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Random tree generation for genetic programming

  • Theoretical Foundations of Evolutionary Computation
  • Conference paper
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Book cover Parallel Problem Solving from Nature — PPSN IV (PPSN 1996)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1141))

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Abstract

This paper introduces a random tree generation algorithm for GP (Genetic Programming). Generating random trees is an essential part of GP. However, the recursive method commonly used in GP does not necessarily generate random trees, i.e the standard GP initialization procedure does not sample the space of possible initial trees uniformly. This paper proposes a truly random tree generation procedure for GP. Our approach is grounded upon a bijection method, i.e., a 1–1 correspondence between a tree with n nodes and some simple word composed by letters x and y. We show how to use this correspondence to generate a GP tree and how GP search is improved by using this “randomness”.

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Author information

Authors and Affiliations

  1. Machine Inference Section, Electrotechnical Laboratory (ETL), 1-1-4 Umezono, Tsukuba Science City, 305, Ibaraki, Japan

    Hitoshi Iba

Authors
  1. Hitoshi Iba
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Editor information

Hans-Michael Voigt Werner Ebeling Ingo Rechenberg Hans-Paul Schwefel

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© 1996 Springer-Verlag Berlin Heidelberg

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Iba, H. (1996). Random tree generation for genetic programming. In: Voigt, HM., Ebeling, W., Rechenberg, I., Schwefel, HP. (eds) Parallel Problem Solving from Nature — PPSN IV. PPSN 1996. Lecture Notes in Computer Science, vol 1141. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61723-X_978

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  • DOI: https://doi.org/10.1007/3-540-61723-X_978

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-61723-5

  • Online ISBN: 978-3-540-70668-7

  • eBook Packages: Springer Book Archive

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