Classification of colon tumor tissues using genetic programming

A Genetic Programming (GP) framework for classification is presented in this paper and applied to a publicly available biomedical microarray dataset representing a collection of expression measurements from colon biopsy experiments [3]. We report experimental results obtained using two different well known fitness criteria: the area under the receiving operating curve (ROC) and the percentage of correctly classified instances (CCI). These results, and their comparison with the ones obtained by three non-evolutionary Machine Learning methods (Support Vector Machines, Voted Perceptron and Random Forests) on the same data, seem to hint that GP is a promising technique for this kind of classification both from the viewpoint of the accuracy of the proposed solutions and of the generalization ability. These results are encouraging and should pave the way to a deeper study of GP for classification applied to biomedical microarray datasets.