Abstract
Genetic algorithm (GA) is one of the most widely used non-traditional optimization tools for various researches and industrial applications. It is a powerful technique for global search. However, it suffers from the problems of poor local search capability and slow convergence rate. There had been several attempts to remove these limitations of a GA in various ways. In this study, for a GA, a new search space reduction technique, which is nothing but a method to confine the search process in the squeezed ranges of variables under some specific conditions, has been proposed to improve its overall performance. The proposed method has been designed in such a way that the convergence rate of a GA can be made faster after keeping a proper balance between the exploration and exploitation phenomena of the algorithm. The performance of a GA with the proposed technique has been tested on a set of ten classical benchmark functions and the results are compared with that of a conventional GA. This experiment clearly reveals the ability of our proposed method to improve the convergence rate of a GA to a considerable amount.
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Das, A.K., Pratihar, D.K. (2019). A New Search Space Reduction Technique for Genetic Algorithms. In: Mandal, J., Sinha, D., Bandopadhyay, J. (eds) Contemporary Advances in Innovative and Applicable Information Technology. Advances in Intelligent Systems and Computing, vol 812. Springer, Singapore. https://doi.org/10.1007/978-981-13-1540-4_12
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DOI: https://doi.org/10.1007/978-981-13-1540-4_12
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