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Exploration and exploitation in evolutionary algorithms

ACM Computing Surveys · 2013 · Vol. 45(3) · pp. 1–33
Matej ČrepinšekShih-Hsi LiuMarjan Mernik

Abstract

“Exploration and exploitation are the two cornerstones of problem solving by search.” For more than a decade, Eiben and Schippers' advocacy for balancing between these two antagonistic cornerstones still greatly influences the research directions of evolutionary algorithms (EAs) [1998]. This article revisits nearly 100 existing works and surveys how such works have answered the advocacy. The article introduces a fresh treatment that classifies and discusses existing work within three rational aspects: (1) what and how EA components contribute to exploration and exploitation; (2) when and how exploration and exploitation are controlled; and (3) how balance between exploration and exploitation is achieved. With a more comprehensive and systematic understanding of exploration and exploitation, more research in this direction may be motivated and refined.

Evolutionary Algorithms and ApplicationsMetaheuristic Optimization Algorithms ResearchAdvanced Multi-Objective Optimization AlgorithmsComputer scienceEvolutionary algorithmBalance (ability)Work (physics)Data scienceManagement scienceArtificial intelligence
Citations
1,264
FWCI
100.13
field-weighted impact
References
187
Percentile
100%
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Citations per year
References
Genetic Programming: On the Programming of Computers by Means of Natural Selection
Medical Entomology and Zoology · 1992 · 13,258 citations
Metaheuristics in combinatorial optimization
ACM Computing Surveys · 2003 · 3,106 citations
When and how to develop domain-specific languages
ACM Computing Surveys · 2005 · 1,694 citations
Hybrid metaheuristics in combinatorial optimization: A survey
Applied Soft Computing · 2011 · 740 citations
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