Abstract
Diagnosis of breast cancer disease depends on human experience. It is time consuming and has an element of human error in the results. This paper presents an intelligent multi-objective classifier to Diagnose breast cancer diseases using multilayer perceptron (MLP) neural network with Differential Evolution technique. The Differential Evolution (DE) algorithm is used to solve multi-objective optimization problems by tuning MLP neural network parameters. The proposed intelligent multi-objective classifier is used for diagnosis of breast cancer disease. In addition, it utilizes the advantages of multi-objective differential evolution to optimize the number of hidden nodes in the hidden layer of the MLP neural network and also to reduce network error rate. The results indicate that the proposed intelligent multi-objective classifier is viable in breast cancer diagnosis.