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Metamodel assisted mixed-integer evolution strategies based on Kendall rank correlation coefficient

Zhuang, L, Tang, K and Jin, Y (2013) Metamodel assisted mixed-integer evolution strategies based on Kendall rank correlation coefficient Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8206 L. pp. 366-375.

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Abstract

Although mixed-integer evolution strategies (MIES) have been successfully applied to optimization of mixed-integer problems, they may encounter challenges when fitness evaluations are time consuming. In this paper, we propose to use a radial-basis-function network (RBFN) trained based on the rank correlation coefficient distance metric to assist MIES. For the distance metric of the RBFN, we modified a heterogeneous metric (HEOM) by multiplying the weight for each dimension. Whilst the standard RBFN aims to approximate the fitness accurately, the proposed RBFN tries to rank the individuals (according to their fitness) correctly. Kendall rank correlation Coefficient (RCC) is adopted to measure the degree of rank correlation between the fitness and each variable. The higher the rank similarity with fitness, the greater the weight one variable will be given. Experimental results show the efficacy of the MIES assisted by the RBFN trained by maximizing the RCC performs. © 2013 Springer-Verlag.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Computing Science
Authors :
AuthorsEmailORCID
Zhuang, LUNSPECIFIEDUNSPECIFIED
Tang, KUNSPECIFIEDUNSPECIFIED
Jin, YUNSPECIFIEDUNSPECIFIED
Date : 2013
Identification Number : 10.1007/978-3-642-41278-3_45
Additional Information : The original publication is available at http://link.springer.com/chapter/10.1007%2F978-3-642-41278-3_45
Depositing User : Symplectic Elements
Date Deposited : 01 Dec 2014 10:00
Last Modified : 16 Jun 2015 13:33
URI: http://epubs.surrey.ac.uk/id/eprint/806713

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