Cellular automata; Chaos; Complex system; Differential evolution; SIR models; SIS models

The article deals with principles of cellular automata(CA) and differential evolution(DE) within simulation of an epidemic process. CA consist of many identical components, each simple, but together capable of complex behaviour. The proposed CA supports to find rules of the transition function that represents the model of a studied epidemic. Search for models a studied epidemic belongs to inverse problems whose solution lies in a finding of local rules guaranteeing a desired global behaviour. The epidemic models have the control parameters and their setting significantly influences the behaviour of the models. One way how to get proper values of the control parameters is use DE. Simulations of illness were performed using both described approaches. The aim of the paper is to show a course of simulations for different rules of the transition function and how to find a suitable model of a studied epidemic in the case of inverse problems using a sufficient amount of local rules of a transition function.