高斯过程下的CMA-ES在医学图像配准中的应用

为了改进协方差矩阵自适应进化策略(CMA-ES)的性能,提出了一种高斯过程协助下的协方差矩阵自适应进化策略(GPACMA-ES)。该策略利用CMA-ES中的协方差矩阵构建核函数,引入高斯过程,在线学习历史经验,并根据历史经验预测全局最优解的最有前景区域,有效地降低了适应度函数的评价次数。同时,为了提高群体的搜索效率,引入了置信区间。群体在置信区间内更高效地采样,使得算法具备更快的收敛速度和全局寻优能力。最后,将GPACMA-ES算法应用于医学图像配准中,配准精度和效率均高于标准的CMA-ES算法。

Gaussian Process Assisted CMA-ES Application in Medical Image Registration

A gaussian process assisted covariance matrix adaptation evolution strategy(GPACMA-ES)optimization algorithm was proposed in this paper.The kernel function used in the GPACMA-ES algorithm is constructed by the cova-riance matrix.Taking advantage of the Gaussian process,which plays a key role in both online learning about the histo-ric experience and predicting the promising region which contains globally optimal solution,the frequency of calculating fitness function in the algorithm is reduced markedly.Meanwhile,in order to improve the efficiency of the algorithm,GPACMA-ES is sampling in the trust region.So it has rapid convergence and good global search capacity.Finally,a case study of medical image registration is examined to demonstrate the ability and applicability of the GPACMA-ES.Expe-riment results show that GPACMA-ES is proper for medical image registration than CMA-ES,and it has a better effect on the precision of registration while reducing the number of calculation of the fitness function.