A New Adaptive Crossover Operator for the Preservation of Useful Schemata

Abstract

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.KeywordsCrossover OperatorCrossover PointDecision TablePromising PerformanceReplacement SelectionThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.