an environment, called Evosphere, in which a population of 3D creatures interact, fight with each other, and evolve freely on the surface of a ``microplanet''. We demonstrate the onset of natural selection and adaptive evolution within this virtual world, both by visual inspection and statistical analysis. We show that the introduction of reproductively isolated species enriches the dynamics of the system, leading to simple evolutionary feedbacks among species.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Fan:2008:cec,
  author = "Kai Fan and Anthony Brabazon and Conall O'Sullivan and Michael O'Neill",
  title = "Benchmarking the Performance of the Real-Valued Quantum-Inspired Evolutionary Algorithm",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0682.pdf},
  url = {},
  size = {},
  abstract =	{Following earlier claims that Quantum-inspired Evolutionary Algorithm (QIEA) may offer advantages in high dimensional environments, this paper tests a real-valued QIEA on a series of benchmark functions of varying dimensionality in order to examine its scalability. The results are compared with those from a genetic algorithm using both a binary and real-valued representation. The results show that the QIEA obtains highly competitive results versus the genetic algorithm, while substantially outperforming both versions of the Genetic Algorithm (GA) in terms of running time. This suggests that QIEA may have substantial utility for real-world high dimensional problems.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Hooshyar:2008:cec,
  author = "B. Hooshyar and A. Rahmani and M. Shenasa",
  title = "A Genetic Algorithm to Time- Cost Trade off in Project Scheduling",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0683.pdf},
  url = {},
  size = {},
  abstract =	{One of the key important issues in project management is trade off between cost and time in such a way that the project is completed in the shortest time and minimum cost. Genetic Algorithm is a practical approach for such optimization problems. In this paper, an algorithm is presented to solve the Time-Cost Tradeoff Problem (TCTP) using genetic algorithm. In this algorithm in order to search the problem space two control variables are used. Also an intelligent mutation operator is presented to approximate to the project's optimal point. Comparing this algorithm with Siemens classical algorithm shows the higher speed of proposed algorithm because in this algorithm many computations of paths in project's network graph are not needed.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(O'Neill:2008:cec,
  author = "Michael O'Neill and Anthony Brabazon",
  title = "Self-Organizing Swarm (SOSwarm) for Financial Credit-Risk Assessment",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0684.pdf},
  url = {},
  size = {},
  abstract =	{This paper applies a self-organizing Particle Swarm algorithm, SOSwarm, for the purposes of credit-risk assessment. SoSwarm can be applied for unsupervised clustering and for classification. In the algorithm, input vectors are projected into a lower dimensional map space producing a visual representation of the input data in a manner similar to a self-organizing map (SOM). However, unlike SOM, the nodes (particles) in this map react to input data during the learning process by modifying their velocities using an adaptation of the Particle Swarm Optimization velocity update step. The utility of SoSwarm is tested by applying it to two important credit-risk assessment problems drawn from the domain of finance, namely the prediction of corporate bond ratings and the prediction of corporate failure. The results obtained on the financial benchmark problems are highly-competitive against those of traditional classification methodologies. The paper makes a further contribution showing that the canonical SOM can be explored within the PSO paradigm. This highlights an important linkage between the heretofore distinct literatures of SOM and PSO.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Benjamin:2008:cec,
  author = "Simon C. Benjamin",
  title = "Evolutionary Route to Computation in Self-Assembled Nanoarrays",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0685.pdf},
  url = {},
  size = {},
  abstract =	{Ordered nanoarrays, i.e. regular patterns of quantum structures at the nanometre scale, can now be synthesised in a range of systems. In this paper I study a form of array computation where the internal dynamics are driven by intrinsic cell-cell interactions and global optical pulses addressing entire structure indiscriminately. The array would need to be ' wired' to conventional technologies only at its boundary. Any self assembled array would have a unique set of defects, therefore I employ an ab initio evolutionary process to subsume such flaws without any need to determine their location or nature. The approach succeeds for various forms of physical interaction within the array.  },
  keywords = {genetic algorithms, genetic programming},
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Naeini:2008:cec,
  author = "Armin Tavakoli Naeini and Maziar Palhang",
  title = "Evolving a Multiagent Coordination Strategy Using Genetic Network Programming for Pursuit Domain",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0686.pdf},
  url = {},
  size = {},
  abstract =	{The design and development of strategies to coordinate the actions of multiple agents is a central research issue in the field of Multiagent Systems (MAS). It is nearly impossible to identify or prove the existence of the best coordination strategy. In most cases a coordination strategy is chosen for a domain, if it is reasonably good.In this paper, we propose a new design methodology using Genetic Network Programming (GNP) to evolve a coordination strategy for a well-known and difficult-to-solve multi agent problem named pursuit domain where cooperation of agents is required. Genetic Network Programming (GNP) is a newly developed evolutionary computation inspired from Genetic Programming (GP). While GP uses a tree structure as genes of an individual, GNP uses a directed graph type structure. We show the effectiveness of proposed methodology through simulations. In addition, the comparison of the performances between GNP and GP is carried out. The results show that performance of GNP solution is significantly superior to GP solution.  },
  keywords = {genetic algorithms, genetic programming},
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Lai:2008:cec,
  author = "K. Robert Lai and Bo-Ruei Kao and Yi-Yuan Chiang",
  title = "Fuzzy Constraint-Directed Negotiation Mechanism as a Framework for Multi-agent Scheduling",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0688.pdf},
  url = {},
  size = {},
  abstract =	{This paper presents a fuzzy constraint-directed negotiation mechanism for agent-based scheduling. Scheduling problem is modeled as a set of fuzzy constraint satisfaction problems (FCSP), interlinked together by inter-agent constraints. Each FCSP represents the perspective of participants and is governed by agents. Negotiation process is considered as a global consistency enforcing via iterative constraint adjustment and relaxation. To facilitate convergence and improve solution quality for a particular performance measure, sharing metascheduling information during negotiation is applied. Experimental results suggest that the proposed approach not only can obtain a high quality schedule in a cost-effective manner, but also provides superior performance in all criteria to other negotiation models for agent-based scheduling.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Zielinski:2008:cec,
  author = "Karin Zielinski and Matthias Joost and Rainer Laur and Bernd Orlik",
  title = "Comparison of Differential Evolution and Particle Swarm Optimization for the Optimization of a PI Cascade Control",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0689.pdf},
  url = {},
  size = {},
  abstract =	{PI cascade controllers are often used in control applications due to their simplicity. Because of uncertain and varying system parameters, a robust control is needed. However, known methods to generate robust controllers often lead to complicated control structures. Unfortunately, there are no analytical solutions to generate robust controllers with a fixed simple structure like the PI cascade. Therefore, easy-to-use optimization algorithms are needed. In this paper it is shown that for a practical approach using recommended parameter settings from literature both Differential Evolution and Particle Swarm Optimization can be used for the optimization of a PI cascade control. A performance comparison shows similar results, so both of them are useful to field engineers who apply optimization algorithms to real-world problems and are often inexperienced concerning optimization.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Helmi:2008:cec,
  author = "B. Hoda Helmi and Adel T. Rahmani",
  title = "An AIS Algorithm for Web Usage Mining with Directed Mutation",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0690.pdf},
  url = {},
  size = {},
  abstract =	{This paper presents a model based on artificial immune system for mining Web usage data. One of the new features of the proposed model is directed mutation that is designed to avoid the random nature of mutation that make the system nondeterministic, besides that the model presents a new method for learning new unseen antigens instead of using the hypermutation which its computational cost is high. In the proposed algorithm each gene in the antigen has its own strength so strong genes are recognized more powerfully. Experimental results show that by exerting the directed mutation and considering item weights in noisy data like Web log data the quality of extracted antibodies are improved and by using the new method for learning new antigens, outliers can't penetrate to set of antibodies. Like the natural immune system, the strongest advantage of immune based learning is its ease of adaptation to the dynamic environment. By introducing the new features, a model which is shown to be more robust and better able to adapt to the dynamic environments such as Web than the similar models is proposed.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Binh:2008:cec,
  author = "Huynh Thi Thanh Binh and Nguyen Xuan Hoai and  R. I. (Bob) McKay",
  title = "A New Hybrid Genetic Algorithm for Solving the Bounded Diameter Minimum Spanning Tree Problem",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0692.pdf},
  url = {},
  size = {},
  abstract =	{In this paper, a new hybrid genetic algorithm - known as HGA - is proposed for solving the Bounded Diameter Minimum Spanning Tree (BDMST) problem. We experiment with HGA on two sets of benchmark problem instances, both Euclidean and Non-Euclidean. On the Euclidean problem instances, HGA is shown to outperform the previous best two Genetic Algorithms (GAs) reported in the BDMST literature, while on the Non-Euclidean problem instance, HGA performs comparably with these two GAs.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Chu2:2008:cec,
  author = "Ying Chu and Hua Mi and Huilian Liao and Zhen Ji and Q. H. Wu",
  title = "A Fast Bacterial Swarming Algorithm For High-Dimensional Function Optimization",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0693.pdf},
  url = {},
  size = {},
  abstract =	{A novel Fast Bacterial Swarming Algorithm (FBSA) for high-dimensional function optimization is presented in this paper. The proposed algorithm combines the foraging mechanism of E-coli bacterium introduced in Bacterial Foraging Algorithm (BFA) with the swarming pattern of birds in block introduced in Particle Swarm Optimization (PSO). It incorporates the merits of the two bio-inspired algorithms to improve the convergence for high-dimensional function optimization. A new parameter called attraction factor is introduced to adjust the bacterial trajectory according to the location of the best bacterium (bacterium with best fitness value). An adaptive step length is adopted to improve the local search ability. The algorithm has been evaluated on standard high-dimensional benchmark functions in comparison with BFA and PSO respectively. The simulation results have demonstrated the fast convergence ability and the improved optimization accuracy of FBSA.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Patnaik:2008:cec,
  author = "Awhan Patnaik and L. Behera",
  title = "Evolutionary Multiobjective Optimization Based Control Strategies for an Inverted Pendulum On a Cart",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0694.pdf},
  url = {},
  size = {},
  abstract =	{We report the design and implementation of three different multiobjective optimization based control strategies for the cart pole system: (1) a multiobjective version of the classic quadratic regulator problem, (2) a multiobjective formulation of a standard H<sub>&#8734;</sub> controller and (3) a mixed norm H<sub>2</sub> /H <sub>&#8734;</sub> controller design problem in a multiobjective setting. The optimization problems have been solved using an elitist Pareto dominance based multiobjective genetic algorithm developed by the authors. Input saturation and bounds on state variables have been incorporated in the problem. It is shown by way of an example that the solution to the scalarized version of muliobjective linear regulator design problem is contained in the set of solutions of the vector objective formulation of the same multiobjective design problem. Finally the validity of the solutions was tested on a real cart pole regulator system.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Keller:2008:cec,
  author = "Robert E. Keller and Riccardo Poli",
  title = "Toward Subheuristic Search",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0695.pdf},
  url = {},
  size = {},
  abstract =	{In previous work, we have introduced an effective, resource-efficient and self-adapting hyperheuristic that uses Genetic Programming (GP) as its method of search in the space of domain-specific metaheuristics. GP employs user-provided, local heuristics from which it produces these metaheuristics (MHs). Here, we show that the hyperheuristic performs even better when working at the subheuristic  level, i.e., when building MHs from generic components and specific elementary operations. In particular, this approach supports efficiency of the better MHs. Specifically, these MHs do not excessively iterate local search steps, i.e., their good performance comes from smart patterns of calls of the provided, basic components. Also, a moderate reduction of the maximum allowed MH size does not reduce performance significantly.  },
  keywords = {genetic algorithms, genetic programming},
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Zhang14:2008:cec,
  author = "Zhenya Zhang and Hongmei Cheng and Shuguang Zhang and Qiansheng Fang",
  title = "Clustering Aggregation Based on Genetic Algorithm for Documents Clustering",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0698.pdf},
  url = {},
  size = {},
  abstract =	{Clustering aggregation problem is a kind of formal description for clustering ensemble problem and technologies for the solving of clustering aggregation problem can be used to construct clustering division with better clustering performance when the clustering performances of each original clustering division are fluctuant or weak. In this paper, an approach based on genetic algorithm for clustering aggregation problem, named as GeneticCA, is presented. To estimate the clustering performance of a clustering division, clustering precision is defined and features of clustering precision are discussed. In our experiments about clustering performances of GeneticCA for document clustering, hamming neural network is used to construct clustering divisions with fluctuant and weak clustering performances. Experimental results show that the clustering performance of clustering division constructed by GeneticCA is better than clustering performance of original clustering divisions with clustering precision as criterion.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Tseng3:2008:cec,
  author = "Chun-Shun Tseng and Ya-Yun Jheng and Sih-Yin Shen and Jung-Hua Wang",
  title = "Fast Symmetric Keys Generation via Mutual Imitating Process",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0699.pdf},
  url = {},
  size = {},
  abstract =	{This paper presents an eavesdropper-proof algorithm that is capable of fast generating symmetric (secret) keys. Instead of literally exchanging secret keys, both the sender and receiver adopt an imitating process based on an improved Hebbian rule that uses identical random inputs to separately train on their reciprocal outputs to generate a pair of exactly identical secret key strings. Important parameters are elaborately characterized to implement a fast information transmission for ephemeral key exchanging. We show the possible performance optimization can be achieved by coordinating the parameters. One essential feature of the proposed algorithm is that even an eavesdropper who acquires entire structure of the algorithm and the transmission data still has no chance to decrypt the encrypted message, thus ensuring security in the subsequent encryption task. Moreover, computation load is well bounded in an acceptable range despite the increasing key length.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(smith:2008:cec,
  author = "James F. {Smith, III}",
  title = "Co-Evolving Fuzzy Decision Trees and Scenarios",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0700.pdf},
  url = {},
  size = {},
  abstract =	{A co-evolutionary data mining algorithm has been invented that automatically generates decision logic in the form of fuzzy decision trees (FDTs). The algorithm initially uses a genetic program (GP) to mine a database of scenarios to automatically create the fuzzy logic. This is followed by the application of a genetic algorithm (GA) that is used to search for pathological scenarios (PS) that result in unsatisfactory performance by the fuzzy logic found by the GP. The fuzzy logic found in the previous step by the GP along with failure criteria (FC) is used to form the fitness function for the GA. If the GA fails to find pathological scenarios then the co-evolution ends; otherwise, the new scenarios are appended to the GP's database followed by GP based data mining and a GA scenario search. A detailed description of the co-evolution of a fuzzy decision tree for real-time control of unmanned air vehicles is provided. The fitness functions for the GP, terminal set, function set, and methods of accelerating convergence are included. The fitness function for the GA and a method of representing scenarios as chromosomes are given. Simulations related to validation of the fuzzy logic are discussed.  },
  keywords = {genetic algorithms, genetic programming},
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Syberfeldt:2008:cec,
  author = "Anna Syberfeldt and Henrik Grimm and Amos Ng and Robert I. John",
  title = "A Parallel Surrogate-Assisted Multi-Objective Evolutionary Algorithm for Computationally Expensive Optimization Problems",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0701.pdf},
  url = {},
  size = {},
  abstract =	{This paper presents a new efficient multiobjective evolutionary algorithm for solving computationallyintensive optimization problems. To support a high degree of parallelism, the algorithm is based on a steady-state design. For improved efficiency the algorithm uses a surrogate to identify promising candidate solutions and filter out poor ones. To handle the uncertainties associated with the approximative surrogate evaluations, a new method for multi-objective optimization is described which is generally applicable to all surrogate techniques. In this method, basically, surrogate objective values assigned to offspring are adjusted to consider the error of the surrogate. The algorithm is evaluated on the ZDT benchmark functions and on a real-world problem of manufacturing optimization. In assessing the performance of the algorithm, a new performance metric is suggested that combines convergence and diversity into one single measure. Results from both the benchmark experiments and the realworld test case indicate the potential of the proposed algorithm.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Yang10:2008:cec,
  author = "Shengxiang Yang and Renato Tin&#243;s",
  title = "Hyper-Selection in Dynamic Environments",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0702.pdf},
  url = {},
  size = {},
  abstract =	{In recent years, several approaches have been developed for genetic algorithms to enhance their performance in dynamic environments. Among these approaches, one kind of methods is to adapt genetic operators in order for genetic algorithms to adapt to a new environment. This paper investigates the effect of the selection pressure on the performance of genetic algorithms in dynamic environments. A hyper-selection scheme is proposed for genetic algorithms, where the selection pressure is temporarily raised whenever the environment changes. The hyper-selection scheme can be combined with other approaches for genetic algorithms in dynamic environments. Experiments are carried out to investigate the effect of different selection pressures on the performance of genetic algorithms in dynamic environments and to investigate the effect of the hyper-selection scheme on the performance of genetic algorithms in combination with several other schemes in dynamic environments. The experimental results indicate that the effect of the hyperselection scheme depends on the problem under consideration and other schemes combined in genetic algorithms.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Zhang15:2008:cec,
  author = "Zhenya Zhang and Hongmei Cheng and Wanli Chen and Qiansheng Fang",
  title = "Correlation Clustering Based on Genetic Algorithm for Documents Clustering",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0703.pdf},
  url = {},
  size = {},
  abstract =	{Correlation clustering problem is a NP hard problem and technologies for the solving of correlation clustering problem can be used to cluster given data set with relation matrix for data in the given data set. In this paper, an approach based on genetic algorithm for correlation clustering problem, named as GeneticCC, is presented. To estimate the performance of a clustering division, data correlation based clustering precision is defined and features of clustering precision are discussed in this paper. Experimental results show that the performance of clustering division for UCI document data set constructed by GeneticCC is better than clustering performance of other clustering divisions constructed by SOM neural network with clustering precision as criterion.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Rani:2008:cec,
  author = "B. Padmaja Rani and B. Vishnu Vardhan and A. Kanaka Durga and A. Vinaya Babu",
  title = "Analysis of N-Gram Model on Telugu Document Classification",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0704.pdf},
  url = {},
  size = {},
  abstract =	{Document classification is one of the recent areas of research evolved as a result of exponential growth in the quantum electronic form of documents. Various document representation methods based on linguistic knowledge are revisited in Literature. Adaptability of N-gram models on various languages is the recent trend. In this paper an attempt is made to analyze character N-gram model on Telugu documents. Tokenization of syllables and the associated complexity of Telugu script is described. A combination of Bayes probabilistic classifier and character N-gram model is discussed in this paper. The performance of the proposed classifier is evaluated in terms of overall accuracy.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Saxena:2008:cec,
  author = "Dhish Kumar Saxena and Kalyanmoy Deb",
  title = "Dimensionality Reduction of Objectives and Constraints in Multi-Objective Optimization Problems: A System Design Perspective",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0705.pdf},
  url = {},
  size = {},
  abstract =	{The notion of Optimal System Design [12] holds that in order to ' truly ' maximize/minimize an objective function, the feasible set needs to be optimized. Inspired by it, the attempt in our recent work [11] was to incorporate constraint-reduction in our earlier proposed procedures on dimensionality reduction of objectives [4,10]. In that, while targetting constrained single-objective optimization problems (SOPs), we could arrive at a critical set of constraints and also their importance based rank-ordering. This information was used to study the shift from the constrained to the unconstrained optima. The methodology above was based on treating the apriori stated constraints as objectives besides the original-objective, and on applying [4,10] to this combined objective set-but- without constraints. In this work, the endeavor is to extend the above notion to the realm of multi-objective optimization problems (MOPs). Towards it, while we hire much from the above methodology, we make a fundamental shift, in that, we retain the a priori stated constraints, while evaluating the combined objective set. The motivation for this shift lies, in that, it allows more effective realization of the notion of System Design than the approach in [11]. Reasonable effort has been spent on establishing this argument. Incorporating this change, a procedure for simultaneous reduction in objectives and constraints (for both SOPs, MOPs) is proposed, which also defines a realizable path towards Optimal System Design. Finally, the procedure is demonstrated on two test problems and one real world problem.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Siwik:2008:cec,
  author = "Leszek Siwik and Piotr Sikorski",
  title = "Efficient Constrained Evolutionary Multi-Agent System for Multi-Objective Optimization",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0706.pdf},
  url = {},
  size = {},
  abstract =	{Evolutionary Multi-agent System approach for optimization (especially for multi-objective optimization) is a very promising computational model. Its computational as well as implemental simplicity causes that approaches based on EMAS model can be widely used for solving optimization tasks. It turns out that introducing some additional mechanisms into basic EMAS-causes that EMAS-based system can be successfully applied for solving constrained multi-objective optimization tasks-and what is important results obtained by proposed approach are better/not worse than results obtained by NSGAII or SPEA2 algorithms. In the course of this paper some extensions that can be introduced into EMAS in order to constrained multi-objective optimization are presented. What is important-any new additional mechanisms do not have to be introduced into EMAS to solve constrained optimization tasks-the only extensions causing that EMAS-based model becomes an efficient and simple both in conception as well as in implementation-is an appropriate strategy regarding the flow among agents crucial non-renewable resource which is usually called life energy. In this paper, both the idea as well as preliminary results of Constrained Evolutionary Multi- Agent System (conEMAS) for Multi-objective Optimization are presented.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(McClintock:2008:cec,
  author = "James McClintock and Gary G. Yen",
  title = "A Two-Tiered, Agent Based Approach for Autonomous, Evolutionary Texture Generation",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0707.pdf},
  url = {},
  size = {},
  abstract =	{This paper proposes a two-tiered, evolutionary architecture for computer based synthesis of textures. In this architecture, a traditional tree based texture generation system is controlled by a set of evolutionary agents. The main contribution of this work is that the user is able to choose the degree of interaction and control they exert over the system. Evolutionary agents are designed to contain information about desirable image features, and they evolve based on user feedback. The agents in turn control the main evolutionary engine for generating textures. This system allows the computer to continue working when the designer leaves without limiting the designer's ability to control the texture generation process when they are available to interact with the system. An experimental implementation is developed to verify the utility of the proposed architecture for texture synthesis. Results show significant improvements in the average user ranking of the agents as the genetic algorithm progresses. },
  keywords = {genetic algorithms, genetic programming},
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Periyasamy:2008:cec,
  author = "Sathish Periyasamy and Alex Gray and Peter Kille",
  title = "The Epigenetic Algorithm",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0708.pdf},
  url = {},
  size = {},
  abstract =	{Evolutionary Computation (EC) paradigms are inspired by the optimization strategies used by biological systems. While these strategies can be found in every level of biological organization, almost all of the EC techniques which comprise techniques from Evolutionary Algorithm (EA) to Swarm Intelligence (SI) have been inspired by organism level optimization strategies. While EA is based on trans-generational genetic adaptation of organisms (biologically inspired), SI is mainly based on intra-generational collective behavioral adaptation of organisms (socially inspired). This paper describes the optimization strategies that bio-molecules use both for intra-generational and trans-generational adaptation of biological cells. These adaptive strategies which are known as epigenetic mechanisms emerged long before any other biological strategy and form the basis for Epigenetic Algorithms (EGA). Further, the paper proposes an intra-generational EGA based on bio-molecular degradation and autocatalysis which are ubiquitous cellular processes and are pivotal for the adaptive dynamics and evolution of intelligent cellular organization.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Song:2008:cec,
  author = "Andy Song",
  title = "Fast Video Analysis by Genetic Programming",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0709.pdf},
  url = {},
  size = {},
  abstract =	{Genetic programming has been applied to various types of vision tasks. This paper extends the use of this powerful problem solving method to a more complex but more common domain, video analysis. We present the methodology as well as the experiments on two video analysis tasks: segmenting texture regions and detecting moving objects. The advantages of GP in this domain can be shown by this study. Firstly GP methods are less dependent on knowledge from domain experts. One methodology is suitable for both tasks. Secondly GP can generate fast video frame analysers which are highly desirable or even critical in real time vision applications.  },
  keywords = {genetic algorithms, genetic programming},
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Sheri:2008:cec,
  author = "Guleng Sheri and David W. Corne",
  title = "The Simplest Evolution/Learning Hybrid: LEM with KNN",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0710.pdf},
  url = {},
  size = {},
  abstract =	{The Learnable Evolution Model (LEM) was introduced by Michalski in 2000, and involves interleaved bouts of evolution and learning. Here we investigate LEM in (we think) its simplest form, using  k-nearest neighbour as the 'learning' mechanism. The essence of the hybridisation is that candidate children are filtered, before evaluation, based on predictions from the learning mechanism (which learns based on previous populations). We test the resulting 'KNNGA' on the same set of problems that were used in the original LEM paper. We find that KNNGA provides very significant advantages in both solution speed and quality over the unadorned GA. This is in keeping with the original LEM paper's results, in which the learning mechanism was AQ and the evolution/learning interface was more sophisticated. It is surprising and interesting to see such beneficial improvement in the GA after such a simple learning-based intervention. Since the only application-specific demand of KNN is a suitable distance measure (in that way it is more generally applicable than many other learning mechanisms), LEM methods using KNN are clearly recommended to explore for large-scale Optimization tasks in which savings in evaluation time are necessary.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Carrano:2008:cec,
  author = "Eduardo G. Carrano and Bruno B. Souza and  Oriane M. Neto",
  title = "An Immune Inspired Memetic Algorithm for Power Distribution System Design under Load Evolution Uncertainties",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0712.pdf},
  url = {},
  size = {},
  abstract =	{This work proposes an Immune Inspired Memetic Algorithm for the expansion planning of electric distribution systems. This algorithm is based on a Clonal Selection Algorithm and a Local Search Method which is built using network distance concepts abstracted from continuous spaces. The memetic algorithm is intended to find not only the optimal solution for the design conditions, but a whole set of viable solutions, that can be considered as alternatives under perturbed operation conditions. Those alternatives are used for handling with load evolution uncertainties, which are inherently related with long term evaluation of the distribution system. The post-optimization analysis of solutions has been made using a Monte Carlo Simulation and a Multiobjective Sensitivity Analysis, in order to estimate their robustness under perturbed load conditions. The results achieved by the proposed algorithm in a practical problem indicate that this method can be more suitable for designing distribution system under load evolution uncertainties.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Carrano2:2008:cec,
  author = "Eduardo G. Carrano and Ricardo H. C. Takahashi and Walmir M. Caminhas",
  title = "A Genetic Algorithm for Multiobjective Training of ANFIS Fuzzy Networks",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0713.pdf},
  url = {},
  size = {},
  abstract =	{The achievement of approximation models may constitute a complex computational task, in the cases of models with non-linear relation between parameters and data. This problem becomes even harder when the system to be modeled is subject to noisy data, since the simple minimization of error over a training data set can give rise to misleading models that fit both the system structure and the noise (the phenomenon of model overfit). This paper proposes a multiobjective genetic algorithm for guiding the training of ANFIS fuzzy networks. This algorithm considers the complexity of network jointly with the error over the training set as relevant objectives, that should be minimized. Results obtained in three regression problems are presented to show the generalization capacity of models constructed with the proposed methodology.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Fraga:2008:cec,
  author = "Luis Gerardo de la Fraga and Israel Vite Silva  ",
  title = "Direct 3D Metric Reconstruction from Two Views Using Differential Evolution",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0714.pdf},
  url = {},
  size = {},
  abstract =	{To obtain a 3D metric reconstruction from two images taken with a same camera without previous calibration, it is necessary to estimate the intrinsic camera parameters and the orientation and position of the two views with respect to the camera. At the present time, there are several algorithms to estimate camera parameters from two views, all of them are based on the epipolar geometry and the estimation of the fundamental matrix. However, it is well known there are some configurations where the fundamental matrix can not be estimated, called critical configurations. In this article we present a novel method to retrieve directly the camera parameters, and orientation and position parameters for two views, from points taken over the two images, using the Differential Evolution (DE) algorithm. This method exploits the reprojection error as the cost function for DE, instead of computing the fundamental matrix. Experimental results show our method recovers 3D points, intrinsic, and orientation and position parameters on non-critical configurations and in the critical configuration of pure translation.We used simulated and real images to prove its effectiveness and robustness.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Togelius:2008:cec,
  author = "Julian Togelius and Faustino Gomez and J&#252;rgen Schmidhuber",
  title = "Learning What to Ignore: Memetic Climbing in Topology and Weight Space",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0715.pdf},
  url = {},
  size = {},
  abstract =	{We present the memetic climber, a simple searchalgorithm that learns topology and weights of neural networkson different time scales. When applied to the problem of learningcontrol for a simulated racing task with carefully selectedinputs to the neural network, the memetic climber outperformsa standard hill-climber. When inputs to the network are lesscarefully selected, the difference is drastic. We also present twovariations of the memetic climber and discuss the generalizationof the underlying principle to population-based neuroevolutionalgorithms.},
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Jin2:2008:cec,
  author = "Nanlin Jin  ",
  title = "Genetic Algorithm-Based Ecosystem for Heather Management",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0716.pdf},
  url = {},
  size = {},
  abstract =	{This paper applies Genetic Algorithms (GA) to simulate a heather moorland ecosystem. We investigate, in this ecosystem how to manage heather for the benefits of survival and reproduction of grouse. A GA candidate solution is a grid, representing spatial relationship of three types of heather. From solutions provided by GA, we have found that the diversity of neighborhood and its distribution are essential. The evenly diversified heather distributions emerge as the best fit solutions for grouse's needs. We compared this finding with data collected from the field work.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Pilat:2008:cec,
  author = "Marcin L. Pilat and Christian Jacob",
  title = "Creature Academy: A System for Virtual Creature Evolution",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0717.pdf},
  url = {},
  size = {},
  abstract =	{In this paper, we present Creature Academy, a virtual laboratory that allows for the evolution of form and function within simulated physical 3D environments. Creature Academy can be used to explore evolutionary mechanisms, design, learning and other processes studied in artificial life simulations. Our system allows to perform hierarchical evolutionary experiments and ecosystem-inspired setups to investigate bodied creatures that interact, compete, adapt, and evolve. As a first proof of concept, we use Creature Academy to evolve morphologies and motion strategies of virtual creatures that walk and jump. We then present results that compare hierarchical evolution scenarios to generate creatures that excel in both walking and jumping, demonstrating how to evolve from creature specialists to generalists.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Arami:2008:cec,
  author = "Arash Arami and Bijan Rahmizadeh Rofoee and  Caro Lucas",
  title = "Multiple Heterogeneous Ant Colonies with Information Exchange",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0719.pdf},
  url = {},
  size = {},
  abstract =	{The method of Multiple Heterogeneous Ant Colonies with Information Exchange (MHACIE) is presented in this paper with emphasis on the speed of finding the optimal solution and the corresponding computational complexity.  The proposed method which is inspired by biology and psychology has a structure composed of several ant colonies. These colonies participate in solving problems in a concurrently manner and also exchange information with each other in communicational steps. Each ant colony is considered as an intelligent agent with behavioral traits. These behavioral traits play a key role in the solving procedure, in interrelation circumstances and in installation of relations. Faster solutions have been achieved using different employments of agents in the algorithm structure. Experimental results show the superiority of Multiple Heterogeneous Ant Colonies algorithm in comparison to the standard ant colony system (ACS) and particle swarm optimization (PSO) algorithms on different benchmarks. A dynamic, control engineering benchmark is also provided in order to gain a more complete evaluation of the proposed algorithm.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Rohling:2008:cec,
  author = "Greg Rohling  ",
  title = "Methods for Decreasing the Number of Objective Evaluations for Independent Computationally Expensive Objective Problems",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0720.pdf},
  url = {},
  size = {},
  abstract =	{In this paper, three new methods for pushing solutions toward a desired region of the objective space more quickly are explored; hypercube distance scaling, dynamic objective thresholding, and hypercube distance objective ordering. These methods are applicable for problems that do not require the entire Pareto front and that require an independent computationally expensive computation for each objective. The performance of these methods is evaluated with the multiple objective 0/1 Knapsack problem.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Li20:2008:cec,
  author = "Hui Li and Dario Landa-Silva",
  title = "Evolutionary Multi-Objective Simulated Annealing with Adaptive and Competitive Search Direction",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0721.pdf},
  url = {},
  size = {},
  abstract =	{In this paper, we propose a population-based implementation of simulated annealing to tackle multi-objective optimisation problems, in particular those of combinatorial nature. The proposed algorithm is called Evolutionary Multi-objective Simulated Annealing Algorithm (EMOSA), which combines local and evolutionary search by incorporating two distinctive features. The first feature is to tune the weight vectors of scalarizing functions (i.e., search directions) for selection during local search using a two-phase strategy. The second feature is the competition between members of the current population with similar weight vectors. We compare the proposed algorithm to three other multi-objective simulated annealing algorithms and also to the Pareto archived evolutionary strategy (PAES). Experiments are carried out on a set of bi-objective travelling salesman problem (TSP) instances with convex or nonconvex Pareto-optimal fronts. Our experimental results demonstrate that the two-phase tuning of weight vectors and the competition between individuals make a significant contribution to the improved performance of EMOSA.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Siwik2:2008:cec,
  author = "Leszek Siwik and Szymon Natanek",
  title = "Elitist Evolutionary Multi-Agent System in Solving Noisy Multi-Objective Optimization Problems",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0722.pdf},
  url = {},
  size = {},
  abstract =	{Evolutionary Multi-Agent System approach for optimization (for multi-objective optimization in particular) is a promising computational model. Its computational as well as implemental simplicity cause that approaches based on EMAS model can be widely used for solving optimization tasks. It turns out that introducing some additional mechanisms into basic EMAS&#8212;such as presented in the course of this paper elitist extensions cause that results obtained with the use of proposed elEMAS (elitist Evolutionary Multi-Agent System) approach are as high-quality results as results obtained by such famous and commonly used algorithms as NSGA-II or SPEA2. Apart from the computational simplicity especially important and interesting aspects of EMAS-based algorithms it is characteristic for them a kind of soft selection which can be additionally easily adjusted depending on a particular situation&#8212;in particular it is possible to introduce auto-adapting selection into such systems. Such a kind of selection seems to be especially important and valuable in solving optimization tasks in uncertain or ``noised'' environments. In the course of this paper the model and experimental results obtained by elEMAS system in solving noisy multi-objective optimization problems are presented and the general conclusion is as follows: EMAS-based optimization system seems to be more effective alternative than classical (i.e. non agent-based) evolutionary algorithms for multi-objective optimization, in particular, in uncertain environment, it seems to be better alternative than NSGA-II algorithm.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Penrod:2008:cec,
  author = "Nathan A. Penrod and Sushil J. Louis and David Carr and Bobby D. Bryant",
  title = "Neuro-Evolving Maintain-Station Behavior for Realistically Simulated Boats",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0723.pdf},
  url = {},
  size = {},
  abstract =	{We evolve a neural network controller for a boat that learns to maintain a given bearing and range with respect to a moving target in the Lagoon 3D game environment. Simulating realistic physics makes maneuvering boats difficult and thus makes an evolutionary approach an attractive alternative to hand coded methods. We evolve the weights of simple recurrent neural networks trained with a fitness function designed to combine multiple fitness objectives based on speed, heading, and position to create a robust maintain station behavior. Results with an enforced subpopulation neural-evolution genetic algorithm indicate that we can consistently evolve robust maintain controllers for realistically simulated boats in Lagoon.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Pilato:2008:cec,
  author = "Christian Pilato and Daniele Loiacono and  Fabrizio Ferrandi and Pier Luca Lanzi",
  title = "High-Level Synthesis with Multi-Objective Genetic Algorithm: A Comparative Encoding Analysis",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0724.pdf},
  url = {},
  size = {},
  abstract =	{The high-level synthesis process involves three interdependent and NP-complete optimization problems: (i) the operation scheduling, (ii) the resource allocation, and (iii) the controller synthesis. Evolutionary algorithms have been effectively applied to high level synthesis in presence conflicting design objectives for finding good tradeoffs in the design space. However, so far the design space exploration has been performed using single-objective evolutionary algorithms with an ad hoc fitness function to achieve the desired tradeoff between the objectives. Recently we proposed a framework based on multi-objective genetic algorithms to perform a fully automated design space exploration. In this paper we focus on the choice of the solution representations that can be used to perform the design space exploration with multi-objective genetic algorithms. In particular we consider two specific representations and compare them on a set of benchmark problems. Our results suggest that they have different biases on the search space that make them more effective in different problems and design subspaces. Accordingly, we present a preliminary investigation on a new representation that exploits the advantages of both of them.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Bader-El-Den:2008:cec,
  author = "Mohamed Bader-El-Den and Riccardo Poli",
  title = "Analysis and Extension of the Inc* on the Satisfiability Testing Problem",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0725.pdf},
  url = {},
  size = {},
  abstract =	{Inc (star) is a general algorithm that can be used in conjunction with any local search heuristic and that has the potential to substantially improve the overall performance of the heuristic. The general idea of the algorithm is the following. Rather than attempting to directly solve a difficult problem, the algorithm dynamically chooses a smaller instance of the problem, and then increases the size of the instance only after the previous simplified instances have been solved, until the full size of the problem is reached. Genetic programming is used to discover new strategies for Inc<sup>*</sup>. Preliminary experiments on the satisfiability problem (SAT) problem have shown that Inc<sup>*</sup> is a competitive approach.  In this paper we enhance Inc<sup>*</sup> and we experimentally test it on larger set of benchmarks, including big instances of SAT. Furthermore, we provide an analysis of the algorithm's behaviour.  },
  keywords = {genetic algorithms, genetic programming},
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Loiacono:2008:cec,
  author = "Daniele Loiacono and Pier Luca Lanzi",
  title = "Computed Prediction in Binary Multistep Problems",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0726.pdf},
  url = {},
  size = {},
  abstract =	{Computed prediction was originally devised to tackle problems defined over real-valued domains. Recent experiments on Boolean functions showed that the concept of computed prediction extends beyond real values and it can also be applied to solve more typical classifier system benchmarks such as Boolean multiplexer and parity functions. So far however, no result has been presented for other well known classifier system benchmarks, i.e., binary multistep problems such as the woods environments. In this paper, we apply XCS with computed prediction to woods environments and show that computed prediction can also tackle this class of problems. Our results demonstrate that (i) XCS with computed prediction converges to optimality faster than XCS, (ii) it solves problems that may be too difficult for XCS and (iii) it evolves solutions that are more compact than those evolved by XCS.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Siwik3:2008:cec,
  author = "Leszek Siwik and Szymon Natanek",
  title = "Solving Constrained Multi-Criteria Optimization Tasks Using Elitist Evolutionary Multi-Agent System",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0727.pdf},
  url = {},
  size = {},
  abstract =	{Introducing elitism into Evolutionary Multi-Agent System for multi-objective optimization proofed to be smooth both conceptually and in realization. Simultaneously it allowed for obtaining results with comparable high quality to such referenced algorithms as Non-dominated Sorting Genetic Algorithm (NSGA-II) or Strength Pareto Evolutionary Algorithm (SPEA2). What is more, applying mentioned agent-based computational paradigm for solving multi-criteria optimization tasks in ``noisy'' environments mainly because of&#8212;characteristic for EMAS-based approach&#8212;a kind of soft selection allowed for obtaining better solutions than mentioned referenced algorithms. From the above observations the following conclusion can be drown: Evolutionary Multi-Agent System (EMAS) (and being the subject of this paper Elitist Evolutionary Multi-Agent System (elEMAS) in particular) seems to be promising computational model in the context of multi-criteria optimization tasks. In previous works however the possibility of applying elEMAS for solving constrained multi-objective optimization task has not been investigated. It is obvious however that in almost all real-life problems constraints are a crucial part of Multi-Objective Optimization Problem (MOOP) definition and it is nothing strange that among (evolutionary) algorithms for multi-objective optimization a special attention is paid to techniques and algorithms for constrained multi-objective optimization and a variety&#8212;more or less effective&#8212;algorithms have been proposed. Thus, the question appears if effective constrained multi-objective optimization with the use of Elitist Evolutionary Multi-Agent System is possible. In the course of this paper preliminary answer for that question is given.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Jewajinda:2008:cec,
  author = "Yutana Jewajinda and Prabhas Chongstitvatana",
  title = "FPGA Implementation of a Cellular Univariate Estimation of Distribution Algorithm and Block-Based Neural Network as an Evolvable Hardware",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0729.pdf},
  url = {},
  size = {},
  abstract =	{This paper presents a hardware implementation of evolvable block-based neural network (BBNN) amd a kind of EDAs called cellular compact genetic algorithm (CCGA) in FPGA. The CCGA and BBNN have cellular-like and array-like structures which are suitable for hardware implementation. The implemented hardware demonstrates the completely intrinsic online evolution in hardware without software running on microprocessors. This work contributes to the field of evolvable hardware by proposing CCGA and a layer-based architecture for integration of BBNN and CCGA as a kind of evolvable hardware. In addition, the proposed CCGA efficiently solves the scalable issues by scaling up to the size of BBNN. The presented approach demonstrates a new kind of evolvable hardware.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Lukac:2008:cec,
  author = "Martin Lukac and Marek Perkowski",
  title = "Evolutionary Approach to Quantum Symbolic Logic Synthesis",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0730.pdf},
  url = {},
  size = {},
  abstract =	{In this paper we present an evolutionary approach to the quantum symbolic logic synthesis that was introduced in [1]. We use a Genetic Algorithm to synthesize quantum circuits from examples, allowing to synthesize functions that are both completely and incompletely specified. The symbolic synthesis is implemented in the GA so as to verify our approach. The Occam Razor principle, fundamental to inductive learning as well as to logic synthesis, is satisfied in this approach by seeking circuits of reduced complexity. The GA is tested on a set of benchmark functions representing single output quantum circuits as well as multiple entangled-qubit state generators.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Wierstra:2008:cec,
  author = "Daan Wierstra and Tom Schaul and Jan Peters and Juergen Schmidhuber",
  title = "Natural Evolution Strategies",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0731.pdf},
  url = {},
  size = {},
  abstract =	{This paper presents Natural Evolution Strategies (NES), a novel algorithm for performing real-valued 'black box' function optimization: optimizing an unknown objective function where algorithm-selected function measurements constitute the only information accessible to the method. Natural Evolution Strategies search the fitness landscape using a multivariate normal distribution with a self-adapting mutation matrix to generate correlated mutations in promising regions. NES shares this property with Covariance Matrix Adaption (CMA), an Evolution Strategy (ES) which has been shown to perform well on a variety of high-precision optimization tasks. The Natural Evolution Strategies algorithm, however, is simpler, less ad-hoc and more principled. Self-adaptation of the mutation matrix is derived using a Monte Carlo estimate of the natural gradient towards better expected fitness. By following the natural gradient instead of the 'vanilla' gradient, we can ensure efficient update steps while preventing early convergence due to overly greedy updates, resulting in reduced sensitivity to local suboptima. We show NES has competitive performance with CMA on unimodal tasks, while outperforming it on several multimodal tasks that are rich in deceptive local optima.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Ahmadi:2008:cec,
  author = "Abbas Ahmadi and Fakhri Karray and Mohamed Kamel",
  title = "Model Order Selection for Multiple Cooperative Swarms Clustering Using Stability Analysis",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0732.pdf},
  url = {},
  size = {},
  abstract =	{Extracting different clusters of the given data is an appealing topic in swarm intelligence applications. This paper introduces multiple cooperative swarms and single swarm clustering approaches and provides mathematical descriptions explaining why the former approach outperform the other one. Moreover, the stability analysis is proposed to obtain the model order of the data using multiple cooperative swarms clustering approach. The proposed clustering approach is evaluated using three data sets and its performance is compared with that of other clustering techniques.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Esmaeili:2008:cec,
  author = "Afshin Esmaeili and Christian Jacob",
  title = "Evolutionary Exploration of Boolean Networks",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0733.pdf},
  url = {},
  size = {},
  abstract =	{Random Boolean networks (RBNs) are abstract models of gene regulatory networks that govern gene expression in cells. We have developed an evolutionary model to explore the dynamic states of random Boolean networks using heuristic optimization methods. The generic behavior of random Boolean networks is investigated as the evolutionary process works its way through different generations, identifying attractors that have been suggested to resemble cell types.   We investigate several fitness functions to tune RBNs with respect to the number of attractors and other network parameters such as excess graph, attractor cycle length, network sensitivity and average basin entropy. We show that by imposing particular constraints on the evolutionary model we can generate ensembles of more stable networks, which are less sensitive to perturbations. Therefore, we demonstrate that an evolutionary approach can be useful for the generation of RBN ensembles, that is sets of regulatory networks that share particular properties.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Siwik4:2008:cec,
  author = "Leszek Siwik and Przemyslaw Sroka and Marek Psiuk",
  title = "Flock-Based Evolutionary Multi-Agent System in Solving Noisy Multi-Objective Optimization Problems",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0734.pdf},
  url = {},
  size = {},
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Romero:2008:cec,
  author = "Andres Romero and Fernando Nino and Gerardo Quintana",
  title = "An Artificial Immune System Model for Knowledge Extraction and Representation",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0735.pdf},
  url = {},
  size = {},
  abstract =	{This paper presents an approach to knowledge extraction and representation based on an artificial immune system. The main idea is to extract the important concepts from a set of text documents, and find the relations between such concepts. At the end, a graph representation is obtained, which is intended to present a picture of the documents' contents. Some experiments were carried out in order to validate the proposed approach, and very promising results were obtained.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Caporale:2008:cec,
  author = "Guglielmo Maria Caporale and Antoaneta Serguieva and Hao Wu",
  title = "A Mixed-Game Agent-Based Model for Simulating Financial Contagion",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0736.pdf},
  url = {},
  size = {},
  abstract =	{Over the past two decades, financial market crises with similar features have occurred in different regions of the world. Unstable cross-market linkages during financial crises are referred to as financial contagion. We simulate the transmission of financial crises in the context of a model of market participants adopting various strategies; this allows testing for financial contagion under alternative scenarios. Using a minority game approach, we develop an agent-based multinational model and investigate the reasons for contagion. Although contagion has been extensively investigated in the financial literature, it has not been studied yet through computational intelligence techniques. Our simulations shed light on parameter values and characteristics which can be exploited to detect contagion at an earlier stage, hence recognising financial crises with the potential to destabilise cross-market linkages. In the real world, such information would be extremely valuable to develop appropriate risk management strategies.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Flores-Mendoza:2008:cec,
  author = "Jorge Isacc Flores-Mendoza and Efren Mezura-Montes",
  title = "Dynamic Adaptation and Multiobjective Concepts in a Particle Swarm Optimizer for Constrained Optimization",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0738.pdf},
  url = {},
  size = {},
  abstract =	{In this paper, we propose a novel approach to solve constrained optimization problems based on particle swarm optimization (PSO). First, an empirical comparison of the most popular PSO variants is presented as to select the most convenient among them. After that, the PSO variant chosen is improved in: (1) its parameter control with a dynamic proposal as to promote a better exploration of the search space and to avoid premature convergence and (2) its constraint-handling mechanism by using multiobjective concepts as to promote a better approach to the feasible region. The algorithm is tested on a set of 13 well-known benchmark problems and the obtained performance is compared against some PSO variants and state-of- the-art approaches. Based on the results presented some conclusions are drawn and the future work is established.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Junior:2008:cec,
  author = "Aranildo R. L. Junior and Tiago A. E. Ferreira and  Ricardo de A. Ara&#250;jo",
  title = "An Experimental Study with a Hybrid Method for Tuning Neural Network for Time Series Prediction",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0739.pdf},
  url = {},
  size = {},
  abstract =	{This paper presents an study of a new Hybrid method based on the Greedy Randomized Adaptive Search Procedure(GRASP) and Evolutionary Strategies(ES) concepts for tuning the structure and parameters of an Artificial Neural Network (ANN). It consists of an ANN trained and adjusted by this new method, which searches for the minimum number of (and their specific) relevant time lags for a correct time series representation, the parameters configuration and the weights of the ANN until the learning performance in terms of fitness value is good enough, which found, for an optimal or sub-optimal forecasting model. An experimental analysis is presented with the proposed method using three relevant time series, and its results are discussed according to five well-known performance measures, showing the effectiveness and robustness of the proposed method.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Prieto:2008:cec,
  author = "Camilo E. Prieto and Fernando Ni&#241;o and Gerardo Quintana",
  title = "A Goalkeeper Strategy in Robot Soccer Based on Danger Theory",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0740.pdf},
  url = {},
  size = {},
  abstract =	{Artificial Immune Systems (AIS) have been successfully modeled and implemented in several engineering applications. In this work, a goalkeeper strategy in robot soccer based on Danger Theory is proposed. Danger Theory is a recent immune theory which has not been widely applied so far. The proposed strategy is implemented and evaluated using middle league SIMUROSOT from FIRA. Experiments carried out yielded promising results.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Beume:2008:cec,
  author = "Nicola Beume and Holger Danielsiek and Christian Eichhorn and Boris Naujoks and Mike Preuss",
  title = "Measuring Flow as Concept for Detecting Game Fun in the Pac-Man Game",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0741.pdf},
  url = {},
  size = {},
  abstract =	{Popular games often have a high-quality graphic design but quite simple-minded non player characters (NPC). Recently, Computational Intelligence (CI) methods have been discovered as suitable methods to revive NPC, making games more interesting, challenging, and funny. We present a fairly large study of human players on the simple arcade game Pac- Man, controlling the ghosts behaviors by simple strategies, neural networks or evolutionary algorithms. The player's fun is of course a subjective experience, but we presume that it is related to the psychological flow concept. We deal with the question whether flow is a more reliable measure than asking human players directly for the fun experienced during the game. In order to detect flow, we introduce a measure based on the interaction time fraction between the human-controlled Pac- Man and the ghosts, and compare the outcome to the results of a fun measure suggested by Yannakakis and Hallam [1].  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Kaylani2:2008:cec,
  author = "A. Kaylani and M. Georgiopoulos and M. Mollaghasemi and G. C. Anagnostopoulos   ",
  title = "Efficient Evolution of ART Neural Networks",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0742.pdf},
  url = {},
  size = {},
  abstract =	{ Genetic algorithms have been used to evolve several neural network architectures. In a previous effort, we introduced the evolution of three well known ART architects; Fuzzy ARTMAP (FAM), Ellipsoidal ARTMAP (EAM) and Gaussian ARTMAP (GAM). The resulting architectures were shown to achieve competitive generalization and exceptionally small size. A major concern regarding these architectures, and any evolved neural network architecture in general, is the added overhead in terms of computational time needed to produce the finally evolved network. In this paper we investigate ways of reducing this computational overhead by reducing the computations needed for the calculation of the fitness value of the evolved ART architectures. The results obtained in this paper can be directly extended to many other evolutionary neural network architectures, beyond the studied evolution of ART neural network architectures.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Beume2:2008:cec,
  author = "Nicola Beume and Tobias Hein and Boris Naujoks and Georg Neugebauer and Nico Piatkowski",
  title = "To Model or Not to Model: Controlling Pac-Man Ghosts without Incorporating Global Knowledge",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0743.pdf},
  url = {},
  size = {},
  abstract =	{The creation of interesting opponents for human players in computer games is an interesting and challenging task. In contrast to up-to-date computer games, e.g. real time strategy games, learning of non-player-character strategies for older games seems to be easier and not that time-consuming. This way, older games, like the famous arcade game Pac-Man, serve as a test bed for the creation of strategies that are fun to play against. The paper at hand uses computational intelligence methods to accomplish this challenge, namely evolutionary algorithms (EA) and artificial neural networks (ANN). The latter are trained on a model of the game whereas the EA learn good behavior by playing. The performance of these two approaches is compared on the original Pac-Man level as well as on other maps with different properties to test the ability of generalizing the learned strategies.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Das:2008:cec,
  author = "Swagatam Das and Sudeshna Sil and  Uday K. Chakraborty",
  title = "Kernel-Based Clustering of Image Pixels with Modified Differential Evolution",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0744.pdf},
  url = {},
  size = {},
  abstract =	{A modified Differential Evolution (DE) algorithm is presented for clustering the pixels of an image in its intensity space. The algorithm requires no prior information about the number of naturally occurring clusters in the image. It employs a kernel-induced similarity measure instead of the conventional sum-of-squares distance. Use of the kernel function makes it possible to partition data that is linearly non-separable and non hyper-spherical in the original input space, into homogeneous groups in a transformed high-dimensional feature space. A novel chromosome representation scheme is adopted for selecting the optimal number of clusters from several possible choices. Extensive performance comparison over a test-suit of five gray scale images (with ground truth) indicates that the proposed algorithm has an edge over a few state-of-the-art algorithms for automatic multi-class image segmentation.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Liu10:2008:cec,
  author = "Li Liu and Wenxin Liu and David A. Cartes and Nian Zhang",
  title = "Real Time Implementation of Particle Swarm Optimization Based Model Parameter Identification and an Application Example",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0745.pdf},
  url = {},
  size = {},
  abstract =	{Particle Swarm Optimization (PSO) has been widely used in optimization problems. If an identification problem can be transformed into an optimization problem, PSO can be used to identify the unknown parameters in the model. Currently, most PSO based identification or optimization applications can only be applied offline. The difficulties of online implementation mainly come from the unavoidable simulation time to evaluate a candidate solution. In this paper, the techniques for faster than real time simulation are introduced and the hardware implementation details of PSO algorithm are presented. We demonstrate the performance of the described approach by applying it to parameter identification of permanent magnet synchronous motor. The method can be easily implemented using dSPACE&#174;. controller and other hardware controllers. The techniques can also be extended to other online identification and optimization problems.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Chang5:2008:cec,
  author = "Yaw-Jen Chang and Jui-Ju Tsai  ",
  title = "Process Optimization Based on Neural Network Model and Orthogonal Arrays",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0746.pdf},
  url = {},
  size = {},
  abstract =	{This paper presents a systematic and cost-effective approach for process optimization with minimal experimental runs. Based on the experimental design scheme of orthogonal arrays, artificial neural network is used to establish the process model. Moreover, Taguchi-genetic algorithm (TGA) is used to search for the global optimum of the fabrication conditions. The procedure starts planning and conducting the initial experiment with fewer levels. By adding experimental points selected from augmented orthogonal arrays, the process model is corrected. This step is continued until the termination condition has been reached. Then, the optimum given by Taguchi-genetic algorithm is the final solution. The proposed approach provides an effective and economical solution for process optimization.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Yu6:2008:cec,
  author = "Tina Yu and Dave Wilkinson and Julian Clark and Morgan Sullivan",
  title = "Evolving Finite State Transducers to Interpret Deepwater Reservoir Depositional Environments",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0748.pdf},
  url = {},
  size = {},
  abstract =	{Predicting oil recovery efficiency of deep water reservoirs is a challenging task. One approach to characterise and predict the producibility of a reservoir is by analysing its depositional information. In a deposition-based stratigraphic interpretation framework, one critical step is the identification and labelling of the stratigraphic components in the reservoir according to their depositional information. This interpretation process is labour intensive and can produce different results depending on the stratigrapher who performs the analysis. To relieve stratigrapher's workload and to produce more consistent results, this research developed a methodology to automate this process using various computational intelligent techniques. Using a well log data set, we demonstrated that the developed methodology and the designed work flow can produce finite state transducer models that interpret deepwater reservoir depositional environments adequately.  },
  keywords = {genetic algorithms, genetic programming},
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Bichot:2008:cec,
  author = "Charles-Edmond Bichot   ",
  title = "A New Meta-Method for Graph Partitioning",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0750.pdf},
  url = {},
  size = {},
  abstract =	{In this paper, a new meta-method based on the physical nuclear process is presented. This meta-method called Fusion-Fission is applied to the two different class of graph partitioning problems. This paper presents results found by this method in comparison with results of classical methods for an air traffic management problem, an image segmentation problem and applied to classical benchmarks. All of these applications of the Fusion-Fission method are successful and the results found by this method outperform state-of-the-art graph partitioning packages both on classical benchmarks and on the air traffic management problem.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Mora:2008:cec,
  author = "A. M. Mora and J. J. Merelo and P. A. Castillo and  J. L. J. Laredo and C. Cotta",
  title = "Influence of Parameters on the Performance of a MOACO Algorithm for Solving the Bi-Criteria Military Path-finding Problem",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0753.pdf},
  url = {},
  size = {},
  abstract =	{This paper presents a statistical parameter analysis of the ant colony optimization algorithm that was implemented to solve the bi-criteria military path-finding problem. Three parameters have been studied using analysis of variance (ANOVA) in order to identify their influence in the results and the most suitable values for them: number of ants, number of iterations and exploration/exploitation factor. In addition, a mean analysis has been performed in order to complete the conclusions obtained. The study has yielded optimal values for the parameters under study, and some internal relationships between them have been identified.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Ponweiser:2008:cec,
  author = "Wolfgang Ponweiser and Tobias Wagner and  Markus Vincze",
  title = "Clustered Multiple Generalized Expected Improvement: A Novel Infill Sampling Criterion for Surrogate Models",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0755.pdf},
  url = {},
  size = {},
  abstract =	{Surrogate model-based optimization is a well-known technique for optimizing expensive black-box functions. By applying this function approximation, the number of real problem evaluations can be reduced because the optimization is performed on the model. In this case two contradictory targets have to be achieved: increasing global model accuracy and exploiting potentially optimal areas. The key to these targets is the criterion for selecting the next point, which is then evaluated on the expensive black-box function &#8211; the 'infill sampling criterion'. Therefore, a novel approach &#8211; the 'Clustered Multiple Generalized Expected Improvement' (CMGEI) &#8211;  is introduced and motivated by an empirical study. Furthermore, experiments benchmarking its performance compared to the state of the art are presented.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Avery:2008:cec,
  author = "Phillipa M. Avery and Garrison W. Greenwood and Zbigniew Michalewicz",
  title = "Coevolving Strategic Intelligence",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0758.pdf},
  url = {},
  size = {},
  abstract =	{Strategic decision making done in parallel with the opposition makes it difficult to predict the opposition's strategy. An important aspect in deciding a move is evaluating your opponent's past moves and using them to predict future movement. In the game of TEMPO this is done through the purchase of intelligence, which gives you a relative view of your opponent's choices. The research presented here seeks to evaluate the way this intelligence is used in the current game, and present an alternative method of representation. This alternate mechanism is then used in a coevolutionary system to obtain a computer player that will self-learn the importance of using opposition data in strategic decision making.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Shyang:2008:cec,
  author = "Woei Shyang and Charles Lakos and Zbigniew Michalewicz and Sven Schellenberg",
  title = "Experiments in Applying Evolutionary Algorithms to Software Verification",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0759.pdf},
  url = {},
  size = {},
  abstract =	{Complex concurrent systems present a significant challenge for software verification. If those systems are safetycritical, the need for software verification becomes particularly pressing, given the serious consequences of unforeseen defects. Complex concurrent systems are characterised by extremely large state spaces. The use of testing techniques for verification means that very little of the state space is explored. On the other hand, model-checking techniques exhaustively examine the state space, but will be stymied by the actual size.  In this paper, we discuss some preliminary experiments on the application of evolutionary algorithms to software verification. This approach does not explore the whole state space, but does use heuristics to guide the search through the most promising parts of the state space for locating errors. },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Koppen:2008:cec,
  author = "Mario Koppen and Yutaka Kinoshita and Kaori Yoshida",
  title = "Auxiliary Objectives for the Evolutionary Multi-Objective Principal Colour Extraction from Logo Images",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0760.pdf},
  url = {},
  size = {},
  abstract =	{In this paper, we present an approach to the selection of principal colours for the class of logo images. The approach is using multiple objectives that can be assigned to a colour set, qualifying the selected colours as being principal colours of the image. Since all these objectives have a different preference, and have different computational complexity and granularity, it is not useful to put them all together into a single objective vector. Instead, a three stages procedure is proposed. The first stage optimizes only objectives of high relevance, and lower computational effort. Here, evolutionary multi-objective optimization is used. The second stage re-evaluates the Pareto set of the first stage according to an additional set of objectives. Finally, one solution of the Pareto set from the second stage is selected according to a single objective of highest preference. As suitable objectives for the first stage, the average minimum distance of the colour set to the image pixels, together with the average number of pixel that are closer than a threshold have been found. The approach was studied on a number of logo images, and it could reconstruct the logo images of good visual quality from the found principal colours in the majority of the cases. The experiments also show that the result is usually improved by searching for more principal colours than are present in the logo image, and by repeating the process to find also small, but notable detail structures.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Bate:2008:cec,
  author = "Iain Bate and Dimitar Kazakov",
  title = "New Directions in Worst-Case Execution Time Analysis",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0761.pdf},
  url = {},
  size = {},
  abstract =	{Most software engineering methods require some form of model populated with appropriate information. Realtime systems are no exception. A significant issue is that the information needed is not always freely available and derived it using manual methods is costly in terms of time and money. Previous work showed how machine learning information derived during software testing can be used to derive loop bounds as part of the Worst-Case Execution Time analysis problem. In this paper we build on this work by investigating the issue of branch prediction.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Siebel:2008:cec,
  author = "Nils T. Siebel and Sven Gr&#252;newald and Gerald Sommer",
  title = "Creating Edge Detectors by Evolutionary Reinforcement Learning",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0764.pdf},
  url = {},
  size = {},
  abstract =	{In this article we present results from experiments where a edge detector was learned from scratch by EANT2, a method for evolutionary reinforcement learning. The detector is constructed as a neural network that takes as input the pixel values from a given image region&#8212;the same way that standard edge detectors do. However, it does not have any perimage parameters. A comparison between the evolved neural networks and two standard algorithms, the Sobel and Canny edge detectors, shows very good results.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Qian:2008:cec,
  author = "Xiaoxue Qian and Xiangrong Zhang and Licheng Jiao and Wenping Ma   ",
  title = "Unsupervised Texture Image Segmentation Using Multiobjective Evolutionary Clustering Ensemble Algorithm",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0765.pdf},
  url = {},
  size = {},
  abstract =	{Multiobjective evolutionary clustering approach has been successfully used in data clustering. In this paper, we propose a novel unsupervised machine learning algorithm namely multiobjective evolutionary clustering ensemble algorithm (MECEA) to perform the texture image segmentation. MECEA comprises two main phases. In the first phase, MECEA uses a multiobjective evolutionary clustering algorithm to optimize two complementary clustering objectives: one based on compactness in the same cluster, and the other based on connectedness of different clusters. The output of the first phase is a set of Pareto solutions, which correspond to different tradeoffs between two clustering objectives, and different numbers of clusters. In the second phase, we make use of the meta-clustering algorithm (MCLA) to combine all the Pareto solutions to get the final segmentation. The segmentation results are evaluated by comparing with three known algorithms: K-means, fuzzy K-means (FCM), and evolutionary clustering algorithm (ECA). It is shown that MECEA is an adaptive clustering algorithm, which outperforms the three algorithms in the experiments we carried out.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Wesolkowski:2008:cec,
  author = "S. Wesolkowski and Z. Zhu",
  title = "Optimizing the Stochastic Fleet Estimation Model Using Evolutionary Computation",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0766.pdf},
  url = {},
  size = {},
  abstract =	{We introduce an evolutionary computation framework using genetic algorithms to optimize the Stochastic Fleet Estimation (SaFE) model. SaFe is a Monte Carlo-based model which generates a vehicle fleet based on the set of requirements that the fleet is supposed to accomplish. A genetic algorithm framework is used in order to alternate solutions between different plausible sets of platforms. We use SaFE coupled with a simple cost evaluation based on the output of SaFe as the genetic algorithm's cost function. Results showing a decrease in fleet cost are shown and analyzed.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Davarynejad:2008:cec,
  author = "M. Davarynejad and M.-R. Akbarzadeh T and  Carlos A. Coello Coello",
  title = "Auto-Tuning Fuzzy Granulation for Evolutionary Optimization",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0767.pdf},
  url = {},
  size = {},
  abstract =	{Much of the computational complexity in employing evolutionary algorithms as optimization tool is due to the fitness function evaluation that may either not exist or be computationally very expensive. With the proposed approach, the expensive fitness evaluation step is replaced by an approximate model. An intelligent guided technique via an adaptive fuzzy similarity analysis for fitness granulation is used to decide on use of expensive function evaluation and dynamically adapt the predicted model. In order to avoid tuning parameters in this approach, a fuzzy supervisor as autotuning algorithm is employed with three inputs. The proposed method is then applied to three traditional optimization benchmarks with four different choices for the dimensionality of the search apace. Effect of number of granules on rate of convergence is also studied. In comparison with standard application of evolutionary algorithms, statistical analysis confirms that the proposed approach demonstrates an ability to reduce the computational complexity of the design problem without sacrificing performance. Furthermore, the auto-tuning of the fuzzy supervisory removes the need for exact parameter determination.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Zhou7:2008:cec,
  author = "Qing Zhou and Xuebin Yang",
  title = "Modeling and Simulation of Contestable Market Based on Classifier Systems",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0768.pdf},
  url = {},
  size = {},
  abstract =	{The contestable markets theory provides a new view of business behaviors and other basic economic problems, such as economies of scale and natural monopolies. In order to study the potential competition from new entrants in the contestable market, a competing frame of agents was designed, and a contestable market model integrated with the theory of complex adaptive systems (CAS) was built. We designed the corresponding dynamic competition mechanism, operational mechanism and especially the classifier system evolutionary learning mechanism. By constructing emulator based on SWARM, the dynamic behavior of retailer agent was simulated and then the results of the multi-agent system were analyzed.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Ishibuchi2:2008:cec,
  author = "Hisao Ishibuchi and Yasuhiro Hitotsuyanagi and Yusuke Nojima",
  title = "Scalability of Multiobjective Genetic Local Search to Many- Objective Problems: Knapsack Problem Case Studies",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0769.pdf},
  url = {},
  size = {},
  abstract =	{It is well-known that Pareto dominance-based evolutionary multiobjective optimization (EMO) algorithms do not work well on many-objective problems. This is because almost all solutions in each population become non-dominated with each other when the number of objectives is large. That is, the convergence property of EMO algorithms toward the Pareto front is severely deteriorated by the increase in the number of objectives. Currently the design of scalable EMO algorithms is a hot issue in the EMO community. In this paper, we examine the scalability of multiobjective genetic local search (MOGLS) to many-objective problems using a hybrid algorithm of NSGA-II and local search. Multiobjective knapsack problems with 2, 4, 6, 8, and 10 objectives are used in computational experiments. It is shown by experimental results that the performance of NSGA-II is improved by the hybridization with local search independent of the number of objectives in the range of 2 to 10 objectives.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Togelius2:2008:cec,
  author = "Julian Togelius and Renzo {De Nardi} and Alberto Moraglio",
  title = "Geometric PSO + GP = Particle Swarm Programming",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0771.pdf},
  url = {},
  size = {},
  abstract =	{Geometric particle swarm Optimization (GPSO) is a recently introduced formal generalisation of traditional particle swarm Optimization (PSO) that applies naturally to both continuous and combinatorial spaces. In this paper we apply GPSO to the space of genetic programs represented as expression trees, uniting the paradigms of genetic programming and particle swarm Optimization. The result is a particle swarm flying through the space of genetic programs. We present initial experimental results for our new algorithm.  },
  keywords = {genetic algorithms, genetic programming},
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Zhao2:2008:cec,
  author = "Yaou Zhao and Yuehui Chen and Meng Pan and Qiang Zhu",
  title = "A Region Reproduction Algorithm for Global Numerical Optimization",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0772.pdf},
  url = {},
  size = {},
  abstract =	{This paper introduces a novel numerical stochastic optimization algorithm called Region Reproduction Algorithm (RRA) to solve global numerical optimization problems. The algorithm firstly generates some regions in space which the individual in the population exists. Then we evaluate the regions according to the fitness of the individuals in them. The number of offspring in the region is reproduced by the fitness in the regions. With the algorithm goes on, there would be more offspring in the superior regions than the poorer regions. Because the algorithm is more concerned in the superior regions, it has more probability to find the optimal solution than traditional algorithms. Experiments show that the algorithm is more effective and stable in terms of the solution quality and standard deviation compared with other existing methods, such as GA, PSO, Canonical PSO and EO.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Doerr:2008:cec,
  author = "Benjamin Doerr and Edda Happ   ",
  title = "Directed Trees: A Powerful Representation for Sorting and Ordering Problems",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0773.pdf},
  url = {},
  size = {},
  abstract =	{We present a simple framework for dealing with search spaces consisting of permutations. To demonstrate its usefulness, we build upon it a simple (1+1)-evolutionary algorithm for one of the most fundamental problems in computer science, namely the problem of sorting n pairwise comparable items. We give a rigorous proof that the optimization time is at most O(n<sup>2</sup>) with high probability. Our experimental evaluation shows that it is much better, namely around O(n log n). This compares favorably with the currently best (1+1)-EAs for sorting, for which an optimization time of O(n<sup>2</sup> log n) was proven (Scharnow, Tinnefeld and Wegener (2004)) and one of similar order is observed experimentally in this work.   Our approach has the particular advantage that it does distinguish between wrong and unexplored information. This allows to retrieve partial, correct information even before the optimal solution has been found.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Yao2:2008:cec,
  author = "Z. Yao and J. Liu and Y.-G. Wang",
  title = "Fusing Genetic Algorithm and Ant Colony Algorithm to Optimize Virtual Enterprise Partner Selection Problem",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0774.pdf},
  url = {},
  size = {},
  abstract =	{The partner selection in virtual enterprises organization is one of the key issues corporate enterprises experience nowadays. Based on the model of Ant Colony Optimization Algorithm (ACA) in virtual enterprise partner selection, in this paper, we fuse the genetic algorithm into ACA, called fusion algorithm, in order to improve the effect of the partner selection. The fusion algorithm has two steps: (1) it uses the GA to optimize the model of partner selection and takes advantages of rapid convergence of GA in initial search periods. (2) When GA search speed has become slow, the ACA takes over the search process, in which it uses the candidates produced by the GA as the seeds of pheromone used by ACA. By experimental comparison with GA optimization and ACA optimization, it shows that the fusion algorithm has performed better than the GA and ACA optimization, respectively, in both speed and accuracy under our selected numerical case. The fusion algorithm presented in this study may be applicable to similar business problems.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Seo:2008:cec,
  author = "Yoonho Seo and Chiung Moon and Young-Hoon Moon and Taioun Kim and Sung Shick Kim",
  title = "Adapting Genetic Algorithm and Tabu Search Approaches for Unidirectional AGV Flowpath Design Problems",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0775.pdf},
  url = {},
  size = {},
  abstract =	{In this paper we suggest an evolutionary computational approach by applying a combination of a genetic algorithm and a tabu search to obtain a good solution for relatively large unidirectional automated guided vehicle flowpath design problems. Unidirectional flowpaths are used to lessen the traffic control loads for large fleets of vehicles and to increase the efficiency in use of space. The flow path design is one of the most important steps in efficient vehicle systems design. We use an genetic algorithm to obtain partially directed networks, which are then completed and afterwards improved by a tabu search. A set of computational experiments is conducted to show the efficiency of the proposed solution procedure and the results are reported.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Fujii:2008:cec,
  author = "Seiya Fujii and Tomoharu Nakashima and Hisao Ishibuchi",
  title = "A Study on Constructing Fuzzy Systems for High-Level Decision Making in a Car Racing Game",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0776.pdf},
  url = {},
  size = {},
  abstract =	{In this paper, we examine the performance of fuzzy rule-based systems in a car racing domain. Fuzzy rulebased systems are used for high-level decision making of a car agent. We examine two methods that generate a set of training patterns for constructing fuzzy rule-based systems. We also examine the effect of sensory information on the high-level decision making. The performance of four types of fuzzy rule-based systems are compared in a series of computational experiments. The analysis of using different types of sensory information and different methods for generating training patterns is also performed.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Ang:2008:cec,
  author = "J. H. Ang and E. J. Teoh and C. H. Tan and K. C. Goh and K. C. Tan",
  title = "Dimension Reduction Using Evolutionary Support Vector Machines",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0777.pdf},
  url = {},
  size = {},
  abstract =	{This paper presents a novel approach of hybridising two conventional machine learning algorithms for dimension reduction. Genetic Algorithm (GA) and Support Vector Machines (SVMs) are integrated effectively based on a wrapper approach. Specifically, the GA component searches for the best attribute set using principles of evolutionary process, after which the reduced dataset is presented to the SVMs. Simulation results show that GA-SVM hybrid is able to produce good classification accuracy and a high level of consistency. In addition, improvements are made to the hybrid by using a correlation measure between attributes as a fitness measure to replace the weaker members in the population with newly formed chromosomes. This correlation measure injects greater diversity and increases the overall fitness of the population  },
  keywords = {genetic algorithms, genetic programming},
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Hu4:2008:cec,
  author = "X. B. Hu and E. Di Paolo and L. Barnett",
  title = "Ripple-Spreading Model and Genetic Algorithm for Random Complex Networks: Preliminary Study",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0778.pdf},
  url = {},
  size = {},
  abstract =	{Recently complex network theory has been broadly applied in various domains. How to effectively and efficiently optimize the topology of complex networks remains largely an unsolved fundamental question. When applied to the network topology optimization, Genetic Algorithms (GAs) are often confronted with permutation representation, memory-inefficiency and stochastic modeling problems, as well as difficulties in the design of problem-specific evolutionary operators. This paper, inspired by the natural ripple spreading phenomenon, reports a deterministic model of random complex networks. Unlike existing stochastic models, the topology of a random network can be thoroughly determined by some ripple-spreading related parameters in the new model. Therefore, the network topology can be improved by optimize these ripple-spreading related parameters. As a result, no purpose-designed GA is required, but a very basic binary GA, compatible to all classic evolutionary operators, can be applied in a straightforward way. Preliminary simulation results demonstrate the potential of the proposed ripple-spreading model and GA for the topology optimization of random complex networks.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Tang2:2008:cec,
  author = "Ke Tang and Zai Wang and Xianbin Cao and Jun Zhang",
  title = "A Multi-Objective Evolutionary Approach to Aircraft Landing Scheduling Problems",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0779.pdf},
  url = {},
  size = {},
  abstract =	{Scheduling aircraft landings has been a complex and challenging problem in air traffic control for long time. In this paper, we propose to solve the aircraft landing scheduling problem (ALSP) using multi-objective evolutionary algorithms (MOEAs). Specifically, we consider simultaneously minimizing the total scheduled time of arrival and the total cost, and formulate the ALSP as a 2-objective optimization problem. A MOEA named Multi-Objective Neighborhood Search Differential Evolution (MONSDE) is applied to solve the 2-objective ALSP. Besides, a ranking scheme named non-dominated average ranking is also proposed to determine the optimal landing sequence. Advantages of our approaches are demonstrated on two example scenarios.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Guo:2008:cec,
  author = "Yuanping Guo and Xianbin Cao and Jun Zhang",
  title = "Multiobjective Evolutionary Algorithm with Constraint Handling for Aircraft Landing Scheduling",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0780.pdf},
  url = {},
  size = {},
  abstract =	{Aircraft landing scheduling is a multiobjective optimization problem with lots of constraints, which is difficult to be dealt with by traditional multiobjective evolutionary algorithms with general constraint handling strategies such as constraint-dominate definition. In this paper we pertinently designed an effective constraint handling method, and then presented a multiobjective evolutionary algorithm using the constraint handing method to solve the aircraft landing scheduling problem. Experiments show that our method is able to locate the feasible region in the search space, obtain the jagged Pareto front, and thereby provide efficient schedule for aircraft landing.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Mallipeddi:2008:cec,
  author = "R. Mallipeddi and P. N. Suganthan",
  title = "Empirical Study on the Effect of Population Size on Differential Evolution Algorithm",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0781.pdf},
  url = {},
  size = {},
  abstract =	{In this paper, we investigate the effect of population size on the quality of solutions and the computational effort required by the Differential evolution (DE) Algorithm. A set of 5 problems chosen from the problem set of CEC 2005 Special Session on Real-Parameter Optimization are used to study the effect of population sizes on the performance of the DE. Results include the effects of various population sizes on the 10 and 30-dimensional versions of each problem for two different mutation strategies. Our study shows a significant influence of the population size on the performance of DE as well as interactions between mutation strategies, population size and dimensionality of the problems.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Jr.2:2008:cec,
  author = "Maury M. Gouv&#234;a Jr. and Aluizio F. R. Ara&#250;jo",
  title = "Diversity Control Based on Population Heterozygosity Dynamics",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0782.pdf},
  url = {},
  size = {},
  abstract =	{Maintaining the population diversity in genetic algorithms (GAs), or minimize its loss, may benefit the evolutionary process in several ways. The premature convergence may lead the GA to a non-optimal result, that is, converging to a local optimum. Specially in dynamic problems, the diversity preservation is a crucial issue. In this work, a study of different diversity models based on several works has been made. From these models a diversity reference-model has been created in order to enhance diversity-reference adaptive control (DRAC) [20] performance. This new version of DRAC method was evaluated in case studies using a dynamic test functions presented in [26]. The validation of the proposed adaptive parameter control method was performed comparing its performance with SGA and other diversity-based algorithm.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Dre&#380;ewski:2008:cec,
  author = "Rafa Dre&#380;ewski and Leszek Siwik",
  title = "Agent-Based Multi-Objective Evolutionary Algorithm with Sexual Selection",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0783.pdf},
  url = {},
  size = {},
  abstract =	{Evolutionary algorithms are (meta-)heuristic techniques used in the case of search, optimization, and adaptation problems, which cannot be solved with the use of traditional methods. Sexual selection mechanism helps to maintain the population diversity in evolutionary algorithms. In this paper the agent-based realization of multi-objective evolutionary algorithm with sexual selection mechanism is presented. The system is evaluated with the use of Zitzler's test problems and compared to "classical" multi-objective evolutionary algorithms.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Vaccaro:2008:cec,
  author = "James Vaccaro and Clark Guest",
  title = "Automated Dynamic Planning and Execution for a Partially Observable Game Model: Tsunami City Search and Rescue",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0784.pdf},
  url = {},
  size = {},
  abstract =	{This paper addresses the problem of autonomous dynamic planning and execution (ADP&amp;E) for partially observable model environments. There are three accomplish-ments illustrated in this paper: (1) develop an ADP&amp;E implementation framework for planning and executing in partially observable model environments, (2) design and implement a methodology for adapting planner parameters to improve the overall planning process, and (3) demonstrate the utility of the planning process on a large complex application (i.e., city search and rescue operations).   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Tavares:2008:cec,
  author = "Jorge Tavares and Francisco B. Pereira and Ernesto Costa",
  title = "Golomb Rulers: A Fitness Landscape Analysis",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0785.pdf},
  url = {},
  size = {},
  abstract =	{Fitness landscape analysis techniques are used to better understand the influence of genetic representations and associated variation operators when solving a combinatorial optimization problem. Several representations for the Optimal Golomb Ruler problem are examined. Common mutation operators such as bit-flip mutation are employed to generate fitness landscapes to study the genetic representations. Furthermore, additional experiments are made to observe the effects of adding heuristics and local improvements to the encodings.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Duro:2008:cec,
  author = "Jo&#227;o Ant&#243;nio Duro and Jose Valente de Oliveira",
  title = "Particle Swarm Optimization Applied to the Chess Game",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0786.pdf},
  url = {},
  size = {},
  abstract =	{To the best of the authors' knowledge this paper investigates for the first time the applicability of particle swarm optimization (PSO) to a chess player agent endowing it with learning abilities, i.e. allowing the agent to improve its performance based on its experience.  A minimax algorithm with alpha beta pruning is used to select the next move of the chess agent. The performance of the agent strongly depends on the heuristic evaluation function available to the minimax algorithm. In this work, board features such as material strength, piece mobility, pawn structure, king safety and control of the centre are used in a parameterized board evaluation function whose weights are optimized using PSO.  The simulation results included, illustrate both the feasibility of the proposed approach and reveals that on average, PSO can provide faster learning results than simulated annealing under similar experimental conditions, especially in the presence of bounded computing time. Unfortunately, results also show that, for this application, PSO is highly sensitive to initial conditions.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Xie2:2008:cec,
  author = "Huayang Xie and Mengjie Zhang and Peter Andreae and Mark Johnston",
  title = "Is the Not-Sampled Issue in Tournament Selection Critical?",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0787.pdf},
  url = {},
  size = {},
  abstract =	{The standard tournament selection samples individuals with replacement. The sampling-with-replacement strategy has its advantages but also has issues. One of the commonly recognised issues is that it is possible to have some individuals not sampled at all during the selection phase. The not-sampled issue aggravates the loss of program diversity. However, it is not clear how the issue affects Genetic Programming (GP) search. This paper investigates the importance of the issue. The theoretical and experimental results show that the issue can be solved and the loss of diversity contributed by not-sampled individuals can be minimised. However, doing so does not appears to significantly improve a GP system. Our conclusion is that the not-sampled issue does not seriously affect the selection performance in the standard tournament selection.  },
  keywords = {genetic algorithms, genetic programming},
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Zamuda:2008:cec,
  author = "Ale&#353; Zamuda and Janez Brest and Borko Bo&#353;kovi&#263; and Viljem Zumer  ",
  title = "Large Scale Global Optimization Using Differential Evolution with Self-Adaptation and Cooperative Co-Evolution",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0788.pdf},
  url = {},
  size = {},
  abstract =	{In this paper, an optimization algorithm is formulated and its performance assessment for large scale global optimization is presented. The proposed algorithm is named DEwSAcc and is based on Differential Evolution (DE) algorithm, which is a floating-point encoding evolutionary algorithm for global optimization over continuous spaces. The original DE is extended by log-normal self-adaptation of its control parameters and combined with cooperative co-evolution as a dimension decomposition mechanism. Experimental results are given for seven high-dimensional test functions proposed for the Special Session on Large Scale Global Optimization at 2008 IEEE World Congress on Computational Intelligence.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Tsang:2008:cec,
  author = "Jeffrey Tsang  ",
  title = "Evolving Trajectories of the N-Body Problem",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0791.pdf},
  url = {},
  size = {},
  abstract =	{ The N-body problem in k dimensions is the task of determining the time evolution of a system of kN second order ordinary differential equations according to Newton's inverse square law. It comes up in astrophysics as an approximation to celestial systems. Separately, evolved art is the use of evolutionary computation to create artistic works, visual or otherwise. This study attempts to use the trajectories of 4-rotationally symmetric 2-dimensional N-body initial conditions computed under leapfrog integration as visual art. The integration routine inevitably accumulates roundoff error; the initial conditions are evolved separately to both minimize and maximize the number of timesteps before the system becomes unstable. Unexpectedly, genes evolved to maximize the number of timesteps can reach thousands of times the number from random genes; evolving to minimize creates configurations declared unstable in the first timestep. Visual inspection of the pictures obtained also reveals common motifs among high and low fitness genes: two co-circling planets for high fitness, circling close to the center and being far off for low fitness. Some genes do not follow the motifs and are considered visually appealing by the author. The fitness landscape under this representation is highly multimodal with lots of sharp peaks and troughs, and mostly flat outside.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Whigham:2008:cec,
  author = "Peter A. Whigham and Grant Dick",
  title = "Exploring the Use of Ancestry as a Unified Network Model of Finite Population Evolution",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0792.pdf},
  url = {},
  size = {},
  abstract =	{The evolution of a population is determined by many factors, including the geographic separation of individuals in the population (spatial structure), parent selection via assortative mating (biasing who breeds with whom), environmental gradients, founder effects, disturbance, selection, stochastic effects characterised as genetic drift and so on. Ultimately the interest in studying a population of organisms is about characterising parent selection over time. This paper will examine the evolution of a population under the neutral conditions of genetic drift and for a simple selection model. For drift two conditions are considered: the first is for a range of spatial (geographic) constraints defined by a network; the second is through the use of a tagging system that models assortative mate selection. A simple selection model for the OneMax problem is used to illustrate the response of a population to selection pressure. An ancestry network is constructed representing the shared parent interactions over time. This structure is analyzed as a method for characterising the interactions of a population. The approach demonstrates a unified model to characterise population dynamics, independent of the underlying evolutionary constraints.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Goh:2008:cec,
  author = "C. K. Goh and Y. S. Ong and K. C. Tan and E. J. Teoh",
  title = "An Investigation on Evolutionary Gradient Search for Multi-Objective Optimization",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0793.pdf},
  url = {},
  size = {},
  abstract =	{Evolutionary gradient search is a hybrid algorithm that exploits the complementary features of gradient search and evolutionary algorithm to achieve a level of efficiency and robustness that cannot be attained by either techniques alone. Unlike the conventional coupling of local search operators and evolutionary algorithm, this algorithm follows a trajectory based on the gradient information that is obtain via the evolutionary process. In this paper, we consider how gradient information can be obtained and used in the context of multi-objective optimization problems. The different types of gradient information are used to guide the evolutionary gradient search to solve multi-objective problems. Experimental studies are conducted to analyze and compare the effectiveness of various implementations.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Nicolau:2008:cec,
  author = "Miguel Nicolau and Marc Schoenauer",
  title = "Evolving Scale-Free Topologies Using a Gene Regulatory Network Model",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0795.pdf},
  url = {},
  size = {},
  abstract =	{A novel approach to generating scale-free network topologies is introduced, based on an existing artificial Gene Regulatory Network model. From this model, different interaction networks can be extracted, based on an activation threshold. By using an Evolutionary Computation approach, the model is allowed to evolve, in order to reach specific network statistical measures. The results obtained show that, when the model uses a duplication and divergence initialisation, such as seen in nature, the resulting regulation networks not only are closer in topology to scale-free networks, but also exhibit a much higher potential for evolution.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Wu6:2008:cec,
  author = "Ling Wu and Changfeng Xing and Faxing Lu and Peifa Jia",
  title = "An Anytime Algorithm Applied to Dynamic Weapon-Target Allocation Problem with Decreasing Weapons and Targets",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0796.pdf},
  url = {},
  size = {},
  abstract =	{In this paper, an anytime algorithm based on the modified genetic algorithm (GA) is presented to solve the dynamic weapon-target allocation (DWTA) problem. With this algorithm, the targets are assigned with weapons one-by-one according to when each target flies away from the launching zone of the weapons, which is also a deadline for completion of pairing weapon for the target. In this paper, the modified GA is applied to the case that the targets are reduced by continuous interception until all of them are neutralized, and along with it in the process, the number of weapons put in use is decreasing. This is a marginal case for an integrated DWTA problem, and the investigation on it provides a basis for solving more complex DWTA problem.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Lanzi:2008:cec,
  author = "Pier Luca Lanzi and Daniele Loiacono and Matteo Zanini",
  title = "Evolving Classifier Ensembles with Voting Predictors",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0797.pdf},
  url = {},
  size = {},
  abstract =	{In XCS with computed prediction, namely XCSF, the classifier prediction parameter is replaced by a parametrized prediction function. So far, the works on the computed prediction in XCSF has been limited to evolve a single type of prediction function at once. Recently, several works studied and extended the computed prediction in XCSF. However, it is still not clear how the most adequate prediction function should be chosen for a given problem. In this paper we introduce XCSF with voting predictors that extends XCSF to let it select best prediction function to use in each problem subspace. We compared XCSFV to XCSF on several problems. Our results suggest that XCSFV performs as well as XCSF with the best prediction function in all the tested problems. In addition, XCSFV finds the most accurate prediction function in each problem subspace  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(O'Neill2:2008:cec,
  author = "Michael O'Neill and Anthony Brabazon and Erik Hemberg",
  title = "Subtree Deactivation Control with Grammatical Genetic Programming in Dynamic Environments",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0798.pdf},
  url = {},
  size = {},
  abstract =	{We investigate the usefulness of a sub-tree deactivation control mechanism which is open to evolutionary learning. It is hypothesised that this representation confers an adaptive advantage in dynamic environments over the standard subtree representation adopted in Genetic Programming. Results presented on benchmark dynamic problem instances provides evidence to support that such an adaptive advantage exists.  },
  keywords = {genetic algorithms, genetic programming, grammatical evolution},
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Hemberg:2008:cec,
  author = "Erik Hemberg and Michael O'Neill and Anthony Brabazon",
  title = "Grammatical Bias and Building Blocks in Meta-Grammar Grammatical Evolution",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0802.pdf},
  url = {},
  size = {},
  abstract =	{This paper describes and tests the utility of a meta Grammar approach to Grammatical Evolution (GE). Rather than employing a fixed grammar as is the case with canonical GE, under a meta Grammar approach the grammar that is used to specify the construction of a syntactically correct solution is itself allowed to evolve. The ability to evolve a grammar in the context of GE means that useful bias towards specific structures and solutions can be evolved and directly incorporated into the grammar during a run. This approach facilitates the evolution of modularity and reuse both on structural and symbol levels and consequently could enhance both the scalability of GE and its adaptive potential in dynamic environments. In this paper an analysis of the extent that building block structures created in the grammars are used in the solution is undertaken. It is demonstrated that building block structures are incorporated into the evolving grammars and solutions at a rate higher than would be expected by random search. Furthermore, the results indicate that grammar design can be an important factor in performance.  },
  keywords = {genetic algorithms, genetic programming, grammatical evolution},
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Mishra:2008:cec,
  author = "K. K. Mishra and Brajesh Kumar Singh and Akash Punhani and Lavkush Sharma",
  title = "Optimizing Melting Rate and Fuel Consumption of Rotary Furnace Using NSGA - II",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0803.pdf},
  url = {},
  size = {},
  abstract =	{In this paper we will study one multi objective optimization problem, which is related to small-scale foundry. Rotary furnace is used in smallscale foundry to melt the metal. To increase the production of a foundry we have to increase melting rate of the rotary furnace. We will use NSGA-II to maximize the melting rate of rotary furnace by minimizing the amount of fuel used.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(O'Neill3:2008:cec,
  author = "Michael O'Neill and Anthony Brabazon",
  title = "Evolving a Logo Design Using Lindenmayer Systems, Postscript and Grammatical Evolution",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0804.pdf},
  url = {},
  size = {},
  abstract =	{We present an application of Grammatical Evolution to the exploration of Lindenmayer systems. The resulting L-systems are expressed in the Postscript language, and as such a Postscript grammar was provided as input to the Grammatical Evolution algorithm. The system takes the form of an interactive evolutionary algorithm, with a human-in-the-loop acting as the fitness function for the generated L-systems. The motivation for this research was to evolve a logo for the UCD Natural Computing Research and Applications group, and to this end the study was a success.  },
  keywords = {genetic algorithms, genetic programming, grammatical evolution, L-System},
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Ast2:2008:cec,
  author = "Jelmer van Ast and Robert Babuskay and Bart De Schutter",
  title = "A General Modeling Framework for Swarms",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0806.pdf},
  url = {},
  size = {},
  abstract =	{Swarms are characterized by the ability to generate complex behavior from the coupling of simple individuals. While the swarm approach to distributed systems of moving agents is gradually finding a way to engineering applications, a true successful demonstration of an engineered swarm is still missing. One of the reasons for this is the gap between the complexity of the swarms studied in fundamental research and the complexity needed for the application to interesting control problems. In the majority of the research on swarm intelligent systems, the moving agents in the swarm are modeled as simple reactive agents. This model comprises too little intelligence to fully exploit the potential of swarms. In this paper, a general comprehensive swarm framework is introduced and related to the established state of the art. Such a framework is novel and it is a first and important step in the development and analysis of more complex and intelligent swarms.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Keller2:2008:cec,
  author = "Robert E. Keller and Riccardo Poli",
  title = "Self-Adaptive Hyperheuristic and Greedy Search",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0809.pdf},
  url = {},
  size = {},
  abstract =	{In previous work, we have introduced an effective and resource-efficient hyperheuristic that uses Genetic Programming as its search heuristic on the space of heuristics. Here, we show that the hyperheuristic performs better than purely greedy and even only mostly greedy flavours of hill climbing. We also introduce a generic principle that allows the hyperheuristic to automatically find good parameter values for its effective and efficient search.  },
  keywords = {genetic algorithms, genetic programming},
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Harvey:2008:cec,
  author = "Nicholas Harvey and Robert Luke and James M. Keller and Derek Anderson   ",
  title = "Speedup of Fuzzy Logic Through Stream Processing on Graphics Processing Units",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {3809--3815},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0811.pdf},
  url = {},
  doi = {doi:10.1109/CEC.2008.4631314},
  size = {},
  abstract =	{As the size and operator complexity of a fuzzy logic system increases, computational tractability becomes a problem. There is a significant amount of parallelism in both the creation of the fuzzy rule base and in fuzzy inference. Traditional processors (CPUs) cannot take full advantage of this natural parallelism. Graphics Processing Units (GPUs) speed up rule construction and inference by using up to 128 processing units operating in parallel. Normally, these processors are used to perform high speed graphics calculations for video games, movies, and other areas of intense graphical work. In this paper, a method is discussed for speeding up fuzzy logic by structuring it into a format such that it resembles the standard rendering procedure for a graphics pipeline based on rasterization.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Jin3:2008:cec,
  author = "Nanlin Jin and Mette Termansen and Klaus Hubacek  ",
  title = "Genetic Algorithms for Dynamic Land-Use Optimization",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0812.pdf},
  url = {},
  size = {},
  abstract =	{This paper concerns the use of Genetic Algorithms designed to optimize agricultural land use based on economic criteria. The agricultural areas considered are heather moorland areas in the UK where sheep farming competes with grouse farming and the land is managed differently for each activity. Additionally, there are tenant farmers who rent land for fixed periods and are more interested in short term economic gain and landlords who are more concerned with land value and capability and economic returns in the longer term. This paper explores the application of Genetic Algorithms (GAs) to what we call an inter-temporal optimization. Inter-temporal optimization aims to maximize outcomes for a period of time, not for a time point. GAs are shown to be able to cope with two important features of intertemporal optimization: (1) dynamics; (2) optimizing areas of landscape. These two features make it difficult for traditional approaches such as econometrics and mathematical dynamic programming to tackle such an optimization problem. This paper exemplifies GA's capabilities by tackling an intertemporal optimization problem in land-use decision making. We use GA to represent land-use decisions, to simulate economic and biologic dynamics, and to optimize decisionmakers' objectives in inter-temporal optimization. Experimental results indicate that a long-term inter-temporal optimization smoothes the impacts of dynamics and reduces the number of decision changes. We also compare the experimental results versus the predictions made by agricultural experts. We have found that a GA system forecasts land-use changes in line with experts' predictions. This work demonstrates how GA successfully deals with dynamics for inter-temporal optimization.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Mo:2008:cec,
  author = "Hongwei Mo and Lifang Xu",
  title = "Research of Immune Network Clone Optimization Algorithm",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0813.pdf},
  url = {},
  size = {},
  abstract =	{Immune clone selection algorithm is a kind of optimization algorithm based on the theory of clone selection. It cannot keep diversity at the end of antibody evolutionary because many antibodies with similar affinity appear. So it leads the algorithm to premature. In order to improve its performance, we use the mechanisms of immune network and crossover operators in genetic algorithm to design immune network clone optimization algorithm(INCOA), which adopts different clone strategies in different processes. The test results show the efficiency of the proposed algorithm in solving optimization problem.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Diedrich:2008:cec,
  author = "Florian Diedrich and Frank Neumann",
  title = "Using Fast Matrix Multiplication in Bio-Inspired Computation for Complex Optimization Problems",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0814.pdf},
  url = {},
  size = {},
  abstract =	{Population-based search heuristics such as evolutionary algorithms or ant colony optimization have been widely used to tackle complex problems in combinatorial optimization. In many cases these problems involve the optimization of an objective function subject to a set of constraints which is very large. In this paper, we examine how population-based search heuristics can be sped up by making use of fast matrix multiplication algorithms. First, we point out that this approach is applicable to the wide class of problems which can be expressed as an Integer Linear Program (ILP). Later on, we investigate the speedup that can be gained by the proposed approach in our experimental studies for the multidimensional knapsack problem. },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Mussetta:2008:cec,
  author = "Marco Mussetta and Paola Pirinoli and Stefano Selleri and Riccardo E. Zich",
  title = "Development and Validation of Differentiated and Undifferentiated Meta-PSO Techniques for Electromagnetics",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0815.pdf},
  url = {},
  size = {},
  abstract =	{Some variations of the Particle Swarm Optimization are here proposed in order to increase the efficiency of the search over the solution space with a negligible overhead in the algorithm complexity and speed. The recently developed Differentiated and Undifferentiated Meta-PSO Technique have been compared in terms of capability and speed of convergence by their application to different test functions; analyses of the optimization technique performances are provided, with respect to the standard PSO convergence rate. Moreover, this paper presents the application of the developed procedures to the optimization of a linear array antenna for mobile dual-band applications.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Mei:2008:cec,
  author = "Florence Choong Chiao Mei and Somnuk Phon-Amnuaisuk and Mohammad Yusoff Alias",
  title = "Adaptive GA: An Essential Ingredient in High-Level Synthesis",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0816.pdf},
  url = {},
  size = {},
  abstract =	{High-level synthesis, a crucial step in VLSI and System on Chip (SoC) design, is the process of transforming an algorithmic or behavioural description into a structural specification of the architecture realizing the behaviour. In the past, researchers have attempted to apply GAs to the HLS domain. This is motivated by the fact that the search space for HLS is large and GAs are known to work well on such problems. However, the process of GA is controlled by several parameters, e.g. crossover rate and mutation rate that largely determine the success and efficiency of GA in solving a specific problem. Unfortunately, these parameters interact with each other in a complicated way and determining which parameter set is best to use for a specific problem can be a complex task requiring much trial and error. This inherent drawback is overcome in this paper where it presents two adaptive GA approaches to HLS, the adaptive GA operator probability (AGAOP) and adaptive operator selection (AOS) and compares the performance to the standard GA (SGA) on eight digital logic benchmarks with varying complexity. The AGAOP and AOS are shown to be far more robust than the SGA, providing fast and reliable convergence across a broad range of parameter settings. The results show considerable promise for adaptive approaches to HLS domain and opens up a path for future work in this area.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Zhao3:2008:cec,
  author = "S. Z. Zhao and J. J. Liang and P. N. Suganthan and M. F. Tasgetiren",
  title = "Dynamic Multi-Swarm Particle Swarm Optimizer with Local Search for Large Scale Global Optimization",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0817.pdf},
  url = {},
  size = {},
  abstract =	{In this paper, the performance of dynamic multi-swarm particle swarm optimizer (DMS-PSO) on the set of benchmark functions provided for the CEC2008 Special Session on Large Scale optimization is reported. Different from the existing multi-swarm PSOs and local versions of PSO, the sub-swarms are dynamic and the sub-swarms' size is very small. The whole population is divided into a large number sub-swarms, these sub-swarms are regrouped frequently by using various regrouping schedules and information is exchanged among the particles in the whole swarm. The Quasi-Newton method is combined to improve its local searching ability.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Folleco:2008:cec,
  author = "Andres Folleco and Taghi M. Khoshgoftaar and Jason Van Hulse and Lofton Bullard   ",
  title = "Software Quality Modeling: The Impact of Class Noise on the Random Forest Classifier",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0818.pdf},
  url = {},
  size = {},
  abstract =	{This study investigates the impact of increasing levels of simulated class noise on software quality classification. Class noise was injected into seven software engineering measurement datasets, and the performance of three learners, random forests, C4.5, and Naive Bayes, was analyzed. The random forest classifier was used for this study because of its strong performance relative to well-known and commonly-used classifiers such as C4.5 and Naive Bayes. Further, relatively little prior research in software quality classification has considered the random forest classifier. The experimental factors considered in this study were the level of class noise and the percent of minority instances injected with noise. The empirical results demonstrate that the random forest obtained the best and most consistent classification performance in all experiments.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Huang8:2008:cec,
  author = "Dong Huang and Cyril Leung and Chunyan Miao",
  title = "Memetic Algorithm for Dynamic Resource Allocation in Multiuser OFDM Based Cognitive Radio Systems",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0819.pdf},
  url = {},
  size = {},
  abstract =	{Cognitive Radio (CR) is a novel concept for improving spectrum use in wireless communication systems by permitting secondary (unlicensed) users to access those frequency bands which are not currently being used by primary (licensed) users. A CR user has the ability to change its transmit parameters rapidly according to the environment it senses. Orthogonal frequency division multiplexing (OFDM) modulation is a good candidate for CR systems due to its flexibility in allocating resources among secondary users. In this paper, the design of a fast and efficient method for dynamically allocating subcarriers, transmit powers and bits to secondary users in a multiuser (MU) OFDM-based CR system is considered. A memetic algorithm (MA) is proposed and shown to provide an improved performance over previously reported algorithms.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Jin4:2008:cec,
  author = "Shuai Jin and Zhaohan Sheng",
  title = "Modeling and Simulation Research on Diffusion of the Public Voice",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0820.pdf},
  url = {},
  size = {},
  abstract =	{The public opinion formation is becoming of a strategic importance at all levels of society. Public voice, a consequence of an asymmetrical informational structure, is interwoven by viewpoints of many individuals involved in the issue. The control and possible handling to manipulate information are now major issues in social organizations, including economy, politics, fashion, and even personal affairs. This paper focuses on simulation of the public voice diffusing based on the social mechanisms at individual level. At first, an analysis was performed to establish a multi-agent-based model analogous to a cellular automata model; it incorporated the variables of individual characters and other individuals' impact to describe the thinking process of individuals, when they were confronted with the public voice. The attributes and hypothesizes referred in the model would be detailed in this section. Then another analyses followed further examine the functions of some parameters and mechanisms in the public voice forming, based on simulation experiments and sensitivity analysis. Finally, exploratory discussions and limitations on the model were presented.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Dhahri:2008:cec,
  author = "H. Dhahri and Adel. M. Alimi and F. Karray",
  title = "The Modified Particle Swarm Optimization for the Design of the Beta Basis Function Neural Networks",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0821.pdf},
  url = {},
  size = {},
  abstract =	{This paper proposes and describes an effective use of the Heuristic optimization. The focus of this research is on a hybrid method combining two heuristic optimization techniques; Differential evolution algorithms (DE) and particle swarm optimization (PSO), to train the Beta Basis Function neural network (BBFNN). Denoted as PSO-DE, this hybrid technique incorporates concepts from DE and PSO and creates individuals in a new generation not only by crossover and mutation operations as found in DE but also by mechanisms of PSO. The results of various experimental studies using the Mackey time prediction have demonstrated the superiority of the hybrid PSO-DE approach over the other four search techniques in terms of solution quality and convergence rates.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Tirronen:2008:cec,
  author = "Ville Tirronen and Ferrante Neri and Kirsi Majava and Tommi K&#228;rkk&#228;inen",
  title = "The ``Natura Non Facit Saltus'' Principle in Memetic Computing",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0822.pdf},
  url = {},
  size = {},
  abstract =	{This paper proposes the employment of continuous probability distributions instead of step functions for adaptive coordination of the local search in fitness diversity based Memetic Algorithms. Two probability distributions are considered in this study: the beta and exponential distributions. These probability distributions have been tested within two memetic frameworks present in literature. Numerical results show that employment of the probability distributions can be beneficial and improve performance of the original Memetic Algorithms on a set of test functions without varying the balance between the evolutionary and local search components.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Atyabi:2008:cec,
  author = "Adham Atyabi and Somnuk Phon-Amnuaisuk and Chin Kuan Ho",
  title = "Cooperative Learning of Homogeneous and Heterogeneous Particles in Area Extension PSO",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0823.pdf},
  url = {},
  size = {},
  abstract =	{Particle Swarm Optimization with Area Extension (AEPSO) is a modified PSO that performs better than basic PSO in static, dynamic, noisy, and real-time environments. This paper investigates the effectiveness of cooperative learning AEPSO in a simulated environment. The environment is a 2D landscape planted with various types of bombs with arbitrary explosion times and locations. The simulated-robots' task (i.e., swarm particles) is to disarm these bombs. Different bombs must be disarmed with appropriate robots (i.e., disarming skills and bomb types must correspond) and the robots (hereafter, referred to as agents) do not have full observations of the environment due to uncertainties in their perceptions. In this study, each agent has the ability to disarm different type of bombs in heterogeneous scenario while each agent has the ability to disarm all types of bombs in homogeneous scenario. We found that AEPSO shows reliable performance in both heterogeneous and homogeneous scenarios as compared to the basic PSO. We also found that the proposed cooperative learning is robust in environment where agents' perception are distorted with noise.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Plant:2008:cec,
  author = "William R. Plant and Gerald Schaefer and Tomoharu Nakashima",
  title = "An Overview of Genetic Algorithms in Simulation Soccer",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0824.pdf},
  url = {},
  size = {},
  abstract =	{This paper discusses the use of genetic algorithms and genetic programming within the simulation soccer domain. Genetic algorithms (GAs) are based on the Darwinian theory of evolution and provide techniques to execute an effective search on a large range of potential solutions to a specific problem. Genetic Programming (GP) uses GA concepts to evolve a computer program. We show how GAs and GP have been applied to the challenging real-time and noisy domain of RoboCup simulation soccer. Among others, genetic approaches can be used to find appropriate actions for a soccer agent during a game, to improve different aspects of team strategy as well as to strengthen the ability of a player or a team in training exercises.  },
  keywords = {genetic algorithms, genetic programming},
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Kononova:2008:cec,
  author = "Anna V. Kononova and Derek B. Ingham and Mohamed Pourkashanian",
  title = "Simple Scheduled Memetic Algorithm for Inverse Problems in Higher Dimensions: Application to Chemical Kinetics",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0825.pdf},
  url = {},
  size = {},
  abstract =	{This paper proposes a scheme for the hybridisation of an Evolution Strategy framework and periodically scheduled Nelder-Mead algorithm. This relatively simple hybridisation scheme turns out to be efficient for the optimisation problems in higher dimensions. The efficiency of the proposed method is tested for a complex engineering problem, namely an inverse problem of chemical kinetics. An extensive parameter analysis and tuning are presented. Numerical results show the superiority of the proposed methods in comparison with some popular metaheuristics and some tailored algorithms presented in the literature for solving the problem under investigation.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Cheeneebash:2008:cec,
  author = "Jayrani Cheeneebash and Jose Antonio Lozano and Harry Coomar Shumsher Rughooputh",
  title = "A Multi-Objective Approach to the Channel Assignment Problem",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0826.pdf},
  url = {},
  size = {},
  abstract =	{With the rapid growth of mobile communications, solving the channel assignment problem has now become a new challenge in research. In this paper, we present the Channel Assignment Problem (CAP) from a multi-objective approach. From this new idea, the communication system can be easily managed in case of an unexpected rise in demand in some particular cells. We carry out the experiments with the Philadelphia problem.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Wang18:2008:cec,
  author = "Yu Wang and Bin Li",
  title = "A Restart Univariate Estimation of Distribution Algorithm: Sampling Under Mixed Gaussian and Levy Probability Distribution",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0827.pdf},
  url = {},
  size = {},
  abstract =	{A univariate EDA denoted as ''LSEDA-gl'' for large scale global optimization (LSGO) problems is proposed in this paper. Three efficient strategies: sampling under mixed Gaussian and Levy probability distribution, Standard Deviation Control strategy and restart strategy are adopted to improve the performance of classical univariate EDA on LSGO problems. The motivation of such work is to extend EDAs to LSGO domain reasonably. Comparison among LSEDA-gl, EDA with standard deviation control strategy only (EDA-STDC) and similar EDA version ''continuous univariate marginal distribution algorithm'' UMDAc is carried out on classical test functions. Based on the general comparison standard, the strengths and weaknesses of the algorithms are discussed. Besides, LSEDA-gl is tested on 7 functions with 100, 500, 1000 dimensions provided in the CEC'2008 Special Session on LSGO. This work is also expected to provide a comparison result for the CEC'2008 special session.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Siebel2:2008:cec,
  author = "Nils T. Siebel and Gerald Sommer",
  title = "Learning Defect Classifiers for Visual Inspection Images by Neuro-Evolution Using Weakly Labelled Training Data",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0828.pdf},
  url = {},
  size = {},
  abstract =	{This article presents results from experiments where a detector for defects in visual inspection images was learned from scratch by EANT2, a method for evolutionary reinforcement learning. The detector is constructed as a neural network that takes as input statistical data on filter responses from a bank of image filters applied to an image region. Training is done on example images with weakly labelled defects. Experiments show good results of EANT2 in an application area where evolutionary methods are rare.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Li21:2008:cec,
  author = "Yingrong Li and Anastasiya Kolesnikova and Won Don Lee",
  title = "A New Classification Approach for Handling New Outcomes",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0829.pdf},
  url = {},
  size = {},
  abstract =	{Classification is an important technique in the field of Data Mining and Machine Learning. The classifier can predict the class of unknown data based on their given attribute values. In ubiquitous computing environment, a great deal information can be obtained from various sensors. However, with the time going on, new sensor may be recruited. The recruited new sensors may bring new outcomes to the existing attribute. How to handle the new outcomes is a difficult issue. This paper first presents the problem and meanwhile a new method for handling new outcomes is proposed. The old rule is generated from the old data with fewer outcomes and modified and combined with the new data smoothly. In the method, the old rule can improve the performance of classifier constructed only from the new data set. The experiments show that the proposed approach is effective in handling new outcomes.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Ji3:2008:cec,
  author = "Zhengping Ji and Matthew D. Luciw and Juyang Weng",
  title = "Epigenetic Sensorimotor Pathways and Its Application to Developmental Object Learning",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0830.pdf},
  url = {},
  size = {},
  abstract =	{A pathway in the central nervous system (CNS) is a path through which nervous signals are processed in an orderly fashion. A sensorimotor pathway starts from a sensory input and ends at a motor output, although almost all pathways are not simply unidirectional. In this paper, we introduce a simple, biologically inspired, unified computational model &#8211; Multi-layer In-place Learning Network (MILN), with a design goal to develop a recurrent network, as a function of sensorimotor signals, for open-ended learning of multiple sensorimotor tasks. The biologically motivated MILN provides automatic feature derivation and pathway refinement from the temporally real-time inputs. The work presented here is applied in the challenging application field of developing reactive behaviours from a video camera and a (noisy) radar range sensor for a vehicle-based robot in open, natural driving environments. An internal model of the agent's experience of the environments is created and refined from the ground-up using a cell-centered model, based on the genomic equivalence principle. The outputs can be imposed by a teacher, at the same time as the learning is active. At any time instant, sensory information from the radar allows the system to focus its visual analysis on relatively small areas within the image plane (attention selection), in a computationally efficient way, suitable for real-time training. This system was trained with data from 10 different city and highway road environments, and cross validation shows that MILN was able to correctly recognize above 95percent of the radarextracted images from the multiple environments. The in-place learning mechanism compares with other learning algorithms favorably, as results of a comparison indicate that in-place learning is the only one to fit all the specified criteria of development of a general-purpose sensorimotor pathway.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Maxville:2008:cec,
  author = "Valerie Maxville and Chiou Peng Lam and Jocelyn Armarego",
  title = "Supporting Component Selection with a Suite of Classifiers",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0833.pdf},
  url = {},
  size = {},
  abstract =	{Software selection involves the assimilation of information and results for each candidate to enable a comparison for decisions to be made. The processes and tools developed assist with software selection to enhance quality, documentation and repeatability. The CdCE process aims to retain and document the information used in selection to assist decisions and to document them for reference as the system evolves. This paper describes the CdCE process and our approach to assist the shortlisting of candidates through a suite of classifiers. The application of the suite is illustrated using a selection and evaluation case study. Applying this approach helps retain the multidimensional nature of the selection process and enhances user awareness in the decision making process.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Luitel:2008:cec,
  author = "Bipul Luitel and Ganesh K. Venayagamoorthy",
  title = "Differential Evolution Particle Swarm Optimization for Digital Filter Design",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0834.pdf},
  url = {},
  size = {},
  abstract =	{In this paper, swarm and evolutionary algorithms have been applied for the design of digital filters. Particle swarm optimization (PSO) and differential evolution particle swarm optimization (DEPSO) have been used here for the design of linear phase finite impulse response (FIR) filters. Two different fitness functions have been studied and experimented, each having its own significance. The first study considers a fitness function based on the passband and stopband ripple, while the second study considers a fitness function based on the mean squared error between the actual and the ideal filter response. DEPSO seems to be promising tool for FIR filter design especially in a dynamic environment where filter coefficients have to be adapted and fast convergence is of importance.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Kawada:2008:cec,
  author = "Kazuo Kawada and Toru Yamamoto",
  title = "Design of an Evolutionary Controller and Its Application",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0839.pdf},
  url = {},
  size = {},
  abstract =	{PID control schemes still continue to be widely used for most industrial control systems. This is mainly because PID controllers have simple control structures, and are simple to maintain and tune. However, it is difficult to find a set of suitable control parameters in the case of time-varying and/or nonlinear systems. For such a problem, the robust controller has been proposed. Although it is important to choose the suitable nominal model in designing the robust controller, it is not usually easy. In this paper, a new robust PD controller design scheme is proposed, in which the suitable nominal model is designed using a real-coded genetic algorithm.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Jiao:2008:cec,
  author = "Jun Jiao and Wu-Wei Chen and Kwong-Sak Leung and Shao-Wen Li and Ji-Xian Wang",
  title = "Intelligent Variable Structure Control for Automated Guided Vehicle",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0840.pdf},
  url = {},
  size = {},
  abstract =	{Aiming at Automated Guided Vehicle (AGV) dynamic model characteristics, a Variable Structure Control based on genetic algorithm (GA) and least square-support vector machine (LS-SVM) was designed. Parameters, predetermined by conventional reaching law, were regulated by LS-SVM online. It was shown that system shattering is eliminated. Simulation results indicated that this method possesses the advantages of higher precision, greater adaptability and robustness, as compared to the conventional Variable Structure Control methods.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Miyagawa:2008:cec,
  author = "Eiji Miyagawa and Toshimichi Saito",
  title = "Particle Swarm Optimizers with Grow-and-Reduce Structure",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0841.pdf},
  url = {},
  size = {},
  abstract =	{This paper presents an improved version of PSO having grow-and-reduce structure. When a particle is trapped into a local optimum, a new particle is born at a position away from the trap and is connected to some/all of existing particles. If a particle can not escape from the trap, the particle is deleted in order to suppress excessive swarm grows. We have adopted three basic population topology: complete graph, ring and tree. Performing basic numerical experiments, the algorithm performance is investigated. The results suggest that the ''growand- reduce'' is very effective for escape from a trap and the tree topology has effective flexibility to realize the optimization.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Wu7:2008:cec,
  author = "Chuansheng Wu and Jinrong He and Xiufen Zou",
  title = "A Genetic Algorithm Approach for Selecting Tikhonov Regularization Parameter",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0842.pdf},
  url = {},
  size = {},
  abstract =	{This paper presents a Genetic Algorithm approach for selecting a Tikhonov regularization parameter. In using Tikhonov parameters regularization for solving ill problems, in terms of Inverse problems of the first category, we could first apply discrete regularization method to transfer it into linear algebraic equations, and then get regular solutions by solving of Euler equations which is of minimum functional equivalence for Tikhonov. As to the selection of regularization parameter, this paper choose a Genetic Algorithm approach, which takes Morozov deviation equation as fitness function for Genetic Algorithm approach, and dynamically selects regularization parameter by designing genetic operation like crossover, mutation and genetic selection. Numerical results show that it is a feasible as well as an effective approach for selecting regularization parameter.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Fieldsend:2008:cec,
  author = "Jonathan E. Fieldsend and Richard M. Everson",
  title = "On the Efficient Use of Uncertainty when Performing Expensive ROC Optimisation",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0843.pdf},
  url = {},
  size = {},
  abstract =	{When optimising receiver operating characteristic (ROC) curves there is an inherent degree of uncertainty associated with the operating point evaluation of a model parameterisation x. This is due to the finite amount of training data used to evaluate the true and false positive rates of x. The uncertainty associated with any particular x can be reduced, but only at the computation cost of evaluating more data. Here we explicitly represent this uncertainty through the use of probabilistically non-dominated archives, and show how expensive ROC optimisation problems may be tackled by only evaluating a small subset of the available data at each generation of an optimisation algorithm. Illustrative results are given on data sets from the well known UCI machine learning repository.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Zhihui:2008:cec,
  author = "Huang Zhihui and Kan Shulin",
  title = "Based on MES for Implement Optimization of Production Scheduling of Auto Electronic Parts Manufacture",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0848.pdf},
  url = {},
  size = {},
  abstract =	{Characteristic of Auto electronic parts production industry are introduced at first. And with the background of the practical demands of Auto electronic parts production industry, with model of Browser/Server (B/S) and program as Microsoft.NET, Visual C++, SQLSever2000 was introduced. Meanwhile, this architecture of Manufacturing Execution System (MES) design theory and database structural charts of the whole system are presented. Information integration and production scheduling optimization function of MES implement information fusion and combination between PCS and ERP, realization the optimization control and management of the Auto electronic parts enterprise production process, which improves enterprise's synthesis competition ability. The two layers integrated automation system production indexes and scheduling as the design core realization optimization of production scheduling indexes. The MES that adopting this method is achieved the integration of management and control. This system has been applied to the Auto electronic parts plant and the production efficiency is increased. The successful implementation of this system has explored a new method for the overall optimization of the large systems in discrete manufacturing enterprise.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Chiu:2008:cec,
  author = "Hsiao-Ya Chiu  ",
  title = "Designing a Satisfaction-Oriented Option Analysis Framework to Support Organization Decisions on Online Training Project",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0851.pdf},
  url = {},
  size = {},
  abstract =	{Online training or web-based training has been widely adopted by organizations. Due to the high cost of time, money and human resources, it is important for decision makers to superintend those projects' performance. However, existing frameworks for evaluating such project's performance are rare. In an attempt to help decision makers monitor their online training projects, this study proposes a satisfaction-oriented framework to evaluate nonprofit-oriented projects' performance using an option pricing approach. This framework can be also seamlessly applied to any IT project that has both quantitative and qualitative factors which require evaluation under uncertainties. In order to construct an ideal evaluation framework, this study proposes a satisfaction-oriented option analysis framework that can be applied to evaluate both quantitative and qualitative measurements on the same scale. Meanwhile, this study constructs a measurement framework that integrates Kirkpatrick's and Black-Scholes models with theoretical groundings to support performance evaluation. At the end of this paper provides an empirical study that demonstrates the analytical procedures to apply the proposed framework to real world applications.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Montero:2008:cec,
  author = "Elizabeth Montero and Mar&#237;a Cristina Riff and Daniel Basterrica",
  title = "Improving MMAS Using Parameter Control",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0852.pdf},
  url = {},
  size = {},
  abstract =	{Tuning parameters values in metaheuristics is a time consuming task. Techniques to control parameters during the execution have been successfully applied into evolutionary algorithms. The key idea is that the algorithm themselves computes its parameters values according to its current state of the search. In this paper, we propose a strategy to include parameters control on ants based algorithms. We have tested our approach to solve hard instances of the travel salesman problem using MMAS. The tests shown that in some cases, it is possible to obtain better results than the reported ones for the same algorithm, by including a parameter control strategy.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Riff:2008:cec,
  author = "Mar&#237;a-Cristina Riff and Teddy Alfaro and   Xavier Bonnaire and Carlos Grand&#243;n",
  title = "EA-MP: An Evolutionary Algorithm for a Mine Planning Problem",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0854.pdf},
  url = {},
  size = {},
  abstract =	{In this paper we introduce an evolutionary algorithm for solving a copper mine planning problem. In the last 10 years this real-world problem has been tackled using linear integer programming and constraint programming. However, because it is a large scale problem, the model must be simplified by relaxing many constraints in order to obtain a near-optimal solution in a reasonable time. We now present an algorithm which takes into account most of the problem constraints and it is able to find better feasible solutions than the approach that has been used until now.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Iclanzan:2008:cec,
  author = "David Iclanzan and D. Dumitrescu",
  title = "How Can Artificial Neural Networks Help Making the Intractable Search Spaces Tractable",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0855.pdf},
  url = {},
  size = {},
  abstract =	{In this paper, we propose the incorporation of Artificial Neural Network (ANN) based supervised and unsupervised Machine Learning techniques into the evolutionary search, in order to detect strongly connected variables. The cost of extending a search method with an ANN based learning skill is relatively low, the memory requirements and model building cost being at most linearithmic in the number of variables.  As a case study, we show how these mechanisms can enable the simple (1+1) Evolutionary Algorithm to efficiently solve hard problems, which are provably intractable using just fixed representation and problem independent operators. Furthermore, simulation results show, that on test suites characterized by strong variable coupling, the ANN extended (1+1) Evolutionary Algorithm qualitatively outperform the best known, full-featured, population based Estimation of Distribution Algorithms.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Moioli:2008:cec,
  author = "Renan C. Moioli and Patricia A. Vargas and Fernando J. Von Zuben",
  title = "Towards the Evolution of an Artificial Homeostatic System",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0856.pdf},
  url = {},
  size = {},
  abstract =	{This paper presents an artificial homeostatic system (AHS) devoted to the autonomous navigation of mobile robots, with emphasis on neuro-endocrine interactions. The AHS is composed of two modules, each one associated with a particular reactive task and both implemented using an extended version of the GasNet neural model, denoted spatially unconstrained GasNet model or simply non-spatial GasNet (NSGasNet). There is a coordination system, which is responsible for the specific role of each NSGasNet at a given operational condition. The switching among the NSGasNets is implemented as an artificial endocrine system (AES), which is based on a system of coupled nonlinear difference equations. The NSGasNets are synthesized by means of an evolutionary algorithm. The obtained neuro-endocrine controller is adopted in simulated and real benchmark applications, and the additional flexibility provided by the use of NSGasNet, together with the existence of an automatic coordination system, guides to convincing levels of performance.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Handoko:2008:cec,
  author = "S. D. Handoko and C. K. Kwoh and Y. S. Ong and M. H. Lim",
  title = "A Study on Constrained MA Using GA and SQP: Analytical vs. Finite-Difference Gradients",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0857.pdf},
  url = {},
  size = {},
  abstract =	{Many deterministic algorithms in the context of constrained optimization require the first-order derivatives, or the gradient vectors, of the objective and constraint functions to determine the next feasible direction along which the search should progress. Although the second-order derivatives, or the Hessian matrices, are also required by some methods such as the sequential quadratic programming (SQP), their values can be approximated based on the first-order information, making the gradients central to the deterministic algorithms for solving constrained optimization problems. In this paper, two ways of obtaining the gradients are compared under the framework of the simple memetic algorithm (MA) employing genetic algorithm (GA) and SQP. Despite the simplicity and straightforwardness of the finite-difference gradients, faster convergence rate can be achieved when the analytical gradients can be made available. The savings on the number of function evaluations as well as the amount of time taken to solve some benchmark problems are presented along with some discussions.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Wang19:2008:cec,
  author = "N. F. Wang and Y. W. Yang and K. Tai",
  title = "Optimization of Structures Under Load Uncertainties Based on Hybrid Genetic Algorithm",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0859.pdf},
  url = {},
  size = {},
  abstract =	{This paper describes a technique for design under uncertainty based on hybrid genetic algorithm. In this work, the proposed hybrid algorithm integrates a simple local search strategy with a constrained multi-objective evolutionary algorithm. The local search is integrated as the worst-casescenario technique of anti-optimization. When anti-optimization is integrated with structural optimization, a nested optimization problem is created, which can be very expensive to solve. The paper demonstrates the use of a technique alternating between optimization (general genetic algorithm) and anti-optimization (local search) which alleviates the computational burden. The method is applied to the optimization of a simply supported structure, to the optimization of a simple problem with conflicting objective functions. The results obtained indicate that the approach can produce good results at reasonable computational costs.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Mallipeddi2:2008:cec,
  author = "R. Mallipeddi and P. N. Suganthan",
  title = "Evaluation of Novel Adaptive Evolutionary Programming on Four Constraint Handling Techniques",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0860.pdf},
  url = {},
  size = {},
  abstract =	{This paper presents empirical studies carried out to evaluate the performance of different constraint handling methods on Constrained Real-Parameter Optimization using a novel adaptive Evolutionary Programming (EP). Twenty five runs have been conducted for each of the 13 test problems considered. Our experimental results show that no single Constraint Handling method can be the best for all problems i.e, each Constraint Handling method is suitable only for a subset of problems. We also show that the novel adaptive EP proposed in this paper has improved performance over the Classical EP (CEP).  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Mantere:2008:cec,
  author = "Timo Mantere and Janne Koljonen",
  title = "Solving and Analyzing Sudokus with Cultural Algorithms",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0862.pdf},
  url = {},
  size = {},
  abstract =	{This paper studies how cultural algorithm suits to solving and analyzing Sudoku puzzles. Sudoku is a number puzzle that has recently become a worldwide phenomenon. It can be regarded as a combinatorial problem, but when solved with evolutionary algorithms it can also be handled as a constraint satisfaction or multi-objective optimization problem. The objectives of this study were (1) to test if a cultural algorithm with a belief space solves Sudoku puzzles more efficiently than a normal permutation genetic algorithm, (2) to see if the belief space gathers information that helps analyze the results and improve the method accordingly, (3) to improve our previous Sudoku solver presented in CEC2007. Experiments showed that proposed the cultural algorithm performed slightly better than the previous genetic algorithm based Sudoku solver.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Bhattacharya:2008:cec,
  author = "Maumita Bhattacharya  ",
  title = "DPGA: A Simple Distributed Population Approach to Tackle Uncertainty",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0863.pdf},
  url = {},
  size = {},
  abstract =	{Evolutionary algorithms (EA) have been widely accepted as efficient optimizers for complex real life problems [2]. However, many real life optimization problems involve time-variant noisy environment, which pose major challenges to EA-based optimization. Presence of noise interferes with the evaluation and the selection process of EA and adversely affects the performance of the algorithm [6]. Also presence of noise means fitness function can not be evaluated and it has to be estimated instead. Several approaches have been tried to overcome this problem, such as introduction of diversity (hyper mutation, random immigrants, special operators) or incorporation of memory of the past (diploidy, case based memory) [5]. In this paper we propose a method, DPGA (distributed population genetic algorithm) that uses a distributed population based architecture to simulate a distributed, self-adaptive memory of the solution space. Local regression is used in each sub-population to estimate the fitness. Specific problem category considered is that of optimization of functions with time variant noisy fitness. Successful applications to benchmark test problems ascertain the proposed method's superior performance in terms of both adaptability and accuracy.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Santana:2008:cec,
  author = "Roberto Santana and Pedro Larra&#241;aga and  Jose A. Lozano",
  title = "Component Weighting Functions for Adaptive Search with EDAs",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0865.pdf},
  url = {},
  size = {},
  abstract =	{This paper introduces the component weighting approach as a general optimization heuristic to increase the likelihood of escaping from local optima by dynamically modifying the fitness function. The approach is tested on the optimization of the simplified hydrophobic-polar (HP) protein problem using estimation of distribution algorithms (EDAs). We show that the use of component weighting together with statistical information extracted from the set of selected solutions considerably improve the results of EDAs for the HP problem. The paper also elaborates on the use of probabilistic modeling for the definition of dynamic fitness functions and on the use of combinations of models.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Caputo:2008:cec,
  author = "D. Caputo and F. Grimaccia and M. Mussetta and R. E. Zich",
  title = "An Enhanced GSO Technique for Wireless Sensor Networks Optimization",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0866.pdf},
  url = {},
  size = {},
  abstract =	{Sensor networks are an emerging field of research which combines many challenges of modern computer science, wireless communication and mobile computing. They present significant systems challenges involving the use of large numbers of resource-constrained nodes operating essentially unattended and exposed to potential local communication failures. The physical constraints of a sensor network, especially in terms of energy, are an intrinsically complex problem and request to take into account many parameters at the same time; in this paper we investigate the possibility of using evolutionary algorithms to optimize the lifetime of a network with a limited power supply. The Genetical Swarm Optimization (GSO) is a recently introduced hybrid technique between GA and PSO. It has developed in order to exploit in the most effective way the uniqueness and peculiarities of these classical optimization approaches, and it can be used to solve combinatorial optimization problems. In this paper the authors present an enhancement of this technique for application in the maximization of the lifetime a wireless sensor network.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(He3:2008:cec,
  author = "Pei He and Lishan Kang and Ming Fu",
  title = "Formality Based Genetic Programming",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {4080--4087},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0867.pdf},
  doi = {doi:10.1109/CEC.2008.4631354},
  size = {},
  abstract =	{Genetic programming (GP) is an illogical method for automatic programming. It shows creativity in discovering a desired program to solve problem, but in essence bases its searching principle on software testing. This paper is dedicated to establishing a novel GP which combines classical GP and formal approaches like Hoare's logic, model checking, and automaton, etc. The result indicates these methods can collaborate in the framework pretty well. As has been demonstrated by the experiment, they work in a way that preserves their advantages while each compensates for the deficiencies of the other. So, once an approximate program is obtained, we can say with certainty it is correct with respect to its corresponding pre- and post-conditions.  },
  keywords = {genetic algorithms, genetic programming},
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Teixeira:2008:cec,
  author = "Flavio Teixeira and Alexandre Romariz",
  title = "Digital Filter Arbitrary Magnitude and Phase Approximations - Statistical Analysis applied to a Stochastic-Based Optimization Approach",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0868.pdf},
  url = {},
  size = {},
  abstract =	{This paper presents a statistical analysis of stochastic-based optimization algorithms applied to a digital filter arbitrary magnitude and phase approximation design problem. Using an already developed rigorous statistical methodology, a completely randomized design is set up and best parameters values are estimated for the adaptive algorithms applied to a specific non-linear approximation problem. After finding the best parameter values, an additional completely randomized design is set up, comparing the performance of the adaptive algorithms with a Quasi-Newton algorithm. Results for the statistical analysis are presented and the performance for different optimization algorithms with the best parameter values are analyzed.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Austermann:2008:cec,
  author = "Anja Austermann and Seiji Yamada",
  title = "Learning to Understand Multimodal Rewards for Human-Robot-Interaction using Hidden Markov Models and Classical Conditioning",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0869.pdf},
  url = {},
  size = {},
  abstract =	{We are proposing an approach to enable a robot to learn the speech, gesture and touch patterns, that its user employs for giving positive and negative reward. The learning procedure uses a combination of Hidden Markov Models and a mathematical model of classical conditioning. To facilitate learning, the robot and the user go through a training task where the goal is known, so that the robot can anticipate its user's commands and rewards. We outline the experimental framework and the training task and give details on the proposed learning method evaluating the applicability of classical conditioning for the task of learning user rewards given in one or more modalities, such as speech, gesture or physical interaction.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Tantar:2008:cec,
  author = "Emilia Tantar and Clarisse Dhaenens and Jose Rui Figueira and El-Ghazali Talbi",
  title = "A priori Landscape Analysis in Guiding Interactive Multi-Objective Metaheuristics",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0871.pdf},
  url = {},
  size = {},
  abstract =	{The integration of information provided by an a priori landscape analysis as a guiding tool for interactive EMO methods is proposed. For this purpose, a new type of a priori landscape analysis is introduced, namely ellipse enclosure of the feasible solutions set in the solution space. The interaction takes place in the solution space, the user having as visual guiding tools the computed enclosure as well as the set of solutions found at the previous search phase. Furthermore, reference points are specified by the user thus directing the search. The effectiveness and efficiency of the method are supported through statistical experimentation performed on the bi-objective permutation flow shop problem.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Zhang16:2008:cec,
  author = "Yan Zhang and Qun Dang and Zhu Jiang and Yong Xuan Huang",
  title = "A Bi-level Blocked Estimation of Distribution Algorithm with Local Search for Maximum Clique Problems",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0872.pdf},
  url = {},
  size = {},
  abstract =	{Maximum Clique Problem (MCP) is a complicated deceptive problem for estimation of distribution algorithms (EDAs). The univariate EDAs cannot use the correlations of the variables and the advanced EDAs perform poor due to the expensive computational cost in building the appropriate probability models. In this paper, by using the special structure of MCP, a new Bi-level Blocked Probability model (BBP) is constructed, which achieves the relationships using in a bivariate probability model at the computational cost of univariate probability model. Integrating promising neighborhood search techniques, a new EDA algorithm, called Bi-level Blocked Estimation of Distribution Algorithm (BBEDA) is proposed for MCP. Comparative experiments on extensive DIMACS Benchmark instances show that the proposed BBEDA can be competitive with the evolutionary algorithm with guided mutation (the best evolutionary algorithm reported so far) in terms of solution quality and computational performance.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Jiang4:2008:cec,
  author = "He Jiang and Zhilei Ren and Yan Hu",
  title = "A Sampling Based FANT for the 3-Dimensional Assignment Problem",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0873.pdf},
  url = {},
  size = {},
  abstract =	{In this paper, we proposed a sampling based FANT (S-FANT) for the 3-Dimensional Assignment Problem (AP3). The AP3 is a well-known NP-hard problem, which aims to choose n disjoint triplets with minimum cost from 3 disjoint sets of size n. Due to its intractability, many heuristics have been proposed to obtain near optimal solutions in reasonable time. Since the solution space size of the AP3 is (n!)<sup>2</sup>, traditional FANT algorithms can't work well for the AP3. In this paper, we showed that, those triplets frequently contained by local optimal solutions are likely to belong to global optimal solutions. Therefore, those triplets can help the ant to converge faster to global optimal solutions. Upon the observation above, the S-FANT consists of two phases. In the sampling phase, a multi-restart scheme is employed to generate local optimal solutions. After that, the pheromone is initialized according to the frequency of triplets appearing in those local optimal solutions. In the FANT phase, a standard FANT algorithm is conducted to explore for better solutions. Extensive experimental results on the standard AP3 benchmark indicated that the new algorithm outperforms the state-of-the-art heuristics in terms of solution quality. Work of this paper not only provides a new efficient heuristic for the AP3, but shows a promising way to design FANT algorithms for those NP-hard problems with large solution space.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Xu6:2008:cec,
  author = "Jian Xu and Gilles Gonvalves and Tinte Hsu",
  title = "Genetic Algorithm for the Vehicle Routing Problem with Time Windows and Fuzzy Demand",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0875.pdf},
  url = {},
  size = {},
  abstract =	{This paper considers a VRP with soft time windows and fuzzy demand (VRPTWFD). The objective is to minimize both the total distance covered by all vehicles as well as the sum of lateness at the customer's due to the violation of time windows. This VRPTWFD is formulated as a two stages recourse model in the context of stochastic programming. The goal is then to minimize the expected cost, which includes the initial cost of the solution found in first stage and the additional cost due to the route failure in second stage. The theory of possibility is applied in the capacity constraint. In addition, a route failure estimation method is proposed to evaluate the additional cost as well as the expected cost. A genetic algorithm, in which a simulation phase based on sampling scenarios to evaluate the fitness of chromosome, is specifically designed to solve the two stages recourse model for the VRPTWFD. Finally an experimental evaluation of this developed algorithm is validated on a few VRPTWFD modified from the Solomon benchmarks.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Rachmawati:2008:cec,
  author = "L. Rachmawati and D. Srinivasan",
  title = "Multi-Objective Evolutionary Algorithm-Assisted Automated Parallel Parking",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0876.pdf},
  url = {},
  size = {},
  abstract =	{The ease with which a human expert driver performs the complex tasks involved in parallel-parking a nonholonomic vehicle motivates the mimicry of an human driving behaviour in automation of the task. This paper presents such an algorithm to achieve automated parallel parking in tight spaces. Unlike other approaches rooted in neural networks and/or fuzzy logic, the proposed algorithm performs maneuvers closely modeled after human driving instructions. Stevens' power law is employed in modeling perceived physical quantities on which the instructions operate while the uncertainty inherent in the natural language formulation is represented by Gaussian distribution.  The algorithm consists of five stages: position alignment in preparation for the backward S-turn, the first half of the Sturn, position alignment for the second part of the S-turn, the second part of the S-turn and longitudinal adjustment. Negotiation of available parking space in the second part of the S-turn, arguably the most difficult part, is performed with the help of a rule base documenting the relation between steering angle, vehicle orientation and distance traversed. To achieve parking accuracy and avoid collision in the maneuver, the appropriate steering angle must be employed. This angle is approximated from the most suitable rule, which identification is essentially a multi-objective problem addressed here by a Multi-Objective Evolutionary Algorithm. Computer simulations demonstrate the success of the approach.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Yamazaki:2008:cec,
  author = "Hirotaka Yamazaki and Ivan Tanev and Tomoyuki Hiroyasu and Katsunori Shimohara",
  title = "On the Generality of the Evolved Driving Rules of an Agent Operating a Model of a Car",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0877.pdf},
  url = {},
  size = {},
  abstract =	{We present an approach for automated evolutionary design of the functionary of driving agent, able to operate a software model of fast running car. The objective of our work is to automatically discover a set of driving rules (if existent) that are general enough to be able to adequately control the car in all sections of predefined circuits. In order to evolve an agent with such capabilities, we propose an indirect, generative representation of the driving rules as algebraic functions of the features of the current surroundings of the car. These functions, when evaluated for the current surrounding of the car yield concrete values of the main attributes of the driving style (e.g., straight line velocity, turning velocity, etc.), applied by the agent in the currently negotiated section of the circuit. Experimental results verify both the very existence of the general driving rules and the ability of the employed genetic programming framework to automatically discover them. The evolved driving rules offer a favourable generality, in that a single rule can be successfully applied (i) not only for all the section of a particular circuit, but also (ii) for the sections in several a priori defined circuits featuring different characteristics.  },
  keywords = {genetic algorithms, genetic programming},
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Garc&#237;a-Sebastian:2008:cec,
  author = "Maite Garc&#237;a-Sebastian and Alex Manhaes Savio and Manuel Gra&#241;a  ",
  title = "Comments on an Evolutionary Intensity Inhomogeneity Correction Algorithm",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0878.pdf},
  url = {},
  size = {},
  abstract =	{We discuss some aspects of a well known algorithm for inhomogeneity intensity correction in Magnetic Resonance Imaging (MRI), the Parametric Bias Correction (PABIC) algorithm. In this approach, the intensity inhomogeneity is modelled by a linear combination of 2D or 3D Legengre polynomials (computed as outer products of 1D polynomials). The model parameter estimation process proposed in the original paper is similar to a (1+1) Evolution Strategy, with some small and subtle differences. In this paper we discuss some features of the algorithm elements, trying to uncover sources of undesired behaviours and the limits to its applicability. We study the energy function proposed in the original paper and its relation to the image formation model. We also discuss the original minimisation algorithm behaviour. We think that this detailed discussion is needed because of the high impact that the original paper had in the literature, leading to an implementation into the well known ITK library, which means that it has become a de facto standard.   },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Langdon2:2008:cec,
  author = "W. B. Langdon",
  title = "Evolving GeneChip Correlation Predictors on Parallel Graphics Hardware",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0881.pdf},
  url = {},
  size = {},
  abstract =	{A GPU is used to datamine five million correlations between probes within Affymetrix HG-U133A probesets across 6685 human tissue samples from NCBI's GEO database. These concordances are used as machine learning training data for genetic programming running on a Linux PC with a RapidMind OpenGL GLSL backend. GPGPU is used to identify technological factors influencing High Density Oligonuclotide Arrays (HDONA) performance. GP suggests mismatch (PM/MM) and Adenosine/Guanine ratio influence microarray quality. Initial results hint that Watson-Crick probe self hybridisation or folding is not important. Under GPGPGPU an nVidia GeForce 8800 GTX interprets 300 million GP primitives/second (300 MGPops, approx 8 GFLOPS).},
  keywords = {genetic algorithms, genetic programming},
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Jang:2008:cec,
  author = "Woo Seok Jang and Hwan Il Kang and Byung Hee Lee",
  title = "Hybrid Simplex-Harmony Search Method for Optimization Problems",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0884.pdf},
  url = {},
  size = {},
  abstract =	{This paper proposes the hybrid Simplex Algorithm(SA)-Harmony Search(HS) Method. HS method is, the evolutionary algorithm, conceptualised using the musical process of searching for optimisation problems. SA helps HS find optimisation solution more accurately and quickly. In this paper, the performances of proposed algorithm are compared with the original HS method and other algorithms through unconstrained functions and constrained functions.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Fukui:2008:cec,
  author = "Shinji Fukui and Yuji Iwahori and Robert J. Woodham",
  title = "GPU Based Extraction of Moving Objects without Shadows Under Intensity Changes",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0885.pdf},
  url = {},
  size = {},
  abstract =	{This paper proposes a GPU based algorithm for extracting moving objects in real time. The whole process of the proposed approach is handled on GPU. GPU is used for acceleration and the proposed approach increases processing speed dramatically. The method uses a* component and b* component of CIELAB colour space without extracting shadow areas as moving objects. It is robust to intensity changes because an estimated background image is generated and moving objects are extracted using background subtraction of the estimated background image and the observed image. The proposed method reduces the times for transferring calculation results from GPU into CPU and the opposite transfer. Reducing the transfer times contributes to speeding up of the proposed method. Results are demonstrated with experiments on real data.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Bhattacharya2:2008:cec,
  author = "Maumita Bhattacharya  ",
  title = "Counter-Niching for Constructive Population Diversity",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0887.pdf},
  url = {},
  size = {},
  abstract =	{Maintaining a desired level of diversity in the Evolutionary Algorithm (EA) population is a requirement to ensure that progress of the EA search is unhindered by premature convergence to suboptimal solutions. Loss of diversity in the EA population pushes the search to a state where the genetic operators can no longer produce superior or even different offspring required to escape the local optimum. Besides diversity's contribution to avoid premature convergence, it is also useful to locate multiple optima where there is more than one solution available. This paper presents a counter-niching technique [8] to introduce and maintain constructive diversity in the EA population. The proposed technique presented here uses informed genetic operations to reach promising, but un-explored or under-explored areas of the search space, while discouraging premature local convergence. Elitism is used at a different level aiming at convergence. The proposed technique's improved performance in terms solution accuracy and computation time is observed through simulation runs on a number of standard benchmark test functions with a genetic algorithm (GA) implementation.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Diaz:2008:cec,
  author = "R. I. Diaz and R. M. Valdovinos and J. H. Pacheco",
  title = "Comparative Study of Genetic Algorithms and Resampling Methods for Ensemble Constructing",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0888.pdf},
  url = {},
  size = {},
  abstract =	{Diversity and accuracy in the members of the classifier ensemble appear as two of the main issues to take into account for its construction and operation. The resampling method has been the strategy to construct the most used ensembles; however, the subsamples here obtained consider both diversity and high accuracy. In this work two different strategies to construct ensembles with those characteristics are analysed: resampling methods as Bagging and Boosting, and an evolution strategy as Genetic Algorithms. Using a dynamic weighting scheme, the Genetic Algorithm strategy demonstrated its effectiveness in searching the best solution to the problem. In addition, we also introduce other modifications in order to reduce the processing time of the Genetic Algorithm. All of them are studied specifically in the framework of the Nearest Neighbour classification algorithm.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Melo:2008:cec,
  author = "Vin&#237;cius Veloso de Melo and Alexandre Claudio Botazzo Delbem  ",
  title = "On Promising Regions and Optimization Effectiveness of Continuous and Deceptive Functions",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0889.pdf},
  url = {},
  size = {},
  abstract =	{This paper evaluates the performance of three evolutionary algorithms to globally optimise complex continuous functions. The performance is evaluated by measuring the algorithms success rate to find the global optimum in several trials. At each set of trials, the search-space is reduced to be closer to the global optimum, so that the starting population is generated in an even more promising region. According to the results, it is possible to can conclude that, in high complexity problems, a good performance of classical evolutionary algorithms can not be expected. The paper also evaluates the performance of an evolutionary algorithm in a deceptive function. In this case, the reduced search-space is the model which generates the deceptive function. The success rates with and without the use of the starting model were compared. In this case, the use of a better starting model substantially increases the performance.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Reynolds:2008:cec,
  author = "Robert G. Reynolds and Mostafa Z. Ali",
  title = "Cultural Algorithms: Knowledge-Driven Engineering Optimization via Weaving a Social Fabric as an Enhanced Influence Function",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0893.pdf},
  url = {},
  size = {},
  abstract =	{Cultural Algorithms employ a basic set of knowledge sources, each related to knowledge observed in various social species. These knowledge sources are then combined to direct the decisions of the individual agents in solving optimisation problems. While many successful real world applications of Cultural Algorithms have been produced, we are interested in studying the fundamental computational processes involved the use of Cultural Systems as problem solvers. In previous work the influence of the knowledge sources have been on individuals in the population only. In this paper we introduce the notion of a social fabric in which the expression of knowledge sources can be distributed through the population. We apply the social fabric function to the solution of a tension/compression spring design problem. We show that different parameter combinations can affect the rate of solution.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)

@inproceedings(Reynolds2:2008:cec,
  author = "Robert G. Reynolds and Mostafa Z. Ali",
  title = "The Social Fabric Approach as an Approach to Knowledge Integration in Cultural Algorithms",
  booktitle = "2008 IEEE World Congress on Computational Intelligence",
  year = 2008,
  editor = "Jun Wang",
  pages = {--},
  address = "Hong Kong",
  month = "1-6 June",
  organization ="IEEE Computational Intelligence Society",
  publisher = "IEEE Press",
  note = {},
  ISBN13 = "978-1-4244-1823-7",
  file = {EC0894.pdf},
  url = {},
  size = {},
  abstract =	{Recently there has been increased interest in socially motivated approaches to problem solving. These approaches include particle Swarm Optimisation, Ant Colony Optimisation, and Cultural Algorithms. Each of these approaches is derived from a social system that operates on potentially different scale. In previous work we introduced a toolkit to model Optimization problem solving using Cultural Algorithms. In this paper we extend the influence and integration function in the Cultural Algorithm Toolkit (CAT) by adding a mechanism by which knowledge sources can spread their influence throughout a population. We then compare this enhanced approach with previous approaches using the Cones World Optimization landscape. Dejong and Morrison proposed the Cones World as an alternative to traditional benchmark optimisation problems in the assessment of optimization algorithms. We demonstrate how the social fabric enhances cultural algorithm performance within this environment relative to earlier system.  },
  notes =	{WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.},
)