Enhancing the diversity of genetic algorithm for improved feature selection

Publisher:
IEEE
Publication Type:
Conference Proceeding
Citation:
2010 IEEE International Conference on Systems Man and Cybernetics (SMC), 2011, pp. 1325 - 1331
Issue Date:
2011-01
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Genetic algorithm (GA) is one of the most widely used population-based evolutionary search algorithms. One of the challenging optimization problems in which GA has been extensively applied is feature selection. It aims at finding an optimal small size subset of features from the original large feature set. It has been found that the main limitation of the traditional GA-based feature selection is that it tends to get trapped in local minima, a problem known as premature convergence. A number of implementations are presented in the literature to overcome this problem based on fitness scaling, genetic operator modification, boosting genetic population diversity, etc. This paper presents a new modified genetic algorithm based on enhanced population diversity, parents' selection and improved genetic operators. Practical results indicate the significance of the proposed GA variant in comparison to many other algorithms from the literature on different datasets.
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