neuroevolution mechanism for hidden markov model

Hidden Markov Model (HMM) is a statistical model based on probabilities. HMM is becoming one of the major models involved in many applications such as natural language<br />processing, handwritten recognition, image processing, prediction systems and many more. In this research we are concerned with finding out the best HMM for a certain application domain. We propose a neuroevolution process that is based first on converting the HMM to a neural network, then generating many neural networks at random where each represents a HMM. We proceed by<br />applying genetic operators to obtain new set of neural networks where each represents HMMs, and updating the population. Finally select the best neural network based on a fitness function.