الفهرس 
acknowledgmentOnly 14 pages are availabe for public view
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Abstract
Genetic and clonal selection algorithms are considered. Three algorithms are designed and executed to obtain purely empirical analysis conclusions in order to turn to purely theoretical analysis results about the behavior of these algorithms as Markov chains, which confirm the conjectures from these experiments and in order to introduce a complete framework toward a new philosophy of MCMC methods. First, we model genetic and clonal selection algorithms using Markov chains. Second, we carryon a particle analysis and analyze the convergence properties of these algorithms. Third, we produce two unified Markov approaches and propose unique chromosomes for C1 purely successful optimization of these algorithms.