Publication detail

Evolutionary Multiobjective Bayesian Optimization Algorithm:Experimental Study

SCHWARZ, J., OČENÁŠEK, J.

Original Title

Evolutionary Multiobjective Bayesian Optimization Algorithm:Experimental Study

English Title

Evolutionary Multiobjective Bayesian Optimization Algorithm:Experimental Study

Type

conference paper

Language

en

Original Abstract

This paper deals with the utilizing of the Bayesian optimization algorithm (BOA) for multiobjective optimization of hypergraph partitioning. The main attention is focused on the incorporation of the Pareto optimality concept. We have modified the standard algorithm BOA for one criterion optimization according to well known niching techniques to find the Pareto optimal set. This approach was compared with standard weighting techniques and the single optimization approach with the constraint. The experiments are focused mainly on the bi-objective optimization because of the visualization simplicity.

English abstract

This paper deals with the utilizing of the Bayesian optimization algorithm (BOA) for multiobjective optimization of hypergraph partitioning. The main attention is focused on the incorporation of the Pareto optimality concept. We have modified the standard algorithm BOA for one criterion optimization according to well known niching techniques to find the Pareto optimal set. This approach was compared with standard weighting techniques and the single optimization approach with the constraint. The experiments are focused mainly on the bi-objective optimization because of the visualization simplicity.

Keywords

Multiobjective optimization, evolutionary algorithms, Bayesian optimization algorithm, Pareto set, niching techniques, hypergraph bisectioning

RIV year

2001

Released

01.01.2001

Location

Hradec nad Moravicí

ISBN

80-85988-57-7

Book

Proceedings of the 35th Spring International Conference MOSIS'01, Vol. 1

Pages from

101

Pages to

108

Pages count

8

URL

Documents

BibTex


@inproceedings{BUT5431,
  author="Josef {Schwarz} and Jiří {Očenášek}",
  title="Evolutionary Multiobjective Bayesian Optimization Algorithm:Experimental Study",
  annote="This paper deals with the utilizing of the Bayesian optimization algorithm (BOA) for multiobjective optimization of hypergraph partitioning. The main attention is focused on the incorporation of the Pareto optimality concept. We have modified the standard algorithm BOA for one criterion optimization according to well known niching techniques to find the Pareto optimal set. This approach was compared with standard weighting techniques and the single optimization approach with the constraint. The experiments are focused mainly on the bi-objective optimization because of the visualization simplicity.",
  booktitle="Proceedings of the 35th Spring International Conference MOSIS'01, Vol. 1",
  chapter="5431",
  year="2001",
  month="january",
  pages="101--108",
  type="conference paper"
}