To live and die in CA

This thesis investigates the nature of elementary cellular automata to better understand their relationship of the models they support to the biological organisms that create the mats and soil crusts found in extreme environments here on earth. Cellular automata have been used to study growth and patterns in forests, arid desert environments, predator-prey problems, and sea shells. It has also been used to study areas of diverse epidemiology and linguistics. Cellular automata have been used as the core of computer games as well. This investigation has led to develop a graphical grammar for simple cellular automata, using L-systems, a grammar system designed by a biologist, Aristid Lindenmayer, to describe growth in biological systems. Also discussed is scaling algorithms, and the associated variable dependencies that create them. All of the cellular automata investigated in this thesis are totalistic (they update simultaneously). Random updating of cells in models to simulate the random availability of resources (water and nutrients) could be especially useful in models of resource limited ecologies like deserts, the artic, and even Mars

Optimal Rules Identification for a Random Number Generator Using Cellular Learning Automata

The cryptography is known as one of most essential ways for protecting information against threats. Among all encryption algorithms, stream ciphering can be indicated as a sample of swift ways for this purpose, in which, a generator is applied to produce a sequence of bits as the key stream. Although this sequence is seems to be random, severely, it contains a pattern that repeats periodically. Linear Feedback Shift Registers and cellular automata have been used as pseudo-random number generator. Some challenges such as error propagation and pattern dependability have motivated the designers to use CA for this purpose. The most important issue in using cellular automata includes determining an optimal set of rules for cells. This paper focuses on selecting optimal rules set for such this generator with using an open cellular learning automata, which is a cellular automata with learning capability and interacts with local and global environments