Detail publikace

The probability models for combinatorial optimization problems

SCHWARZ, J.

Originální název

The probability models for combinatorial optimization problems

Typ

článek ve sborníku mimo WoS a Scopus

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova

probabilistic models, EDA algorithms, Bayesian networks

Autoři

SCHWARZ, J.

Vydáno

1. 1. 2000

Nakladatel

unknown

Místo

Brno

Strany od

72

Strany do

75

Strany počet

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"
}