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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
  • 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 expandQuick Abstract | 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 turn exploited by the evolutionary part of the algorithm in order to improve the quality of its simulated population. The results of several experiments conducted on different TSP instances with up to 13,509 cities show the efficacy of the symbiosis between the two heuristics 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 expandQuick Abstract | 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 + 1) EA maximizes one in polynomial time and needs exponential time for the other while the (1 + 1)* EA has the opposite behavior. These results demonstrate that small changes of an EA may change its behavior significantly. Since the (1 + 1) EA and the (1 + 1)* EA differ only on plateaus of constant fitness, the results also show how EAs behave on such plateaus. The (1 + 1) EA can pass a path of constant fitness and polynomial length in polynomial time. Finally, for these functions, it is shown that local performance measures like the quality gain and the progress rate do not describe the global behavior of EAs 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 expandQuick Abstract | 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 outputs of the demixing system. The proposed method utilizes a genetic algorithm (GA) to minimize the highly nonlinear and nonconvex cost functions. The GA-based global optimization technique is able to obtain superior separation solutions to the nonlinear blind separation problem from any random initial values. Compared to conventional gradient-based approaches, the GA-based approach for blind source separation is characterized by high accuracy, robustness, and convergence rate. In particular, it is very suitable for the case of limited available data. Simulation results are discussed to demonstrate that the proposed GA-based approach is capable of separating independent sources from their nonlinear mixtures generated by a parametric separation model View full abstract»

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  • 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 expandQuick Abstract | 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 adaptively according to the online discovered tradeoff surface and its desired population distribution density. It incorporates the method of fuzzy boundary local perturbation with interactive local fine tuning for broader neighborhood exploration. This achieves better convergence as well as discovering any gaps or missing tradeoff regions at each generation. Other advanced features include a proposed preserved strategy to ensure better stability and diversity of the Pareto front and a convergence representation based on the concept of online population domination to provide useful information. Extensive simulations are performed on two benchmark and one practical engineering design problems 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 expandQuick Abstract | 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 operators are designed to process meaningful topological information as geno-type substructures and turn the radiality and connectivity into genetic transmissible properties. First, a theoretical example is presented to illustrate important differences between other common approaches and the approach taken. Then, a real electrical industry application is presented to illustrate the ability of the approach to handle large-scale distribution-network problems 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.
 

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