An Evaluation of Over-Fit Control Strategies for Multi-Objective Evolutionary Optimization

Abstract

The optimization of classification systems is often confronted by the solution over-fit problem. Solution over-fit occurs when the optimized classifier memorizes the training data sets instead of producing a general model. This paper compares two validation strategies used to control the over-fit phenomenon in classifier optimization problems. Both strategies are implemented within the multi-objective NSGA-II and MOMA algorithms to optimize a projection distance classifier and a multiple layer perceptron neural network classifier, in both single and ensemble of classifier configurations. Results indicated that the use of a validation stage during the optimization process is superior to validation performed after the optimization process.