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Efficient Search for Robust Solutions by Means of Evolutionary Algorithms and Fitness Approximation


Ingo PaenkeJürgen Branke, Yaochu Jin



Veröffentlicht: 2006 August

Journal: IEEE Transactions on Evolutionary Computation
Nummer: 4
Seiten: 405- 420

Volume: 10
Bemerkung: * Impact Factor (ISI 2005): 3.257
Referierte Veröffentlichung
BibTeX




Kurzfassung
For many real-world optimization problems, the robustness of a solution is of great importance in addition to the solution's quality. By robustness, we mean that small deviations from the original design, e.g., due to manufacturing tolerances, should be tolerated without a severe loss of quality. One way to achieve that goal is to evaluate each solution under a number of different scenarios, and use the average solution quality as fitness. However, this approach is often impractical, because the cost for evaluating each individual several times is unacceptable. In this paper, we present a new and efficient approach to estimating a solution's expected quality and variance. Basically, we propose to construct local approximate models of the fitness function, and then use these approximate models to estimate expected fitness and variance. Based on a variety of test function, we demonstrate empirically that our approach significantly outperforms the implicit averaging approach as well as the explicit averaging approaches using existing estimation techniques reported in the literature.

ISSN: 1089-778X
VG Wort-Seiten: 201
Weitere Informationen unter: LinkLink

Projekt

EVOLEARN



Forschungsgruppe

Effiziente Algorithmen


Forschungsgebiet
Optimierung unter Unsicherheit