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Identifying and exploiting the scale of a search space in differential evolution


Montgomery, J and Chen, S and Gonzalez-Fernandez, Y, Identifying and exploiting the scale of a search space in differential evolution, Proceedings of 2014 IEEE Congress on Evolutionary Computation, 6-11 July 2014, Beijing, China, pp. 1427-1434. ISBN 9781479966264 (2014) [Refereed Conference Paper]

Copyright Statement

Copyright 2014 IEEE

DOI: doi:10.1109/CEC.2014.6900579


Optimisation in multimodal landscapes involves two distinct tasks: identifying promising regions and location of the (local) optimum within each region. Progress towards the second task can interfere with the first by providing a misleading estimate of a regionís value. Thresheld convergence is a generally applicable "meta"-heuristic designed to control an algorithmís rate of convergence and hence which mode of search it is using at a given time. Previous applications of thresheld convergence in differential evolution (DE) have shown considerable promise, but the question of which threshold values to use for a given (unknown) function landscape remains open. This work explores the use of clustering-based method to infer the distances between local optima in order to set a series of decreasing thresholds in a multi-start DE algorithm. Results indicate that on those problems where normal DE converges, the proposed strategy can lead to sizable improvements.

Item Details

Item Type:Refereed Conference Paper
Keywords:optimisation, differential evolution, search space analysis
Research Division:Information and Computing Sciences
Research Group:Machine learning
Research Field:Neural networks
Objective Division:Expanding Knowledge
Objective Group:Expanding knowledge
Objective Field:Expanding knowledge in the information and computing sciences
UTAS Author:Montgomery, J (Dr James Montgomery)
ID Code:91687
Year Published:2014
Web of Science® Times Cited:2
Deposited By:Information and Communication Technology
Deposited On:2014-05-26
Last Modified:2017-12-06

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