Disk schedulers in current operating systems schedule a request as soon as the previous request has been completed. In many common applications disk read requests are issued synchronously with short period between them In this case, the scheduler switches to a request from another process with the assumption that the last process has no more requests. This condition is called deceptive idleness. It can be overcome by anticipatory disk scheduling framework, which introduces a short, controlled delay period, during which the disk scheduler waits for additional requests to arrive from the process that issued the last serviced request. Genetic algorithms, powerful and broadly applicable optimization techniques and the most widely known types of evolutionary computation methods can be used for optimizing the anticipatory scheduling. In this paper, we propose to use the cross over operators while tuning the anticipatory scheduling with Genetic algorithms. In general, a cross over operator is regarded as a main genetic operator and the performance of genetic algorithms depends to a great extent on the performance of the cross over operator used. We make the study on Linux Operating Systems kernel 2.6.18. We use various cross over operators and make a comparison on the performance of anticipatory scheduling.