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Heuristic methods for evolutionary computation techniques

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Abstract

Evolutionary computation techniques, which are based on a powerful principle of evolution—survival of the fittest, constitute an interesting category of heuristic search. In other words, evolutionary techniques are stochastic algorithms whose search methods model some natural phenomena: genetic inheritance and Darwinian strife for survival.

Any evolutionary algorithm applied to a particular problem must address the issue of genetic representation of solutions to the problem and genetic operators that would alter the genetic composition of offspring during the reproduction process. However, additional heuristics should be incorporated in the algorithm as well; some of these heuristic rules provide guidelines for evaluating (feasible and infeasible) individuals in the population. This paper surveys such heuristics and discusses their merits and drawbacks.

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Authors and Affiliations

  1. Department of Computer Science, University of North Carolina, 28223, Charlotte, NC, USA

    Zbigniew Michalewicz

  2. Institute of Computer Science, Polish Academy of Sciences, ul.Ordona21, 01-237, Warsaw, Poland

    Zbigniew Michalewicz

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  1. Zbigniew Michalewicz
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“An abridged version of this paper appears in the volume entitled META-HEURISTICS: Theory & Application, edited by Ibrahim H. Osman and James P. Kelly, to be published by Kluwer Academic Publishers in March 1996.”

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Michalewicz, Z. Heuristic methods for evolutionary computation techniques. J Heuristics 1, 177–206 (1996). https://doi.org/10.1007/BF00127077

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