Abstract
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 crossover and mutation strategies.
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References
Back, T. and H.-P. Schwefel (1993). „An Overview of Evolutionary Algorithms for Parameter Optimization.“ Evolutionary Computation 1(1): 1–23.
Bonzano, A., P. Cunningham, et al. (1997). Using Introspective Learning to Improve Retrieval in CBR: A Case Study in Air Trafic Control. International Conference on Case-Based Reasoning (ICCBR’97), Providence, RI, USA, Springer-Verlag.
Bruninghaus, S. andK. Ashley (1997). Using Machine Learning for Assigning Indice to Textual Cases. Second International Conference on Case-Based Reasoning (ICCBR’97), Providence, RI, USA, Springer-Verlag.
Fox, S. and D. Leake (1995). Learning to Refine Indexing by Introspective Learning. International Conference on Case-Based Reasoning (ICCBR’95), Sesimbra, Portugal, Springer-Verlag.
Goel, A. (1992). Representation of Design Functions in Experience-Based Design. Intelligent Computer Aided Design. D. Brown, M. Waldron and H. Yosnikawa, Elsevier Science.
Gomes, P. and C. Bento (1999). Converting Programs into Cases for Software Reuse. International Joint Conference on Artificial Intelligence (IJCAI 99) Workshop: Automating the Construction of Case-Based Reasoners, Stockholm, Sweden.
Katalagarianos, P. and Y. Vassiliou (1995). „On the reuse of software: a case-based approach employing a repository.“ Automated Software Engineering 2: 55–86.
Prieto-Diaz, R. (1993). „Status Report: Software Reusability.“ IEEE Software(May).
Ricci, F. and P. Avesani (1995). Learning a Local Similarity Metric for Case-Based Reasoning. International Conference on Case-Based Reasoning (ICCBR’95), Sesimbra, Portugal, Springer-Verlag.
Watson, I. (1997). Applying Case-Based Reasoning: techniques for enterprise systems. Morgan Kaufmann.
Wettschereck, D. and D. Aha (1995). Weighting Features. International Conference on Case-Based Reasoning (ICCBR’95), Sesimbra, Portugal, Springer-Verlag.
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Gomes, P., Bento, C. (2000). Learning User Preferences in Case-Based Software Reuse. In: Blanzieri, E., Portinale, L. (eds) Advances in Case-Based Reasoning. EWCBR 2000. Lecture Notes in Computer Science, vol 1898. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44527-7_11
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DOI: https://doi.org/10.1007/3-540-44527-7_11
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