In this paper, we revisit a general class of multi-criteria multi-constrained network design problems and attempt to solve, in a novel way, with Evolutionary Algorithms (EAs). A ma...
Mutation-based Evolutionary Algorithms, also known as Evolutionary Programming (EP) are commonly applied to Artificial Neural Networks (ANN) parameters optimization. This paper pre...
Kristina Davoian, Alexander Reichel, Wolfram-Manfr...
A multiobjective genetic algorithm for detecting communities in dynamic networks, i.e., networks that evolve over time, is proposed. The approach leverages on the concept of evolu...
This paper presents an evolutionary artificial neural network approach based on the pareto differential evolution algorithm augmented with local search for the prediction of breas...
Selection of the topology of a neural network and correct parameters for the learning algorithm is a tedious task for designing an optimal artificial neural...