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Multi-objective genetic algorithms in the study of the genetic code’s adaptability

de Oliveira, Lariza Laura, Freitas, Alex A., Tinós, Renato (2017) Multi-objective genetic algorithms in the study of the genetic code’s adaptability. Information Sciences, 425 . pp. 48-61. ISSN 0020-0255. (doi:10.1016/j.ins.2017.10.022)

Abstract

Using a robustness measure based on values of the polar requirement of amino acids, Freeland and Hurst (1998) showed that less than one in one million random hypothetical codes are better than the standard genetic code. In this paper, instead of comparing the standard code with randomly generated codes, we use an optimisation algorithm to find the best hypothetical codes. This approach has been used before, but considering only one objective to be optimised. The robustness measure based on the polar requirement is considered the most effective objective to be optimised by the algorithm. We propose here that the polar requirement is not the only property to be considered when computing the robustness of the genetic code. We include the hydropathy index and molecular volume in the evaluation of the amino acids using three multi-objective approaches: the weighted formula, lexicographic and Pareto approaches. To our knowledge, this is the first work proposing multi-objective optimisation approaches with a non-restrictive encoding for studying the evolution of the genetic code. Our results indicate that multi-objective approaches considering the three amino acid properties obtain better results than those obtained by single objective approaches reported in the literature. The codes obtained by the multi-objective approach are more robust and structurally more similar to the standard code.

Item Type: Article
DOI/Identification number: 10.1016/j.ins.2017.10.022
Uncontrolled keywords: Genetic codeGenetic algorithmsLexicographic approachMulti-objective genetic algorithm
Divisions: Faculties > Sciences > School of Computing
Depositing User: Alex Freitas
Date Deposited: 23 Nov 2017 09:32 UTC
Last Modified: 29 May 2019 19:53 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/64639 (The current URI for this page, for reference purposes)
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