Abstract
In the field of Evolutionary Strategy, parameter estimation for functions with multiple minima is a difficult task when interdependencies between parameters have to be investigated. Most of the current routines that are used to estimate such parameters leverage state-of-the-art machine learning approaches to identify the global minimum, ignoring the relevance of the potential local minima. In this paper, we present a novel Evolutionary Strategy routine that uses sampling tools deriving from the Bayesian field to find the best parameters according to a certain loss function. The Bayesian Recursive Global Optimizer (BaRGO) presented in this work explores the parameter space identifying both local and global minima. Applications of BaRGO to 2D minimization problems and to parameter estimation of Red Blood Cell model are reported.
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Acknowledgements
The authors acknowledge support from the Swiss National Science Foundation grants 200021L_204817 and 192549. Simulations were carried out at the Swiss National Supercomputer Center under project u4.
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Miotti, P., Filippi-Mazzola, E., Wit, E.C., Pivkin, I.V. (2023). Estimating Parameters of 3D Cell Model Using a Bayesian Recursive Global Optimizer (BaRGO). In: Mikyška, J., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M. (eds) Computational Science – ICCS 2023. ICCS 2023. Lecture Notes in Computer Science, vol 14075. Springer, Cham. https://doi.org/10.1007/978-3-031-36024-4_27
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