Comparing natural evolution strategies to BIPOP-CMA-ES on noiseless and noisy black-box optimization testbeds

Natural Evolution Strategies (NES) are a recent member of the class of preal-valued optimization algorithms that are based on adapting search distributions. Exponential NES (xNES) are the most common instantiation of NES, and particularly appropriate for the BBOB 2012 benchmarks, given that many are non-separable, and their relatively small problem dimensions. Here, we augment xNES with adaptation sampling, which adapts learning rates online, and compare the resulting performance directly to the BIPOP-CMA-ES algorithm, the winner of the 2009 black-box optimization benchmarking competition (BBOB). This report provides an extensive empirical comparison, both on the noise-free and noisy BBOB testbeds.

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