Evolution of appropriate crossover and mutation operators in a genetic process

Traditional genetic algorithms use only one crossover and one mutation oper ator to generate the next generation. The chosen crossover and mutation ope rators are critical to the success of genetic algorithms. Different crossov er or mutation operators, however, are suitable for different problems, eve n for different stages of the genetic process in a problem. Determining whi ch crossover and mutation operators should be used is quite difficult and i s usually done by trial-and-error. In this paper, a new genetic algorithm, the dynamic genetic algorithm (DGA), is proposed to solve the problem. The dynamic genetic algorithm simultaneously uses more than one crossover and m utation operators to generate the next generation. The crossover and mutati on ratios change along with the evaluation results of the respective offspr ing in the next generation. By this way, we expect that the really good ope rators will have an increasing effect in the genetic process. Experiments a re also made, with results showing the proposed algorithm performs better t han the algorithms with a single crossover and a single mutation operator.