Combining Maximum Likelihood Estimate with Constraint Generation (ML-CG) Method by Using the Genetic Algorithm to Estimate the Parameter of Boltzmann's Distribution Represented by RNA

Abstract

With the enormous scientific progress that technology is witnessing at the moment, new types of systems have emerged called smart systems. That have been developed and used in many current applications Genetic algorithms. Were used in this research to study the distribution of Boltzmann, which is subject to the composition of ribosomal RNA, and included the suggestion of a genetic algorithm that combines the method of the maximum likelihood with the method of generation constraint to estimate the Boltzmann distribution parameter represented by the RNA parameter. The results showed that the embedded genetic algorithm is better for estimating the RNA string parameter than previous methods.Matlab has been used in writing research algorithms and finding results.