Why Are Clonal Selection Algorithms Markov?
by Khiria E . El-Nady, Usama H. Abou El-Enien, and Amr A. Badr.
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 finite
dimensional Markov and lumped Markov chains, which confirm 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 finite
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 unified 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 classification 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 of
Clonal selection algorithms, lumped Markov chains,
Classification, central limit theorem,
Stationary multivariate normal distribution, Unique chromosomes
Khiria E . El-Nady, firstname.lastname@example.org
Usama H. Abou El-Enien, email@example.com
Amr A. Badr, firstname.lastname@example.org
Di Lorenzo, Renato, email@example.com
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