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2015 | OriginalPaper | Chapter

A Feature-Based Comparison of Evolutionary Computing Techniques for Constrained Continuous Optimisation

Authors : Shayan Poursoltan, Frank Neumann

Published in: Neural Information Processing

Publisher: Springer International Publishing

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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.

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previous chapter Automated Detection of Galaxy Groups Through Probabilistic Hough Transform
next chapter A Feature-Based Analysis on the Impact of Set of Constraints for -Constrained Differential Evolution
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Metadata
Title
A Feature-Based Comparison of Evolutionary Computing Techniques for Constrained Continuous Optimisation
Authors
Shayan Poursoltan
Frank Neumann
Copyright Year
2015
Book
Neural Information Processing

Print ISBN: 978-3-319-26554-4

Electronic ISBN: 978-3-319-26555-1

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-26555-1_38

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