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Evolving Vision Controllers with a Two-Phase Genetic Programming System Using Imitation

Renaud Barate 1, * Antoine Manzanera 1
* Corresponding author
Abstract : We present a system that automatically selects and param-eterizes 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.
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Renaud Barate, Antoine Manzanera. Evolving Vision Controllers with a Two-Phase Genetic Programming System Using Imitation. 10th International Conference on the Simulation of Adaptive Behavior (SAB'08), Jul 2008, Osaka, Japan. ⟨10.1007/978-3-540-69134-1_8⟩. ⟨hal-01222604⟩

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