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Simplify Your Covariance Matrix Adaptation Evolution Strategy
IEEE Transactions on Evolutionary Computation, 2017The standard covariance matrix adaptation evolution strategy (CMA-ES) comprises two evolution paths, one for the learning of the mutation strength and one for the rank-1 update of the covariance matrix. In this paper, it is shown that one can approximately transform this algorithm in such a manner that one of the evolution paths and the covariance ...
Hans-Georg Beyer, Bernhard Sendhoff
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A covariance matrix adaptation evolution strategy variant and its engineering application
Applied Soft Computing, 2019Abstract This paper proposes a novel covariance matrix adaptation evolution strategy (CMA-ES) variant, named AEALSCE, for single-objective numerical optimization problems in the continuous domain. To avoid premature convergence and strengthen the exploration capacity of the basic CMA-ES, AEALSCE is obtained by integrating the CMA-ES with two ...
Yajun Liang +5 more
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An adaptive penalty based covariance matrix adaptation–evolution strategy
Computers & Operations Research, 2013zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Kusakci, Ali Osman, Can, Mehmet
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ADVANCED ALGORITHM OF EVOLUTION STRATEGIES OF COVARIATION MATRIX ADAPTATION
Bukovinian Mathematical Journal, 2022The 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
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Improving Evolution Strategies through Active Covariance Matrix Adaptation
2006 IEEE International Conference on Evolutionary Computation, 2006This 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 ...
G.A. Jastrebski, D.V. Arnold
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Static Loadabilty Assessment Using Covariance Matrix Adapted Evolution Strategy
Advanced Materials Research, 2013This paper discusses application of Covariance Matrix Adapted Evolution Strategy (CMAES) algorithm for maximizing loadability margin of power system. CMAES is a class of continuous evolutionary algorithm that generates new population members by sampling from a probability distribution that is constructed during the optimization process.
S. Alamelu +3 more
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Adapting the Covariance Matrix in Evolution Strategies
Proceedings of the 3rd International Conference on Operations Research and Enterprise Systems, 2014Evolution 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.
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Dynamic Niching in Evolution Strategies with Covariance Matrix Adaptation
2005 IEEE Congress on Evolutionary Computation, 2005Evolutionary 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.
O.M. Shir, T. Back
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Enhancing covariance matrix adaptation evolution strategy through fitness inheritance
2016 IEEE Congress on Evolutionary Computation (CEC), 2016Evolution strategy (ES) has shown to be effective in many search and optimization problems. In particular, the ES with covariance matrix adaptation (CMAES) achieves great successes and is viewed as a state-of-the-art evolutionary algorithm for complex numerical optimization.
Rung-Tzuo Liaw, Chuan-Kang Ting
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Overview of surrogate-model versions of covariance matrix adaptation evolution strategy
Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2017Evaluation 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
Pitra, Z. +3 more
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