Detail publikačního výsledku

Bayesian Optimization Algorithms for Multi-Objective Optimization

LAUMANNS, M.; OČENÁŠEK, J.

Originální název

Bayesian Optimization Algorithms for Multi-Objective Optimization

Anglický název

Bayesian Optimization Algorithms for Multi-Objective Optimization

Druh

Článek recenzovaný mimo WoS a Scopus

Originální abstrakt

In recent years, several researchers have concentrated on usingprobabilistic models in evolutionary algorithms. These EstimationDistribution Algorithms (EDA) incorporate methods for automatedlearning of correlations between variables of the encoded solutions.The process of sampling new individuals from a probabilistic modelrespects these mutual dependencies among genes such that disruption ofimportant building blocks is avoided, in comparison with classicalrecombination operators. The goal of this paper is to investigate theusefulness of this concept in multi-objective evolutionaryoptimization, where the aim is to approximate the set of Pareto-optimalsolutions. We integrate the model building and sampling techniques of aspecial EDA called Bayesian Optimization Algorithm based on binarydecision trees into a general evolutionary multi-objective optimizer. Apotential performance gain is empirically tested in comparison withother state-of-the-art multi-objective EA on the bi-objective 0/1knapsack problem.

Anglický abstrakt

In recent years, several researchers have concentrated on usingprobabilistic models in evolutionary algorithms. These EstimationDistribution Algorithms (EDA) incorporate methods for automatedlearning of correlations between variables of the encoded solutions.The process of sampling new individuals from a probabilistic modelrespects these mutual dependencies among genes such that disruption ofimportant building blocks is avoided, in comparison with classicalrecombination operators. The goal of this paper is to investigate theusefulness of this concept in multi-objective evolutionaryoptimization, where the aim is to approximate the set of Pareto-optimalsolutions. We integrate the model building and sampling techniques of aspecial EDA called Bayesian Optimization Algorithm based on binarydecision trees into a general evolutionary multi-objective optimizer. Apotential performance gain is empirically tested in comparison withother state-of-the-art multi-objective EA on the bi-objective 0/1knapsack problem.

Klíčová slova

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

Klíčová slova v angličtině

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

Autoři

LAUMANNS, M.; OČENÁŠEK, J.

Vydáno

07.09.2002

Nakladatel

Springer Verlag

Místo

Granada

ISBN

3-540-444139-5

Kniha

Parallel Problem Solving from Nature - PPSN VII

ISSN

0302-9743

Periodikum

Lecture Notes in Computer Science

Svazek

2002

Číslo

2439

Stát

Spolková republika Německo

Strany od

298

Strany do

307

Strany počet

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"
}