Extension of Genetic Programming with Multiple Trees for Agent Learning

Created by W.Langdon from gp-bibliography.bib Revision:1.4216

  author =       "Takashi Ito and Kenichi Takahashi and 
                 Michimasa Inaba",
  title =        "Extension of Genetic Programming with Multiple Trees
                 for Agent Learning",
  journal =      "Journal of Computers",
  year =         "2016",
  number =       "4",
  volume =       "11",
  pages =        "329--340",
  keywords =     "genetic algorithms, genetic programming, Autonomous
                 agent, conditional probability, island model",
  bibdate =      "2016-06-09",
  bibsource =    "DBLP,
  ISSN =         "1796-203X",
  URL =          "http://www.jcomputers.us/index.php?m=content&c=index&a=show&catid=179&id=2649",
  URL =          "http://www.jcomputers.us/vol11/jcp1104-07.pdf",
  DOI =          "doi:10.17706/jcp.11.4.329-340",
  size =         "12 pages",
  abstract =     "This paper proposes an extension of genetic
                 programming (GP) with multiple trees. In order to
                 improve the performance, GP with control node (GPCN)
                 and its three kinds of modification have been proposed.
                 In GPCN, an individual consists of several trees which
                 have the number P of executions. In previous work, the
                 two kinds of modification, the conditional probability
                 and the cross-cultural island model are employed. This
                 paper proposes two methods: the new island model that
                 combines the conditional probability with two islands
                 in the cross-cultural island model and a method
                 exchanges multiple trees in an individual in a suitable
                 order. Experiments are conducted to show the
                 performance in the garbage collection problem and the
                 Santa Fe Trail problem.",

Genetic Programming entries for Takashi Ito Ken-ichi Takahashi Michimasa Inaba