We present a system that automatically selects and parameterizes a vision based obstacle avoidance method adapted to a given visual context. This system uses genetic programming and a robotic simulation to evaluate the candidate algorithms. As the number of evaluations is restricted, we introduce a novel method using imitation to guide the evolution toward promising solutions. We show that for this problem, our two-phase evolution process performs better than other techniques. © 2008 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Barate, R., & Manzanera, A. (2008). Evolving vision controllers with a two-phase genetic programming system using imitation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5040 LNAI, pp. 73–82). https://doi.org/10.1007/978-3-540-69134-1_8
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