Complexity-based fitness evaluation

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InCollection{Iba:1997:HEC,
  author =       "Hitoshi Iba",
  title =        "Complexity-based fitness evaluation",
  booktitle =    "Handbook of Evolutionary Computation",
  publisher =    "Oxford University Press",
  publisher_2 =  "Institute of Physics Publishing",
  year =         "1997",
  editor =       "Thomas Baeck and David B. Fogel and 
                 Zbigniew Michalewicz",
  chapter =      "section C4.4",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-7503-0392-1",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.375.6494.pdf",
  DOI =          "doi:10.1201/9781420050387.ptc",
  size =         "8 pages",
  abstract =     "This section describes the complexity-based fitness
                 evaluation for evolutionary algorithms. We first
                 introduce and compare the leading competing model
                 selection criteria, namely, an MDL
                 (minimum-description-length) principle, the AIC (Akaike
                 information criterion), an MML (minimum-message-length)
                 principle, the PLS (predictive least-squares) measure,
                 cross-validation, and the maximum-entropy principle.
                 Then we give an illustrative example to show the
                 effectiveness of the complexity-based fitness by
                 experimenting with evolving decision trees using
                 genetic programming (GP). Thereafter, we describe
                 various research on complexity-based fitness
                 evaluation, that is, controlling genetic algorithm or
                 GP search strategies by means of the MDL criterion.",
}

Genetic Programming entries for Hitoshi Iba

Citations