提出了基于遗传算法的改进广义 Lloyd算法 ( GLA) .它以种群为基础 ,使用选择算子对种群进行有针对性的操作 ,通过变异算子以提高种群的平均适应值 ,使其逃离局部最小点 ,最后采用交叉算子以增加个体的多样性 ,又降低了该算法对初始码书的敏感程度 .高斯 -马尔科夫序列实验表明 ,该算法较好地实现了全局最优 ,并有助于克服对初始码书较为敏感的缺点 This paper proposes an improved generalized Lloyd algorithm (GLA) based on genetic algorithm.It uses population operator as the basis and selects operators to make targeted operations on the population. Through mutation operator, the average fitness of the population is increased to get away from the local minimum Finally, the crossover operator is used to increase the diversity of individuals and the sensitivity of the algorithm to the initial codebook is also reduced.Gaussian-Markov sequence experiments show that this algorithm can achieve global optimality well and help To overcome the initial codebook is more sensitive to the shortcomings