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.