The coherent point drift (CPD) algorithm is a powerful approach for point set registration. However, it suffers from a serious problem-there is a weight parameter w that reflects the assumption about the amount of noise and number of outliers in the Gaussian mixture model, and its value has an influence on the point set registration performance In the original CPD algorithm, the value of w is set manually, and hence an improper value will lead to poor registration results. To solve this problem, a fully automatic algorithm for the selection of an optimal weight parameter is proposed using a hybrid optimization scheme that combines the genetic algorithm with the Nelder-Mead simplex method. The experiments show that the refined CPD algorithm is more effective and extends the original CPD algorithm in its methodology and applications. The coherent point drift (CPD) algorithm is a powerful approach for point set registration. However, it suffers from a serious problem-there is a weight parameter w that reflects the assumption about the amount of noise and number of outliers in the Gaussian mixture model , and its value has an influence on the point set registration performance In the original CPD algorithm, the value of w is set manually, and therefore an improper value will lead to poor registration results. To solve this problem, a fully automatic algorithm for the selection of an optimal weight parameter is proposed using a hybrid optimization scheme that combines the genetic algorithm with the Nelder-Mead simplex method. The experiments show that the refined CPD algorithm is more effective and extends the original CPD algorithm in its methodology and applications.