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Proceedings of the Genetic and Evolutionary Computation Conference, 2018 The population size, i.e., the number of candidate solutions generated at each iteration, is the most critical strategy parameter in the covariance matrix adaptation evolution strategy, CMA-ES, which is one of the state-of-the-art search algorithms for black-box continuous optimization.
Kouhei Nishida, Youhei Akimoto
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Scientific Reports, 2022 Among population-based metaheuristics, both Differential Evolution (DE) and Covariance Matrix Adaptation Evolution Strategy (CMA-ES) perform outstanding for real parameter single objective optimization.
Zhe Chen, Yuanxing Liu
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Ship Autonomous Berthing Simulation Based on Covariance Matrix Adaptation Evolution Strategy
Journal of Marine Science and Engineering, 2023 Existing research on auto-berthing of ships has mainly focused on the design and implementation of controllers for automatic berthing. For the real automatic docking processes, not only do external environmental perturbations need to be taken into ...
Guoquan Chen, Jian Yin, Shenhua Yang
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International Journal for Simulation and Multidisciplinary Design Optimization, 2022 The electro-thermomechanical modeling study of the High Electron Mobility Transistor (HEMT) has been presented, all the necessary equations are detailed and coupled.
Amar Abdelhamid +2 more
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Frontiers in Immunology, 2023 PurposeThough copy number variants (CNVs) have been suggested to play a significant role in inborn errors of immunity (IEI), the precise nature of this role remains largely unexplored.
Breanna J. Beers +36 more
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Online selection of CMA-ES variants [PDF]
Proceedings of the Genetic and Evolutionary Computation Conference, 2019 In the field of evolutionary computation, one of the most challenging topics is algorithm selection. Knowing which heuristics to use for which optimization problem is key to obtaining high-quality solutions. We aim to extend this research topic by taking a first step towards a selection method for adaptive CMA-ES algorithms.
Diederick Vermetten +3 more
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Про одне узагальнення еволюційних алгоритмів
Міжнародний науково-технічний журнал "Проблеми керування та інформатики", 2023 У реальному світі існує безліч сценаріїв оптимізації з великою кількістю обмежень та цільових функцій, які за своєю природою є розривними, нелінійними, невипуклими та мультимодальними.
Юлія Анатоліївна Літвінчук
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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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Optimizing CMA-ES with CMA-ES - Data and Code
, 2023 Data and Code - Conference Paper at ECTA Optimizing CMA-ES with CMA ...
Thomaser, André +3 more
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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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