Probabilistic model building in genetic programming: a critical review

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

  author =       "Kangil Kim and Yin Shan and Xuan Hoai Nguyen and 
                 R. I. McKay",
  title =        "Probabilistic model building in genetic programming: a
                 critical review",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2014",
  volume =       "15",
  number =       "2",
  pages =        "115--167",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming, Probabilistic
                 model building, Estimation of distribution, Ant colony,
                 Iterated density estimation, Prototype tree, Stochastic
  ISSN =         "1389-2576",
  DOI =          "doi:10.1007/s10710-013-9205-x",
  size =         "53 pages",
  abstract =     "Probabilistic model-building algorithms (PMBA), a
                 subset of evolutionary algorithms, have been successful
                 in solving complex problems, in addition providing
                 analytical information about the distribution of fit
                 individuals. Most PMBA work has concentrated on the
                 string representation used in typical genetic
                 algorithms. A smaller body of work has aimed to apply
                 the useful concepts of PMBA to genetic programming
                 (GP), mostly concentrating on tree representation.
                 Unfortunately, the latter research has been
                 sporadically carried out, and reported in several
                 different research streams, limiting substantial
                 communication and discussion. In this paper, we aim to
                 provide a critical review of previous applications of
                 PMBA and related methods in GP research, to facilitate
                 more vital communication. We illustrate the current
                 state of research in applying PMBA to GP, noting
                 important perspectives. We use these to categorise
                 practical PMBA models for GP, and describe the main
                 varieties on this basis.",

Genetic Programming entries for Kangil Kim Yin Shan Nguyen Xuan Hoai R I (Bob) McKay