A species conserving genetic algorithm for multimodal function optimization

  • Authors:

  • Jian-Ping Li;Marton E. Balazs;Geoffrey T. Parks;P. John Clarkson

  • Affiliations:

  • Department of Mechanical, Aerospace and Manufacturing Engineering, UMIST, PO Box 88, Manchester M60 1QD, UK;Department of Computing, Mathematics and Sciences, Richmond the American International University in London, Queens Road, Richmond upon Thames TW10 6JP, UK;Engineering Design Centre, Cambridge University Engineering Department, Trumpington Street, Cambridge CB2 1PZ, UK;Engineering Design Centre, Cambridge University Engineering Department, Trumpington Street, Cambridge CB2 1PZ, UK

  • Venue:
  • Evolutionary Computation
  • Year:
  • 2002

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Abstract

This paper introduces a new technique called species conservation for evolving parallel subpopulations. The technique is based on the concept of dividing the population into several species according to their similarity. Each of these species is built around a dominating individual called the species seed. Species seeds found in the current generation are saved (conserved) by moving them into the next generation. Our technique has proved to be very effective in finding multiple solutions of multimodal optimization problems. We demonstrate this by applying it to a set of test problems, including some problems known to be deceptive to genetic algorithms.