Optimization of DHMM Based on Chaotic Migration-Based GA for Chinese Signature Verification

DOI

10.2991/iske.2007.108How to use a DOI?

Keywords

Genetic Algorithm; Hidden Markov Model; Chaos; Migration Strategy

Abstract

In this paper, Genetic Algorithm (GA) is used to train the parameters of Discrete Hidden Markov Model (DHMM). To overcome the premature convergence in GA, a chaotic migration strategy is introduced to the pseudo parallel genetic algorithm to increase the diversity of population. Because the GA’s evolution speed is very slow, the Baum-Welch is applied to the GA. The Baum-Welch algorithm, which is used as speed operator, generates part of initial population of GA and is applied to the solutions, so the needs of evolution generations of GA can be decreased, which helps for overcoming the lacking that GA needs a long time to converge. A floating matrix encoding mechanism is used for reflecting internal relations consisted in parameters of DHMM. This encoding method reduces the searching range of solutions space and increases the searching efficiency further. By using GA, the number of states can be adjusted dynamically. At last, the proposed method is used for signature verification. The promising experiment result indicates that the chaotic migration-based GA can optimize DHMM effectively.

Copyright

© 2007, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Zhenhua Wu
PY  - 2007/10
DA  - 2007/10
TI  - Optimization of DHMM Based on Chaotic Migration-Based GA for Chinese Signature Verification
BT  - Proceedings of the 2007 International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2007)
PB  - Atlantis Press
SP  - 630
EP  - 635
SN  - 1951-6851
UR  - https://doi.org/10.2991/iske.2007.108
DO  - 10.2991/iske.2007.108
ID  - Wu2007/10
ER  -