An improved adaptive genetic algorithm and its application in function optimization

To speed up convergence rates and resolve local convergence issues in traditional adaptive genetic algorithms,an improved adaptive genetic algorithm was developed.According to the concentrating degree of fitness of the populations,a kind of adaptive crossover probability and mutation probability were designed in terms of three variables of maximal fitness,minimal fitness and average fitness of the populations,whereby the crossover probabilities and mutation probabilities of the whole populations could be adjusted.Based on this,an improved adaptive genetic algorithm was developed.Simulation results prove that the new adaptive algorithm can converge faster than the unimproved algorithm and is highly effective at avoiding the premature convergence of the adaptive genetic algorithm.