Abstract
In this paper, an enhanced version of Harmony Search (HS), called Tournament Selective Harmony Search (TSHS) is used to obtain an optimal set of weights for Functional Link Artificial Neural Network (FLANN) with Gradient Descent Learning (GDL) for the task of classification in data mining. The TSHS performs better than HS and Improved HS (IHS) by avoiding random selection of harmonies for their improvisation by introducing tournament selection strategy. This approach of TSHS to acquire optimal harmony in a population of harmony memory is adopted to find out optimal set of weights for FLANN model. The proposed TSHS-GDL-FLANN is compared with other alternatives by examining on various benchmark datasets from UCI Machine Learning repository. In order to get statistical correctness of results, the proposed method is analyzed by using ANOVA statistical test under null-hypothesis.
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