Detail publikace

Estimation Distribution Algorithm for mixed continuous-discrete optimization problems

SCHWARZ, J., OČENÁŠEK, J.

Originální název

Estimation Distribution Algorithm for mixed continuous-discrete optimization problems

Anglický název

Estimation Distribution Algorithm for mixed continuous-discrete optimization problems

Jazyk

en

Originální abstrakt

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.

Anglický abstrakt

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.

Dokumenty

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"
}