A natural evolution strategy for multi-objective optimization

Tobias Glasmachers, Tom Schaul, Jürgen Schmidhuber

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Scopus citations


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. © 2010 Springer-Verlag.
Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number of pages10
StatePublished - Nov 12 2010
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2022-09-14

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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