Publication detail

Advanced Bayesian Optimization Algorithms Applied in Decomposition Problems

SCHWARZ, J., OČENÁŠEK, J., JAROŠ, J.

Original Title

Advanced Bayesian Optimization Algorithms Applied in Decomposition Problems

Type

conference paper

Language

English

Original Abstract

This paper deals 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. BOA algorithms are based on the estimation and sampling of probabilistic model unlike classical genetic algorithms. We also propose the utilization of prior knowledge about the structure of a task graph to increase the convergence speed and the quality of solutions. The performance of KMBOA algorithm on the multiprocessor scheduling problem is empirically investigated and confirmed.

Keywords

Bayesian optimization algorithm, hypergraph-partitioning problem, multiprocessor scheduling problem, specific problem knowledge

Authors

SCHWARZ, J., OČENÁŠEK, J., JAROŠ, J.

RIV year

2004

Released

28. 6. 2004

Publisher

IEEE Computer Society

Location

Los Alamitos

ISBN

0-7695-2125-8

Book

Proceedings of ECBS 2004

Pages from

102

Pages to

111

Pages count

10

BibTex

@inproceedings{BUT17153,
  author="Josef {Schwarz} and Jiří {Očenášek} and Jiří {Jaroš}",
  title="Advanced Bayesian Optimization Algorithms Applied in Decomposition Problems",
  booktitle="Proceedings of ECBS 2004",
  year="2004",
  pages="102--111",
  publisher="IEEE Computer Society",
  address="Los Alamitos",
  isbn="0-7695-2125-8"
}