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Conference Paper

Fitness Expectation Maximization

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons84135

Schaul T, Peters,  J
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Citation

Wierstra, D., Schaul T, Peters, J., & Schmidhuber, J. (2008). Fitness Expectation Maximization. Parallel Problem Solving from Nature – PPSN X, 337-346.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-C743-4
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 EM’s 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.