Genetic programming incorporating biased mutation for evolution and adaptation of Snakebot

Authors:
Ivan Tanev
Affiliations:
Department of Information Systems Design, Faculty of Engineering, Doshisha University, Kyotanabe, Japan 610-0321
Venue:
Genetic Programming and Evolvable Machines
Year:
2007

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Abstract

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.