Publication detail

The Distributed Bayesian Optimization Algorithm for Combinatorial Optimization

OČENÁŠEK, J., SCHWARZ, J.

Original Title

The Distributed Bayesian Optimization Algorithm for Combinatorial Optimization

English Title

The Distributed Bayesian Optimization Algorithm for Combinatorial Optimization

Type

conference paper

Language

en

Original Abstract

The Bayesian Optimization Algorithms (BOA) belong to the probabilistic model building evolutionary algorithms where crossover and mutation operators are replaced by probability distribution estimation and sampling techniques. The learned Bayesian network BN as the most general graphical probability model is used to encode the structure of solved combinatorial problems. In [1] we proposed and simulated the pipeline hardware architecture for BOA. The aim of this paper is to propose the distributed version of BOA algorithm with a coarse-grained parallelism. We focused primarily on the construction of Bayesian network in the distributed environment. In addition, methods for overlapping the communication latency during generation, evaluation and broadcasting of new population among the processes are described. Much attention was devoted to the implementation of proposed approaches using a cluster of workstations as a computational platform.

English abstract

The Bayesian Optimization Algorithms (BOA) belong to the probabilistic model building evolutionary algorithms where crossover and mutation operators are replaced by probability distribution estimation and sampling techniques. The learned Bayesian network BN as the most general graphical probability model is used to encode the structure of solved combinatorial problems. In [1] we proposed and simulated the pipeline hardware architecture for BOA. The aim of this paper is to propose the distributed version of BOA algorithm with a coarse-grained parallelism. We focused primarily on the construction of Bayesian network in the distributed environment. In addition, methods for overlapping the communication latency during generation, evaluation and broadcasting of new population among the processes are described. Much attention was devoted to the implementation of proposed approaches using a cluster of workstations as a computational platform.

Keywords

genetic algorithm, estimation of distribution algorithm, Distributed Bayesian Optimization Algorithm, Bayesian network, dependency graph, cluster computing, coarse-grained parallelism

Released

19.09.2001

Location

Athens

ISBN

84-89925-97-6

Book

EUROGEN 2001 - Evolutionary Methods for Design, Optimisation and Control with Applications to Industrial Problems

Pages from

115

Pages to

120

Pages count

8

Documents

BibTex


@inproceedings{BUT10031,
  author="Jiří {Očenášek} and Josef {Schwarz}",
  title="The Distributed Bayesian Optimization Algorithm for Combinatorial Optimization",
  annote="The Bayesian Optimization Algorithms (BOA) belong to the probabilistic model building evolutionary algorithms where crossover and mutation operators are replaced by probability distribution estimation and sampling techniques. The learned Bayesian network BN as the most general graphical probability model is used to encode the structure of solved combinatorial problems. In [1] we proposed and simulated the pipeline hardware architecture for BOA. The aim of this paper is to propose the distributed version of BOA algorithm with a coarse-grained parallelism. We focused primarily on the construction of Bayesian network  in the distributed environment.  In addition, methods for overlapping  the communication latency during generation, evaluation and broadcasting of new population among the processes are described. Much attention was devoted to the implementation of proposed approaches using a cluster of workstations as a computational platform.",
  booktitle="EUROGEN 2001 - Evolutionary Methods for Design, Optimisation and Control with Applications to Industrial Problems",
  chapter="10031",
  year="2001",
  month="september",
  pages="115--120",
  type="conference paper"
}