Evolving Dynamic Fitness Measures for Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

@Article{Ragalo18EWSA,
  author =       "Anisa Ragalo and Nelishia Pillay",
  title =        "Evolving Dynamic Fitness Measures for Genetic
                 Programming",
  journal =      "Expert Systems with Applications",
  year =         "2018",
  volume =       "109",
  pages =        "162--187",
  month =        "1 " # nov,
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1016/j.eswa.2018.03.060",
  abstract =     "This research builds on the hypothesis that the use of
                 different fitness measures on the different generations
                 of genetic programming (GP) is more effective than the
                 convention of applying the same fitness measure
                 individually throughout GP. Whereas the previous study
                 used a genetic algorithm (GA) to induce the sequence in
                 which fitness measures should be applied over the GP
                 generations, this research uses a meta- (or high-level)
                 GP to evolve a combination of the fitness measures for
                 the low-level GP. The study finds that the meta-GP is
                 the preferred approach to generating dynamic fitness
                 measures. GP systems applying the generated dynamic
                 fitness measures consistently outperform the previous
                 approach, as well as standard GP on benchmark and real
                 world problems. Furthermore, the generated dynamic
                 fitness measures are shown to be reusable, whereby they
                 can be used to solve unseen problems to optimality.",
}

Genetic Programming entries for Anisa Waganda Ragalo Nelishia Pillay

Citations