Optimal interaction of mutation and crossover in the breeder genetic algorithm

The dynamic behavior of mutation and crossover is investigated with the Breeder Genetic Algorithm. The main emphasis is on binary functions. The genetic operators are compared near their optimal performance. It is shown that mutation is most e cient in small populations. Crossover critically depends on the size of the population. Mutation is the more robust search operator. But the BGA combines the two operators in such away that the performance is better than that of a single operator. For the DECEPTION function it is shown that increasing the size of the population above a certain number decreases the quality of the solutions obtained.