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

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From Animals to Animats 10 (SAB 2008)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5040))

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Abstract

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.

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Authors and Affiliations

  1. ENSTA - UEI, 32 bd Victor, 75739, Paris, Cedex 15, France

    Renaud Barate & Antoine Manzanera

Authors
  1. Renaud Barate
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  2. Antoine Manzanera
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Editor information

Minoru Asada John C. T. Hallam Jean-Arcady Meyer Jun Tani

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© 2008 Springer-Verlag Berlin Heidelberg

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Barate, R., Manzanera, A. (2008). Evolving Vision Controllers with a Two-Phase Genetic Programming System Using Imitation. In: Asada, M., Hallam, J.C.T., Meyer, JA., Tani, J. (eds) From Animals to Animats 10. SAB 2008. Lecture Notes in Computer Science(), vol 5040. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69134-1_8

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  • DOI: https://doi.org/10.1007/978-3-540-69134-1_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-69133-4

  • Online ISBN: 978-3-540-69134-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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