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European Conference on Genetic Programming

EuroGP 2013: Genetic Programming pp 97–108Cite as

Robustness and Evolvability of Recombination in Linear Genetic Programming

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7831))

Abstract

The effect of neutrality on evolutionary search is known to be crucially dependent on the distribution of genotypes over phenotypes. Quantitatively characterizing robustness and evolvability in genotype and phenotype spaces greatly helps to understand the influence of neutrality on Genetic Programming. Most existing robustness and evolvability studies focus on mutations with a lack of investigation of recombinational operations. Here, we extend a previously proposed quantitative approach of measuring mutational robustness and evolvability in Linear GP. By considering a simple LGP system that has a compact representation and enumerable genotype and phenotype spaces, we quantitatively characterize the robustness and evolvability of recombination at the phenotypic level. In this simple yet representative LGP system, we show that recombinational properties are correlated with mutational properties. Utilizing a population evolution experiment, we demonstrate that recombination significantly accelerates the evolutionary search process and particularly promotes robust phenotypes that innovative phenotypic explorations.

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Author information

Authors and Affiliations

  1. Computational Genetics Laboratory, Geisel School of Medicine, Dartmouth College, Lebanon, NH, 03756, USA

    Ting Hu & Jason H. Moore

  2. Department of Computer Science, Memorial University, St. John’s, NL, A1B 3X5, Canada

    Wolfgang Banzhaf

Authors
  1. Ting Hu
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  2. Wolfgang Banzhaf
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  3. Jason H. Moore
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Editor information

Editors and Affiliations

  1. Institute of Computing Science, Poznan University of Technology, Piotowo 2, 60-965, Poznań, Poland

    Krzysztof Krawiec

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

    Alberto Moraglio

  3. Geisel School of Medicine, Dartmouth College, 03755, Hanover, NH, USA

    Ting Hu

  4. Department of Computer Engineering, Istanbul Technical University, 34469, Masla, Istanbul, Turkey

    A. Şima Etaner-Uyar

  5. Institute of Computer Graphics and Algorithms, Vienna University of Technology, 1040, Vienna, Austria

    Bin Hu

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Hu, T., Banzhaf, W., Moore, J.H. (2013). Robustness and Evolvability of Recombination in Linear Genetic Programming. In: Krawiec, K., Moraglio, A., Hu, T., Etaner-Uyar, A.Ş., Hu, B. (eds) Genetic Programming. EuroGP 2013. Lecture Notes in Computer Science, vol 7831. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37207-0_9

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  • DOI: https://doi.org/10.1007/978-3-642-37207-0_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37206-3

  • Online ISBN: 978-3-642-37207-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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