A natural evolution strategy for multi-objective optimization

Authors:
Tobias Glasmachers;Tom Schaul;Jürgen Schmidhuber
Affiliations:
IDSIA, University of Lugano, Switzerland;IDSIA, University of Lugano, Switzerland;IDSIA, University of Lugano, Switzerland
Venue:
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Year:
2010

Citing 9
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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.