Function Approximation by means of Multi-Branches Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Katya Rodriguez-Vazquez and Carlos Oliver-Morales",
  title =        "Function Approximation by means of Multi-Branches
                 Genetic Programming",
  booktitle =    "Late Breaking Papers at the 2004 Genetic and
                 Evolutionary Computation Conference",
  year =         "2004",
  editor =       "Maarten Keijzer",
  address =      "Seattle, Washington, USA",
  month =        "26 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "",
  abstract =     "This work presents a performance analysis of a
                 Multi-Branches Genetic Programming (MBGP) approach
                 applied in symbolic regression (e.g. function
                 approximation) problems. Genetic Programming (GP) has
                 been previously applied to this kind of regression.
                 However, one of the main drawbacks of GP is the fact
                 that individuals tend to grow in size through the
                 evolution process without a significant improvement in
                 individual performance. In Multi-Branches Genetic
                 Programming (MBGP), an individual is composed of
                 several branches, each branch can evolve a part of
                 individual solution, and final solution is composed of
                 the integration of these partial solutions. Accurate
                 solutions emerge by using MBGP consisting of a less
                 complex structure in comparison with solutions
                 generated by means of traditional GP encoding without
                 considering any additional mechanisms such as a
                 multi-objective fitness functions evaluation for tree
                 size controlling.",
  notes =        "Part of \cite{keijzer:2004:GECCO:lbp}",

Genetic Programming entries for Katya Rodriguez-Vazquez Carlos Oliver-Morales