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Detail publikačního výsledku
SCHWARZ, J.; JAROŠ, J.
Originální název
A PROBLEM KNOWLEDGE BASED BAYESIAN OPTIMIZATION ALGORITHM APPLIED IN MULTIPROCESSOR SCHEDULING
Anglický název
Druh
Stať ve sborníku mimo WoS a Scopus
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.
Anglický abstrakt
Klíčová slova
optimization problems, multiprocessor scheduling problem, evolutionary algorithms, Bayesian optimization algorithm, problem knowledge.
Klíčová slova v angličtině
Autoři
Vydáno
28.06.2004
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" }