DocumentCode
2182955
Title
Hybrid Genetic Algorithm with Baum-Welch Algorithm by using Diversity Population Technique
Author
Nootyaskool, Supakit ; Kruatrachue, Boontee
Author_Institution
Dept. of Comput. Eng., King Mongkut´´s Inst. of Technol., Bangkok
fYear
2006
fDate
Oct. 18 2006-Sept. 20 2006
Firstpage
15
Lastpage
20
Abstract
Baum-Welch Algorithm (BWA) have used in recognition systems, many researchers have improved BWA performances by using hybrid genetic algorithm (HGA). This paper presents a new HGA technique by using diversity population structure. We surveyed HGA techniques and divided into four types. There were separate processes, population types, fitness determiners, and diversity population structure. A technique of diversity population structure protected applying BWA to similar population. Different population structures make available GA to find optimum point quickly. This paper compared all of HGA techniques, which there trained on hidden Markov models (HMM), in an application Thai off-line handwritten recognition, we used database from NECTEC. An experiment of HGA, HMM probability of diversity population techniques get better than techniques of population types 52.65% improvement and there better than techniques of separately process 37.91% improvement. Moreover, HGA experimented five times repeatedly, standard derivation value of diversity population techniques showed closely results
Keywords
genetic algorithms; handwritten character recognition; hidden Markov models; image recognition; Baum-Welch algorithm; diversity population structure; handwritten recognition; hidden Markov models; hybrid genetic algorithm; recognition systems; Algorithm design and analysis; Biological cells; Databases; Evolutionary computation; Genetic algorithms; Genetic engineering; Handwriting recognition; Hidden Markov models; Mathematics; Protection;
fLanguage
English
Publisher
ieee
Conference_Titel
Communications and Information Technologies, 2006. ISCIT '06. International Symposium on
Conference_Location
Bangkok
Print_ISBN
0-7803-9741-X
Electronic_ISBN
0-7803-9741-X
Type
conf
DOI
10.1109/ISCIT.2006.339879
Filename
4141505
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