Abstract

Cellular automata (CA) are parallel computational models that comprise of a grid of cells. CA is mainly used for modeling complex systems in various fields, where the geometric structure of the lattices is different. In the absence of a CA model to accommodate different types of lattices in CA, an angle-based CA model is proposed to accommodate various lattices. In the proposed model, the neighborhood structure in a two dimensional cellular automata (2D-CA) is viewed as a star graph. The vertices of the proposed graph are determined by a parameter, angle ( θ ) . Based on the angle ( θ ) , the neighborhood of the CA, which is treated as the vertices of the graph, varies. So this model is suitable for the representation of different types of two dimensional lattices such as square lattice, rectangular lattice, hexagonal lattice, etc. in CA. A mathematical model is formulated for representing CA rules which suit for different types of symmetric lattices. The star graph representation helps to find out the internal symmetries exists in CA rules. Classification of CA rules based on the symmetry exists in the rules, which generates symmetric patterns are discussed in this work.

Highlights

  • Cellular automata (CA) [1] are computational model which consists of a grid of cells where each of the cells acts as a finite automaton

  • We propose the classification of CA rules based on the amount of the internal symmetries that exists in the CA rules, which will lead to symmetric patterns generated by a CA

  • In order to measure the symmetry in a rule, which is represented as r-star graph, a Θ-transform based on graph automorphism is used

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Summary

Introduction

Cellular automata (CA) [1] are computational model which consists of a grid of cells where each of the cells acts as a finite automaton. The neighborhood of a cell Cij in a two dimensional CA is the set of surrounding cells on which the updation of Cij depends upon. The updation of each cell is based on a transition function, which is called a rule of the CA. The states of each cell get updated in unit time steps in parallel, based on rules which are characterized by the neighborhood in synchronous fashion. The states of the CA get updated by the application of rules uniformly in each cell in the CA and it produces patterns at each time step. The output pattern of the CA shows the dynamic and complex behavior. The pattern generated by the CA is the cumulative observation of the output of the CA in successive time steps

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