In this paper, we present the results of the tests we have performed with different encoding strategies for evolving controllers for a snake-like robot. This study is aimed at finding the best encoding for on-line learning of basic skills, such as locomotion (both free and directed to an objective) and obstacle avoidance. The snake moves in a virtual world, which realistically simulates all the physical conditions of the real world. This is the first step of our research on on-line, embedded and open-ended evolution of robot controllers, where robots have to learn how to survive during their lifetime, and occasionally mate with other robots. A simple (1+1) evolutionary strategy has been adopted for lifetime learning. The results of the tests have shown that the best results, tested on the locomotion skills, is the 'He1Sig' controller, that uses a different set of parameters for each segment of the snake but only one mutation rate, common to all parameters, that is encoded in the chromosome and therefore undergoes evolution itself. © Springer-Verlag Berlin Heidelberg 2013.
CITATION STYLE
Pérez-Moneo Suárez, D., & Rossi, C. (2013). A comparison between different encoding strategies for snake-like robot controllers. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7835 LNCS, pp. 560–568). Springer Verlag. https://doi.org/10.1007/978-3-642-37192-9_56
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