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Author Schwarz, J. ♦ Ocenasek, J. ♦ Jaros, J.
Sponsorship IEEE Comput. Soc. ♦ Brno Univ. of Technol
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2004
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science ♦ Technology ♦ Engineering & allied operations
Subject Keyword Bayesian methods ♦ Sampling methods ♦ Scheduling algorithm ♦ Processor scheduling ♦ Evolutionary computation ♦ Genetic algorithms ♦ Testing ♦ Partitioning algorithms ♦ Information technology ♦ Couplings
Abstract We deal with the usage of Bayesian optimization algorithm (BOA) and its advanced variants for solving complex NP-complete combinatorial optimization problems. We focus on the hypergraph-partitioning problem and multiprocessor scheduling problem, which belong to the class of frequently solved decomposition tasks. One of the goals is to use these problems to experimentally compare the performance of the recently proposed Mixed Bayesian optimization algorithm (MBOA) with the performance of several other evolutionary algorithms based on the estimation and sampling of probabilistic model. We also propose the utilization of prior knowledge about the structure of hypergraphs and task graphs to increase the convergence speed and the quality of solutions. The performance of knowledge based MBOA (KMBOA) algorithms on the multiprocessor scheduling problem is empirically investigated and confirmed.
Description Author affiliation: Dept. of Comput. Syst., Brno Univ. of Technol., Czech Republic (Schwarz, J.)
ISBN 0769521258
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2004-05-27
Publisher Place Czech Republic
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Size (in Bytes) 378.73 kB
Page Count 10
Starting Page 102
Ending Page 111

Source: IEEE Xplore Digital Library