الفهرس 
ACKNOWLEDGMENTOnly 14 pages are availabe for public view
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Abstract
In this thesis, we proposed a model that can work on both of DNA evolution and DNA reconstruction with the aid of Probabilistic Cellular Automata. A strand of DNA is formed by a sequence of four bases (A, C, G and T). This strand can be viewed as a row of cells with each cell holds one of the four bases. The focus here will be on models that are based on the concept of cellular automata (CA). CAs are discrete computational models that are capable of universal computation, in other words, they are capable of doing any computation that a normal computer can do. Occasionally, mutation arises within replication which led to a DNA sequence which is distinct from the actual one. These mutations are the main causes of evolution. Firstly, DNA evolution is studied as a probabilistic model. We have followed the concept that takes into account neighbor-dependent mutation effects. Besides, this principle has been strengthened more by adding the effect of the left neighbor beside the right one as the cell affected by them together. Our model concentrates on the neighbor-dependent strategy of DNA sequence changes by incorporating stochastic elements to it through using PCA. Secondly, a modified method for the reconstruction process based on Probabilistic Cellular Automata (PCA) integrated with Particle Swarm Optimization (PSO) algorithm is introduced. PSO is used to find out the optimal and appropriate transition rules of cellular automata (CA) for the reconstruction task. This combination increases the efficiency of the algorithm. The CA rules are used for analysis and predictions of DNA sequence.