Web Mining Based on Hybrid Simulated Annealing Genetic Algorithm and HMM
Gong Xiang-jian · Computer Technology and Development · 2012
The training algorithm which is used to training HMM is a sub-optimal algorithm and sensitive to initial parameters.Typical hidden Markov model often leads to sub-optimal when training it with random parameters.It is ineffective when mining Web information with typical HMM.GA has the excellent ability of global searching and has the defect of slow convergence rate.SA has the excellent ability of local searching and has the defect of randomly roaming.It combines the advantages of genetic algorithm and simulated annealing algorithm,proposes hybrid simulated annealing genetic algorithm(SGA).SGA chooses the best SGA parameters by experiment and optimizes HMM combining Baum-Welch during the course of Web mining.The experimental results show that the SGA significantly improves the performance in precision and recall.