In this paper

a genetic algorithm using multi-species and multi-encoding method is proposed to combat premature convergence inherent in Standard Genetic Algorithms(SGA). It involves with three species evolved simultaneously. By using float encoding method and binary encoding method respectively

the first species has stronger local search ability and the second has stronger global search ability. The third species

which called "elitist species"

aims to keep the elitist individuals in the evolution process. And at the same time

it evolves too

which will enhances the convergence speed and improves the perfomance of GA. And the migration strategy adopted in the proposed method which immigrates elitist individuals among the three species can keep the population diversity efficiently. This multi-species method can help genetic algorithms to escape from possible local entrapment and obtain good tradeoff between exploration ability and exploitation ability. The experimental results of this method on a series of classical complex multimodal functions have shown its efficience and superiority.