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Deriving Genetic Programming Fitness Properties by Static Analysis

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Genetic Programming (EuroGP 2002)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2278))

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Abstract

The aim of this paper is to introduce the idea of using static analysis of computer programs as a way of measuring fitness in genetic programming. Such techniques extract information about the programs without explicitly running them, and in particular they infer properties which hold across the whole of the input space of a program. This can be applied to measure fitness, and has a numbe of advantages over measuring fitness by running members of the population on test cases. The most important advantage is that if a solution is found then it is possible to formally trust that solution to be correct across all inputs. This paper introduces these ideas, discusses various ways in which they could be applied, discusses the type of problems for which they are appropriate, and ends by giving a simple test example and some questions for future research.

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Author information

Authors and Affiliations

  1. Computing Laboratory, University of Kent at Canterbury, CT2 7NF, Canterbury, Kent, England

    Colin G. Johnson

Authors
  1. Colin G. Johnson
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Idaho, 83844-1010, Moscow, ID, USA

    James A. Foster

  2. INRIA Rocquencourt, B.P. 105, 78153, Le Chesnay Cedex, France

    Evelyne Lutton

  3. School of Computer Science, University of Birmingham, B15 2TT, Birmingham, UK

    Julian Miller

  4. Department of Computer Science and Information Systems, University of Limerick, Schuman Building, Limerick, Ireland

    Conor Ryan

  5. Dip. Tecnologie dell’Informazione, Università degli Studi di Milano, Via Bramante 65, 26013, Crema, Italy

    Andrea Tettamanzi

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© 2002 Springer-Verlag Berlin Heidelberg

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Johnson, C.G. (2002). Deriving Genetic Programming Fitness Properties by Static Analysis. In: Foster, J.A., Lutton, E., Miller, J., Ryan, C., Tettamanzi, A. (eds) Genetic Programming. EuroGP 2002. Lecture Notes in Computer Science, vol 2278. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45984-7_29

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  • DOI: https://doi.org/10.1007/3-540-45984-7_29

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-43378-1

  • Online ISBN: 978-3-540-45984-2

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