A Novel Multi-Objective Genetic Algorithm Approach to Artificial Neural Network Topology Optimisation: The Breast 
Cancer Classification Problem

Abstract

This paper presents a novel approach to artificial neural network (ANN) topology optimisation that uses multi-objective 
genetic algorithm in order to find the best network configuration for the Wisconsin breast cancer database (WBCD) classification 
problem. The WBCD [Mangasarian, OL., et al., 1995][Mangasarian, OL., et al.][Wolberg, WH., et al., 1995] is a publicly available 
database composed by 699 cases, each of which is defined by 11 parameters. The former first 10 values of each record account 
for geometrical features of cells extracted with FNA biopsy. The last parameter represents the nature of the tumour; two classes 
of tumour are considered in this database: benignant and malignant tumours. An Intelligent System, IDEST, was designed and implemented. 
At the core of this system there's an Artificial Neural Network that is able to classify cases. The design of such an ANN is a non 
trivial task and choices incoherent with the problem could lead to instability of the network. For these reasons a fixed topology 
genetic algorithm (GA) approach was used to find an optimal topology for the given problem. In a second step a multi-objective GA 
(MOGA) was developed and employed in order to refine the search in the "topology space". Results shown by the IDEST demonstrate 
the great potentialities of similar approaches.