Publication 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

conference paper

Language

en

Original Abstract

The paper summarizes our recent work on the design, analysis and applications of the Bayesian optimization algorithm (BOA) and its advanced accelerated variants for solving complex - sometimes NP-complete - combinatorial optimization problems from circuit design. We review the methods for accelerating BOA for hypergraph-partitioning problem. The first method accelerates the convergence of sequential BOA by utilizing specific knowledge about the optimized problem and the second method is based on the parallel construction of a probabilistic model. In the experimental part we analyze the advantages of acceleration techniques and prove that BOA is able to solve hypergraph partitioning problems reliably, effectively, and without the need for specifying control parameters and encoding schemes as in recombination-based genetic algorithms.

English abstract

The paper summarizes our recent work on the design, analysis and applications of the Bayesian optimization algorithm (BOA) and its advanced accelerated variants for solving complex - sometimes NP-complete - combinatorial optimization problems from circuit design. We review the methods for accelerating BOA for hypergraph-partitioning problem. The first method accelerates the convergence of sequential BOA by utilizing specific knowledge about the optimized problem and the second method is based on the parallel construction of a probabilistic model. In the experimental part we analyze the advantages of acceleration techniques and prove that BOA is able to solve hypergraph partitioning problems reliably, effectively, and without the need for specifying control parameters and encoding schemes as in recombination-based genetic algorithms.

Keywords

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

RIV year

2003

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

Documents

BibTex


@inproceedings{BUT13984,
  author="Josef {Schwarz} and Jiří {Očenášek}",
  title="ACCELERATED BAYESIAN OPTIMIZATION ALGORITHMS FOR ADVANCED HYPERGRAPH PARTITIONING, accepted paper",
  annote="The paper summarizes our recent work on the design, analysis and
applications of the Bayesian optimization algorithm (BOA) and its
advanced accelerated variants for solving complex - sometimes
NP-complete - combinatorial optimization problems from circuit design.
We review the methods for accelerating BOA for hypergraph-partitioning
problem. The first method accelerates the convergence of sequential BOA
by utilizing specific knowledge about the optimized problem and the
second method is based on the parallel construction of a probabilistic
model. In the experimental part we analyze the advantages of
acceleration techniques and prove that BOA is able to solve hypergraph
partitioning problems reliably, effectively, and without the need for
specifying control parameters and encoding schemes as in
recombination-based genetic algorithms.",
  address="Faculty of Mechanical Engineering BUT",
  booktitle="Procceedings of MENDEL 2003",
  chapter="13984",
  institution="Faculty of Mechanical Engineering BUT",
  year="2003",
  month="may",
  pages="133--141",
  publisher="Faculty of Mechanical Engineering BUT",
  type="conference paper"
}