Feature selection of modulation recognition based on dynamic adaptive genetic algorithm
基于动态自适应遗传算法的调制信号特征选择

Neural network classifier has been widely used in automatic recognition. It's performance and structure are dependent on the dimension of the input data. Genetic algorithm is a kind of global optimization algorithm and is appropriate to be used in feature selection. Two drawbacks of the Simple Genetic Algorithm (SGA), i.e. premature convergence and poor local searching ability, hinder SGA from wide application. This paper proposed a novel genetic algorithm in which the median of the population are used to control the probability of the crossover and the mutation. Simulation results show that our method can get the optimal subset quickly and the performance of the neural network classifier could be improved markedly.