Genetic 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 genetic algorithms as a finite dimensional Markov and....

Clonal selection algorithms are considered. Two algorithms are designed and executed to obtain purely empirical analysis conclusions in order to turn to purely theoretical analysis results about the behavior of clonal selection algorithms as a nite dimensional Markov and lumped Markov chains, which conrm the conjectures from these experiments and in order to introduce a complete framework toward a new philosophy of machine intelligence. First, we model clonal selection algorithms using a nite dimensional Markov and lumped Markov chains. Second, we carry on a particle analysis (the basic component) and analyze the convergence properties of these algorithms. Third, we produce two unied Markov and lumped Markov approaches for analysis for a complete framework and propose unique chromosomes for a purely successful optimization of these algorithms. Furthermore, for the Markov approach, we obtain purely theoretical analysis for a classication and Stationary distributions of chains. For the lumped Markov approach, we obtain purely theoretical analysis for all possible conditional multivariate normal distributions of transition probability matrices and stationary multivariate normal distributions ofchains.