Modeling non-equilibrium population using variable-chromosome-length genetic algorithm

Research output: Chapter in Book/Report/Conference proceedingConference proceedingpeer-review

1 Citation (Scopus)

Abstract

Codon usage bias is the preferential use of synonymous codons. First models that studied this phenomenon assumed that the population is at mutation-selection-drift equilibrium, but more advanced models were proposed later to incorporate demographic changes. One of these models proposed by Zeng and Charlesworth represents the evolutionary process by a Markov model, allowing for changes in the population size. Their model is, however, too simple to reflect many realistic demographic changes. In this paper, we extend their model by allowing complex demographies with many changes in population size. Such extension requires a more powerful optimization algorithm compared with the simple one used in the model proposed by Zeng and Charlesworth. The optimization algorithm we use is a version of the genetic algorithm that we develop particularly for this purpose. We validate our method using simulated data.

Original languageEnglish
Title of host publication2017 IEEE Congress on Evolutionary Computation, CEC 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2566-2573
Number of pages8
ISBN (Electronic)9781509046010
DOIs
Publication statusPublished - 5 Jul 2017
Externally publishedYes
Event2017 IEEE Congress on Evolutionary Computation, CEC 2017 - Donostia-San Sebastian, Spain
Duration: 5 Jun 20178 Jun 2017

Publication series

Name2017 IEEE Congress on Evolutionary Computation, CEC 2017 - Proceedings

Conference

Conference2017 IEEE Congress on Evolutionary Computation, CEC 2017
Country/TerritorySpain
CityDonostia-San Sebastian
Period5/06/178/06/17

Keywords

  • Codon usage bias
  • Genetic algorithms
  • Modeling
  • Optimization
  • Population genetics

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Signal Processing

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