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2013 | OriginalPaper | Chapter

The Application of Ant Colony Optimization in CBR

Author : Jianhua Shu

Published in: Proceedings of The Eighth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA), 2013

Publisher: Springer Berlin Heidelberg

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Abstract

In the Case-Based Reasoning (CBR) System, the retrieval efficiency and system performance are reduced because of the unlimited increasing case base with the incremental learning. This paper proposes the method of ant colony optimization (ACO) in the CBR system. This method combines the increased efficiency of case retrieval, the effective case base indexing, and the validity of maintenances by adding or reducing cases. Through the all processes we have used the clustering and classification algorithm based ACO. The implementation of the ACO algorithm into the CBR system is successful and the experimental results verify its effectiveness.

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Metadata
Title
The Application of Ant Colony Optimization in CBR
Author
Jianhua Shu
Copyright Year
2013
Publisher
Springer Berlin Heidelberg
Book
Proceedings of The Eighth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA), 2013

Print ISBN: 978-3-642-37501-9

Electronic ISBN: 978-3-642-37502-6

Copyright Year: 2013

DOI
https://doi.org/10.1007/978-3-642-37502-6_143

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