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Fitness Expectation Maximization


Conference Paper


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.

Author(s): Wierstra, D. and Schaul, T. and Peters, J. and Schmidhuber, J.
Book Title: PPSN 2008
Journal: Parallel Problem Solving from Nature – PPSN X
Pages: 337-346
Year: 2008
Month: September
Day: 0
Editors: Rudolph, G. , T. Jansen, S. Lucas, C. Poloni, N. Beume
Publisher: Springer

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

DOI: 10.1007/978-3-540-87700-4_34
Event Name: 10th International Conference on Parallel Problem Solving From Nature
Event Place: Dortmund, Germany

Address: Berlin, Germany
Digital: 0
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF


  title = {Fitness Expectation Maximization},
  author = {Wierstra, D. and Schaul, T. and Peters, J. and Schmidhuber, J.},
  journal = {Parallel Problem Solving from Nature – PPSN X},
  booktitle = {PPSN 2008},
  pages = {337-346},
  editors = {Rudolph, G. , T. Jansen, S. Lucas, C. Poloni, N. Beume},
  publisher = {Springer},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Berlin, Germany},
  month = sep,
  year = {2008},
  month_numeric = {9}