Empirical methods for artificial intelligence
Empirical methods for artificial intelligence
Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
Global Optimization of Stochastic Black-Box Systems via Sequential Kriging Meta-Models
Journal of Global Optimization
An experimental investigation of model-based parameter optimisation: SPO and beyond
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Generalizing surrogate-assisted evolutionary computation
IEEE Transactions on Evolutionary Computation
Comparison-based optimizers need comparison-based surrogates
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
On the distribution of EMOA hypervolumes
LION'10 Proceedings of the 4th international conference on Learning and intelligent optimization
Ordinal regression in evolutionary computation
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
A benchmark of kriging-based infill criteria for noisy optimization
Structural and Multidisciplinary Optimization
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Parameter tuning of evolutionary algorithms (EAs) is attracting more and more interest. In particular, the sequential parameter optimization (SPO) framework for the model-assisted tuning of stochastic optimizers has resulted in established parameter tuning algorithms. In this paper, we enhance the SPO framework by introducing transformation steps before the response aggregation and before the actual modeling. Based on design-of-experiments techniques, we empirically analyze the effect of integrating different transformations. We show that in particular, a rank transformation of the responses provides significant improvements. A deeper analysis of the resulting models and additional experiments with adaptive procedures indicates that the rank and the Box-Cox transformation are able to improve the properties of the resultant distributions with respect to symmetry and normality of the residuals. Moreover, model-based effect plots document a higher discriminatory power obtained by the rank transformation.