Titlebook: Experimental Methods for the Analysis of Optimization Algorithms; Thomas Bartz-Beielstein,Marco Chiarandini,Mike Pre Book 2010 Springer-Ve

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Yu Li,Jonathan Li,Michael A. Chapmanance is optimized. In this chapter, we review ., a racing algorithm for the task of automatic algorithm configuration. . is based on a statistical approach for selecting the best configuration out of a set of candidate configurations under stochastic evaluations. We review the ideas underlying this

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https://doi.org/10.1007/978-3-031-34765-8actical and theoretical optimization problems. We describe the mechanics and interfaces employed by SPOT to enable users to plug in their own algorithms. Furthermore, two case studies are presented to demonstrate how SPOT can be applied in practice, followed by a discussion of alternative metamodels

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Florian W. H. Smit,Michael John Welchild a response surface model and use this model for finding good parameter settings of the given algorithm. We evaluated two methods from the literature that are based on Gaussian process models: sequential parameter optimization (SPO) (Bartz-Beielstein et al. 2005) and sequential Kriging optimizati