On the mapping of genotype to phenotype in evolutionary algorithms

• Published in: Genetic programming and evolvable machines Vol. 18; no. 3; pp. 353 - 361
• Main Authors: Whigham, Peter A., Dick, Grant, Maclaurin, James
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.09.2017; Springer Nature B.V
• Subjects: Artificial Intelligence; Biology; Biomedical Engineering and Bioengineering; Compilers; Computer Science; Decoupling; Electrical Engineering; Evolutionary algorithms; Evolutionary design method; Genetic algorithms; Interpreters; Mapping of Genotype to Phenotype in Evolutionary Algorithms; Molecular biology; Programming Languages; Programming Techniques; Representations; Searching; Software Engineering/Programming and Operating Systems; Biological analogy; Grammatical evolution; Representation; Genetic programming
• ISSN: 1389-2576; 1573-7632
• DOI: 10.1007/s10710-017-9288-x

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.