Re-inspiring the genetic algorithm with multi-level selection theory: Multi-Level Selection Genetic Algorithm (MLSGA)
Re-inspiring the genetic algorithm with multi-level selection theory: Multi-Level Selection Genetic Algorithm (MLSGA)
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 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.
Evolutionary theory, algorithm development, multi-level selection, multi-objective optimisation, single objective optimisation
1-13
Sobey, A.J.
e850606f-aa79-4c99-8682-2cfffda3cd28
Grudniewski, P.A.
30349b11-4667-41d6-9b45-587c46254a2f
1 September 2018
Sobey, A.J.
e850606f-aa79-4c99-8682-2cfffda3cd28
Grudniewski, P.A.
30349b11-4667-41d6-9b45-587c46254a2f
Sobey, A.J. and Grudniewski, P.A.
(2018)
Re-inspiring the genetic algorithm with multi-level selection theory: Multi-Level Selection Genetic Algorithm (MLSGA).
Bioinspiration & Biomimetics, 13 (5), .
(doi:10.1088/1748-3190/aad2e8).
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 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.
Text
Bioinspiration and Biomimetics (2)
- Accepted Manuscript
More information
Accepted/In Press date: 12 July 2018
e-pub ahead of print date: 12 July 2018
Published date: 1 September 2018
Keywords:
Evolutionary theory, algorithm development, multi-level selection, multi-objective optimisation, single objective optimisation
Identifiers
Local EPrints ID: 422443
URI: http://eprints.soton.ac.uk/id/eprint/422443
ISSN: 1748-3182
PURE UUID: dc65652c-d6ff-4b05-b516-b6246d7dd74d
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Date deposited: 24 Jul 2018 16:30
Last modified: 16 Mar 2024 06:54
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