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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Neurocomputing (Amsterdam) Jg. 503; S. 129 - 139
Hauptverfasser: Zou, Feng, Chen, Debao, Liu, Hui, Cao, Siyu, Ji, Xuying, Zhang, Yan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 07.09.2022
Schlagworte:
Algorithm selection
Fitness landscape
Heuristic algorithms
Landscape analysis
Survey
Landscape analysis
Algorithm selection
Survey
Heuristic algorithms
Fitness landscape
ISSN:0925-2312, 1872-8286
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:•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.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2022.06.084