Knowledge-independent data mining with fine-grained parallel evolutionary algorithms

This paper illustrates the application of evolutionary algorithms (EA) to data mining problems. The objectives are to demonstrate that EA can provide a competitive general purpose data mining scheme for classification tasks without constraining the knowledge representation, and that it can be achieved reducing the amount of time required using the inherent parallel processing nature of EA. Experiments were performed with GALE, a fine-grained parallel evolutionary algorithm, on several artificial, public domain and private datasets. The empirical results suggest that EA are competitive and robust data mining schemes that scale up better than non-evolutionary well-known schemes.

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