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Bayesian Optimization Algorithm: From Single Level to Hierarchy (2002)
| Citations: | 101 - 19 self |
BibTeX
@TECHREPORT{Pelikan02bayesianoptimization,
author = {Martin Pelikan},
title = {Bayesian Optimization Algorithm: From Single Level to Hierarchy },
institution = {},
year = {2002}
}
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Abstract
There are four primary goals of this dissertation. First, design a competent optimization algorithm capable of learning and exploiting appropriate problem decomposition by sampling and evaluating candidate solutions. Second, extend the proposed algorithm to enable the use of hierarchical decomposition as opposed to decomposition on only a single level. Third, design a class of difficult hierarchical problems that can be used to test the algorithms that attempt to exploit hierarchical decomposition. Fourth, test the developed algorithms on the designed class of problems and several real-world applications. The dissertation proposes the Bayesian optimization algorithm (BOA), which uses Bayesian networks to model the promising solutions found so far and sample new candidate solutions. BOA is theoretically and empirically shown to be capable of both learning a proper decomposition of the problem and exploiting the learned decomposition to ensure robust and scalable search for the optimum across a wide range of problems. The dissertation then identifies important features that must be incorporated into the basic BOA to solve problems that are not decomposable on a single level, but that can still be solved by decomposition over multiple levels of difficulty. Hierarchical
Keyphrases
single level bayesian optimization algorithm hierarchical decomposition bayesian network several real-world application promising solution appropriate problem decomposition proper decomposition sample new candidate solution important feature multiple level scalable search candidate solution wide range difficult hierarchical problem competent optimization algorithm primary goal developed algorithm learned decomposition basic boa
