Memetic Pareto Evolutionary Artificial Neural Networks for the Determination of Growth Limits of Listeria Monocytogenes

Authors:
J. C. Fernández;P. A. Gutiérrez;C. Hervás;F. J. Martínez
Affiliations:
-;-;-;-
Venue:
HIS '08 Proceedings of the 2008 8th International Conference on Hybrid Intelligent Systems
Year:
2008

Citing 0
Cited 2

Memetic Pareto Differential Evolution for Designing Artificial Neural Networks in Multiclassification Problems Using Cross-Entropy Versus Sensitivity

HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
Neural network ensembles to determine growth multi-classes in predictive microbiology

HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II

Quantified Score

Hi-index	0.01

Visualization

Abstract

The main objective of this work is to automatically design neural network models with sigmoidal basis units for classification tasks, so that classifiers are obtained in the most balanced way possible in terms of CCR and Sensitivity (given by the lowest percentage of examples correctly predicted to belong to each class). We present a Memetic Pareto Evolutionary NSGA2 (MPENSGA2) approach based on the Pareto-NSGAII evolution (PNSGAII) algorithm. We propose to augmente it with a local search using the improved Rprop—IRprop algorithm for the prediction of growth/no growth of L. monocytogenes as a function of the storage temperature, pH, citric (CA) and ascorbic acid (AA). The results obtained show that the generalization ability can be more efficiently improved within a framework that is multi-objective instead of a within a single-objective one.