Doc. Ing. Josef Schwarz, CSc.
SCHWARZ Josef. The probability models for combinatorial optimization problems. In: Proceedings of The 4th JapanCentral Europe Joint Workshop on Energy and Information in NonLinear Systems. Brno, Czech Republic, November 1012, 2000. Brno: neznámá, 2000, s. 7275.  Jazyk publikace:  angličtina 

Název publikace:  The probability models for combinatorial optimization problems 

Název (cs):  The probability models for combinatorial optimization problems 

Strany:  7275 

Sborník:  Proceedings of The 4th JapanCentral Europe Joint Workshop on Energy and Information in NonLinear Systems. Brno, Czech Republic, November 1012, 2000 

Konference:  The 4th JapanCentral Europe Joint Workshop on Energy and Information in NonLinear Systems 

Místo vydání:  Brno, CZ 

Rok:  2000 

Vydavatel:  neznámá 

Klíčová slova 

probabilistic models, EDA algorithms, Bayesian networks 
Anotace 

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. 
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. 
BibTeX: 

@INPROCEEDINGS{
author = {Josef Schwarz},
title = {The probability models for combinatorial optimization
problems},
pages = {7275},
booktitle = {Proceedings of The 4th JapanCentral Europe Joint Workshop
on Energy and Information in NonLinear Systems. Brno,
Czech Republic, November 1012, 2000},
year = {2000},
location = {Brno, CZ},
language = {english},
url = {http://www.fit.vutbr.cz/research/view_pub.php.cs?id=6437}
} 
