Using Perturbation To Improve Robustness Of Solutions Generated By Genetic Programming For Robot Learning

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

@Article{oai:CiteSeerPSU:421006,
  title =        "Using Perturbation To Improve Robustness Of Solutions
                 Generated By Genetic Programming For Robot Learning",
  author =       "Prabhas Chongstitvatana",
  journal =      "Journal of Circuits, Systems and Computers",
  year =         "1999",
  volume =       "9",
  number =       "1-2",
  pages =        "133--143",
  publisher =    "World Scientific Publishing Company",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.worldscinet.com/123/09/0901n02/S0218126699000128.html",
  DOI =          "doi:10.1142/S0218126699000128",
  citeseer-isreferencedby = "oai:CiteSeerPSU:397249;
                 oai:CiteSeerPSU:59033",
  citeseer-references = "oai:CiteSeerPSU:212034; oai:CiteSeerPSU:51923;
                 oai:CiteSeerPSU:70404; oai:CiteSeerPSU:23925;
                 oai:CiteSeerPSU:61708; oai:CiteSeerPSU:14506;
                 oai:CiteSeerPSU:160348; oai:CiteSeerPSU:115106",
  annote =       "The Pennsylvania State University CiteSeer Archives",
  language =     "en",
  oai =          "oai:CiteSeerPSU:421006",
  rights =       "unrestricted",
  URL =          "http://citeseer.ist.psu.edu/421006.html",
  abstract =     "This paper proposes a method to improve robustness of
                 the robot programs generated by genetic programming.
                 The main idea is to inject perturbation into the
                 simulation during the evolution of the solutions. The
                 resulting robot programs are more robust because they
                 have evolved to tolerate the changes in their
                 environment. We set out to test this idea using the
                 problem of navigating a mobile robot from a starting
                 point to a target in an unknown cluttered environment.
                 The result of the experiments shows the effectiveness
                 of this scheme. The analysis of the result shows that
                 the robustness depends on the {"}experience{"} that a
                 robot program acquired during evolution. To improve
                 robustness, the size of the set of {"}experience{"}
                 should be increased and/or the amount of reusing the
                 {"}experience{"} should be increased.",
  notes =        "discrete 2D 500x750 simulation, smellLeft,smellRight",
}

Genetic Programming entries for Prabhas Chongstitvatana

Citations