College of Mechanical Engineering, University of Shanghai for Science and Technology, 200093, Shanghai

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Abstract—Aiming at improving search efficiency limitations of canonical real coded genetic algorithm, this paper improves three aspects for the canonical real coded genetic algorithm, that are initial population generating, overall process of algorithm and the mutation operator, then puts forward an improved real coded genetic algorithm. This improved algorithm combines the series operation and parallel operation of the three basic operators (selection, crossover and mutation), presents new methods of uniform generating initial population and progressive variation, the former contributes to generating good initial population, the latter processes more active mutation operation on the basic of controlling the average fitness in a certain range, which can obtain advantage individual and reduce evolution time. Using typical standard test functions for two algorithms’ simulation research, the simulation results show that, the proposed real coded genetic algorithm is superior to the canonical real coded genetic algorithm on the aspects of search speed and search accuracy, improving search efficiency of canonical real coded genetic algorithm greatly.

Index Terms—real coded genetic algorithm, improve, series operation and parallel operation, progressive variation, uniform generating of initial population

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Cite: Jianmin Zhu, Donger Zhou, Fucai Li, Tingting Fu, "Improved Real Coded Genetic Algorithm and Its Simulation," Journal of Software vol. 9, no. 2, pp. 389-397, 2014.