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IEEE Transactions on Evolutionary Computation

Issue 6 • Date Dec. 2008

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Displaying Results 1 - 20 of 20
  • Table of contents

    Publication Year: 2008, Page(s): C1
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  • IEEE Transactions on Evolutionary Computation publication information

    Publication Year: 2008, Page(s): C2
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  • Editorial Continued Evolution of Evolutionary Computation - Transition to a New Editor-in-Chief

    Publication Year: 2008, Page(s): 661
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  • Efficient Evolution of Accurate Classification Rules Using a Combination of Gene Expression Programming and Clonal Selection

    Publication Year: 2008, Page(s):662 - 678
    Cited by:  Papers (10)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (768 KB) | HTML iconHTML

    A hybrid evolutionary technique is proposed for data mining tasks, which combines a principle inspired by the immune system, namely the clonal selection principle, with a more common, though very efficient, evolutionary technique, gene expression programming (GEP). The clonal selection principle regulates the immune response in order to successfully recognize and confront any foreign antigen, and ... View full abstract»

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  • A Multiobjective Evolutionary Conceptual Clustering Methodology for Gene Annotation Within Structural Databases: A Case of Study on the Gene Ontology Database

    Publication Year: 2008, Page(s):679 - 701
    Cited by:  Papers (8)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2069 KB) | HTML iconHTML

    Current tools and techniques devoted to examine the content of large databases are often hampered by their inability to support searches based on criteria that are meaningful to their users. These shortcomings are particularly evident in data banks storing representations of structural data such as biological networks. Conceptual clustering techniques have demonstrated to be appropriate for uncove... View full abstract»

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  • Biogeography-Based Optimization

    Publication Year: 2008, Page(s):702 - 713
    Cited by:  Papers (277)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (746 KB) | HTML iconHTML

    Biogeography is the study of the geographical distribution of biological organisms. Mathematical equations that govern the distribution of organisms were first discovered and developed during the 1960s. The mindset of the engineer is that we can learn from nature. This motivates the application of biogeography to optimization problems. Just as the mathematics of biological genetics inspired the de... View full abstract»

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  • A Fast Incremental Hypervolume Algorithm

    Publication Year: 2008, Page(s):714 - 723
    Cited by:  Papers (17)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1307 KB) | HTML iconHTML

    When hypervolume is used as part of the selection or archiving process in a multiobjective evolutionary algorithm, it is necessary to determine which solutions contribute the least hypervolume to a front. Little focus has been placed on algorithms that quickly determine these solutions and there are no fast algorithms designed specifically for this purpose. We describe an algorithm, IHSO, that qui... View full abstract»

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  • Binary String Fitness Characterization and Comparative Partner Selection in Genetic Programming

    Publication Year: 2008, Page(s):724 - 735
    Cited by:  Papers (9)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1009 KB) | HTML iconHTML

    The premise behind all evolutionary methods is ldquosurvival of the fittest,rdquo and consequently, individuals require a quantitative fitness measure. This paper proposes a novel strategy for evaluating individual's relative strengths and weaknesses, as well as representing these in the form of a binary string fitness characterization (BSFC); in addition, as customary, an overall fitness value is... View full abstract»

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  • Coevolution of Fitness Predictors

    Publication Year: 2008, Page(s):736 - 749
    Cited by:  Papers (24)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (838 KB) | HTML iconHTML

    We present an algorithm that coevolves fitness predictors, optimized for the solution population, which reduce fitness evaluation cost and frequency, while maintaining evolutionary progress. Fitness predictors differ from fitness models in that they may or may not represent the objective fitness, opening opportunities to adapt selection pressures and diversify solutions. The use of coevolution add... View full abstract»

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  • A Bayesian Network Approach to Program Generation

    Publication Year: 2008, Page(s):750 - 764
    Cited by:  Papers (15)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (774 KB) | HTML iconHTML

    Genetic programming (GP) is a powerful optimization algorithm that has been applied to a variety of problems. This algorithm can, however, suffer from problems arising from the fact that a crossover, which is a main genetic operator in GP, randomly selects crossover points, and so building blocks may be destroyed by the action of this operator. In recent years, evolutionary algorithms based on pro... View full abstract»

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  • Item-Location Assignment Using Fuzzy Logic Guided Genetic Algorithms

    Publication Year: 2008, Page(s):765 - 780
    Cited by:  Papers (4)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1317 KB) | HTML iconHTML

    In today's logistics environment, large-scale combinatorial problems will inevitably be met during industrial operations. This paper deals with a novel real-world optimization problem, called the item-location assignment problem, faced by a logistics company in Shenzhen, China. The objective of the company in this particular operation is to assign items to suitable locations such that the required... View full abstract»

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  • Corrections to “A Robust Stochastic Genetic Algorithm (StGA) for Global Numerical Optimization”

    Publication Year: 2008, Page(s): 781
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (39 KB) | HTML iconHTML

    In the above titled paper (ibid., vol 8, no. 5, pp. 456-470, Oct 04), there is an error in the programming of the stochastic genetic algorithm (StGA) presented in that paper. The origin and implications of the error are discussed here. View full abstract»

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  • Erratum to "Dominance-Based Multiobjective Simulated Annealing"

    Publication Year: 2008, Page(s): 781
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (39 KB) | HTML iconHTML

    For original paper see Z. Tu et al., ibid., vol. 8, no.5, p.456-70, (2004). We have recently discovered an error in the programming of the stochastic genetic algorithm (StGA). The main program was written in C++ except for a subroutine which was coded in MATLAB. This particular subroutine was used to generate the NS number of stochastic children for a chromosome. The NS stochastic children were st... View full abstract»

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  • Acknowledgment to Reviewers

    Publication Year: 2008, Page(s):782 - 785
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  • IEEE Congress on Evolutionary Computation

    Publication Year: 2008, Page(s): 786
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  • 2008 Index IEEE Transactions on Evolutionary Computation Vol. 12

    Publication Year: 2008, Page(s):787 - 794
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  • Scitopia.org [advertisement]

    Publication Year: 2008, Page(s): 795
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    Publication Year: 2008, Page(s): 796
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  • IEEE Computational Intelligence Society Information

    Publication Year: 2008, Page(s): C3
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  • IEEE Transactions on Evolutionary Computation Information for authors

    Publication Year: 2008, 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