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Evolutionary Computation, IEEE Transactions on

Issue 6 • Date Dec. 2001

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Displaying Results 1 - 9 of 9
  • The third nasa/dod workshop on evolvable hardware [Book Reviews]

    Publication Year: 2001 , Page(s): 631 - 633
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    Freely Available from IEEE
  • Acknowledgment to reviewers

    Publication Year: 2001 , Page(s): 634 - 635
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    Freely Available from IEEE
  • Author index

    Publication Year: 2001 , Page(s): 636 - 637
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    Freely Available from IEEE
  • Subject index

    Publication Year: 2001 , Page(s): 637 - 641
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    Freely Available from IEEE
  • Evolutionary algorithms with dynamic population size and local exploration for multiobjective optimization

    Publication Year: 2001 , Page(s): 565 - 588
    Cited by:  Papers (105)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (528 KB) |  | HTML iconHTML  

    Evolutionary algorithms have been recognized to be well suited for multiobjective optimization. These methods, however, need to "guess" for an optimal constant population size in order to discover the usually sophisticated tradeoff surface. This paper addresses the issue by presenting a novel incrementing multiobjective evolutionary algorithm (IMOEA) with dynamic population size that is computed a... View full abstract»

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  • Evolutionary algorithms - how to cope with plateaus of constant fitness and when to reject strings of the same fitness

    Publication Year: 2001 , Page(s): 589 - 599
    Cited by:  Papers (33)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (282 KB) |  | HTML iconHTML  

    The most simple evolutionary algorithm (EA), the so-called (1 + 1) EA, accepts an offspring if its fitness is at least as large (in the case of maximization) as the fitness of its parent. The variant (1 + 1)* EA only accepts an offspring if its fitness is strictly larger than the fitness of its parent. Here, two functions related to the class of long-path functions are presented such that the (1 +... View full abstract»

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  • Nonlinear blind source separation using higher order statistics and a genetic algorithm

    Publication Year: 2001 , Page(s): 600 - 612
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (269 KB) |  | HTML iconHTML  

    This paper presents a novel method for blindly separating unobservable independent source signals from their nonlinear mixtures. The demixing system is modeled using a parameterized neural network whose parameters can be determined under the criterion of independence of its outputs. Two cost functions based on higher order statistics are established to measure the statistical dependence of the out... View full abstract»

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  • A hybrid heuristic for the traveling salesman problem

    Publication Year: 2001 , Page(s): 613 - 622
    Cited by:  Papers (71)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (204 KB) |  | HTML iconHTML  

    The combination of genetic and local search heuristics has been shown to be an effective approach to solving the traveling salesman problem (TSP). This paper describes a new hybrid algorithm that exploits a compact genetic algorithm in order to generate high-quality tours, which are then refined by means of the Lin-Kernighan (LK) local search. The local optima found by the LK local search are in t... View full abstract»

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  • On spanning-tree recombination in evolutionary large-scale network problems - application to electrical distribution planning

    Publication Year: 2001 , Page(s): 623 - 630
    Cited by:  Papers (15)
    Save to Project icon | Request Permissions | Click to expandAbstract | PDF file iconPDF (177 KB) |  | HTML iconHTML  

    We report the key algorithms involved in the recombination-based evolutionary software developed for planning electrical distribution networks. We focus on the dimensionality problem of large-scale networks and on the specificities of its search space. We report the difficulties in handling topology constraints and present both the geno-type and the operators to overcome such difficulties. The ope... 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