Genetic programming with a new representation and a new mutation operator for image classification
Qinglan Fan, Ying Bi, Bing Xue, Mengjie Zhang · Proceedings of the Genetic and Evolutionary Computation Conference Companion · 2021
Due to the high dimensionality and variations of the image data, it is challenging to develop an image classification method that is able to capture useful information from images and then conduct classification effectively. This paper proposes a new GP approach to image classification, which can perform feature extraction, feature construction, and classification simultaneously. The new approach can extract and construct multiple informative features to effectively handle image variations. Furthermore, a new mutation operator is developed to dynamically adjust the size of the evolved GP programs. The experimental results show that the proposed approach achieves significantly better or similar performance than/to the baseline methods on two datasets.