Doc. Ing. Josef Schwarz, CSc.

SCHWARZ Josef. The probability models for combinatorial optimization problems. In: Proceedings of The 4th Japan-Central Europe Joint Workshop on Energy and Information in Non-Linear Systems. Brno, Czech Republic, November 10-12, 2000. Brno: neznámá, 2000, s. 72-75.
Jazyk publikace:angličtina
Název publikace:The probability models for combinatorial optimization problems
Název (cs):The probability models for combinatorial optimization problems
Strany:72-75
Sborník:Proceedings of The 4th Japan-Central Europe Joint Workshop on Energy and Information in Non-Linear Systems. Brno, Czech Republic, November 10-12, 2000
Konference:The 4th Japan-Central Europe Joint Workshop on Energy and Information in Non-Linear 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 = {72--75},
   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},
   location = {Brno, CZ},
   language = {english},
   url = {http://www.fit.vutbr.cz/research/view_pub.php.cs?id=6437}
}

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