Evolutionary optimization programming with probabilistic models

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@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