  This paper investigates the use of more than one crossover operator to
enhance the performance of genetic algorithms. Novel crossover operators are
proposed such as the Collision crossover, which is based on the physical rules
of elastic collision, in addition to proposing two selection strategies for the
crossover operators, one of which is based on selecting the best crossover
operator and the other randomly selects any operator. Several experiments on
some Travelling Salesman Problems (TSP) have been conducted to evaluate the
proposed methods, which are compared to the well-known Modified crossover
operator and partially mapped Crossover (PMX) crossover. The results show the
importance of some of the proposed methods, such as the collision crossover, in
addition to the significant enhancement of the genetic algorithms performance,
particularly when using more than one crossover operator.
