Program Search with a Hierarchical Variable Length Representation: Genetic Programming, Simulated Annealing and Hill Climbing

Una-May O'Reilly and Franz Oppacher

Working Papers from Santa Fe Institute

Abstract: This paper emphasizes the general value of a hierarchical variable length representation for program induction by demonstrating that different search strategies and operators complementary to them can be used to obtain solutions. It presents a comparison of Genetic Programming (GP) with Simulated Annealing (SA) and Stochastic Iterated Hill Climbing (SIHC). All three search algorithms employ the hierarchical variable length representation for programs brought into recent prominence with the GP paradigm [K-92]. We experiment with three GP crossover operators and a new hierarchical variable length mutation operator developed for use in SA and SIHC. The results do not favor any one search technique which bears out the observation that a search strategy should be chosen in view of the landscape determined by fitness function and representation.

Date: 1994-04
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Persistent link: https://EconPapers.repec.org/RePEc:wop:safiwp:94-04-21

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