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On the mapping of genotype to phenotype in evolutionary algorithms

Genetic Programming and Evolvable Machines volume 18, pages 353–361 (2017)Cite this article

A Commentary to this article was published on 29 March 2017

A Commentary to this article was published on 28 February 2017

A Commentary to this article was published on 24 February 2017

A Commentary to this article was published on 24 February 2017

A Commentary to this article was published on 24 February 2017

A Commentary to this article was published on 24 February 2017

A Commentary to this article was published on 24 February 2017

A Commentary to this article was published on 24 February 2017

A Commentary to this article was published on 23 February 2017

Abstract

Analogies with molecular biology are frequently used to guide the development of artificial evolutionary search. A number of assumptions are made in using such reasoning, chief among these is that evolution in natural systems is an optimal, or at least best available, search mechanism, and that a decoupling of search space from behaviour encourages effective search. In this paper, we explore these assumptions as they relate to evolutionary algorithms, and discuss philosophical foundations from which an effective evolutionary search can be constructed. This framework is used to examine grammatical evolution (GE), a popular search method that draws heavily upon concepts from molecular biology. We identify several properties in GE that are in direct conflict with those that promote effective evolutionary search. The paper concludes with some recommendations for designing representations for effective evolutionary search.

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Fig. 1
Fig. 2

(Adaptation of Fig. 4.1 from [14])

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Authors and Affiliations

  1. Department of Information Science, University of Otago, Dunedin, New Zealand

    Peter A. Whigham & Grant Dick

  2. Department of Philosophy, University of Otago, Dunedin, New Zealand

    James Maclaurin

Authors
  1. Peter A. Whigham
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  2. Grant Dick
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  3. James Maclaurin
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Correspondence to Peter A. Whigham.

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Whigham, P.A., Dick, G. & Maclaurin, J. On the mapping of genotype to phenotype in evolutionary algorithms. Genet Program Evolvable Mach 18, 353–361 (2017). https://doi.org/10.1007/s10710-017-9288-x

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  • DOI: https://doi.org/10.1007/s10710-017-9288-x

Keywords

  • Genetic programming
  • Biological analogy
  • Grammatical evolution
  • Representation