Conference paper

SCHWARZ Josef and JAROŠ Jiří. A PROBLEM KNOWLEDGE BASED BAYESIAN OPTIMIZATION ALGORITHM APPLIED IN MULTIPROCESSOR SCHEDULING. In: Mendel Conference on Soft Computing. Brno: Faculty of Mechanical Engineering BUT, 2004, pp. 83-88. ISBN 80-214-2676-4.
Publication language:english
Original title:A PROBLEM KNOWLEDGE BASED BAYESIAN OPTIMIZATION ALGORITHM APPLIED IN MULTIPROCESSOR SCHEDULING
Title (cs):Znalostně orientovaný Bayesovský optimalizační algoritmus
Pages:83-88
Proceedings:Mendel Conference on Soft Computing
Conference:Tenth International Mendel Conference on Soft Computing
Place:Brno, CZ
Year:2004
ISBN:80-214-2676-4
Publisher:Faculty of Mechanical Engineering BUT
Keywords
optimization problems, multiprocessor scheduling problem, evolutionary algorithms, Bayesian optimization algorithm, problem knowledge.
Annotation
This paper deals with the multiprocessor scheduling problem, which  belongs to the class of frequently solved decomposition tasks. The goals is to experimentally compare the performance of the recently proposed Mixed Bayesian Optimization Algorithm (MBOA) based on probabilistic model with  the newly derived  knowledge  based MBOA version (KMBOA) This algorithm includes  utilization of prior knowledge about the structure of a task graph to speed-up the  convergence  and the  solution quality. The performance of standard  genetic algorithm was also tested on the same benchmarks.
BibTeX:
@INPROCEEDINGS{
   author = {Josef Schwarz and Ji{\v{r}}{\'{i}} Jaro{\v{s}}},
   title = {A PROBLEM KNOWLEDGE BASED BAYESIAN OPTIMIZATION ALGORITHM
	APPLIED IN MULTIPROCESSOR SCHEDULING},
   pages = {83--88},
   booktitle = {Mendel Conference on Soft Computing},
   year = {2004},
   location = {Brno, CZ},
   publisher = {Faculty of Mechanical Engineering BUT},
   ISBN = {80-214-2676-4},
   language = {english},
   url = {http://www.fit.vutbr.cz/research/view_pub.php?id=7519}
}

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