Publication detail

Bayesian Optimization Algorithms for Multi-Objective Optimization

LAUMANNS, M. OČENÁŠEK, J.

Original Title

Bayesian Optimization Algorithms for Multi-Objective Optimization

Type

journal article - other

Language

English

Original Abstract

In recent years, several researchers have concentrated on using probabilistic models in evolutionary algorithms. These Estimation Distribution Algorithms (EDA) incorporate methods for automated learning of correlations between variables of the encoded solutions. The process of sampling new individuals from a probabilistic model respects these mutual dependencies among genes such that disruption of important building blocks is avoided, in comparison with classical recombination operators. The goal of this paper is to investigate the usefulness of this concept in multi-objective evolutionary optimization, where the aim is to approximate the set of Pareto-optimal solutions. We integrate the model building and sampling techniques of a special EDA called Bayesian Optimization Algorithm based on binary decision trees into a general evolutionary multi-objective optimizer. A potential performance gain is empirically tested in comparison with other state-of-the-art multi-objective EA on the bi-objective 0/1 knapsack problem.

Keywords

probabilistic models,Estimation Distribution Algorithms, multi-objective evolutionary optimization, Pareto-optimal solutions, Bayesian Optimization Algorithm, binary decision trees, knapsack problem.

Authors

LAUMANNS, M.; OČENÁŠEK, J.

RIV year

2004

Released

7. 9. 2002

Publisher

Springer Verlag

Location

Granada

ISBN

3-540-444139-5

Book

Parallel Problem Solving from Nature - PPSN VII

ISBN

0302-9743

Periodical

Lecture Notes in Computer Science

Year of study

2002

Number

2439

State

Federal Republic of Germany

Pages from

298

Pages to

307

Pages count

10

BibTex

@article{BUT41072,
  author="Marco {Laumanns} and Jiří {Očenášek}",
  title="Bayesian Optimization Algorithms for Multi-Objective Optimization",
  journal="Lecture Notes in Computer Science",
  year="2002",
  volume="2002",
  number="2439",
  pages="298--307",
  issn="0302-9743"
}