Natural gradient works efficiently in learning
Neural Computation
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
Stochastic search using the natural gradient
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
The Journal of Machine Learning Research
Exponential natural evolution strategies
Proceedings of the 12th annual conference on Genetic and evolutionary computation
A natural evolution strategy for multi-objective optimization
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Natural evolution strategies converge on sphere functions
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
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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 an extensive empirical comparison to study the impact of adaptation sampling in xNES, both on the noise-free and noisy BBOB testbeds.