Modeling of Physical Phenomena by Probabilistic Cellular Automaton

A new approach of modeling of physica1 phenomena by using a probabilistic cellular automaton is proposed. Our model aims to reproduce a spatio-temporal black and white pattern (e.g.， patterns obtained by Ising model， Diffusion Limited Aggregation model). Our model has three model parameters that characterize the nearest neighbor interaction. Optimization of the parameters is carried out by genetic algorithms. Two kinds of genetic algorithms are tested here: a classical one and more sophisticated one. Although convergence of the model parameters by sophisticated one is faster than classical one， the same optimum prameters is obtained by both algorithms