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Genetic algorithms for numerical optimization

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Abstract

Genetic algorithms (GAs) are stochastic adaptive algorithms whose search method is based on simulation of natural genetic inheritance and Darwinian striving for survival. They can be used to find approximate solutions to numerical optimization problems in cases where finding the exact optimum is prohibitively expensive, or where no algorithm is known. However, such applications can encounter problems that sometimes delay, if not prevent, finding the optimal solutions with desired precision. In this paper we describe applications of GAs to numerical optimization, present three novel ways to handle such problems, and give some experimental results.

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Authors and Affiliations

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

    Zbigniew Michalewicz

  2. Department of Computer Science, University of North Carolina, 27599, Chapel Hill, NC, USA

    Cezary Z. Janikow

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  1. Zbigniew Michalewicz
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  2. Cezary Z. Janikow
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Michalewicz, Z., Janikow, C.Z. Genetic algorithms for numerical optimization. Stat Comput 1, 75–91 (1991). https://doi.org/10.1007/BF01889983

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