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https://hdl.handle.net/2440/72021
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Type: | Conference paper |
Title: | Weighted preferences in evolutionary multi-objective optimization |
Author: | Friedrich, T. Kroeger, T. Neumann, F. |
Citation: | AI 2011: Advances in Artificial Intelligence: 24th Australasian Joint Conference, Perth, Australia, December 5-8, 2011. Proceedings / D. Wang and M. Reynolds (eds.): pp.291-300 |
Publisher: | Springer |
Publisher Place: | Germany |
Issue Date: | 2011 |
Series/Report no.: | Lecture notes in Computer Science ; 7106 |
ISBN: | 9783642258312 |
ISSN: | 0302-9743 1611-3349 |
Conference Name: | Australasian Joint Conference on Artificial Intelligence (24th : 2011 : Perth, Western Australia) |
Editor: | Wang, D.H. Reynolds, M. |
Statement of Responsibility: | Tobias Friedrich, Trent Kroeger, and Frank Neumann |
Abstract: | Evolutionary algorithms have been widely used to tackle multi-objective optimization problems. Incorporating preference information into the search of evolutionary algorithms for multi-objective optimization is of great importance as it allows one to focus on interesting regions in the objective space. Zitzler et al. have shown how to use a weight distribution function on the objective space to incorporate preference information into hypervolume-based algorithms. We show that this weighted information can easily be used in other popular EMO algorithms as well. Our results for NSGA-II and SPEA2 show that this yields similar results to the hypervolume approach and requires less computational effort. |
Rights: | © Springer-Verlag Berlin Heidelberg 2011 |
DOI: | 10.1007/978-3-642-25832-9 |
Published version: | https://doi.org/10.1007/978-3-642-25832-9 |
Appears in Collections: | Aurora harvest Computer Science publications |
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