Abstract
In evolutionary computation, the fitness of an individual corresponds to its evaluation as a candidate solution. Fitness surrogate models are intended to achieve a reduction in the number of real fitness computations in situations where these computations are expensive. This paper evaluates the effectiveness of three global fitness surrogates: a quadratic model, the inverse distance weighting (IDW) interpolation algorithm and a variant from the later (IDWR). The evaluation is performed using four benchmark functions form the optimization literature. The IDWR algorithm was able to achieve a significantly lower number of real fitness evaluations until convergence when compared to the alternative models for most of the functions considered.
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Emmendorfer, L.R. (2020). An Empirical Evaluation of Global Fitness Surrogate Models in Evolutionary Computation. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2020. Lecture Notes in Computer Science(), vol 12415. Springer, Cham. https://doi.org/10.1007/978-3-030-61401-0_36
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