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IEEE Transactions on Evolutionary Computation

Issue 6 • Date Dec. 2011

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Displaying Results 1 - 13 of 13
  • Table of contents

    Publication Year: 2011, Page(s): C1
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  • IEEE Transactions on Evolutionary Computation publication information

    Publication Year: 2011, Page(s): C2
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  • An Evolutionary Algorithm That Makes Decision Based on the Entire Previous Search History

    Publication Year: 2011, Page(s):741 - 769
    Cited by:  Papers (12)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1626 KB) | HTML iconHTML

    In this paper, we report a novel evolutionary algorithm that enhances its performance by utilizing the entire previous search history. The proposed algorithm, namely history driven evolutionary algorithm (HdEA), employs a binary space partitioning tree structure to memorize the positions and the fitness values of the evaluated solutions. Benefiting from the space partitioning scheme, a fast fitnes... View full abstract»

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  • R-EVO: A Reactive Evolutionary Algorithm for the Maximum Clique Problem

    Publication Year: 2011, Page(s):770 - 782
    Cited by:  Papers (5)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (817 KB) | HTML iconHTML

    An evolutionary algorithm with guided mutation (EA/G) has been proposed recently for solving the maximum clique problem. In the framework of estimation-of-distribution algorithms, guided mutation uses a model distribution to generate offspring by combining the local information of solutions found so far with global statistical information. Each individual is then subjected to a Marchiori's repair ... View full abstract»

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  • Local Optima Networks of NK Landscapes With Neutrality

    Publication Year: 2011, Page(s):783 - 797
    Cited by:  Papers (4)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1194 KB) | HTML iconHTML

    In previous work, we have introduced a network based model that abstracts many details of the underlying landscape and compresses the landscape information into a weighted, oriented graph which we call the local optima network. The vertices of this graph are the local optima of the given fitness landscape, while the arcs are transition probabilities between local optima basins. Here, we extend thi... View full abstract»

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  • Cluster Guide Particle Swarm Optimization (CGPSO) for Underdetermined Blind Source Separation With Advanced Conditions

    Publication Year: 2011, Page(s):798 - 811
    Cited by:  Papers (9)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (628 KB) | HTML iconHTML

    The underdetermined blind source separation (BSS), which based on sparse representation, is discussed in this paper; moreover, some difficulties (or real assumptions) that were left out of consideration before are aimed. For instance, the number of sources, , is unknown, large-scale, or time-variant; the mixing matrix is ill-conditioned. For the proposed algorithm, in order to detect a time-varian... View full abstract»

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  • Neighborhood Knowledge-Based Evolutionary Algorithm for Multiobjective Optimization Problems

    Publication Year: 2011, Page(s):812 - 831
    Cited by:  Papers (7)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1812 KB) | HTML iconHTML

    Although there are a variety of approaches to solve multiobjective optimization problems, few of them makes systematic use of the neighborhood relationship between the candidate solutions observed during the search process to improve the final results. In this paper, a new evolutionary algorithm, referred to as the neighborhood knowledge-based evolutionary algorithm (NKEA), is proposed to solve th... View full abstract»

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  • Orthogonal Learning Particle Swarm Optimization

    Publication Year: 2011, Page(s):832 - 847
    Cited by:  Papers (74)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (593 KB) | HTML iconHTML

    Particle swarm optimization (PSO) relies on its learning strategy to guide its search direction. Traditionally, each particle utilizes its historical best experience and its neighborhood's best experience through linear summation. Such a learning strategy is easy to use, but is inefficient when searching in complex problem spaces. Hence, designing learning strategies that can utilize previous sear... View full abstract»

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  • A Multicriteria Statistical Based Comparison Methodology for Evaluating Evolutionary Algorithms

    Publication Year: 2011, Page(s):848 - 870
    Cited by:  Papers (10)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (477 KB) | HTML iconHTML

    This paper presents a statistical based comparison methodology for performing evolutionary algorithm comparison under multiple merit criteria. The analysis of each criterion is based on the progressive construction of a ranking of the algorithms under analysis, with the determination of significance levels for each ranking step. The multicriteria analysis is based on the aggregation of the differe... View full abstract»

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  • Acknowledgment to Reviewers

    Publication Year: 2011, Page(s):871 - 875
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  • 2012 IEEE world congress on computational intelligence

    Publication Year: 2011, Page(s): 876
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  • IEEE Computational Intelligence Society Information

    Publication Year: 2011, Page(s): C3
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  • IEEE Transactions on Evolutionary Computation information for authors

    Publication Year: 2011, 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.
 

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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