Results 221 to 230 of about 3,655 (256)

Evolution Strategy with Covariance Matrix and decreasing step-size Adaptation (CMDSA-ES)

2016 IEEE International Conference on Automation Science and Engineering (CASE), 2016
The Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is state-of-the-art among evolution strategies. However, its adaptation process and parameters selection is rather complicated. The simplified version Covariance Matrix Adaptation and Step-size Self Adaptation Evolution Strategy (CMSA-ES) successfully reduces this complexity by turning to the
Zhuo Yang, Xi Chen
exaly   +2 more sources

Adapting the Covariance Matrix in Evolution Strategies

Proceedings of the 3rd International Conference on Operations Research and Enterprise Systems, 2014
Evolution strategies belong to the best performing modern metaheuristics for continuous optimization. This paper addresses the covariance matrix adaptation in evolution strategies which is central to the algorithm. Nearly all approaches so far consider the sample covariance matrix as one of the main factors for the adaptation.
Silja Meyer-Nieberg, Erik Kropat
openaire   +1 more source

ADVANCED ALGORITHM OF EVOLUTION STRATEGIES OF COVARIATION MATRIX ADAPTATION

Bukovinian Mathematical Journal, 2022
The paper considers the extension of the CMA-ES algorithm using mixtures of distributions for finding optimal hyperparameters of neural networks. Hyperparameter optimization, formulated as the optimization of the black box objective function, which is a necessary condition for automation and high performance of machine learning approaches. CMA-ES is an
Yu. Litvinchuk, I. Malyk
openaire   +1 more source

Dynamic Niching in Evolution Strategies with Covariance Matrix Adaptation

2005 IEEE Congress on Evolutionary Computation, 2005
Evolutionary algorithms (EAs) have the tendency to converge quickly into a single solution in the search space. However, many complex search problems require the identification and maintenance of multiple solutions. Niching methods are the extension of EAs to address this issue.
Ofer M. Shir, Thomas Bäck
openaire   +1 more source

Improving Evolution Strategies through Active Covariance Matrix Adaptation

2006 IEEE International Conference on Evolutionary Computation, 2006
This paper proposes a novel modification to the derandomised covariance matrix adaptation algorithm used in connection with evolution strategies. In existing variants of that algorithm, only information gathered from successful offspring candidate solutions contributes to the adaptation of the covariance matrix, while old information passively decays ...
Grahame A. Jastrebski, Dirk V. Arnold
openaire   +1 more source

Overview of surrogate-model versions of covariance matrix adaptation evolution strategy

Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2017
Evaluation of real-world black-box objective functions is in many optimization problems very time-consuming or expensive. Therefore, surrogate regression models, used instead of the expensive objective function and in that way decreasing the number of its evaluations, have received a lot of attention. Here, we briefly survey surrogate-assisted versions
Zbynek Pitra, Lukás Bajer, Jakub Repický, Martin Holena   +3 more
openaire   +2 more sources

Sparse Covariance Matrix Adaptation Techniques for Evolution Strategies

2015
Evolution strategies are variants of evolutionary algorithms. In contrast to genetic algorithms, their search process depends strongly on mutation. Since the search space is often continuous, evolution strategies use a multivariate normal distribution as search distribution. This necessitates the tuning and adaptation of the covariance matrix.
Silja Meyer-Nieberg, Erik Kropat
openaire   +1 more source

Covariance Matrix Adaptation Revisited – The CMSA Evolution Strategy –

2008
The covariance matrix adaptation evolution strategy (CMA-ES) rates among the most successful evolutionary algorithms for continuous parameter optimization. Nevertheless, it is plagued with some drawbacks like the complexity of the adaptation process and the reliance on a number of sophisticatedly constructed strategy parameter formulae for which no or ...
Hans-Georg Beyer, Bernhard Sendhoff
openaire   +1 more source

Adapting arbitrary normal mutation distributions in evolution strategies: the covariance matrix adaptation

Proceedings of IEEE International Conference on Evolutionary Computation, 2002
A new formulation for coordinate system independent adaptation of arbitrary normal mutation distributions with zero mean is presented. This enables the evolution strategy (ES) to adapt the correct scaling of a given problem and also ensures invariance with respect to any rotation of the fitness function (or the coordinate system).
Nikolaus Hansen, Andreas Ostermeier
openaire   +1 more source

Modified box constraint handling for the covariance matrix adaptation evolution strategy

Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2017
We propose a modified box constraint handling technique for the covariance matrix adaptation evolution strategy (CMA-ES). The existing box constraint handling turns the box-constrained optimization problem into an unconstrained optimization by introducing an artificial fitness landscape, where a penalty function is added to the function values at the ...
Naoki Sakamoto, Youhei Akimoto
openaire   +1 more source