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
