  Mutation is one of the most important stages of the genetic algorithm because
of its impact on the exploration of global optima, and to overcome premature
convergence. There are many types of mutation, and the problem lies in
selection of the appropriate type, where the decision becomes more difficult
and needs more trial and error. This paper investigates the use of more than
one mutation operator to enhance the performance of genetic algorithms. Novel
mutation operators are proposed, in addition to two selection strategies for
the mutation operators, one of which is based on selecting the best mutation
operator and the other randomly selects any operator. Several experiments on
some Travelling Salesman Problems (TSP) were conducted to evaluate the proposed
methods, and these were compared to the well-known exchange mutation and
rearrangement mutation. The results show the importance of some of the proposed
methods, in addition to the significant enhancement of the genetic algorithm's
performance, particularly when using more than one mutation operator.

Artificial Intelligence;Neural and Evolutionary Computing