Veröffentlicht: 2006 August
Journal: IEEE Transactions on Evolutionary Computation
Seiten: 405- 420
- Impact Factor (ISI 2005): 3.257
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.
VG Wort-Seiten: 201
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