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    Courant Institute, New York University

    Efficient natural evolution strategies

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    Efficient Natural Evolution Strategies (eNES) is a novel alternative to conventional evolutionary algorithms, using the natural gradient to adapt the mutation distribution. Unlike previous methods based on natural gradients, eNES uses a fast algorithm to calculate the inverse of the exact Fisher information matrix, thus increasing both robustness and performance of its evolution gradient estimation, even in higher dimensions. Additional novel aspects of eNES include optimal fitness baselines and importance mixing (a procedure for updating the population with very few fitness evaluations). The algorithm yields competitive results on both unimodal and multimodal benchmarks.

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    Description

    Title : Efficient natural evolution strategies
    Author(s) : Yi Sun, Daan Wierstra, Tom Schaul, Juergen Schmidhuber
    Abstract : Efficient Natural Evolution Strategies (eNES) is a novel alternative to conventional evolutionary algorithms, using the natural gradient to adapt the mutation distribution. Unlike previous methods based on natural gradients, eNES uses a fast algorithm to calculate the inverse of the exact Fisher information matrix, thus increasing both robustness and performance of its evolution gradient estimation, even in higher dimensions. Additional novel aspects of eNES include optimal fitness baselines and importance mixing (a procedure for updating the population with very few fitness evaluations). The algorithm yields competitive results on both unimodal and multimodal benchmarks.
    Keywords : evolution strategies, natural gradient, optimization

    Subject : unspecified
    Area : Other
    Language : English
    Year : 2009

    Affiliations Courant Institute, New York University
    Journal : Proceedings of the 11th Annual conference on Genetic and evoluti
    Publisher : ACM Press
    City : New York, New York, USA
    Pages : 539 -
    Url : http://portal.acm.org/citation.cfm?doid=1569901.1569976
    Isbn : 9781605583259
    Doi : 10.1145/1569901.1569976

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