Genetic Algorithms Principles Towards Hidden Markov Model

Abstract

In this paper we propose a general approach based on Genetic Algorithms (GAs) to evolve Hidden Markov Models (HMM). The problem appears when experts assign probability values for HMM, they use only some limited inputs. The assigned probability values might not be accurate to serve in other cases related to the same domain. We introduce an approach based on GAs to find
out the suitable probability values for the HMM to be mostly correct in more cases than what have been used to assign the probability values.