Natural evolution strategies

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

Research output: Contribution to journalArticlepeer-review

243 Scopus citations

Abstract

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. © 2014 Daan Wierstra, Tom Schaul, Tobias Glasmachers, Yi Sun, Jan Peters and Jürgen Schmidhuber.
Original languageEnglish (US)
Pages (from-to)949-980
Number of pages32
JournalJournal of Machine Learning Research
Volume15
StatePublished - Jan 1 2014
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2022-09-14

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Statistics and Probability
  • Control and Systems Engineering

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