Efficient annealing -inspired genetic algorithm for information retrieval from web-document

Authors:
Yuan Xu;Yang Deli;Liu Yu
Affiliations:
Software School, Dalian University of Technology, Dalian, China;Software School, Dalian University of Technology, Dalian, China;Software School, Dalian University of Technology, Dalian, China
Venue:
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Year:
2009

Citing 5
Cited 0

Applying genetic algorithms to query optimization in document retrieval

Information Processing and Management: an International Journal
Sub optimal scheduling in a grid using genetic algorithms

Parallel Computing - Special issue: Parallel and nature-inspired computational paradigms and applications
The Anatomy of the Grid: Enabling Scalable Virtual Organizations

International Journal of High Performance Computing Applications
Efficient Genetic Algorithm Based Data Mining Using Feature Selection with Hausdorff Distance

Information Technology and Management
Web mining in soft computing framework: relevance, state of the art and future directions

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

With the huge amount of information available online, the World Wide Web is a fertile area for data mining research. The Web mining research is at the cross road of research from several research is at the cross road of research from several research communities. In this paper, a new adaptive method of mining web documents is proposed. We give an algorithm which combines genetic algorithm and simulated annealing based on vector space model. This algorithm avoids the disadvantage of web documents by using pure genetic algorithm which can not be utilized accurately .Experimental results indicate that this adaptive method significantly improves the performance in retrieval accuracy.