Enhancing Genetic Algorithms using Multi Mutations

Mutation is one of the most important stages of genetic algorithms because of its impact on the exploration of the search space, and in overcoming premature convergence. Since there are many types of mutations one common problem lies in selecting the appropriate type. The decision then becomes more difficult and needs more trial and error to find the best mutation to be used. This paper investigates the use of more than one mutation operator to enhance the performance of genetic algorithms. New mutation operators are proposed, in addition to two election strategies for the mutation operators. One is based on selecting the best mutation operator and the other randomly selects any operator. Several experiments were conducted on the Travelling Salesman Problem (TSP) to evaluate the proposed methods. 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 algorithms' performance, particularly when using more than one mutation operator.