Publication Details

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. Mendel Conference on Soft Computing. Brno: Faculty of Mechanical Engineering BUT, 2004. p. 83-88. ISBN: 80-214-2676-4.

Czech title

Znalostně orientovaný Bayesovský optimalizační algoritmus

Type

conference paper

Language

English

Authors

Schwarz Josef, doc. Ing., CSc. (CM-SFE)
Jaroš Jiří, doc. Ing., Ph.D. (DCSY)

Keywords

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

Abstract

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.

Published

2004

Pages

83–88

Proceedings

Mendel Conference on Soft Computing

ISBN

80-214-2676-4

Publisher

Faculty of Mechanical Engineering BUT

Place

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"
}