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Slightly Beyond Turing’s Computability for Studying Genetic Programming

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Machines, Computations, and Universality (MCU 2007)

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Abstract

Inspired by genetic programming (GP), we study iterative algorithms for non-computable tasks and compare them to naive models. This framework justifies many practical standard tricks from GP and also provides complexity lower-bounds which justify the computational cost of GP thanks to the use of Kolmogorov’s complexity in bounded time.

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Authors and Affiliations

  1. TAO, INRIA Futurs, LRI, UMR 8623 (CNRS - Univ. Paris-Sud),  

    Olivier Teytaud

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  1. Olivier Teytaud
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Jérôme Durand-Lose Maurice Margenstern

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Teytaud, O. (2007). Slightly Beyond Turing’s Computability for Studying Genetic Programming. In: Durand-Lose, J., Margenstern, M. (eds) Machines, Computations, and Universality. MCU 2007. Lecture Notes in Computer Science, vol 4664. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74593-8_24

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  • DOI: https://doi.org/10.1007/978-3-540-74593-8_24

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-74592-1

  • Online ISBN: 978-3-540-74593-8

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