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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| Vydáno v: | Information sciences Ročník 279; s. 94 - 104 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier Inc
20.09.2014
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| ISSN: | 0020-0255, 1872-6291 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Tang, Ke Yao, Xin Peng, Fei Chen, Guoliang |
| Author_xml | – sequence: 1 givenname: Ke surname: Tang fullname: Tang, Ke email: ketang@ustc.edu.cn organization: USTC-Birmingham Joint Research Institute in Intelligent Computation and Its Applications (UBRI), School of Computer Science and Technology, University of Science and Technology of China, China – sequence: 2 givenname: Fei surname: Peng fullname: Peng, Fei organization: USTC-Birmingham Joint Research Institute in Intelligent Computation and Its Applications (UBRI), School of Computer Science and Technology, University of Science and Technology of China, China – sequence: 3 givenname: Guoliang surname: Chen fullname: Chen, Guoliang organization: USTC-Birmingham Joint Research Institute in Intelligent Computation and Its Applications (UBRI), School of Computer Science and Technology, University of Science and Technology of China, China – sequence: 4 givenname: Xin surname: Yao fullname: Yao, Xin organization: USTC-Birmingham Joint Research Institute in Intelligent Computation and Its Applications (UBRI), School of Computer Science and Technology, University of Science and Technology of China, China |
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| Keywords | Algorithm subset selection Global optimization Population-based Algorithm Portfolios Evolutionary optimization |
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| Snippet | Population-based Algorithm Portfolios (PAP) is an appealing framework for integrating different Evolutionary Algorithms (EAs) to solve challenging numerical... |
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| SubjectTerms | Algorithm subset selection Evolutionary optimization Global optimization Population-based Algorithm Portfolios |
| Title | Population-based Algorithm Portfolios with automated constituent algorithms selection |
| URI | https://dx.doi.org/10.1016/j.ins.2014.03.105 |
| Volume | 279 |
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