2023-06-212023-06-212020-01-01Springer Nature Switzerland AG 2020http://hdl.handle.net/20.500.12272/8073Different parameters feed mathematical and/or empirical models. However, the uncertainty (or lack of precision) present in such parameters usually impacts in the quality of the output/recommendation of prediction models. Fortunately, there exist uncertainty reduction methods which enable the obtention of more accurate solutions. One of such methods is ESSIM-DE (Evolutionary Statistical System with Island Model and Differential Evolution), a general purpose method for prediction and uncertainty reduction. ESSIM-DE has been used for the forest fireline prediction, and it is based on statistical analysis, parallel computing, and differential evolution. In this work, we enrich ESSIM-DE with an automatic and dynamic tuning strategy, to adapt the generational parameter of the evolutionary process in order to avoid premature convergence and/or stagnation, and to improve the general performance of the predictive tool. We describe the metrics, the tuning points and actions, and we show the results for different controlled fires.pdfengopenAccesshttp://creativecommons.org/publicdomain/zero/1.0/CC0 1.0 UniversalDynamic tuning, Fire prediction, Differential Evolution, Parallel computinginfo:eu-repo/semantics/articleUniversidad Tecnológica Nacional. Facultad Regional MendozaAtribución10.1007/978-3-030-48325-8_2