Evolutionary optimization programming with probabilistic models
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Oh:2009:BIC-TA,
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author = "Sanghoun Oh and Sangwook Lee and Moongu Jeon",
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title = "Evolutionary optimization programming with
probabilistic models",
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booktitle = "Fourth International Conference on Bio-Inspired
Computing, BIC-TA '09",
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year = "2009",
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month = oct,
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pages = "1--6",
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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)",
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DOI = "doi:10.1109/BICTA.2009.5338075",
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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.",
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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