Using a genetic algorithm approach to solve the dynamic channel-assignment problem

The Channel Assignment Problem is an NP-complete problem to assign a minimum number of channels under certain constraints to requested calls in a cellular radio system. Examples of the many approaches to solve this problem include using neural-networks, simulated annealing, graph colouring, genetic algorithms, and heuristic searches. We present a new heuristic algorithm that consists of three stages: 1) determine-lower-bound cell regular interval assignment; 2) greedy region assignment; and 3) genetic algorithm assignment. Through simulation, we show that our heuristic algorithm achieves lower bound solutions for 11 of the 13 instances of the well known Philadelphia benchmark problem. Our algorithm also has the advantage of being able to find optimum solutions faster than existing approaches that use neural networks.