Evolutionary optimization programming with probabilistic models

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

@InProceedings{Oh:2009:BIC-TA,
  author =       "Sanghoun Oh and Sangwook Lee and Moongu Jeon",
  title =        "Evolutionary optimization programming with
                 probabilistic models",
  booktitle =    "Fourth International Conference on Bio-Inspired
                 Computing, BIC-TA '09",
  year =         "2009",
  month =        oct,
  pages =        "1--6",
  keywords =     "genetic algorithms, genetic programming, chi-ary
                 extended compact genetic algorithm, conditional
                 probability table, evolutionary optimization
                 programming, expanded parse tree, marginal product
                 model, multivariate dependence model, probabilistic
                 models, probability distribution, statistical
                 distributions, trees (mathematics)",
  DOI =          "doi:10.1109/BICTA.2009.5338075",
  abstract =     "Genetic programming is a powerful optimization
                 technique thanks to its capacity of discovering
                 automatically a proper set of programs, rules or
                 functions of a given problem. Regardless of such
                 strengths, GP does not handle a key genetic operator,
                 crossover effectively, resulting in the disruption of
                 good building blocks. To overcome such a problem, we
                 propose a probabilistic model-based evolutionary
                 optimization programming in this paper. It uses an
                 enhanced expanded parse tree that transforms the tree
                 into linear-type chromosomes by inserting nulls and
                 selectors, and that reduces the size of a conditional
                 probability table. Also, a multivariate dependence
                 model, chi-ary extended compact genetic algorithm,
                 chi-eCGA, is employed to find a good probability
                 distribution in the form of marginal product model for
                 the problem. Experimental results provide grounds for
                 the dominance of the proposed approach over existing
                 algorithms.",
  notes =        "Slides
                 http://www.evocomputing.net/attachment/1030346332.pdf
                 Also known as \cite{5338075}",
}

Genetic Programming entries for Sanghoun Oh Sangwook Lee Moongu Jeon

Citations