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    Post Doctorate

    Courant Institute, New York University

    Fitness expectation maximization

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    We present Fitness Expectation Maximization (FEM), a novel method for performing black box function optimization. FEM searches the fitness landscape of an objective function using an instantiation of the well-known Expectation Maximization algorithm, producing search points to match the sample distribution weighted according to higher expected fitness. FEM updates both candidate solution parameters and the search policy, which is represented as a multinormal distribution. Inheriting EMs stability and strong guarantees, the method is both elegant and competitive with some of the best heuristic search methods in the field, and performs well on a number of unimodal and multimodal benchmark tasks. To illustrate the potential practical applications of the approach, we also show experiments on finding the parameters for a controller of the challenging non-Markovian double pole balancing task.

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    Description

    Title : Fitness expectation maximization
    Author(s) : Daan Wierstra, Tom Schaul, Jan Peters, J Schmidhuber
    Abstract : We present Fitness Expectation Maximization (FEM), a novel method for performing black box function optimization. FEM searches the fitness landscape of an objective function using an instantiation of the well-known Expectation Maximization algorithm, producing search points to match the sample distribution weighted according to higher expected fitness. FEM updates both candidate solution parameters and the search policy, which is represented as a multinormal distribution. Inheriting EMs stability and strong guarantees, the method is both elegant and competitive with some of the best heuristic search methods in the field, and performs well on a number of unimodal and multimodal benchmark tasks. To illustrate the potential practical applications of the approach, we also show experiments on finding the parameters for a controller of the challenging non-Markovian double pole balancing task.
    Subject : unspecified
    Area : Other
    Language : English
    Year : 2008

    Affiliations Courant Institute, New York University
    Editors : Günter Rudolph, Thomas Jansen, Simon Lucas, Carlo Poloni, Nicola Beume
    Journal : Parallel Problem Solving from Nature–PPSN X
    Volume : 5199
    Publisher : Springer
    Pages : 337-346
    Url : http://www.springerlink.com/index/l11461v304q10401.pdf
    Doi : 10.1007/978-3-540-87700-4

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