Advanced Bayesian Optimization Algorithms Applied in Decomposition Problems
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F04%3APU49180" target="_blank" >RIV/00216305:26230/04:PU49180 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Advanced Bayesian Optimization Algorithms Applied in Decomposition Problems
Original language description
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 severall other evolutionary algorithms. BOA algorithms are based on the estimation and sampling of probabilistic model unlike classical genetic algorithms. We also propose the utilization of prior knowledge about the structure of a task graph to increase the convergence speed and the quality of solutions. The performance of KMBOA algorithm on the multiprocessor scheduling problem is empirically investigated and confirmed.
Czech name
Pokročilé Bayesovské optimalizační algoritmy aplikované na dekompoziční problémy
Czech description
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 severall other evolutionary algorithms. BOA algorithms are based on the estimation and sampling of probabilistic model unlike classical genetic algorithms. We also propose the utilization of prior knowledge about the structure of a task graph to increase the convergence speed and the quality of solutions. The performance of KMBOA algorithm on the multiprocessor scheduling problem is empirically investigated and confirmed.
Classification
Type
D - Article in proceedings
CEP classification
JC - Computer hardware and software
OECD FORD branch
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Result continuities
Project
<a href="/en/project/GA102%2F02%2F0503" target="_blank" >GA102/02/0503: Parallel performance prediction and tuning</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2004
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of ECBS 2004
ISBN
0-7695-2125-8
ISSN
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e-ISSN
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Number of pages
10
Pages from-to
102-111
Publisher name
IEEE Computer Society
Place of publication
Los Alamitos
Event location
Brno
Event date
May 23, 2004
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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