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Genetic Algorithms and Heuristic Search

Author

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  • Terry Jones
  • Stephanie Forrest

Abstract

Genetic Algorithms (GAs) and heuristic search are shown to be structurally similar. The strength of the correspondence and its practical consequences are demonstrated by considering the relationship between fitness functions in GAs and the heuristic functions of AI. By examining the extent to which fitness functions approximate an AI ideal, a measure of GA search difficulty is defined and applied to previously studied problems. The success of the measure in predicting GA performance (1) illustrates the potential advantages of viewing evolutionary search from a heuristic search perspective and (2) appears to be an important step toward answering a question that has been the subject of much research in the GAs community: what makes search hard (or easy) for a GA?

Suggested Citation

  • Terry Jones & Stephanie Forrest, 1995. "Genetic Algorithms and Heuristic Search," Working Papers 95-02-021, Santa Fe Institute.
    • Handle: RePEc:wop:safiwp:95-02-021
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    References listed on IDEAS

    as
    1. Terry Jones & Gregory J. E. Rawlins, 1993. "Reverse Hillclimbing, Genetic Algorithms and the Busy Beaver Problem," Working Papers 93-04-024, Santa Fe Institute.
    2. Terry Jones, 1994. "A Description of Holland's Royal Road Function," Working Papers 94-11-059, Santa Fe Institute.
    3. Terry Jones, 1995. "Evolutionary Algorithms, Fitness Landscapes and Search," Working Papers 95-05-048, Santa Fe Institute.
    4. Terry Jones & Stephanie Forrest, 1995. "Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms," Working Papers 95-02-022, Santa Fe Institute.
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    Cited by:

    1. Terry Jones, 1995. "One Operator, One Landscape," Working Papers 95-02-025, Santa Fe Institute.

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