Robustness and evolvability of recombination in linear genetic programming

  • Authors:

  • Ting Hu;Wolfgang Banzhaf;Jason H. Moore

  • Affiliations:

  • Computational Genetics Laboratory, Geisel School of Medicine, Dartmouth College, Lebanon, NH;Department of Computer Science, Memorial University, St. John's, NL, Canada;Computational Genetics Laboratory, Geisel School of Medicine, Dartmouth College, Lebanon, NH

  • Venue:
  • EuroGP'13 Proceedings of the 16th European conference on Genetic Programming
  • Year:
  • 2013

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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.