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Promoting Phenotypic Diversity in Genetic Programming

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Parallel Problem Solving from Nature, PPSN XI (PPSN 2010)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6239))

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Abstract

Population diversity is generally seen as playing a crucial role in the ability of evolutionary computation techniques to discover solutions. In genetic programming, diversity metrics are usually based on structural properties of individual program trees, but are also sometimes based on the spread of fitness values in the population. We explore the use of a further interpretation of diversity, in which differences are measured in terms of the behaviour of programs when executed. Although earlier work has shown that improving behavioural diversity in initial GP populations can have a marked beneficial effect on performance, further analysis reveals that lack of behavioural diversity is a problem throughout whole runs, even when other diversity levels are high. To address this, we enhance phenotypic diversity via modifications to the crossover operator, and show that this can lead to additional performance improvements.

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Author information

Authors and Affiliations

  1. Dept. of Computer Science, University of Liverpool, Liverpool, L69 3BX, United Kingdom

    David Jackson

Authors
  1. David Jackson
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Editor information

Editors and Affiliations

  1. Faculty of Electrical Engineering, Automatics, Computer Science and Electronics, Department of ComputerScience, AGH University of Science and Technology, Mickiewicza 30, 30-059, Kraków, Poland

    Robert Schaefer

  2. Departamento de Lenguajes y Ciencias de la Computación, ETSI Informática, Universidad de Málaga, Campus Teatinos, 29071, Málaga, Spain

    Carlos Cotta

  3. Department of Mathematics and Computer Science, University of Bielsko-Biala, Willowa 2, 43-309, Bielsko-Biala, Poland

    Joanna Kołodziej

  4. Fakultät für Informatik, Technische Universität Dortmund, 44221, Dortmund, Germany

    Günter Rudolph

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Jackson, D. (2010). Promoting Phenotypic Diversity in Genetic Programming. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds) Parallel Problem Solving from Nature, PPSN XI. PPSN 2010. Lecture Notes in Computer Science, vol 6239. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15871-1_48

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  • DOI: https://doi.org/10.1007/978-3-642-15871-1_48

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15870-4

  • Online ISBN: 978-3-642-15871-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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