HMXT-GP: an information-theoretic approach to genetic programming that maintains diversity

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{conf/sac/SantosaMK11,
  author =       "Henry Santosa and John Milton and Paul J. Kennedy",
  title =        "{HMXT-GP}: an information-theoretic approach to
                 genetic programming that maintains diversity",
  booktitle =    "Proceedings of the 2011 ACM Symposium on Applied
                 Computing",
  year =         "2011",
  editor =       "William C. Chu and W. Eric Wong and 
                 Mathew J. Palakal and Chih-Cheng Hung",
  pages =        "1070--1075",
  address =      "TaiChung, Taiwan",
  month =        "21-25 " # mar,
  publisher =    "ACM",
  keywords =     "genetic algorithms, genetic programming, HMXT,
                 diversity, evolutionary computation, information
                 theory",
  acmid =        "1982420",
  isbn13 =       "978-1-4503-0113-8",
  DOI =          "doi:10.1145/1982185.1982420",
  size =         "6 pages",
  abstract =     "This paper applies a recent information--theoretic
                 approach to controlling Genetic Algorithms (GAs) called
                 HMXT to tree--based Genetic Programming (GP). HMXT, in
                 a GA domain, requires the setting of selection
                 thresholds in a population and the application of high
                 levels of crossover to thoroughly mix alleles. Applying
                 these in a tree--based GP setting is not trivial. We
                 present results comparing HMXT--GP to Koza--style GP
                 for varying amounts of crossover and over three
                 different optimisation (minimisation) problems. Results
                 show that average fitness is better with HMXT--GP
                 because it maintains more diversity in populations, but
                 that the minimum fitness found was better with Koza.
                 HMXT allows straightforward tuning of population
                 diversity and selection pressure by altering the
                 position of the selection thresholds.",
  notes =        "cart, binomial-3, Mario AI. SAC'11 University of
                 Technology, Sydney, Broadway NSW, Australia",
}

Genetic Programming entries for Henry Santosa John Milton Paul J Kennedy

Citations