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This tutorial introduces the CMA Evolution Strategy (ES), where CMA stands for Covariance Matrix Adaptation.
A simplex method for function minimization
Nelder JA, Mead R · 1965
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Evolutionsstrategie ’94
Rechenberg I · 1994
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Adapting arbitrary normal mutation distributions in evolution strategies: The covariance matrix adaptation
Hansen N, Ostermeier A · 1996
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Convergence properties of evolution strategies with the derandomized covariance matrix adaptation: The ( μ / μ I , λ \mu/\mu_{I},\lambda )-CMA-ES
Hansen N, Ostermeier A · 1997
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Verallgemeinerte individuelle Schrittweitenregelung in der Evolutionsstrategie
Hansen N · 1998
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Invariance, self-adaptation and correlated mutations in evolution strategies
Hansen N · 2000
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The Theory of Evolution Strategies
Beyer HG · 2001
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On self-adaptive features in real-parameter evolutionary algorithms
Beyer HG, Deb K · 2001
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Completely derandomized self-adaptation in evolution strategies
Hansen N, Ostermeier A · 2001
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Estimation of distribution algorithms based on multivariate normal and Gaussian networks
Larrañaga P, Lozano JA, Bengoetxea E · 2001
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A review on estimation of distribution algorithms
Larrañaga P · 2002
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Qualms regarding the optimality of cumulative path length control in CSA/CMA-evolution strategies
Beyer HG, Arnold DV · 2003
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Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES)
Hansen N, Müller SD, Koumoutsakos P · 2003
Cited alongside, same era.
Performance analysis of evolutionary optimization with cumulative step length adaptation
Arnold DV, Beyer HG · 2004
Cited alongside, same era.
Evaluating the CMA evolution strategy on multimodal test functions
Hansen N, Kern S · 2004
Cited alongside, same era.
Learning probability distributions in continuous evolutionary algorithms – a comparative review
Kern S, Müller SD, Hansen N, Büche D, Ocenasek J, Koumoutsakos P · 2004
Cited alongside, same era.
The Cross-Entropy Method: a unified approach to combinatorial optimization, Monte-Carlo simulation, and machine learning
Rubenstein RY, Kroese DP · 2004
Cited alongside, same era.
Multidisciplinary optimisation in the design of future space launchers
Collange G, Delattre N, Hansen N, Quinquis I, Schoenauer M · 2010
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Exponential natural evolution strategies
Glasmachers T, Schaul T, Yi S, Wierstra D, Schmidhuber J · 2010
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Variable Metrics in Evolutionary Computation
Hansen N · 2010
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Benchmarking a weighted negative covariance matrix update on the BBOB-2010 noiseless testbed
Hansen N, Ros R · 2010
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Injecting External Solutions Into CMA-ES
Hansen N · 2011
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Principled design of continuous stochastic search: From theory to practice
Hansen N, Auger A · 2014
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Auger A, Hansen N · 2005
Cited alongside, same era.
Weighted multirecombination evolution strategies
Arnold DV · 2006
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The CMA evolution strategy: a comparing review
Hansen N · 2006
Cited alongside, same era.
Improving evolution strategies through active covariance matrix adaptation
Jastrebski G, Arnold DV · 2006
Cited alongside, same era.
Benchmarking a BI-Population CMA-ES on the BBOB-2009 Function Testbed
Hansen N · 2009
Cited alongside, same era.
A method for handling uncertainty in evolutionary optimization with an application to feedback control of combustion
Hansen N, Niederberger SPN, Guzzella L, Koumoutsakos P · 2009
Cited alongside, same era.
Efficient Covariance Matrix Update for Variable Metric Evolution Strategies
Suttorp T, Hansen N, Igel C · 2009
Cited alongside, same era.
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How to Assess Step-Size Adaptation Mechanisms in Randomised Search
Hansen N, Atamna A, Auger A · 2014
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Quality gain analysis of the weighted recombination evolution strategy on general convex quadratic functions
Akimoto Y, Auger A, Hansen N · 2017
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Diagonal acceleration for covariance matrix adaptation evolution strategies
Akimoto Y, Hansen N · 2020
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Augmented Lagrangian, penalty techniques and surrogate modeling for constrained optimization with CMA-ES
Dufossé P, Hansen N · 2021
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Learning rate adaptation by line search in evolution strategies with recombination
Gissler A, Auger A, Hansen N · 2022
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