An Empirical Study on the Accuracy of Computational Effort in Genetic Programming

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@InProceedings{Barrero:2011:AESotAoCEiGP,
  title =        "An Empirical Study on the Accuracy of Computational
                 Effort in Genetic Programming",
  author =       "David F. Barrero and Maria R-Moreno and 
                 Bonifacio Castano and David Camacho",
  pages =        "1169--1176",
  booktitle =    "Proceedings of the 2011 IEEE Congress on Evolutionary
                 Computation",
  year =         "2011",
  editor =       "Alice E. Smith",
  month =        "5-8 " # jun,
  address =      "New Orleans, USA",
  organization = "IEEE Computational Intelligence Society",
  publisher =    "IEEE Press",
  ISBN =         "0-7803-8515-2",
  keywords =     "genetic algorithms, genetic programming, computational
                 effort, estimation, variability sources, computational
                 complexity, estimation theory",
  DOI =          "doi:10.1109/CEC.2011.5949748",
  abstract =     "Some commonly used performance measures in Genetic
                 Programming are those defined by John Koza in his first
                 book. These measures, mainly computational effort and
                 number of individuals to be processed, estimate the
                 performance of the algorithm as well as the difficulty
                 of a problem. Although Koza's performance measures have
                 been widely used in the literature, their behaviour is
                 not well known. In this paper we try to study the
                 accuracy of these measures and advance in the
                 understanding of the factors that influence them. In
                 order to achieve this goal, we report an empirical
                 study that attempts to systematically measure the
                 effects of two variability sources in the estimation of
                 the number of individuals to be processed and the
                 computational effort. The results obtained in those
                 experiments suggests that these measures, in common
                 experimental setups, and under certain circumstances,
                 might have a high relative error.",
  notes =        "CEC2011 sponsored by the IEEE Computational
                 Intelligence Society, and previously sponsored by the
                 EPS and the IET.",
}

Genetic Programming entries for David F Barrero Ma Dolores Rodriguez Moreno Bonifacio Castano David Camacho

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