Global optimization
Evolution strategies –A comprehensive introduction
Natural Computing: an international journal
Efficient Global Optimization of Expensive Black-Box Functions
Journal of Global Optimization
Toward the Optimization of a Class of Black Box Optimization Algorithms
ICTAI '97 Proceedings of the 9th International Conference on Tools with Artificial Intelligence
Local convergence rates of simple evolutionary algorithms withCauchy mutations
IEEE Transactions on Evolutionary Computation
Randomized algorithms for robust controller synthesis using statistical learning theory
Automatica (Journal of IFAC)
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One of the most serious problem concerning global optimization methods is their correct configuration. Usually algorithms are described by some number of external parameters for which optimal values strongly depend on the objective function. If there is a lack of knowledge on the function under consideration the optimization algorithms can by adjusted using trail-and-error method. Naturally, this kind of approach gives rise to many computational problems. Moreover, it can be applied only when a lot of function evaluations is allowed. In order to avoid trial-and-error method it is reasonable to use an optimization algorithm which is characterized by the highest degree of robustness according to the variations in its control parameters. In this paper, the robustness issue of evolutionary strategy with isotropic stable mutations is discussed. The experimental simulations are conducted with the help of special search environment - the so-called general search space.