A New Adaptive Crossover Operator for the Preservation of Useful Schemata

In genetic algorithms, commonly used crossover operators such as one-point, two-point and uniform crossover operator are likely to destroy the information obtained in the evolution because of their random choices of crossover points. To overcome this defect, a new adaptive crossover operator based on the Rough Set theory is proposed in this paper. By using this specialized crossover operator, useful schemata can be found and have a higher probability of surviving recombination regardless of their defining length. We compare the proposed crossover operator’s performance with the two-point crossover operator on several typical function optimization problems. The experiment results show that the proposed crossover operator is more efficient.