Incorporating Learning Probabilistic Context-Sensitive Grammar in Genetic Programming for Efficient Evolution and Adaptation of Snakebot

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@InProceedings{eurogp:Tanev05,
  author =       "Ivan Tanev",
  editor =       "Maarten Keijzer and Andrea Tettamanzi and 
                 Pierre Collet and Jano I. {van Hemert} and Marco Tomassini",
  title =        "Incorporating Learning Probabilistic Context-Sensitive
                 Grammar in Genetic Programming for Efficient Evolution
                 and Adaptation of Snakebot",
  booktitle =    "Proceedings of the 8th European Conference on Genetic
                 Programming",
  publisher =    "Springer",
  series =       "Lecture Notes in Computer Science",
  volume =       "3447",
  year =         "2005",
  address =      "Lausanne, Switzerland",
  month =        "30 " # mar # " - 1 " # apr,
  organisation = "EvoNet",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-25436-6",
  pages =        "155--166",
  DOI =          "doi:10.1007/b107383",
  bibsource =    "DBLP, http://dblp.uni-trier.de",
  abstract =     "In this work we propose an approach of incorporating
                 probabilistic learning context-sensitive grammar
                 (PLCSG) in genetic programming (GP) employed for
                 evolution and adaptation of locomotion gaits of
                 simulated snake-like robot (Snakebot). In our approach
                 PLCSG is derived from the originally defined
                 context-free grammar, which usually expresses the
                 syntax of genetic programs in canonical GP. During the
                 especially introduced {"}steered{"} mutation the
                 probabilities of applying each of particular production
                 rules with multiple right-hand side alternatives in
                 PLCSG depend on the context, and these probabilities
                 are {"}learned{"} from the aggregated reward values
                 obtained from the evolved best-of-generation Snakebots.
                 Empirically obtained results verify that employing
                 PLCSG contributes to the improvement of computational
                 effort of both (i) the evolution of the fastest
                 possible locomotion gaits for various fitness
                 conditions and (ii) adaptation of these locomotion
                 gaits to challenging environment and de-graded
                 mechanical abilities of Snakebot. In all of the cases
                 considered in this study, the locomotion gaits, evolved
                 and adapted employing GP with PLCSG feature higher
                 velocity and are obtained faster than with canonical
                 GP.",
  notes =        "Part of \cite{keijzer:2005:GP} EuroGP'2005 held in
                 conjunction with EvoCOP2005 and EvoWorkshops2005",
}

Genetic Programming entries for Ivan T Tanev

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