Multi-Objective Evolutionary Algorithm based on NSGA-II for Neural Network Optimization Application to the Prediction of Severe Diseases

Abstract

Neural networks have become usable as classifiers in several domains, including medicine. The choice of topology, internal structure, and learning algorithm characterize the type of neural network, resulting in incredible diversity among these networks. Until now, the most challenging problem to solve for classifiers in a neural network has been finding an optimal point between three chosen surfaces: architecture, synaptic weight, and input variables.

To address this problem, we propose a multi-objective neuro-genetic system that simultaneously optimizes these three surfaces. To demonstrate the effectiveness of our approach, we have implemented and compared two types of classifiers - the classical neural classifier and the multi-objective neuro-genetic classifier - using several medical databases. The results obtained show the efficiency of our method, with correct classification rates of up to 100%, which is a very promising result. The comparison between the two approaches employed demonstrates the effectiveness of the multi-objective genetic approach."