A natural evolution strategy for multi-objective optimization

Tobias Glasmachers; Tom Schaul; Jürgen Schmidhuber

doi:10.1007/978-3-642-15844-5_63

A natural evolution strategy for multi-objective optimization

Tobias Glasmachers, Tom Schaul, Jürgen Schmidhuber

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

13 Scopus citations

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. © 2010 Springer-Verlag.

Original language	English (US)
Title of host publication	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages	627-636
Number of pages	10
DOIs	https://doi.org/10.1007/978-3-642-15844-5_63
State	Published - Nov 12 2010
Externally published	Yes

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

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title = "A natural evolution strategy for multi-objective optimization",

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. {\textcopyright} 2010 Springer-Verlag.",

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PY - 2010/11/12

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N2 - 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.

AB - 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.

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BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ER -

A natural evolution strategy for multi-objective optimization

Abstract

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