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CMA-ES and Advanced Adaptation Mechanisms

Authors:

Youhei Akimoto,

Anne Auger,

Nikolaus HansenAuthors Info & Claims

GECCO '16 Companion: Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion

Pages 533 - 562

https://doi.org/10.1145/2908961.2926980

Published: 20 July 2016 Publication History

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Zhang JTang YWang HXu K(2023)ASRO-DIO: Active Subspace Random Optimization Based Depth Inertial OdometryIEEE Transactions on Robotics10.1109/TRO.2022.320850339:2(1496-1508)Online publication date: Apr-2023
https://doi.org/10.1109/TRO.2022.3208503
Archetti FCandelieri AGaluzzi BPerego R(2021)Learning Enabled Constrained Black-Box OptimizationBlack Box Optimization, Machine Learning, and No-Free Lunch Theorems10.1007/978-3-030-66515-9_1(1-33)Online publication date: 4-Jan-2021
https://doi.org/10.1007/978-3-030-66515-9_1
Benhamou ESaltiel DGuez BParis N(undefined)BCMA-ES II: Revisiting Bayesian CMA-ESSSRN Electronic Journal10.2139/ssrn.3365453
https://doi.org/10.2139/ssrn.3365453
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Index Terms

CMA-ES and Advanced Adaptation Mechanisms
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Bio-inspired approaches

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GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation

Evolution Strategies (ESs) and many continuous domain Estimation of Distribution Algorithms (EDAs) are stochastic optimization procedures that sample a multivariate normal (Gaussian) distribution in the continuous search space, Rⁿ. Many of them can be ...
Functionally specialized CMA-ES: a modification of CMA-ES based on the specialization of the functions of covariance matrix adaptation and step size adaptation
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This paper aims the design of efficient and effective optimization algorithms for function optimization. This paper presents a new framework of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES). Recent studies modified the ...

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Published In

GECCO '16 Companion: Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion

July 2016

1510 pages

ISBN:9781450343237

DOI:10.1145/2908961

Editor:
Tobias Friedrich
Hasso Plattner Institute
,
General Chair:
Frank Neumann
University of Adelaide
,
Program Chair:
Andrew M. Sutton
Hasso Plattner Institute

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: 20 July 2016

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GECCO '16

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SIGEVO

GECCO '16: Genetic and Evolutionary Computation Conference

July 20 - 24, 2016

Colorado, Denver, USA

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GECCO '16 Companion Paper Acceptance Rate 137 of 381 submissions, 36%;

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

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Zhang JTang YWang HXu K(2023)ASRO-DIO: Active Subspace Random Optimization Based Depth Inertial OdometryIEEE Transactions on Robotics10.1109/TRO.2022.320850339:2(1496-1508)Online publication date: Apr-2023
https://doi.org/10.1109/TRO.2022.3208503
Archetti FCandelieri AGaluzzi BPerego R(2021)Learning Enabled Constrained Black-Box OptimizationBlack Box Optimization, Machine Learning, and No-Free Lunch Theorems10.1007/978-3-030-66515-9_1(1-33)Online publication date: 4-Jan-2021
https://doi.org/10.1007/978-3-030-66515-9_1
Benhamou ESaltiel DGuez BParis N(undefined)BCMA-ES II: Revisiting Bayesian CMA-ESSSRN Electronic Journal10.2139/ssrn.3365453
https://doi.org/10.2139/ssrn.3365453
Benhamou EAtif JLaraki RAuger A(undefined)A Discrete Version of CMA-ESSSRN Electronic Journal10.2139/ssrn.3307212
https://doi.org/10.2139/ssrn.3307212
Benhamou E(undefined)Kalman Filter Demystified: From Intuition to Probabilistic Graphical Model to Real Case in Financial MarketsSSRN Electronic Journal10.2139/ssrn.3292762
https://doi.org/10.2139/ssrn.3292762

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Recommendations

Improved step size adaptation for the MO-CMA-ES

CMA-ES: evolution strategies and covariance matrix adaptation

Functionally specialized CMA-ES: a modification of CMA-ES based on the specialization of the functions of covariance matrix adaptation and step size adaptation

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