Multistage Evolutionary Strategies for Adjusting a Cellular Automata-based Epidemiological Model

An epidemiological model based on cellular automata (CA) rules is tuned through several parameters to provide a more accurate simulation of the real phenomena. CA are dynamic systems capable of describing complexity from simple components and local iterations. The parameters setting discussed here is guided by reference values that were obtained with real field data. We started from a recent study in which an adequate parameters configuration was sought for a stochastic CA-based epidemiological model of Chagas Disease through an evolutionary approach. The results were satisfactory but the performance of the standard genetic algorithm (GA) previously employed declines with the expansion of the search space. In order to improve performance, we present a multistage evolutionary strategy, where different settings are applied based on the current stage of the GA search. The proposed evolutionary approach provided solutions with the least error in the set of experiments, confirming the improvement over the previous approach.