Hill-climbing genetic algorithm optimization in cognitive radio decision engine

To dynamically adjust the radio parameters is one of the basic capabilities of cognitive radio decision engine. This paper proposed a hill-climbing genetic algorithm which optimize optimal individual after one genetic iterative operation by hill-climbing algorithm. The proposed method would enhance the local search capability at the later stage of each generation of GA. We designed a multi-carrier system for performance analysis. Through different weighting scenarios multiple objective fitness functions, the simulation results illustrate the trade-off between the fitness function and the transmission parameters configuration. And the results show that the hill-climbing genetic algorithm is better than pure genetic algorithm in stability and average fitness value.