Result 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.
Type
conference paper
Language
English
Authors
Schwarz Josef, doc. Ing., CSc., DCSY (FIT)
Jaroš Jiří, prof. Ing., Ph.D., DCSY (FIT)
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.

Keywords

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

Published
2004
Pages
83–88
Proceedings
Mendel Conference on Soft Computing
Conference
Mendel 2004, 10th International 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"
}
Projects
Parallel system performance prediction and tuning, GACR, Standardní projekty, GA102/02/0503, start: 2002-01-01, end: 2004-12-31, completed
Departments
Department of Computer Systems (DCSY)
Back to top