Abstract
This paper presents a new hybrid genetic algorithm (HGA) for feature selection (FS) called as HGAFS. HGAFS incorporates a new local search operation that is devised and embedded in HGA to fine-tune the search in FS. The proposed local search operation works on basis of the distinct and informative nature of input features that is computed by their correlation information. The aim of using correlation information is to encourage the local search strategy for selecting less correlated (distinct) features. Such an encouragement reduces the redundancy of information in the generated subset of salient features. We have tested our methods on several real-world datasets and have compared the performances with the results of other existing algorithms. It is found that HGAFS produces consistently better performances.
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