NDLI: Learned mutation strategies in genetic programming for evolution and adaptation of simulated snakebot

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Learned mutation strategies in genetic programming for evolution and adaptation of simulated snakebot

Content Provider	ACM Digital Library
Author	Tanev, Ivan
Abstract	In this work we propose an approach of incorporating learned mutation strategies (LMS) in genetic programming (GP) employed for evolution and adaptation of locomotion gaits of simulated snake-like robot (Snakebot). In our approach the LMS are implemented via learned probabilistic context-sensitive grammar (LPCSG). The LPCSG is derived from the originally defined context-free grammar, which usually expresses the syntax of genetic programs in canonical GP. Applying LMS implies that the probabilities of applying each of particular production rules in LPCGS during the mutation depend on the context. These probabilities are learned from the aggregated reward values obtained from the parsed syntax of the evolved best-of-generation Snakebots. Empirically obtained results verify that LMS contributes to the improvement of computational effort of both (i) the evolution of the fastest possible locomotion gaits for various fitness conditions and (ii) the adaptation of these locomotion gaits to challenging environment and degraded mechanical abilities of Snakebot. In all of the cases considered in this study, the locomotion gaits, evolved and adapted employing GP with LMS feature higher velocity and are obtained faster than with canonical GP.
Starting Page	687
Ending Page	694
Page Count	8
File Format	PDF
ISBN	1595930108
DOI	10.1145/1068009.1068125
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2005-06-25
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Locomotion Mutation strategies Context-sensitive grammar Genetic programming Snakebot
Content Type	Text
Resource Type	Article

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