Incremental evolution of complex general behavior
Adaptive Behavior - Special issue on environment structure and behavior
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
Exponential natural evolution strategies
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Evolving a single scalable controller for an octopus arm with a variable number of segments
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part II
High dimensions and heavy tails for natural evolution strategies
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Scalable neuroevolution for reinforcement learning
TPNC'12 Proceedings of the First international conference on Theory and Practice of Natural Computing
| Hi-index | 0.00 |
The Natural Evolution Strategies (NES) family of search algorithms have been shown to be efficient black-box optimizers, but the most powerful version xNES does not scale to problems with more than a few hundred dimensions. And the scalable variant, SNES, potentially ignores important correlations between parameters. This paper introduces Block Diagonal NES (BD-NES), a variant of NES which uses a block diagonal covariance matrix. The resulting update equations are computationally effective on problems with much higher dimensionality than their full-covariance counterparts, while retaining faster convergence speed than methods that ignore covariance information altogether. The algorithm has been tested on the Octopus-arm benchmark, and the experiments section presents performance statistics showing that BD-NES achieves better performance than SNES on networks that are too large to be optimized by xNES.