Applying case-based reasoning: techniques for enterprise systems
Applying case-based reasoning: techniques for enterprise systems
Status Report: Software Reusability
IEEE Software
Learning to Refine Indexing by Introspective Reasoning
ICCBR '95 Proceedings of the First International Conference on Case-Based Reasoning Research and Development
Learning a Local Similarity Metric for Case-Based Reasoning
ICCBR '95 Proceedings of the First International Conference on Case-Based Reasoning Research and Development
ICCBR '95 Proceedings of the First International Conference on Case-Based Reasoning Research and Development
Using Introspective Learning to Improve Retrieval in CBR: A Case Study in Air Traffic Control
ICCBR '97 Proceedings of the Second International Conference on Case-Based Reasoning Research and Development
Using Machine Learning for Assigning Indices to Textual Cases
ICCBR '97 Proceedings of the Second International Conference on Case-Based Reasoning Research and Development
An overview of evolutionary algorithms for parameter optimization
Evolutionary Computation
An Analysis of Research Themes in the CBR Conference Literature
ECCBR '08 Proceedings of the 9th European conference on Advances in Case-Based Reasoning
Combining case-based and similarity-based product recommendation
ECCBR'06 Proceedings of the 8th European conference on Advances in Case-Based Reasoning
| Hi-index | 0.00 |
Case-Based Reasoning is a good framework for Software Reuse because it provides a flexible and powerful searching mechanism for software components. In a CBR system for software reuse it is important to learn the user preferences adapting the system software choices to the user. In a complex domain as software design, the similarity metric will also be complex, thus creating the necessity for a learning algorithm capable of weight learning. In this paper we present an evolutionary approach to similarity weight learning in a CBR system for software reuse. This approach is justified by the similarity metric complexity and recursive nature, which makes other learning methods to fail. We present experimental work showing the feasibility of this approach and we also present a parametric study, exploring several cross-over and mutation strategies.