Classification of Cellular Automata

We consider hexagonal cellular automata with immediate cell neighbourhood and three cell-states. Every cell calculates its next state depending on the integral representation of states in its neighbourhood, i.e., how many neighbours are in each one state. We employ evolutionary algorithms to breed local transition functions that support mobile localizations (gliders), and characterize sets of the functions selected in terms of quasi-chemical systems. Analysis of the set of functions evolved allows to speculate that mobile localizations are likely to emerge in the quasi-chemical systems with limited diffusion of one reagent, a small number of molecules are required for amplification of travelling localizations, and reactions leading to stationary localizations involve relatively equal amount of quasi-chemical species. Techniques developed can be applied in cascading signals in nature-inspired spatially extended computing devices, and phenomenological studies and classification of non-linear discrete systems.

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Classification of Satellite Images Using the Cellular Automata Approach

The Open Knowlege Society. A Computer Science and Information Systems Manifesto - Communications in Computer and Information Science ◽

10.1007/978-3-540-87783-7_66 ◽

2008 ◽

pp. 521-526 ◽

Cited By ~ 3

Author(s):

Moisés Espínola ◽

Rosa Ayala ◽

Saturnino Leguizamón ◽

Massimo Menenti

Keyword(s):

Cellular Automata ◽

Satellite Images

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Automatic Texture Based Classification of the Dynamics of One-Dimensional Binary Cellular Automata

International Journal of Natural Computing Research ◽

10.4018/ijncr.2019100104 ◽

2019 ◽

Vol 8 (4) ◽

pp. 41-61

Author(s):

Marcelo Arbori Nogueira ◽

Pedro Paulo Balbi de Oliveira

Keyword(s):

Cellular Automata ◽

Dynamic Behavior ◽

Temporal Evolution ◽

Computational Cost ◽

Great Variability ◽

Texture Descriptor ◽

One Dimensional ◽

Binary Images ◽

Elementary Cellular Automata

Cellular automata present great variability in their temporal evolutions due to the number of rules and initial configurations. The possibility of automatically classifying its dynamic behavior would be of great value when studying properties of its dynamics. By counting on elementary cellular automata, and considering its temporal evolution as binary images, the authors created a texture descriptor of the images - based on the neighborhood configurations of the cells in temporal evolutions - so that it could be associated to each dynamic behavior class, following the scheme of Wolfram's classic classification. It was then possible to predict the class of rules of a temporal evolution of an elementary rule in a more effective way than others in the literature in terms of precision and computational cost. By applying the classifier to the larger neighborhood space containing 4 cells, accuracy decreased to just over 70%. However, the classifier is still able to provide some information about the dynamics of an unknown larger space with reduced computational cost.

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