Improving Convergence in Cartesian Genetic Programming Using Adaptive Crossover, Mutation and Selection

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Kalkreuth:2015:ieeeSSCI,
  author =       "Roman Kalkreuth and Guenter Rudolph and Jorg Krone",
  booktitle =    "2015 IEEE Symposium Series on Computational
                 Intelligence",
  title =        "Improving Convergence in Cartesian Genetic Programming
                 Using Adaptive Crossover, Mutation and Selection",
  year =         "2015",
  pages =        "1415--1422",
  abstract =     "Genetic programming (GP) can be described as a
                 paradigm which opens the automatic derivation of
                 programs for problem solving. GP as popularized by Koza
                 uses tree representation. The application of GP takes
                 place on several types of complex problems and became
                 very important for Symbolic Regression. Miller and
                 Thomson introduced a new directed graph representation
                 called Cartesian Genetic Programming (CGP). We use this
                 representation for very complex problems. CGP enables a
                 new application on classification and image processing
                 problems. Previous research showed that CGP has a low
                 convergence rate on complex problems. Like in other
                 approaches of evolutionary computation, premature
                 convergence is also a common issue. Modern GP systems
                 produce population statistics in every iteration. In
                 this paper we introduce a new adaptive strategy which
                 uses population statistics to improve the convergence
                 of CGP. A new metric for CGP is introduced to classify
                 the healthy population diversity. Our strategy
                 maintains population diversity by adapting the
                 probabilities of the genetic operators and selection
                 pressure. We demonstrate our strategy on several
                 regression problems and compare it to the traditional
                 algorithm of CGP. We conclude this paper by giving
                 advices about parametrisation of the adaptive
                 strategy.",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/SSCI.2015.201",
  month =        dec,
  notes =        "Also known as \cite{7376777}",
}

Genetic Programming entries for Roman Kalkreuth Guenter Rudolph Jorg Krone

Citations