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
Available under License : See the attached licence file.
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|
|Identification Number :||https://doi.org/10.1007/978-3-540-30217-9_80|
|Additional Information :||The original publication is available at http://www.springerlink.com|
|Depositing User :||Symplectic Elements|
|Date Deposited :||20 Jul 2012 12:20|
|Last Modified :||23 Sep 2013 19:27|
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