Memetic pareto differential evolutionary artificial neural networks to determine growth multi-classes in predictive microbiology

Abstract

The main objective of this research is to automatically design Artificial Neural Network models with sigmoid basis units for multiclassification tasks in predictive microbiology. The classifiers obtained achieve a double objective: a high classification level in the dataset and high classification levels for each class. The Memetic Pareto Differential Evolution Neural Network chosen to learn the structure and weights of the Neural Networks is a Differential Evolutionary approach based on the Pareto Differential Evolution multiobjective evolutionary algorithm. The Pareto Differential Evolution algorithm is augmented with a local search using the improved Resilient Backpropagation with backtracking–iRprop + algorithm. To analyze the robustness of this methodology, it has been applied to two complex classification problems in predictive microbiology (Staphylococcus aureus and Shigella flexneri). The results obtained show that the generalization ability and the classification rate in each class can be more efficiently improved within this multiobjective algorithm.