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PAC learning and genetic programming

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Published:12 July 2011Publication History

ABSTRACT

Genetic programming (GP) is a very successful type of learning algorithm that is hard to understand from a theoretical point of view. With this paper we contribute to the computational complexity analysis of genetic programming that has been started recently. We analyze GP in the well-known PAC learning framework and point out how it can observe quality changes in the the evolution of functions by random sampling. This leads to computational complexity bounds for a linear GP algorithm for perfectly learning any member of a simple class of linear pseudo-Boolean functions. Furthermore, we show that the same algorithm on the functions from the same class finds good approximations of the target function in less time.

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    • Published in

      cover image ACM Conferences
      GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation
      July 2011
      2140 pages
      ISBN:9781450305570
      DOI:10.1145/2001576

      Copyright © 2011 ACM

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      New York, NY, United States

      Publication History

      • Published: 12 July 2011

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