Typed Cartesian Genetic Programming for Image Classification

This paper introduces an extension to Cartesian Genetic Programming (CGP), aimed at image classification problems. Individuals in the population consist of two layers of functions: image processing functions, and traditional mathematical functions. Information can be passed between these layers, and the final result can either be an image or a numerical value. This has been applied to image classification, by using CGP to evolve image processing algorithms for feature extraction. This paper presents results which show that these automatically extracted features can substantially increase classification accuracy on a medical problem concerned with the analysis of potentially cancerous cells.