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OČENÁŠEK, J. SCHWARZ, J.
Original Title
The Distributed Bayesian Optimization Algorithm for Combinatorial Optimization
Type
article in a collection out of WoS and Scopus
Language
English
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.
Keywords
genetic algorithm, estimation of distribution algorithm, Distributed Bayesian Optimization Algorithm, Bayesian network, dependency graph, cluster computing, coarse-grained parallelism
Authors
OČENÁŠEK, J.; SCHWARZ, J.
Released
19. 9. 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
BibTex
@inproceedings{BUT10031, author="Jiří {Očenášek} and Josef {Schwarz}", title="The Distributed Bayesian Optimization Algorithm for Combinatorial Optimization", booktitle="EUROGEN 2001 - Evolutionary Methods for Design, Optimisation and Control with Applications to Industrial Problems", year="2001", pages="115--120", address="Athens", isbn="84-89925-97-6" }