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An Analysis of Koza’s Computational Effort Statistic for Genetic Programming

  • Steffen Christensen
  • Franz Oppacher
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2278)

Abstract

As research into the theory of genetic programming progresses, more effort is being placed on systematically comparing results to give an indication of the effectiveness of sundry modifications to traditional GP. The statistic that is commonly used to report the amount of computational effort to solve a particular problem with 99% probability is Koza’s I(M, i, z) statistic. This paper analyzes this measure from a statistical perspective. In particular, Koza’s I tends to underestimate the true computational effort, by 25% or more for commonly used GP parameters and run sizes. The magnitude of this underestimate is nonlinearly decreasing with increasing run count, leading to the possibility that published results based on few runs may in fact be unmatchable when replicated at higher resolution. Additional analysis shows that this statistic also underreports the generation at which optimal results are achieved.

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References

  1. Keijzer, M., Babovic, V., Ryan, C., O’Neill, M., Cattolico, M.: Adaptive Logic Programming. In: Spector, L., Goodman, D. et al (eds.): Proceedings of the 2001 Genetic and Evolutionary Computation Conference. Morgan Kaufmann, San Francisco (2001)Google Scholar
  2. Koza, John R.: Genetic Programming: on the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge London (1992)zbMATHGoogle Scholar
  3. Luke, Sean. ECJ 7: A Java-based evolutionary computation and genetic programming system. http://www.cs.umd.edu/projects/plus/ec/ecj/ (2001)

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Steffen Christensen
    • 1
  • Franz Oppacher
    • 1
  1. 1.Computer Science Dept.Carleton UniversityOttawaCanada

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