The research of advances in adaptive genetic algorithm

The traditional genetic algorithm works with a fixed probability of genetic operators. It brings inconvenience to the individual adaptive, where the population is easy to get evolved into a stagnant state, resulting in local convergence. In this paper, progressive optimization is introduced to perform 5 times of improvement on crossover operator and mutation operator. The other part of the research is focused on the solution to the maximum optimization of Shaffer´s F6 test function by way of comparative experiments on improved genetic algorithms. Experimental results show that the improved genetic algorithms are effective.