Speeding Up the Genetic Algorithm Convergence Using Sequential Mutation and Circular Gene Methods

Genetic algorithms (GAs) are intelligent computational tools which their simplicity, accuracy and adaptable topology cause them to be used in globally minimum or maximum finding problems. Developing the GAs to increase their speed in finding the global minimum or maximum of a cost function has been a big challenge until now and many variants of GA has been evolved to accomplish this goal. This paper presents two new sequential mutation method and circular gene method to increase the speed of the GA. These methods attain a better final answer accompanied by lesser use of cost function evaluations in comparison with the original GA and some other known complementary methods. In addition, it speeds up reaching the minimum or maximum point regarding the number of generations. A number of common test functions with known minimum values and points are tested and the results are compared with some other algorithms such as original GA, bacterial evolutionary algorithm, jumping gene and PSO. Simulation results show that the presented methods in this paper can reach the global minimum point through lesser generations and evaluations of the cost function in comparison with the traditional methods.