A survey of fitness landscape analysis for optimization
•FLA has attracted attention of researchers for its research significance and application.•This paper attempts to give an overview of fitness landscape analysis for optimization.•Future research works are discussed and given in four aspects. Over past few decades, as a powerful analytical tool to ch...
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| Published in: | Neurocomputing (Amsterdam) Vol. 503; pp. 129 - 139 |
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| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
07.09.2022
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| ISSN: | 0925-2312, 1872-8286 |
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| Abstract | •FLA has attracted attention of researchers for its research significance and application.•This paper attempts to give an overview of fitness landscape analysis for optimization.•Future research works are discussed and given in four aspects.
Over past few decades, as a powerful analytical tool to characterize the fitness landscape of a problem, fitness landscape analysis (FLA) has been widely concerned and utilized for all kinds of optimization areas. Since its introduction by Sewell Wright in 1932, FLA has attracted more and more attention of researchers for its research significance and application value such as an intuitive understanding features of complex optimization problems, explaining evolutionary algorithm behavior, assessing performances of algorithms, and guiding selections and/or configures of algorithms. This paper attempts to give an overview of fitness landscape analysis and its typical application for optimization so far. We hope that this survey can help to understand features of complex optimization problems in depth and thus to improve the certain algorithm performance of for a given optimization problem. |
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| AbstractList | •FLA has attracted attention of researchers for its research significance and application.•This paper attempts to give an overview of fitness landscape analysis for optimization.•Future research works are discussed and given in four aspects.
Over past few decades, as a powerful analytical tool to characterize the fitness landscape of a problem, fitness landscape analysis (FLA) has been widely concerned and utilized for all kinds of optimization areas. Since its introduction by Sewell Wright in 1932, FLA has attracted more and more attention of researchers for its research significance and application value such as an intuitive understanding features of complex optimization problems, explaining evolutionary algorithm behavior, assessing performances of algorithms, and guiding selections and/or configures of algorithms. This paper attempts to give an overview of fitness landscape analysis and its typical application for optimization so far. We hope that this survey can help to understand features of complex optimization problems in depth and thus to improve the certain algorithm performance of for a given optimization problem. |
| Author | Zou, Feng Chen, Debao Cao, Siyu Ji, Xuying Liu, Hui Zhang, Yan |
| Author_xml | – sequence: 1 givenname: Feng orcidid: 0000-0001-5373-8779 surname: Zou fullname: Zou, Feng organization: School of Physics and Electronic Information, Huaibei Normal University, Huaibei 235000, China – sequence: 2 givenname: Debao surname: Chen fullname: Chen, Debao email: chendb_8@163.com organization: School of Computer Science and Technology, Huaibei Normal University, Huaibei 235000, China – sequence: 3 givenname: Hui surname: Liu fullname: Liu, Hui organization: School of Physics and Electronic Information, Huaibei Normal University, Huaibei 235000, China – sequence: 4 givenname: Siyu surname: Cao fullname: Cao, Siyu organization: School of Physics and Electronic Information, Huaibei Normal University, Huaibei 235000, China – sequence: 5 givenname: Xuying surname: Ji fullname: Ji, Xuying organization: School of Physics and Electronic Information, Huaibei Normal University, Huaibei 235000, China – sequence: 6 givenname: Yan surname: Zhang fullname: Zhang, Yan organization: School of Physics and Electronic Information, Huaibei Normal University, Huaibei 235000, China |
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| Keywords | Landscape analysis Algorithm selection Survey Heuristic algorithms Fitness landscape |
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