Breast cancer diagnosis using an artificial neural network trained by group search optimizer

Abstract

This paper presents a novel optimization algorithm: a group search optimizer (GSO) for training an artificial neural network (ANN) used for diagnosis of breast cancer. The GSO is inspired by animal social searching behaviour. Its global search performance has been proved competitive to other evolutionary algorithms and the particle swarm optimizer. The parameters of a three-layer feed-forward ANN, including connection weights and bias are tuned by the GSO algorithm. Wisconsin diagnostic breast cancer data from the UCI Machine Learning repository are employed as a benchmark classification problem to evaluate the proposed method. In comparison with other sophisticated machine learning techniques used for ANN training, including some ANN ensembles, the GSO for ANN, GSOANN, has a better convergence rate and generalization performances for the breast cancer diagnosis problem.