Knowledge-Based Genetic Algorithms and its Application in Multi-Sensor Fusion

Author

Niu, Yuguang ; Yan, Gaowei ; Gang Xie ; Chen, Zehua ; Xie, Gang

Author_Institution

Coll. of Inf. Eng., Taiyuan Univ. of Technol., Taiyuan

Volume

1

fYear

2008

fDate

2-4 Sept. 2008

Firstpage

485

Lastpage

490

Abstract

In this paper, rough set theory (RST) was introduced to discovery knowledge hidden in the evolution process of Genetic Algorithm. Firstly it was used to analyze correlation between individual variables and their fitness function. Secondly, eigenvector was defined to judge the characteristic of the problem. And then the knowledge discovered was used to select evolution subspace and to realize knowledge-based evolution. The result of weight-value optimization of the neural network in multi-sensor information fusion system shows that this method is able to effectively improve the study efficiency and study precision for neural networks.

Keywords

data mining; eigenvalues and eigenfunctions; genetic algorithms; knowledge based systems; neural nets; rough set theory; sensor fusion; eigenvector; evolution process; evolution subspace selection; fitness function; knowledge discovery; knowledge-based genetic algorithm; multi sensor information fusion system; neural network; rough set theory; weight-value optimization; Computer networks; Data analysis; Evolution (biology); Genetic algorithms; Humans; Information analysis; Knowledge representation; Neural networks; Optimization methods; Set theory; Genetic Algorithms (GAs); Granular Computing; Neural Network; Rough set theory; knowledge discovery; knowledge evolution;

fLanguage

English

Publisher

ieee

Conference_Titel

Networked Computing and Advanced Information Management, 2008. NCM '08. Fourth International Conference on

Conference_Location

Gyeongju

Print_ISBN

978-0-7695-3322-3

Type

conf

DOI

10.1109/NCM.2008.14

Filename

4624056