Natural Evolution Strategies

This paper presents Natural Evolution Strategies (NES), a recent family of black-box optimization algorithms that use the natural gradient to update a parameterized search distribution in the direction of higher expected fitness. We introduce a collection of techniques that address issues of convergence, robustness, sample complexity, computational complexity and sensitivity to hyperparameters. This paper explores a number of implementations of the NES family, such as general-purpose multi-variate normal distributions and separable distributions tailored towards search in high dimensional spaces. Experimental results show best published performance on various standard benchmarks, as well as competitive performance on others. [abs] [ pdf ][ bib ] &copy JMLR 2014. ( edit, beta )

Authors

• Daan Wierstra
• Tom Schaul
• Tobias Glasmachers
• Yi Sun
• Jan Peters
• Jürgen Schmidhuber

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