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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.
Vermetten, Diederick +3 more
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Alternative Restart Strategies for CMA-ES [PDF]
, 2012 This paper focuses on the restart strategy of CMA-ES on multi-modal functions. A first alternative strategy proceeds by decreasing the initial step-size of the mutation while doubling the population size at each restart. A second strategy adaptively allocates the computational budget among the restart settings in the BIPOP scheme.
Loshchilov, Ilya +2 more
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Injecting External Solutions Into CMA-ES [PDF]
, 2011 This report considers how to inject external candidate solutions into the CMA-ES algorithm. The injected solutions might stem from a gradient or a Newton step, a surrogate model optimizer or any other oracle or search mechanism. They can also be the result of a repair mechanism, for example to render infeasible solutions feasible.
Hansen, Nikolaus
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Local-meta-model CMA-ES for partially separable functions [PDF]
Proceedings of the 13th annual conference on Genetic and evolutionary computation, 2011 In this paper, we propose a new variant of the covariance matrix adaptation evolution strategy with local meta-models (lmm-CMA) for optimizing partially separable functions. We propose to exploit partial separability by building at each iteration a meta-model for each element function (or sub-function) using a full quadratic local model.
Bouzarkouna, Zyed +2 more
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Parallelizing the CMA-ES algorithm [PDF]
, 2017 Nowadays, with the development of increasingly better computer hardware we can witness an ever-growing need for faster execution of algorithms. The basis of this thesis is an attempt to speed-up the evolutionary algorithm CMA-ES using various parallel approaches.
Rihar , Nejc
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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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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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