Publication result detail

ACCELERATED BAYESIAN OPTIMIZATION ALGORITHMS FOR ADVANCED HYPERGRAPH PARTITIONING, accepted paper

SCHWARZ, J.; OČENÁŠEK, J.

Original Title

ACCELERATED BAYESIAN OPTIMIZATION ALGORITHMS FOR ADVANCED HYPERGRAPH PARTITIONING, accepted paper

English Title

ACCELERATED BAYESIAN OPTIMIZATION ALGORITHMS FOR ADVANCED HYPERGRAPH PARTITIONING, accepted paper

Type

Paper in proceedings outside WoS and Scopus

Original Abstract

The paper summarizes our recent work on the design, analysis andapplications of the Bayesian optimization algorithm (BOA) and itsadvanced accelerated variants for solving complex - sometimesNP-complete - combinatorial optimization problems from circuit design.We review the methods for accelerating BOA for hypergraph-partitioningproblem. The first method accelerates the convergence of sequential BOAby utilizing specific knowledge about the optimized problem and thesecond method is based on the parallel construction of a probabilisticmodel. In the experimental part we analyze the advantages ofacceleration techniques and prove that BOA is able to solve hypergraphpartitioning problems reliably, effectively, and without the need forspecifying control parameters and encoding schemes as inrecombination-based genetic algorithms.

English abstract

The paper summarizes our recent work on the design, analysis andapplications of the Bayesian optimization algorithm (BOA) and itsadvanced accelerated variants for solving complex - sometimesNP-complete - combinatorial optimization problems from circuit design.We review the methods for accelerating BOA for hypergraph-partitioningproblem. The first method accelerates the convergence of sequential BOAby utilizing specific knowledge about the optimized problem and thesecond method is based on the parallel construction of a probabilisticmodel. In the experimental part we analyze the advantages ofacceleration techniques and prove that BOA is able to solve hypergraphpartitioning problems reliably, effectively, and without the need forspecifying control parameters and encoding schemes as inrecombination-based genetic algorithms.

Keywords

Optimization problems, decomposition and allocation problems, graphicalprobabilistic model, Bayesian network, Bayesian-Dirichlet metric,Bayesian optimization algorithm, problem knowledge, parallelization,hypergraph partitioning.

Key words in English

Optimization problems, decomposition and allocation problems, graphicalprobabilistic model, Bayesian network, Bayesian-Dirichlet metric,Bayesian optimization algorithm, problem knowledge, parallelization,hypergraph partitioning.

Authors

SCHWARZ, J.; OČENÁŠEK, J.

RIV year

2011

Released

09.05.2003

Publisher

Faculty of Mechanical Engineering BUT

Location

Brno

ISBN

80-214-2411-7

Book

Procceedings of MENDEL 2003

Pages from

133

Pages to

141

Pages count

9

BibTex

@inproceedings{BUT13984,
  author="Josef {Schwarz} and Jiří {Očenášek}",
  title="ACCELERATED BAYESIAN OPTIMIZATION ALGORITHMS FOR ADVANCED HYPERGRAPH PARTITIONING, accepted paper",
  booktitle="Procceedings of MENDEL 2003",
  year="2003",
  pages="133--141",
  publisher="Faculty of Mechanical Engineering BUT",
  address="Brno",
  isbn="80-214-2411-7"
}