Please use this identifier to cite or link to this item:
https://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: | Lecture Notes in Artificial Intelligence, 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) (9 Nov 2015 - 12 Nov 2015 : Istanbul, Turkey) |
Editor: | Arik, S. Huang, T. Lai, W. Liu, Q. |
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 |
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: | Aurora harvest 8 Computer Science publications |
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