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A new approach to dynamic systems modeling is given. Stochastic Cellular Automata (SCA) are used as the basic computational module. The dynamic systems are considered as time and space dependent, where time dependencies are supposed to be given with some differential equations (DE), while space influences are not known. The basic idea of our approach is to use heuristics for the design of SCA and some stochastic search algorithm to optimize free model parameters. Two non-gradient optimization algorithms are used and evaluated on the two case studies: diffusion and migration of Cs in soil and forest fire spread problem. They are Evolutionary Algorithm (EA) and Stochastic Correlation Algorithm (ALOPEX). We show that with some modifications, both algorithms are capable to solve the two case problems, though there are some important differences between them

Dynamic Systems Modeling with Stochastic Cellular Automata (Evolutionary versus Stochastic Correlation Approach)