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On the Effectiveness of Sampling for Evolutionary Optimization in Noisy Environments

Qian, C, Yu, Y, Zhou, Z-H and Jin, Y (2014) On the Effectiveness of Sampling for Evolutionary Optimization in Noisy Environments Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8672. pp. 302-311.

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Abstract

Sampling has been often employed by evolutionary algorithms to cope with noise when solving noisy real-world optimization problems. It can improve the estimation accuracy by averaging over a number of samples, while also increasing the computation cost. Many studies focused on designing efficient sampling methods, and conflicting empirical results have been reported. In this paper, we investigate the effectiveness of sampling in terms of rigorous running time, and find that sampling can be ineffective. We provide a general sufficient condition under which sampling is useless (i.e., sampling increases the running time for finding an optimal solution), and apply it to analyzing the running time performance of (1+1)-EA for optimizing OneMax and Trap problems in the presence of additive Gaussian noise. Our theoretical analysis indicates that sampling in the above examples is not helpful, which is further confirmed by empirical simulation results.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Computing Science
Authors :
AuthorsEmailORCID
Qian, CUNSPECIFIEDUNSPECIFIED
Yu, YUNSPECIFIEDUNSPECIFIED
Zhou, Z-HUNSPECIFIEDUNSPECIFIED
Jin, YUNSPECIFIEDUNSPECIFIED
Date : 2014
Identification Number : 10.1007/978-3-319-10762-2_30
Contributors :
ContributionNameEmailORCID
PublisherSpringer, UNSPECIFIEDUNSPECIFIED
Additional Information : The original publication is available at http://www.springerlink.com
Depositing User : Symplectic Elements
Date Deposited : 03 Mar 2015 10:45
Last Modified : 12 Mar 2015 02:33
URI: http://epubs.surrey.ac.uk/id/eprint/807233

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