Functions with noise-induced multimodality: a test for evolutionary robust Optimization-properties and performance analysis

This paper proposes and analyzes a class of test functions for evolutionary robust optimization, the "functions with noise-induced multimodality" (FNIMs). After a motivational introduction gleaned from a real-world optimization problem, the robust optimizer properties of this test class are investigated with respect to different robustness measures. The steady-state behavior of evolution strategies on FNIMs will be investigated empirically. Being based on the empirical results, a subclass of FNIMs is identified which is amenable to an asymptotical performance analysis. The results of this analysis will be used to derive recommendations for the choice of strategy-specific parameters such as population size and truncation ratio

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