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|Title:||Weighted preferences in evolutionary multi-objective optimization|
|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|
|Series/Report no.:||Lecture notes in Computer Science ; 7106|
|Conference Name:||Australasian Joint Conference on Artificial Intelligence (24th : 2011 : Perth, Western Australia)|
|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|
|Appears in Collections:||Aurora harvest|
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