Abstract
In Artificial Neural Network (ANN), the selection of connection weights is a key issue and Genetic and Evolution Strategies have been found to be promising algorithms to solve this important task. Motivated by that, this study investigates the applicability of using two novel Multi-Objective Evolutionary Algorithms (MOEA): Speed constrained Multi-Objective Particle Swarm Optimization (SMPSO) and Multi-Objective Differential Evolution Algorithm based on Decomposition with Dynamical Resource Allocation (MOEA/D-DE-DRA). ANNs are training to learn data classification using sensibility and specificity for different UCI databases. The results are compared using the Hypervolume as quality indicator and statistical test.
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