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IEEE Transactions on Evolutionary Computation

Issue 3 • Date June 2004

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Displaying Results 1 - 15 of 15
  • Table of contents

    Publication Year: 2004, Page(s): c1
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  • IEEE Transactions on Evolutionary Computation publication information

    Publication Year: 2004, Page(s): c2
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  • Guest Editorial Special Issue on Particle Swarm Optimization

    Publication Year: 2004, Page(s):201 - 203
    Cited by:  Papers (58)  |  Patents (1)
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  • The fully informed particle swarm: simpler, maybe better

    Publication Year: 2004, Page(s):204 - 210
    Cited by:  Papers (676)  |  Patents (17)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (240 KB) | HTML iconHTML

    The canonical particle swarm algorithm is a new approach to optimization, drawing inspiration from group behavior and the establishment of social norms. It is gaining popularity, especially because of the speed of convergence and the fact that it is easy to use. However, we feel that each individual is not simply influenced by the best performer among his neighbors. We, thus, decided to make the i... View full abstract»

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  • On the computation of all global minimizers through particle swarm optimization

    Publication Year: 2004, Page(s):211 - 224
    Cited by:  Papers (315)  |  Patents (18)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (808 KB) | HTML iconHTML

    This paper presents approaches for effectively computing all global minimizers of an objective function. The approaches include transformations of the objective function through the recently proposed deflection and stretching techniques, as well as a repulsion source at each detected minimizer. The aforementioned techniques are incorporated in the context of the particle swarm optimization (PSO) m... View full abstract»

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  • A Cooperative approach to particle swarm optimization

    Publication Year: 2004, Page(s):225 - 239
    Cited by:  Papers (767)  |  Patents (16)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (624 KB) | HTML iconHTML

    The particle swarm optimizer (PSO) is a stochastic, population-based optimization technique that can be applied to a wide range of problems, including neural network training. This paper presents a variation on the traditional PSO algorithm, called the cooperative particle swarm optimizer, or CPSO, employing cooperative behavior to significantly improve the performance of the original algorithm. T... View full abstract»

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  • Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients

    Publication Year: 2004, Page(s):240 - 255
    Cited by:  Papers (1054)  |  Patents (15)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (632 KB) | HTML iconHTML

    This paper introduces a novel parameter automation strategy for the particle swarm algorithm and two further extensions to improve its performance after a predefined number of generations. Initially, to efficiently control the local search and convergence to the global optimum solution, time-varying acceleration coefficients (TVAC) are introduced in addition to the time-varying inertia weight fact... View full abstract»

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  • Handling multiple objectives with particle swarm optimization

    Publication Year: 2004, Page(s):256 - 279
    Cited by:  Papers (970)  |  Patents (15)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1539 KB) | HTML iconHTML

    This paper presents an approach in which Pareto dominance is incorporated into particle swarm optimization (PSO) in order to allow this heuristic to handle problems with several objective functions. Unlike other current proposals to extend PSO to solve multiobjective optimization problems, our algorithm uses a secondary (i.e., external) repository of particles that is later used by other particles... View full abstract»

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  • Learning to play games using a PSO-based competitive learning approach

    Publication Year: 2004, Page(s):280 - 288
    Cited by:  Papers (73)  |  Patents (14)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (472 KB) | HTML iconHTML

    A new competitive approach is developed for learning agents to play two-agent games. This approach uses particle swarm optimizers (PSO) to train neural networks to predict the desirability of states in the leaf nodes of a game tree. The new approach is applied to the TicTacToe game, and compared with the performance of an evolutionary approach. A performance criterion is defined to quantify perfor... View full abstract»

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  • An approach to multimodal biomedical image registration utilizing particle swarm optimization

    Publication Year: 2004, Page(s):289 - 301
    Cited by:  Papers (147)  |  Patents (13)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (920 KB) | HTML iconHTML

    Biomedical image registration, or geometric alignment of two-dimensional and/or three-dimensional (3D) image data, is becoming increasingly important in diagnosis, treatment planning, functional studies, computer-guided therapies, and in biomedical research. Registration based on intensity values usually requires optimization of some similarity metric between the images. Local optimization techniq... View full abstract»

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  • Call for papers FUZZ-IEEE 2005

    Publication Year: 2004, Page(s): 302
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  • Special issue of analysis and design of representations and operators

    Publication Year: 2004, Page(s): 303
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  • Special issue on autonomous mental development

    Publication Year: 2004, Page(s): 304
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  • IEEE Neural Networks Society Information

    Publication Year: 2004, Page(s): c3
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  • IEEE Transactions on Evolutionary Computation Information for authors

    Publication Year: 2004, Page(s): c4
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Aims & Scope

IEEE Transactions on Evolutionary Computation publishes archival quality original papers in evolutionary computation and related areas including nature-inspired algorithms, population-based methods, and optimization where selection and variation are integral, and hybrid systems where these paradigms are combined. Purely theoretical papers are considered as are application papers that provide general insights into these areas of computation.
 

Full Aims & Scope

Meet Our Editors

Editor-in-Chief

Dr. Kay Chen Tan (IEEE Fellow)

Department of Electrical and Computer Engineering

National University of Singapore

Singapore 117583

Email: eletankc@nus.edu.sg

Website: http://vlab.ee.nus.edu.sg/~kctan