Investigating the parameter space of evolutionary algorithms

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Moshe Sipper and Weixuan Fu and Karuna Ahuja and 
                 Jason H. Moore",
  title =        "Investigating the parameter space of evolutionary
  journal =      "BioData Mining",
  year =         "2018",
  volume =       "11",
  number =       "1",
  month =        "17 " # feb,
  keywords =     "genetic algorithms, genetic programming, evolutionary
                 algorithms, Meta-genetic algorithm, Parameter tuning,
  ISSN =         "1756-0381",
  DOI =          "doi:10.1186/s13040-018-0164-x",
  size =         "14 pages",
  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.",

Genetic Programming entries for Moshe Sipper Weixuan Fu Karuna Ahuja Jason H Moore