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2015•Shuxue de shijian yu renshiRequires access

An Improved Adaptive Genetic Algorithm

YU Guang-shua

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Abstract

In view of the problem of the premature convergence of the IAGA adaptive genetic algorithm,an improved adaptive genetic algorithm(NIAGA algorithm) is proposed.The custom discriminant determines whether a group appeard a trend of the premature convergence,then both macroeconomic regulation and control and micro processing method are used to set the crossover probability Pc and mutation probability Pm respectively by the different situation and reduce the possibility of trapping in the premature convergenceThe simulation result shows that the new algorithm improves the problem of the premature convergence of the IAGA algorithm effectively and has better global convergence.

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What this paper is about

In view of the problem of the premature convergence of the IAGA adaptive genetic algorithm,an improved adaptive genetic algorithm(NIAGA algorithm) is proposed.The custom discriminant determines whether a group appeard a trend of the premature convergence,then both macroeconomic regulation and control and micro processing method are used to set the crossover probability Pc and mutation probability Pm respectively by the different situation and reduce the possibility of trapping in the premature convergenceThe simulation result shows that the new algorithm improves the problem of the premature convergence of the IAGA algorithm effectively and has better global convergence.

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Available abstract

In view of the problem of the premature convergence of the IAGA adaptive genetic algorithm,an improved adaptive genetic algorithm(NIAGA algorithm) is proposed.The custom discriminant determines whether a group appeard a trend of the premature convergence,then both macroeconomic regulation and control and micro processing method are used to set the crossover probability Pc and mutation probability Pm respectively by the different situation and reduce the possibility of trapping in the premature convergenceThe simulation result shows that the new algorithm improves the problem of the premature convergence of the IAGA algorithm effectively and has better global convergence.

Key concepts: Premature convergence, Crossover, Convergence (economics), Algorithm, Computer science, Genetic algorithm, Mutation, Set (abstract data type)

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