Knowledge-based genetic algorithms

Authors:
Gaowei Yan;Gang Xie;Zehua Chen;Keming Xie
Affiliations:
College of Information Engineering, Taiyuan University of Technology, Taiyuan, Shanxi, P.R. China;College of Information Engineering, Taiyuan University of Technology, Taiyuan, Shanxi, P.R. China;College of Information Engineering, Taiyuan University of Technology, Taiyuan, Shanxi, P.R. China;College of Information Engineering, Taiyuan University of Technology, Taiyuan, Shanxi, P.R. China
Venue:
RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
Year:
2008

Citing 7
Cited 0

Rough sets: probabilistic versus deterministic approach

International Journal of Man-Machine Studies
Rough sets and dependency analysis among attributes in computer implementations of expert's inference models

International Journal of Man-Machine Studies
Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
Classification of meteorological volumetric radar data using rough set methods

Pattern Recognition Letters - Special issue: Rough sets, pattern recognition and data mining
On Modeling Data Mining with Granular Computing

COMPSAC '01 Proceedings of the 25th International Computer Software and Applications Conference on Invigorating Software Development
Rough Genetic Algorithms

RSFDGrC '99 Proceedings of the 7th International Workshop on New Directions in Rough Sets, Data Mining, and Granular-Soft Computing
Rough sets and information granulation

IFSA'03 Proceedings of the 10th international fuzzy systems association World Congress conference on Fuzzy sets and systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, Rough Set Theory (RST) was introduced to discover 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. Experiment results have shown that the proposed method has higher searching efficiency, faster convergent speed, and good performance for deceptive problem and multi-modal problems.