Population-based Algorithm Portfolios with automated constituent algorithms selection

Population-based Algorithm Portfolios (PAP) is an appealing framework for integrating different Evolutionary Algorithms (EAs) to solve challenging numerical optimization problems. Particularly, PAP has shown significant advantages to single EAs when a number of problems need to be solved simultaneou...

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Veröffentlicht in:Information sciences Jg. 279; S. 94 - 104
Hauptverfasser: Tang, Ke, Peng, Fei, Chen, Guoliang, Yao, Xin
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Inc 20.09.2014
Schlagworte:
Algorithm subset selection
Evolutionary optimization
Global optimization
Population-based Algorithm Portfolios
Algorithm subset selection
Global optimization
Population-based Algorithm Portfolios
Evolutionary optimization
ISSN:0020-0255, 1872-6291
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Zusammenfassung:Population-based Algorithm Portfolios (PAP) is an appealing framework for integrating different Evolutionary Algorithms (EAs) to solve challenging numerical optimization problems. Particularly, PAP has shown significant advantages to single EAs when a number of problems need to be solved simultaneously. Previous investigation on PAP reveals that choosing appropriate constituent algorithms is crucial to the success of PAP. However, no method has been developed for this purpose. In this paper, an extended version of PAP, namely PAP based on Estimated Performance Matrix (EPM-PAP) is proposed. EPM-PAP is equipped with a novel constituent algorithms selection module, which is based on the EPM of each candidate EAs. Empirical studies demonstrate that the EPM-based selection method can successfully identify appropriate constituent EAs, and thus EPM-PAP outperformed all single EAs considered in this work.
ISSN:0020-0255
1872-6291
DOI:10.1016/j.ins.2014.03.105