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- W2121912524 abstract "In order to improve code reliability and development productivity, software reuse is a clear solution and a reuse library based on object-oriented technology is essential. It is also very important to classify components elaborately and retrieve them accurately in the reuse library. In this paper, we present genetic algorithms for multi-way clustering, in which the number of clusters, similarity in a cluster and similarity between clusters are taken into consideration with the aim of finding optimized clusters into which components are classified, and for cluster-based linear retrieval with the aim of finding an optimal query which retrieves clusters containing components similar to a given query. We compare genetic algorithms with simulated annealing algorithms for multi-way clustering and cluster-based retrieval. The results of our experiments demonstrate that generic algorithms produce better solutions than those obtained by simulated annealing algorithms. We implemented a Reusable Class Library (RCL) using these methods, which is based on CORBA." @default.
- W2121912524 created "2016-06-24" @default.
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- W2121912524 date "2002-11-27" @default.
- W2121912524 modified "2023-09-26" @default.
- W2121912524 title "Optimization of multi-way clustering and retrieval using genetic algorithms in reusable class library" @default.
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- W2121912524 doi "https://doi.org/10.1109/apsec.1998.733547" @default.
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