Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/109187
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Type: Conference paper
Title: A feature-based comparison of evolutionary computing techniques for constrained continuous optimisation
Author: Poursoltan, S.
Neumann, F.
Citation: Proceedings of the 22nd International Conference on Neural Information Processing, 2015 / Arik, S., Huang, T., Lai, W., Liu, Q. (ed./s), vol.9491, iss.Part III, pp.332-343
Publisher: Springer
Issue Date: 2015
Series/Report no.: Lecture Notes in Computer Science (LNCS, vol. 9491)
ISBN: 9783319265544
ISSN: 0302-9743
1611-3349
Conference Name: 22nd International Conference on Neural Information Processing (ICONIP 2015) (09 Nov 2015 - 12 Nov 2015 : Istanbul, Turkey)
Statement of
Responsibility: 
Shayan Poursoltan and Frank Neumann
Abstract: Evolutionary algorithms have been frequently applied to constrained continuous optimisation problems. We carry out feature based comparisons of different types of evolutionary algorithms such as evolution strategies, differential evolution and particle swarm optimisation for constrained continuous optimisation. In our study, we examine how sets of constraints influence the difficulty of obtaining close to optimal solutions. Using a multi-objective approach, we evolve constrained continuous problems having a set of linear and/or quadratic constraints where the different evolutionary approaches show a significant difference in performance. Afterwards, we discuss the features of the constraints that exhibit a difference in performance of the different evolutionary approaches under consideration.
Rights: © Springer International Publishing Switzerland 2015
RMID: 0030041420
DOI: 10.1007/978-3-319-26555-1_38
Grant ID: http://purl.org/au-research/grants/arc/DP130104395
http://purl.org/au-research/grants/arc/DP140103400
Published version: http://dx.doi.org/10.1007/978-3-319-26555-1
Appears in Collections:Computer Science publications

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