Publication detail

The probability models for combinatorial optimization problems

SCHWARZ, J.

Original Title

The probability models for combinatorial optimization problems

Type

article in a collection out of WoS and Scopus

Language

English

Original Abstract

In the last few years there has been a growing interest in the field of Estimation of Distribution Algorithms (EDAs), where crossover and mutation genetic operators are replaced by probability estimation and sampling techniques. The Bayesian Optimization Algorithm incorporates methods for learning Bayesian networks and uses these to model the promising solutions and generate new ones. The aim of this paper is to propose the parallel version of this algorithm, where the optimization time decreases linearly with the number of processors. During the parallel construction of network, the explicit topological ordering of variables is used to keep the model acyclic. The performance of the optimization process seems to be not affected by this constraint and our version of algorithm was successfully tested for the discrete combinatorial problem represented by graph partitioning as well as for deceptive functions.

Keywords

probabilistic models, EDA algorithms, Bayesian networks

Authors

SCHWARZ, J.

Released

1. 1. 2000

Publisher

unknown

Location

Brno

Pages from

72

Pages to

75

Pages count

4

BibTex

@inproceedings{BUT191578,
  author="Josef {Schwarz}",
  title="The probability models for combinatorial optimization problems",
  booktitle="Proceedings of The 4th Japan-Central Europe Joint Workshop on  Energy and Information in Non-Linear Systems. Brno, Czech Republic, November 10-12, 2000",
  year="2000",
  pages="72--75",
  publisher="unknown",
  address="Brno"
}