Automatic construction method for image classification algorithms has been required. Genetic Image Network for Image Classification (GIN-IC) is the automatic construction method for image classification algorithms which include image transformation component using evolutionary computation, and its effectiveness has already been proven. In our study, we try to improve the performance of GIN-IC with AdaBoost algorithm using GIN-IC as weak classifiers to complement with each other. We apply our proposed method to three types of image classification problems, and show the results in this paper. In our method, discrimination rates for training images and test images improved in the experiments compared with the previous method GIN-IC.