Detail publikace

A PROBLEM KNOWLEDGE BASED BAYESIAN OPTIMIZATION ALGORITHM APPLIED IN MULTIPROCESSOR SCHEDULING

SCHWARZ, J., JAROŠ, J.

Originální název

A PROBLEM KNOWLEDGE BASED BAYESIAN OPTIMIZATION ALGORITHM APPLIED IN MULTIPROCESSOR SCHEDULING

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální 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.

Klíčová slova

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

Autoři

SCHWARZ, J., JAROŠ, J.

Rok RIV

2004

Vydáno

28. 6. 2004

Nakladatel

Faculty of Mechanical Engineering BUT

Místo

Brno

ISBN

80-214-2676-4

Kniha

Mendel Conference on Soft Computing

Strany od

83

Strany do

88

Strany počet

6

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"
}