Average convergence rate of evolutionary algorithms

He, J ORCID: https://orcid.org/0000-0002-5616-4691 and Lin, G, 2016. Average convergence rate of evolutionary algorithms. IEEE Transactions on Evolutionary Computation, 20 (2), pp. 316-321. ISSN 1089-778X

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Abstract

In evolutionary optimization, it is important to understand how fast evolutionary algorithms converge to the optimum per generation, or their convergence rates. This letter proposes a new measure of the convergence rate, called the average convergence rate. It is a normalized geometric mean of the reduction ratio of the fitness difference per generation. The calculation of the average convergence rate is very simple and it is applicable for most evolutionary algorithms on both continuous and discrete optimization. A theoretical study of the average convergence rate is conducted for discrete optimization. Lower bounds on the average convergence rate are derived. The limit of the average convergence rate is analyzed and then the asymptotic average convergence rate is proposed.

Item Type:	Journal article
Publication Title:	IEEE Transactions on Evolutionary Computation
Creators:	He, J. and Lin, G.
Publisher:	Institute of Electrical and Electronics Engineers
Date:	2016
Volume:	20
Number:	2
ISSN:	1089-778X
Identifiers:	Number Type 10.1109/tevc.2015.2444793 DOI
Divisions:	Schools > School of Science and Technology
Record created by:	Jonathan Gallacher
Date Added:	09 Apr 2018 15:10
Last Modified:	09 Apr 2018 15:10
URI:	https://irep.ntu.ac.uk/id/eprint/33231

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