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On test functions for evolutionary multi-objective optimization

Okabe, T, Jin, Y, Olhofer, M and Sendhoff, B (2004) On test functions for evolutionary multi-objective optimization

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In order to evaluate the relative performance of optimization algorithms benchmark problems are frequently used. In the case of multi-objective optimization (MOO), we will show in this paper that most known benchmark problems belong to a constrained class of functions with piecewise linear Pareto fronts in the parameter space. We present a straightforward way to define benchmark problems with an arbitrary Pareto front both in the fitness and parameter spaces. Furthermore, we introduce a difficulty measure based on the mapping of probability density functions from parameter to fitness space. Finally, we evaluate two MOO algorithms for new benchmark problems. © Springer-Verlag 2004.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Computing Science
Authors :
Okabe, T
Jin, Y
Olhofer, M
Sendhoff, B
Date : 2004
DOI : 10.1007/978-3-540-30217-9_80
Additional Information : The original publication is available at
Depositing User : Symplectic Elements
Date Deposited : 20 Jul 2012 12:20
Last Modified : 31 Oct 2017 14:35

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