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Comparison of 2D Stochastic and Probabilistic Cellular Automata based on Unique Rules in Terms of Runtime and Convergence

SACI 2025 IEEE 19th International Symposium on Applied Computational Intelligence and Informatics

Óbuda University, Budapest, Hungary; Politehnica University of Timişoara, Romania; IEEE Chapter of SMC, Romania

Timişoara

Romania

2025.05.19.

2025.05.24.

Cellular automata models are a prominent tool for modelling discrete dynamical systems. Especially in the study of the evolution of spatial patterns over time. The aim of the present research is to statistically compare stochastic and probabilistic cellular automata models based on individual rules in terms of running time and convergence. Stochastic models use random rules, while probabilistic models are based on probabilistic rules, which may lead to more deterministic behaviour. In the analyses, the Moran index was used to measure the convergence of spatial patterns, while the running time was measured directly in terms of iterations. The results show that probabilistic models are significantly faster and more stable, while stochastic models show higher variability. A deeper understanding of these differences may contribute to the development of a complex cellular automata application.