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Natural gradient evolution strategies for adaptive sampling

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Ann NoweAuthors Info & Claims

GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference Companion

Pages 73 - 74

https://doi.org/10.1145/3520304.3533939

Published: 19 July 2022 Publication History

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Abstract

We evaluate two (1+1)-natural evolution strategies (NES) turned into adaptive Markov chain Monte Carlo (MCMC) samplers on a test suite of probability distributions. We compare their performance with the AM-family of samplers considered to be the state of the art in adaptive MCMC. Our experiments show that natural gradient based adaptation used in NES further improves adaptive MCMC.

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Index Terms

Natural gradient evolution strategies for adaptive sampling
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Markov decision processes

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Information & Contributors

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Published In

GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference Companion

July 2022

2395 pages

ISBN:9781450392686

DOI:10.1145/3520304

Editor:
Jonathan E. Fieldsend
University of Exeter
,
General Chair:
Markus Wagner
The University of Adelaide

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 July 2022

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GECCO '22

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SIGEVO

GECCO '22: Genetic and Evolutionary Computation Conference

July 9 - 13, 2022

Massachusetts, Boston

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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