GAQPR and its application in discovering frequent structures in time series

Author

Bin, Li ; Junan, Yang ; Zhenquan, Zhuang

Author_Institution

Dept. of Electron. Sci. & Technol., Univ. of Sci. & Technol. of China, Hefei, China

Volume

1

fYear

2003

fDate

14-17 Dec. 2003

Firstpage

399

Abstract

A new genetic algorithm based on the quantum probability representation (GAQPR) is proposed, in which each individual evolves independently; a new crossover operator is designed to integrate searching processes of multiple individuals into a more efficient global searching process; a new mutation operator is also proposed. The algorithm is used to discover frequent structures in time series, experiment results show that the GAQPR is efficient for the complex multi-peak searching problem.

Keywords

genetic algorithms; probability; quantum computing; time series; GAQPR; complex multipeak searching problem; crossover operator; frequent structures discovery; genetic algorithm based on the quantum probability representation; global searching process; mutation operator; searching processes; time series; Algorithm design and analysis; Biological cells; Data mining; Genetic algorithms; Genetic mutations; Heuristic algorithms; Merging; Quantum computing; Quantum mechanics;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks and Signal Processing, 2003. Proceedings of the 2003 International Conference on

Conference_Location

Nanjing

Print_ISBN

0-7803-7702-8

Type

conf

DOI

10.1109/ICNNSP.2003.1279293

Filename

1279293