Re-inspiring the genetic algorithm with multi-level selection theory: multi-level selection genetic algorithm

Abstract

Genetic algorithms are integral to a range of applications. They utilise Darwin’s theory of evolution to find optimal solutions in large complex spaces such as engineering, to visualise the design space, artificial intelligence, for pattern classification, and financial modelling, improving predictions. Since the original genetic algorithm was developed, new theories have been proposed which are believed to be integral to the evolution of biological systems. However, genetic algorithm development has focused on mathematical or computational methods as the basis for improvements to the mechanisms, moving it away from its original evolutionary inspiration. There is a possibility that the new evolutionary mechanisms are vital to explain how biological systems developed but they are not being incorporated into the genetic algorithm; it is proposed that their inclusion may provide improved performance or interesting feedback to evolutionary theory. Multi-level selection is one example of an evolutionary theory that has not been successfully implemented into the genetic algorithm and these mechanisms are explored in this paper. The resulting multi-level selection genetic algorithm (MLSGA) is unique in that it has different reproduction mechanisms at each level and splits the fitness function between these mechanisms. There are two variants of this theory and these are compared with each other alongside a unified approach. This paper documents the behaviour of the two variants, which show a difference in behaviour especially in terms of the diversity of the population found between each generation. The multi-level selection 1 variant moves rapidly towards the optimal front but with a low diversity amongst its children. The multi-level selection 2 variant shows a slightly slower evolution speed but with a greater diversity of children. The unified selection exhibits a mixed behaviour between the original variants. The different performance of these variants can be utilised to provide specific solvers for different problem types when using the MLSGA methodology.