Biomimetic use of genetic algorithms

TL;DR

This paper examines the biological inspiration behind genetic algorithms, analyzing their similarities and differences with natural genomes, and suggests new approaches for applying GAs to complex problems based on these insights.

Contribution

It provides a critical analysis of the biomimetic aspects of GAs and proposes novel strategies for their application inspired by natural genome features.

Findings

GAs rely mainly on implicit parallelism, unlike biological genomes.

Differences between GAs and natural genomes suggest new application methods.

Using complex codings and homogeneous populations may enhance GA performance.

Abstract

Genetic algorithms are considered as an original way to solve problems, probably because of their generality and of their "blind" nature. But GAs are also unusual since the features of many implementations (among all that could be thought of) are principally led by the biological metaphor, while efficiency measurements intervene only afterwards. We propose here to examine the relevance of these biomimetic aspects, by pointing out some fundamental similarities and divergences between GAs and the genome of living beings shaped by natural selection. One of the main differences comes from the fact that GAs rely principally on the so-called implicit parallelism, while giving to the mutation/selection mechanism the second role. Such differences could suggest new ways of employing GAs on complex problems, using complex codings and starting from nearly homogeneous populations.