Benchmarking Natural Evolution Strategies with adaptation sampling on the noiseless and noisy black-box optimization testbeds

Abstract

Natural Evolution Strategies (NES) are a recent member of the class of real-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. The technique of adaptation sampling, which adapts learning rates online further improves the algorithm's performance. This report provides the the most extensive empirical results on that combination (xNES-as) to date, on both the noise-free and noisy BBOB testbeds. Copyright 2012 ACM.