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Surrogate-assisted evolutionary computation: Recent advances and future challenges

Jin, Y (2011) Surrogate-assisted evolutionary computation: Recent advances and future challenges Swarm and Evolutionary Computation, 1 (2). pp. 61-70.

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Abstract

Surrogate-assisted, or meta-model based evolutionary computation uses efficient computational models, often known as surrogates or meta-models, for approximating the fitness function in evolutionary algorithms. Research on surrogate-assisted evolutionary computation began over a decade ago and has received considerably increasing interest in recent years. Very interestingly, surrogate-assisted evolutionary computation has found successful applications not only in solving computationally expensive single- or multi-objective optimization problems, but also in addressing dynamic optimization problems, constrained optimization problems and multi-modal optimization problems. This paper provides a concise overview of the history and recent developments in surrogate-assisted evolutionary computation and suggests a few future trends in this research area.

Item Type: Article
Authors :
AuthorsEmailORCID
Jin, YUNSPECIFIEDUNSPECIFIED
Date : 2011
Identification Number : https://doi.org/10.1016/j.swevo.2011.05.001
Related URLs :
Depositing User : Symplectic Elements
Date Deposited : 28 Mar 2017 14:42
Last Modified : 28 Mar 2017 14:42
URI: http://epubs.surrey.ac.uk/id/eprint/532116

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