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Crossover, Macromutation, and Population-Based Search

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  • Terry Jones

Abstract

A Genetic Algorithm (GA) maintains a population of individuals for the express purpose of improving performance via communication of information between contemporary individuals. This is achieved in a GA through the use of a crossover operator. If crossover is not a useful method for this exchange, the GA should not, on average, perform any better than a variety of simpler algorithms that are not population-based. A simple method for testing the usefulness of crossover for a particular problem is presented. This makes it possible to identify situations in which crossover is apparently useful but is in fact producing gains that are only equal to (or less than) those that can be obtained with macromutation and no population.

Suggested Citation

  • Terry Jones, 1995. "Crossover, Macromutation, and Population-Based Search," Working Papers 95-02-024, Santa Fe Institute.
  • Handle: RePEc:wop:safiwp:95-02-024
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    Cited by:

    1. Mayer, D. G. & Belward, J. A. & Widell, H. & Burrage, K., 1999. "Survival of the fittest--genetic algorithms versus evolution strategies in the optimization of systems models," Agricultural Systems, Elsevier, vol. 60(2), pages 113-122, May.

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