An improved adaptive genetic algorithm for function optimization

Function optimization based on traditional genetic algorithm is easy to fall into local extremum, so that adaptive genetic algorithm is proposed to solve this problem. However, traditional adaptive genetic algorithm has some disadvantages, such as low efficiency and instability. This study presents an improved adaptive genetic algorithm. Specifically, the crossover probability and the mutation probability were dynamically adjusted according to the concentrating and dispersing degree of the fitness values of the whole populations. In complex function optimization problems, the result of the simulation shows that the improved adaptive genetic algorithm has a great improvement in many aspects of the global optimization, such as the convergence rate, the optimal solution and the stability.