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IEEE Transactions on Evolutionary Computation

Issue 4 • Date Nov. 1999

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Displaying Results 1 - 6 of 6
  • Proceedings of the first NASD/DoD workshop on evolvable hardware [Book Reveiws]

    Publication Year: 1999, Page(s):304 - 306
    Cited by:  Papers (1)
    Request permission for commercial reuse | PDF file iconPDF (27 KB)
    Freely Available from IEEE
  • 1999 Index IEEE Transactions on Evolutionary Computation Vol. 3

    Publication Year: 1999, Page(s):1 - 4
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    Freely Available from IEEE
  • Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach

    Publication Year: 1999, Page(s):257 - 271
    Cited by:  Papers (1486)  |  Patents (8)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (564 KB)

    Evolutionary algorithms (EAs) are often well-suited for optimization problems involving several, often conflicting objectives. Since 1985, various evolutionary approaches to multiobjective optimization have been developed that are capable of searching for multiple solutions concurrently in a single run. However, the few comparative studies of different methods presented up to now remain mostly qua... View full abstract»

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  • The compact genetic algorithm

    Publication Year: 1999, Page(s):287 - 297
    Cited by:  Papers (199)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (272 KB)

    Introduces the compact genetic algorithm (cGA) which represents the population as a probability distribution over the set of solutions and is operationally equivalent to the order-one behavior of the simple GA with uniform crossover. It processes each gene independently and requires less memory than the simple GA. The development of the compact GA is guided by a proper understanding of the role of... View full abstract»

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  • Macroevolutionary algorithms: a new optimization method on fitness landscapes

    Publication Year: 1999, Page(s):272 - 286
    Cited by:  Papers (35)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (496 KB)

    Introduces an approach to optimization problems based on a previous theoretical work on extinction patterns in macroevolution. We name them macroevolutionary algorithms (MA). Unlike population-level evolution, which is employed in standard evolutionary algorithms, evolution at the level of higher taxa is used as the underlying metaphor. The model exploits the presence of links between “speci... View full abstract»

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  • Genetic drift in genetic algorithm selection schemes

    Publication Year: 1999, Page(s):298 - 303
    Cited by:  Papers (27)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (116 KB)

    A method for calculating genetic drift in terms of changing population fitness variance is presented. The method allows for an easy comparison of different selection schemes and exact analytical results are derived for traditional generational selection, steady-state selection with varying generation gap, a simple model of Eshelman's CHC algorithm (1991), and (μ+λ) evolution strategies. ... View full abstract»

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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