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11th IEEE International Conference and Workshop on the Engineering of Computer-Based Systems (ECBS'04)
Advanced Bayesian Optimization Algorithms Applied in Decomposition Problems
Brno, Czech Republic
May 24-May 27
ISBN: 0-7695-2125-8
ASCII Text
x 
 
Josef Schwarz, Jir? Ocen?sek, Jir? Jaros, "Advanced Bayesian Optimization Algorithms Applied in Decomposition Problems," Engineering of Computer-Based Systems, IEEE International Conference on the, pp. 102, 11th IEEE International Conference and Workshop on the Engineering of Computer-Based Systems (ECBS'04), 2004.
 
 
BibTex
x
 
@article{ 10.1109/ECBS.2004.1316688,
author = {Josef Schwarz and Jir? Ocen?sek and Jir? Jaros},
title = {Advanced Bayesian Optimization Algorithms Applied in Decomposition Problems},
journal ={Engineering of Computer-Based Systems, IEEE International Conference on the},
volume = {0},
year = {2004},
isbn = {0-7695-2125-8},
pages = {102},
doi = {http://doi.ieeecomputersociety.org/10.1109/ECBS.2004.1316688},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Engineering of Computer-Based Systems, IEEE International Conference on the
TI - Advanced Bayesian Optimization Algorithms Applied in Decomposition Problems
SN - 0-7695-2125-8
SP
EP
A1 - Josef Schwarz,
A1 - Jir? Ocen?sek,
A1 - Jir? Jaros,
PY - 2004
KW - null
VL - 0
JA - Engineering of Computer-Based Systems, IEEE International Conference on the
ER -
 
Josef Schwarz, Brno University of Technology, CZ
Jir? Ocen?sek, Swiss Federal Institute of Technology (ETH), Switzerland
Jir? Jaros, Brno University of Technology, CZ
This paper deals with the usage of Bayesian optimization algorithm (BOA) and its advanced variants for solving complex NP-complete combinatorial optimization problems. We focus on the hypergraph-partitioning problem and multiprocessor scheduling problem, which belong to the class of frequently solved decomposition tasks. One of the goals is to use these problems to experimentally compare the performance of the recently proposed Mixed Bayesian Optimization Algorithm (MBOA) with the performance of several other evolutionary algorithms based on the estimation and sampling of probabilistic model. We also propose the utilization of prior knowledge about the structure of hypergraphs and task graphs to increase the convergence speed and the quality of solutions. The performance of knowledge based MBOA (KMBOA) algorithms on the multiprocessor scheduling problem is empirically investigated and confirmed.
Citation:
Josef Schwarz, Jir? Ocen?sek, Jir? Jaros, "Advanced Bayesian Optimization Algorithms Applied in Decomposition Problems," ecbs, pp.102, 11th IEEE International Conference and Workshop on the Engineering of Computer-Based Systems (ECBS'04), 2004
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