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OČENÁŠEK, J. SCHWARZ, J.
Original Title
The Parallel Bayesian Optimization Algorithm
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
EDA, BOA, Bayesian network, probabilistic model, fine-grained parallelism, parallel computing
Authors
OČENÁŠEK, J.; SCHWARZ, J.
Released
1. 1. 2000
Publisher
Springer Verlag
Location
Košice
ISBN
3-7908-1322-2
Book
Proceedings of the European Symposium on Computational Inteligence
1615-3871
Periodical
Advances in Soft Computing
State
unknown
Pages from
61
Pages to
67
Pages count
7
URL
http://www.fit.vutbr.cz/~schwarz/PDFCLANKY/ISCI00.pdf
BibTex
@inproceedings{BUT191579, author="Jiří {Očenášek} and Josef {Schwarz}", title="The Parallel Bayesian Optimization Algorithm", booktitle="Proceedings of the European Symposium on Computational Inteligence", year="2000", journal="Advances in Soft Computing", pages="61--67", publisher="Springer Verlag", address="Košice", isbn="3-7908-1322-2", issn="1615-3871", url="http://www.fit.vutbr.cz/~schwarz/PDFCLANKY/ISCI00.pdf" }