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Program search with a hierarchical variable length representation: Genetic Programming, simulated annealing and hill climbing

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Parallel Problem Solving from Nature — PPSN III (PPSN 1994)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 866))

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Abstract

This paper presents a comparison of Genetic Programming(GP) with Simulated Annealing (SA) and Stochastic Iterated Hill Climbing (SIHC) based on a suite of program discovery problems which have been previously tackled only with GP. All three search algorithms employ the hierarchical variable length representation for programs brought into recent prominence with the GP paradigm [8]. We feel it is not intuitively obvious that mutation-based adaptive search can handle program discovery yet, to date, for each GP problem we have tried, SA or SIHC also work.

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Authors and Affiliations

  1. Santa Fe Institute, 87505, Santa Fe, NM, USA

    Una-May O'Reilly

  2. School of Computer Science, Carleton University, Ottawa, Canada

    Franz Oppacher

Authors
  1. Una-May O'Reilly
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  2. Franz Oppacher
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Editor information

Yuval Davidor Hans-Paul Schwefel Reinhard Männer

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© 1994 Springer-Verlag Berlin Heidelberg

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O'Reilly, UM., Oppacher, F. (1994). Program search with a hierarchical variable length representation: Genetic Programming, simulated annealing and hill climbing. In: Davidor, Y., Schwefel, HP., Männer, R. (eds) Parallel Problem Solving from Nature — PPSN III. PPSN 1994. Lecture Notes in Computer Science, vol 866. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-58484-6_283

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  • DOI: https://doi.org/10.1007/3-540-58484-6_283

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-58484-1

  • Online ISBN: 978-3-540-49001-2

  • eBook Packages: Springer Book Archive

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