A fitness based scanning multi-parent crossover operator using a probabilistic selection

Several multi-parent crossover operators had been proposed to increase performance of genetic algorithms. The operators allow several parents to simultaneously take part in creating offspring. The operators need to balance between the two conflicting goals, exploitation and exploration. Strong exploitation allows fast convergence to succeed but can lead to premature convergence while strong exploration can lead to better solution quality but slower convergence. This paper proposes a new fitness based scanning multi-parent crossover operator for genetic algorithms. The new operator looks for the optimal setting for the two goals to achieve the highest benefits from both. The operator uses a probabilistic selection with an incremental threshold value to allow strong exploration at early stages of the algorithms and strong exploitation at late stages of the algorithms. The experiments conducted on some testing functions show that the operator can give better solution quality and faster convergence when compared with some well-known multi-parent crossover operators.