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Clustering algorithms, a fundamental base for data mining procedures and learning techniques, suffer from the lack of efficient methods for determining the optimal number of clusters to be found in an arbitrary dataset. The few methods existing in the literature always use some sort of evolutionary algorithm having a cluster validation index as its objective function. In this article, a new evolutionary...
This work presents a stochastic local search method for efficiently solve the scheduling problem in heterogeneous computing environments. The research community has been searching for accurate schedulers for heterogeneous computing systems, able to perform in reduced times. The stochastic search proposed in this work is based on simple operators in order to keep the computational complexity as low...
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