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A Natural Evolution Strategy for Multi-objective Optimization

  • Tobias Glasmachers
  • Tom Schaul
  • Jürgen Schmidhuber
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6238)

Abstract

The recently introduced family of natural evolution strategies (NES), a novel stochastic descent method employing the natural gradient, is providing a more principled alternative to the well-known covariance matrix adaptation evolution strategy (CMA-ES). Until now, NES could only be used for single-objective optimization. This paper extends the approach to the multi-objective case, by first deriving a (1 + 1) hillclimber version of NES which is then used as the core component of a multi-objective optimization algorithm. We empirically evaluate the approach on a battery of benchmark functions and find it to be competitive with the state-of-the-art.

Keywords

Pareto Front Multiobjective Optimization Benchmark Function Natural Gradient Covariance Matrix Adaptation Evolution Strategy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Tobias Glasmachers
    • 1
  • Tom Schaul
    • 1
  • Jürgen Schmidhuber
    • 1
  1. 1.IDSIAUniversity of LuganoSwitzerland

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