Training HMM Structure and Parameters with Genetic Algorithm and Harmony Search Algorithm

Abstract

-In this paper, we utilize training strategy of hidden Markov model (HMM) to use in versatile issues such as classification of time-series sequential data such as electric transient disturbance problem in power system. For this, an automatic means of optimizing HMMs would be highly desirable, but it raises important issues: model interpretation and complexity control. With this in mind, we explore the possibility of using genetic algorithm (GA) and harmony search (HS) algorithm for optimizing the HMM. GA is flexible to allow incorporating other methods, such as Baum-Welch, within their cycle. Furthermore, operators that alter the structure of HMMs can be designed to simple structures. HS algorithm with parameter-setting free technique is proper for optimizing the parameters of HMM. HS algorithm is flexible so as to allow the elimination of requiring tedious parameter assigning efforts. In this paper, a sequential data analysis simulation is illustrated, and the optimized-HMMs are evaluated. The optimized HMM was capable of classifying a sequential data set for testing compared with the normal HMM.

A novel algorithm for parameter estimation of Hidden Markov Model inspired by Ant Colony Optimization

“…The problem studied in this paper is the UPE problem. There are many papers that have tried to solve this problem by heuristic methods such as [29], [30], [31], [32], [10]. The UPE problem is to estimate matrices A0.25em,E.…”

The Reputation Evaluation Based on Optimized Hidden Markov Model in E-Commerce

“…Recently, various intelligent evolutionary algorithms are introduced to optimize HMM and achieve good performance. Reference [25] optimizes HMM by tabu search algorithm; [26,27] propose to train HMM structure with genetic algorithm (GA); [28] trains HMM by Particle Swarm Optimization (PSO) algorithm; [29,30] make a comparison between PSO and GA for HMM training and demonstrate that the hybrid algorithm based on PSO and BW is superior to BW algorithm and the hybrid algorithm based on GA and BW. For HMM, model parameters need to satisfy statistical characteristics: the optimization of model parameters in HMM can be considered as a constraint problem.…”