Improved adaptive genetic algorithm and its application in constrained function optimization

An Adaptive Genetic Algorithm(AGA) based on Adaptive Penalty Function(AGA-APF) has been proposed.On the one hand,the disruptive selection operator is employed to enhance the survival probability of potential better individuals in the population.On the other hand,the probabilities of crossover and mutation based on superiority inheritance are introduced so as to prevent the algorithm from premature convergence.What's more,the improved optimal reserved strategy is applied in AGA-APF,which can guarantee the convergence of the algorithm and validity of the convergent solutions.The simulation results of the con strained function optimization have demonstrated that AGA-APF can converge to optimal solutions rapidly and own higher robustness.