Publication Details

SELECT * FROM publications WHERE Record_Number=10164
Reference TypeConference Proceedings
Author(s)Wierstra, D.; Schaul, T.; Peters, J.; Schmidhuber, J.
TitleNatural Evolution Strategies
Journal/Conference/Book Title2008 IEEE Congress on Evolutionary Computation
AbstractThis paper presents Natural Evolution Strategies (NES), a novel algorithm for performing real-valued black box function optimization: optimizing an unknown objective function where algorithm-selected function measurements con- stitute the only information accessible to the method. Natu- ral Evolution Strategies search the fitness landscape using a multivariate normal distribution with a self-adapting mutation matrix to generate correlated mutations in promising regions. NES shares this property with Covariance Matrix Adaption (CMA), an Evolution Strategy (ES) which has been shown to perform well on a variety of high-precision optimization tasks. The Natural Evolution Strategies algorithm, however, is simpler, less ad-hoc and more principled. Self-adaptation of the mutation matrix is derived using a Monte Carlo estimate of the natural gradient towards better expected fitness. By following the natural gradient instead of the �vanilla� gradient, we can ensure efficient update steps while preventing early convergence due to overly greedy updates, resulting in reduced sensitivity to local suboptima. We show NES has competitive performance with CMA on several tasks, while outperforming it on one task that is rich in deceptive local optima, the Rastrigin benchmark. found and the algorithm�s sensitivity to local suboptima on the fitness landscape.
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