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

Created by W.Langdon from gp-bibliography.bib Revision:1.8129

@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:10.1007/978-3-540-31989-4_14",
• 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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