Publication detail

Estimation Distribution Algorithm for mixed continuous-discrete optimization problems

SCHWARZ, J., OČENÁŠEK, J.

Original Title

Estimation Distribution Algorithm for mixed continuous-discrete optimization problems

English Title

Estimation Distribution Algorithm for mixed continuous-discrete optimization problems

Type

conference paper

Language

en

Original Abstract

In recent few years expressive progress in the theory and practice of Estimation of Distribution Algorithms (EDA) [1] has appeared, where the classical genetic recombination operators are replaced by probability estimation and stochastic sampling techniques. In this paper we identify some disadvantages of present probabilistic models used in EDAs and propose more general and efficient model for continuous optimization problems based on the decision trees. The new variant of EDA is capable to solve mixed continuous-discrete optimization problems.

English abstract

In recent few years expressive progress in the theory and practice of Estimation of Distribution Algorithms (EDA) [1] has appeared, where the classical genetic recombination operators are replaced by probability estimation and stochastic sampling techniques. In this paper we identify some disadvantages of present probabilistic models used in EDAs and propose more general and efficient model for continuous optimization problems based on the decision trees. The new variant of EDA is capable to solve mixed continuous-discrete optimization problems.

Keywords

Estimation Distribution Algorithm, Bayesian Optimization Algorithm, Bayesian network, Gaussian network, decision tree, CART model

RIV year

2002

Released

16.06.2002

Publisher

IOS Press

Location

Kosice

ISBN

1-58603-256-9

Book

Proceedings of the 2nd Euro-International Symposium on Computational Intelligence

Pages from

227

Pages to

232

Pages count

6

Documents

BibTex


@inproceedings{BUT10030,
  author="Jiří {Očenášek} and Josef {Schwarz}",
  title="Estimation Distribution Algorithm for mixed continuous-discrete optimization problems",
  annote="In recent few years expressive progress in the theory and practice of Estimation of Distribution Algorithms (EDA) [1] has appeared, where the classical genetic recombination operators are replaced by probability estimation and stochastic sampling techniques. In this paper we identify some disadvantages of present probabilistic models used in EDAs and propose more general and efficient model for continuous optimization problems based on the decision trees. The new variant of EDA is capable to solve mixed continuous-discrete optimization problems.",
  address="IOS Press",
  booktitle="Proceedings of the 2nd Euro-International Symposium on Computational Intelligence",
  chapter="10030",
  institution="IOS Press",
  year="2002",
  month="june",
  pages="227--232",
  publisher="IOS Press",
  type="conference paper"
}