Genetic programming incorporating biased mutation for evolution and adaptation of Snakebot

  • Ivan TanevEmail author


In this work we propose an approach for incorporating learning probabilistic context-sensitive grammar (LPCSG) in genetic programming (GP), employed for evolution and adaptation of locomotion gaits of a simulated snake-like robot (Snakebot). Our approach is derived from the original context-free grammar which usually expresses the syntax of genetic programs in canonical GP. Empirically obtained results verify that employing LPCSG 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 degraded mechanical abilities of the Snakebot.


Adaptation Genetic programming Grammar Locomotion Snakebot 



The author thanks Katsunori Shimohara, Thomas Ray and Andrzej Buller for their support of this work. The research was supported in part by the National Institute of Information and Communications Technology of Japan.


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Copyright information

© Springer Science + Business Media, LLC 2006

Authors and Affiliations

  1. 1.Department of Information Systems DesignFaculty of Engineering, Doshisha UniversityKyotanabeJapan

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