The application and optimization of genetic algorithms in formula problems

The genetic algorithm is widely applied to all kinds of formula problems for its characteristics of simpleness, universality, strong robustness and less mathematical demands for optimization problems. However, the traditional standard genetic algorithm has a great blindness when generating the initial population and in the crossover and mutation process, which results in extremely low efficiency. In this paper, according to the characteristics of the formula problems, we propose to add constraints of formula problems to the initial population generation process and the crossover and mutation process and this reduces the blindness and improves the algorithm efficiency. In view of recipe issues, a quick generation method for the initial population is presented and a new crossover and mutation method is presented. We implemented the optimized genetic algorithm on Matlab and verified the feasibility and high-efficiency of the algorithm.