Improved sampling using loopy belief propagation for probabilistic model building genetic programming

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

@Article{Sato:2015:SEC,
  author =       "Hiroyuki Sato and Yoshihiko Hasegawa and 
                 Danushka Bollegala and Hitoshi Iba",
  title =        "Improved sampling using loopy belief propagation for
                 probabilistic model building genetic programming",
  journal =      "Swarm and Evolutionary Computation",
  year =         "2015",
  volume =       "23",
  pages =        "1--10",
  month =        aug,
  keywords =     "genetic algorithms, genetic programming, Estimation of
                 distribution algorithms, Loopy belief propagation,
                 Probabilistic model building GP",
  ISSN =         "2210-6502",
  DOI =          "doi:10.1016/j.swevo.2015.02.002",
  URL =          "http://www.sciencedirect.com/science/article/pii/S2210650215000176",
  size =         "10 pages",
  abstract =     "In recent years, probabilistic model building genetic
                 programming (PMBGP) for program optimisation has
                 attracted considerable interest. PMBGPs generally use
                 probabilistic logic sampling (PLS) to generate new
                 individuals. However, the generation of the most
                 probable solutions (MPSs), i.e., solutions with the
                 highest probability, is not guaranteed. In the present
                 paper, we introduce loopy belief propagation (LBP) for
                 PMBGPs to generate MPSs during the sampling process. We
                 selected program optimization with linkage estimation
                 (POLE) as the foundation of our approach and we refer
                 to our proposed method as POLE-BP. We apply POLE-BP and
                 existing methods to three benchmark problems to
                 investigate the effectiveness of LBP in the context of
                 PMBGPs, and we describe detailed examinations of the
                 behaviours of LBP. We find that POLE-BP shows better
                 search performance with some problems because LBP
                 boosts the generation of building blocks.",
}

Genetic Programming entries for Hiroyuki Sato Yoshihiko Hasegawa Danushka Bollegala Hitoshi Iba

Citations