An Automatical And Efficient Image Classification Based On Improved Genetic Programming
Yang Lu, Fazhi He, Li Dai, Lin Zhang · 2022 IEEE 25th International Conference on Computer Supported Cooperative Work in Design (CSCWD) · 2022
Image classification is a basic task in machine intelligence, but challenging due to high variations across images. Traditional methods use hand-crafted features to solve it, which require much domain knowledge. Genetic Programming (GP) can automatically solve problems without much knowledge about the structure and form of the solution. And GP is interpretable and needs less time to adjust the parameters compared with deep image classification methods. However, the existing GP-based image classification methods have some disadvantages, such as poor classification performance and long training time. This paper proposed a new image classification algorithm based on multilayer genetic programming with cache (MCGP). MCGP designs a new hierarchical individual program structure with a classification layer and uses a subtree cache strategy to reduce training time. The experiments show that MCGP can get better or competitive results compared with traditional methods, other GP methods, and convolutional neural network methods. In addition, the training speed of MCGP is much faster than other GP methods.