Results 21 to 30 of about 2,841,488 (326)
Covariance matrix adaptation evolution strategy based optical phase control
Electronics Letters, 2021 In this letter, an investigation of the use of a covariance matrix adaptation evolution strategy (CMA‐ES) algorithm is conducted as the phase‐locking method for multi‐channel coherent beam combining (CBC) for the first time.
Hansol Kim, Yoonchan Jeong
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Frontiers in Genetics, 2023 Objective: In the study, we investigated the genetic etiology of the ventricular septal defect (VSD) and comprehensively evaluated the diagnosis rate of prenatal chromosomal microarray analysis (CMA) and exome sequencing (ES) for VSD to provide evidence ...
You Wang +7 more
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Diagnostics, 2022 Fetal malformations occur in 2–3% of pregnancies. They require invasive procedures for cytogenetics and molecular testing. “Structural anomalies” include non-transient anatomic alterations.
Gioia Mastromoro +5 more
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Benchmarking MO-CMA-ES and COMO-CMA-ES on the bi-objective bbob-biobj testbed [PDF]
Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2019 In this paper, we propose a comparative benchmark of MO-CMAES, COMO-CMA-ES (recently introduced in [12]) and NSGA-II,using the COCO framework for performance assessment and the Bi-objective test suite bbob-biobj. For a fixed number of pointsp, COMO-CMA-ES approximates an optimal p-distribution of the Hypervolume Indicator. While not designed to perform
Dufossé, Paul, Touré, Cheikh
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A CMA‐ES Algorithm Allowing for Random Parameters in Model Calibration
Journal of Advances in Modeling Earth Systems, 2023 In geoscience and other fields, researchers use models as a simplified representation of reality. The models include processes that often rely on uncertain parameters that reduce model performance in reflecting real‐world processes.
Volkmar Sauerland +3 more
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(1+1)-CMA-ES with Margin for Discrete and Mixed-Integer Problems [PDF]
Annual Conference on Genetic and Evolutionary Computation, 2023 The covariance matrix adaptation evolution strategy (CMA-ES) is an efficient continuous black-box optimization method. The CMA-ES possesses many attractive features, including invariance properties and a well-tuned default hyperparameter setting ...
Yohei Watanabe +5 more
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Optimization of solder joints in embedded mechatronic systems via Kriging-assisted CMA-ES algorithm
International Journal for Simulation and Multidisciplinary Design Optimization, 2019 In power electronics applications, embedded mechatronic systems (MSs) must meet the severe operating conditions and high levels of thermomechanical stress.
Hamdani Hamid +2 more
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The Effects of CMA-ES Style Selection and Restart Criteria on DE
Mendel, 2018 Over the years, a lot of research has gone into the creation of different mutation operators and adaptive parameters for differential evolution (DE).
Mark Wineberg, Samuel Opawale
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Matrix Adaptation Evolution Strategy with Multi-Objective Optimization for Multimodal Optimization
Algorithms, 2019 The standard covariance matrix adaptation evolution strategy (CMA-ES) is highly effective at locating a single global optimum. However, it shows unsatisfactory performance for solving multimodal optimization problems (MMOPs).
Wei Li
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BCMA-ES II: Revisiting Bayesian CMA-ES [PDF]
SSRN Electronic Journal, 2019 This paper revisits the Bayesian CMA-ES and provides updates for normal Wishart. It emphasizes the difference between a normal and normal inverse Wishart prior. After some computation, we prove that the only difference relies surprisingly in the expected covariance. We prove that the expected covariance should be lower in the normal Wishart prior model
Benhamou, Eric +3 more
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