Investigating the parameter space of evolutionary algorithms

Moshe Sipper, Weixuan Fu, Karuna Ahuja, Jason H. Moore

Research output: Contribution to journalArticlepeer-review

40 Scopus citations

Abstract

Evolutionary computation (EC) has been widely applied to biological and biomedical data. The practice of EC involves the tuning of many parameters, such as population size, generation count, selection size, and crossover and mutation rates. Through an extensive series of experiments over multiple evolutionary algorithm implementations and 25 problems we show that parameter space tends to be rife with viable parameters, at least for the problems studied herein. We discuss the implications of this finding in practice for the researcher employing EC.

Original languageEnglish
Article number2
JournalBioData Mining
Volume11
Issue number1
DOIs
StatePublished - 17 Feb 2018

Keywords

  • Evolutionary algorithms
  • Genetic programming
  • Hyper-parameter
  • Meta-genetic algorithm
  • Parameter tuning

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Biology
  • Genetics
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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