The function optimization problem solved by the improved adaptive Genetic Algorithm
To solve the problems of slow convergence speed,low search efficiency and the ease of falling into local optimum in the process of complex function optimization of genetic algorithm,an improved adap-tive genetic algorithm is proposed.The algorithm optimizes the coding length,population initialization meth-od,selection method,crossover and mutation operator adaptation mechanism and fitness function construc-tion method from the global perspective.The simulation results show that the algorithm has a significant im-provement in convergence speed,solution accuracy,stability,global optimization ability and so on,and shows excellent performance in complex function optimization problems.
Genetic Algorithmpopulation initializationrate of convergenceadaptiveoptimization of function
万方数据
维普
