Different 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.
