Improving the performance of a Genetic Algorithm for minimum span frequency assignment problem with an adaptive mutation rate and a new initialization method

We propose a new Genetic Algorithm (GA) for solving the minimum span frequency assignment problem (MSFAP). The MSFAP is minimizing the range of the frequencies assigned to each transmitter in a region satisfying a number of constraints. The basic framework of the GA involves finding and ordering of the cells for use in a greedy (sequential) assignment process, and it also utilizes graph theoretic constraint to reduce search space. Because the performance of the GA heavily depends on the mutation rate, we use an adaptive mutation rate. And also a novel initialization method is utilized to improve the performance. Results are given which show that our GA produces optimal solutions to several practical problems,and the performance of our GA is far better than the existing GAs.