Detail publikace

A PROBLEM KNOWLEDGE BASED BAYESIAN OPTIMIZATION ALGORITHM APPLIED IN MULTIPROCESSOR SCHEDULING

SCHWARZ, J., JAROŠ, J. A PROBLEM KNOWLEDGE BASED BAYESIAN OPTIMIZATION ALGORITHM APPLIED IN MULTIPROCESSOR SCHEDULING. In Mendel Conference on Soft Computing. Brno: Faculty of Mechanical Engineering BUT, 2004. p. 83-88. ISBN: 80-214-2676-4.
Název česky
Znalostně orientovaný Bayesovský optimalizační algoritmus
Typ
článek ve sborníku konference
Jazyk
anglicky
Autoři
Schwarz Josef, doc. Ing., CSc. (CK-SZZ)
Jaroš Jiří, doc. Ing., Ph.D. (UPSY)
Klíčová slova

optimization problems, multiprocessor scheduling problem, evolutionary algorithms, Bayesian optimization algorithm, problem knowledge.

Abstrakt

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.

Rok
2004
Strany
83–88
Sborník
Mendel Conference on Soft Computing
ISBN
80-214-2676-4
Vydavatel
Faculty of Mechanical Engineering BUT
Místo
Brno
BibTeX 
@inproceedings{BUT17336,
  author="Josef {Schwarz} and Jiří {Jaroš}",
  title="A PROBLEM KNOWLEDGE BASED BAYESIAN OPTIMIZATION ALGORITHM APPLIED IN MULTIPROCESSOR SCHEDULING",
  booktitle="Mendel Conference on Soft Computing",
  year="2004",
  pages="83--88",
  publisher="Faculty of Mechanical Engineering BUT",
  address="Brno",
  isbn="80-214-2676-4"
}
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