On the mapping of genotype to phenotype in evolutionary algorithms

Article

DOI: 10.1007/s10710-017-9288-x

Cite this article as:
Whigham, P.A., Dick, G. & Maclaurin, J. Genet Program Evolvable Mach (2017). doi:10.1007/s10710-017-9288-x
Part of the following topical collections:
  1. Mapping of Genotype to Phenotype in Evolutionary Algorithms

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.

Keywords

Genetic programming Biological analogy Grammatical evolution Representation 

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Peter A. Whigham
    • 1
  • Grant Dick
    • 1
  • James Maclaurin
    • 2
  1. 1.Department of Information ScienceUniversity of OtagoDunedinNew Zealand
  2. 2.Department of PhilosophyUniversity of OtagoDunedinNew Zealand

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