Success Effort and Other Statistics for Performance Comparisons in Genetic Programming

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  author =       "Matthew Walker and Howard Edwards and Chris Messom",
  title =        "Success Effort and Other Statistics for Performance
                 Comparisons in Genetic Programming",
  booktitle =    "2007 IEEE Congress on Evolutionary Computation",
  year =         "2007",
  editor =       "Dipti Srinivasan and Lipo Wang",
  pages =        "4631--4638",
  address =      "Singapore",
  month =        "25-28 " # sep,
  organization = "IEEE Computational Intelligence Society",
  publisher =    "IEEE Press",
  ISBN =         "1-4244-1340-0",
  file =         "1658.pdf",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/CEC.2007.4425079",
  abstract =     "This paper looks at the statistics used to compare
                 variations to the genetic programming method. Previous
                 work in this area has been dominated by the use of mean
                 best-of-run fitness and Koza's minimum computational
                 effort. This article re-introduces a statistic we name
                 success effort and analyses two methods to produce
                 confidence intervals for the statistic. We then compare
                 success effort and four other performance measures and
                 conclude that success effort is a sometimes more
                 powerful statistic than computational effort and a more
                 desirable measure than the other statistics.",
  notes =        "CEC 2007 - A joint meeting of the IEEE, the EPS, and
                 the IET.

                 IEEE Catalog Number: 07TH8963C",

Genetic Programming entries for Matthew Walker Howard Edwards Chris Messom