A Study of Good Predecessor Programs for Reducing Fitness Evaluation Cost in Genetic Programming

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

@TechReport{vuw-CS-TR-06-3,
  author =       "Huayang Xie and Mengjie Zhang and Peter Andreae",
  title =        "A Study of Good Predecessor Programs for Reducing
                 Fitness Evaluation Cost in Genetic Programming",
  institution =  "Computer Science, Victoria University of Wellington",
  year =         "2006",
  number =       "CS-TR-06-3",
  address =      "New Zealand",
  keywords =     "genetic algorithms, genetic programming, Fitness
                 evaluation, good predecessor programs, population
                 clustering",
  URL =          "http://www.mcs.vuw.ac.nz/comp/Publications/CS-TR-06-3.abs.html",
  URL =          "http://www.mcs.vuw.ac.nz/comp/Publications/archive/CS-TR-06/CS-TR-06-3.pdf",
  abstract =     "Good Predecessor Programs (GPPs) are the ancestors of
                 the best program found in a Genetic Programming (GP)
                 evolution. This paper reports on an investigation into
                 GPPs with the ultimate goal of reducing fitness
                 evaluation cost in tree-based GP systems. A framework
                 is developed for gathering information about GPPs and a
                 series of experiments is conducted on a symbolic
                 regression problem, a binary classification problem,
                 and a multi-class classification program with
                 increasing levels of difficulty in different domains.
                 The analysis of the data shows that during evolution,
                 GPPs typically constitute between less than 33per cent
                 of the total programs evaluated, and may constitute
                 less than 5per cent. The analysis results further shows
                 that in all evaluated programs, the proportion of GPPs
                 is reduced by increasing tournament size and to a less
                 extent, affected by population size. Problem difficulty
                 seems to have no clear influence on the proportion of
                 GPPs.",
}

Genetic Programming entries for Huayang Jason Xie Mengjie Zhang Peter Andreae

Citations