article

The effects of recombination on phenotypic exploration and robustness in evolution

Authors:

Wolfgang Banzhaf,

Jason H. MooreAuthors Info & Claims

Artificial Life, Volume 20, Issue 4

Pages 457 - 470

https://doi.org/10.1162/ARTL_a_00145

Published: 01 October 2014 Publication History

Abstract

Recombination is a commonly used genetic operator in artificial and computational evolutionary systems. It has been empirically shown to be essential for evolutionary processes. However, little has been done to analyze the effects of recombination on quantitative genotypic and phenotypic properties. The majority of studies only consider mutation, mainly due to the more serious consequences of recombination in reorganizing entire genomes. Here we adopt methods from evolutionary biology to analyze a simple, yet representative, genetic programming method, linear genetic programming. We demonstrate that recombination has less disruptive effects on phenotype than mutation, that it accelerates novel phenotypic exploration, and that it particularly promotes robust phenotypes and evolves genotypic robustness and synergistic epistasis. Our results corroborate an explanation for the prevalence of recombination in complex living organisms, and helps elucidate a better understanding of the evolutionary mechanisms involved in the design of complex artificial evolutionary systems and intelligent algorithms.

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Cited By

Zhang JHu TWagner M(2022)Regulatory genotype-to-phenotype mappings improve evolvability in genetic programmingProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3520304.3529043(623-626)Online publication date: 9-Jul-2022
https://dl.acm.org/doi/10.1145/3520304.3529043
Hu TTomassini MBanzhaf W(2020)A network perspective on genotype–phenotype mapping in genetic programmingGenetic Programming and Evolvable Machines10.1007/s10710-020-09379-021:3(375-397)Online publication date: 1-Sep-2020
https://dl.acm.org/doi/10.1007/s10710-020-09379-0
O’Neill MBrabazon A(2019)Mutational Robustness and Structural Complexity in Grammatical Evolution2019 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2019.8790010(1338-1344)Online publication date: 10-Jun-2019
https://dl.acm.org/doi/10.1109/CEC.2019.8790010
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The effects of recombination on phenotypic exploration and robustness in evolution
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cover image Artificial Life

Artificial Life Volume 20, Issue 4

Fall 2014

123 pages

ISSN:1064-5462

EISSN:1530-9185

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Publisher

MIT Press

Cambridge, MA, United States

Publication History

Published: 01 October 2014

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Cited By

Zhang JHu TWagner M(2022)Regulatory genotype-to-phenotype mappings improve evolvability in genetic programmingProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3520304.3529043(623-626)Online publication date: 9-Jul-2022
https://dl.acm.org/doi/10.1145/3520304.3529043
Hu TTomassini MBanzhaf W(2020)A network perspective on genotype–phenotype mapping in genetic programmingGenetic Programming and Evolvable Machines10.1007/s10710-020-09379-021:3(375-397)Online publication date: 1-Sep-2020
https://dl.acm.org/doi/10.1007/s10710-020-09379-0
O’Neill MBrabazon A(2019)Mutational Robustness and Structural Complexity in Grammatical Evolution2019 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2019.8790010(1338-1344)Online publication date: 10-Jun-2019
https://dl.acm.org/doi/10.1109/CEC.2019.8790010
Hu TTomassini MBanzhaf W(2019)Complex Network Analysis of a Genetic Programming Phenotype NetworkGenetic Programming10.1007/978-3-030-16670-0_4(49-63)Online publication date: 24-Apr-2019
https://dl.acm.org/doi/10.1007/978-3-030-16670-0_4
Nickerson KChen YWang FHu TTakadama K(2018)Measuring evolvability and accessibility using the hyperlink-induced topic search algorithmProceedings of the Genetic and Evolutionary Computation Conference10.1145/3205455.3205633(1175-1182)Online publication date: 2-Jul-2018
https://dl.acm.org/doi/10.1145/3205455.3205633
Hu TBanzhaf WNeumann FSutton A(2016)Quantitative Analysis of Evolvability using Vertex Centralities in Phenotype NetworkProceedings of the Genetic and Evolutionary Computation Conference 201610.1145/2908812.2908940(733-740)Online publication date: 20-Jul-2016
https://dl.acm.org/doi/10.1145/2908812.2908940

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