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Precomputation for rapid hypothesis generation in evolutionary robotics

Published:15 July 2017Publication History

ABSTRACT

A common aim in evolutionary search is to skillfully navigate complex search spaces. Achieving this aim requires creating search algorithms that exploit the structure of such spaces. Yet studying such structure directly is challenging because of the expansiveness of most search spaces. In the context of evolutionary robotics, this paper suggests a middle-ground approach that combines a full-fledged domain with an expressive but limited encoding, and then precomputes the behavior of all possible individuals, enabling evaluation as a look-up table. The product is an experimental playground in which search is non-trivial yet which offers extreme computational efficiency and ground truth about search-space structure. This paper describes the approach and demonstrates a range of its applications, directly exploring deception, behavioral rarity, and generalizations of evolvability in a popular benchmark task. The hope is that the extensible framework enables quick experimentation and idea generation, aiding brainstorming of new search algorithms and measures.

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References

  1. Joel Lehman and Kenneth O. Stanley. Abandoning objectives: Evolution through the search for novelty alone. Evolutionary Computation, 19(2):189--223, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Terry Jones and Stephanie Forrest. Fitness distance correlation as a measure of problem didculty for genetic algorithms. 1995.Google ScholarGoogle Scholar
  3. M. Kirschner and J. Gerhart. Evolvability. Proceedings of the National Academy of Sciences of the United States of America, 95(15):8420, 1998.Google ScholarGoogle ScholarCross RefCross Ref
  4. Henok Mengistu, Joel Lehman, and Jeff Clune. Evolvability search: Directly selecting for evolvability in order to study and produce it. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2016). ACM, 2016. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Joel Lehman and Kenneth O. Stanley. Beyond open-endedness: Quantifying impressiveness. In Proceedings of Artificial Life Thirteen (ALIFE XIII), 2012.Google ScholarGoogle ScholarCross RefCross Ref

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        cover image ACM Conferences
        GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference Companion
        July 2017
        1934 pages
        ISBN:9781450349390
        DOI:10.1145/3067695

        Copyright © 2017 Owner/Author

        Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 15 July 2017

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