A FITNESS-BASED MULTI-PARENT CROSSOVER OPERATOR WITH PROBABILISTIC SELECTION
Several multi-parent crossover operators have been proposed to increase the performance of genetic algorithms. In these cases, the operators allow several parents to simultaneously take part in creating offspring. However, the operators need to find a balance between the two conflicting goals of 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 seeks out the optimal setting for the two goals in order to achieve the highest benefits from both. The operator uses a probabilistic selection with an incremental threshold value to allow strong exploration in the early stages of the algorithms and strong exploitation in their later stages. Experiments conducted on some test functions show that the operator can give better solution quality and more convergence consistency when compared with some other well-known multi-parent crossover operators.
multi-parent crossover probabilistic selection fitness based scanning crossover