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Confidence intervals of success rates in evolutionary computation

Published:07 July 2010Publication History

ABSTRACT

Success Rate (SR) is a statistic straightforward to use and interpret, however a number of non-trivial statistical issues arises when it is examinated in detail. We address some of those issues, providing evidence that suggests that SR follows a binomial density function, therefore its statistical properties are independent of the flavour of the Evolutionary Algorithm (EA) and its domain. It is fully described by the SR and the number of runs. Moreover, the binomial distribution is a well known statistical distribution with a large corpus of tools available that can be used in the context of EC research. One of those tools, confidence intervals (CIs), is studied.

References

  1. L. D. Brown, T. T. Cai, and A. Dasgupta. Interval estimation for a binomial. Statistical Science, 16:101--133, 2001.Google ScholarGoogle ScholarCross RefCross Ref
  2. S. Christensen and F. Oppacher. An analysis of koza's computational effort statistic for genetic programming. In EuroGP'02, pages 182--191, London, UK, 2002. Springer-Verlag. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. M. Walker, H. Edwards, and C. H. Messom. Confidence intervals for computational effort comparisons. In EuroGP, pages 23--32, 2007.. Google ScholarGoogle ScholarDigital LibraryDigital Library

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      cover image ACM Conferences
      GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation
      July 2010
      1520 pages
      ISBN:9781450300728
      DOI:10.1145/1830483

      Copyright © 2010 Copyright is held by the author/owner(s)

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 7 July 2010

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