Methodology - Multiobjective evolutionary algorithms

Descripción

Desarrollo de algoritmos evolutivos multiobjetivo para llevar a cabo metodologías de clasificación.

Publicaciones

• Cluster analysis and forecasting of viruses incidence growth curves: Application to SARS-CoV-2
• How fair can we go in machine learning? Assessing the boundaries of fairness in decision trees
• Short- and long-term energy flux prediction using Multi-Task Evolutionary Artificial Neural Networks
• Multi-objective evolutionary optimization using the relationship between F1 and accuracy metrics in classification tasks
• Applying an Evolutionary Algorithm for the Analysis of Mental Disorders in Macro-urban Areas: The Case of Barcelona.
• Memetic Multi-Objective Algorithm based on Differential Evolution to Design Artificial Neural Networks: Applications (New trends on intelligent systems and soft computing)
• Sensitivity Versus Accuracy in Multiclass Problems Using Memetic Pareto Evolutionary Neural Networks
• E}studio de un Modelo de Ensemble formado por los Extremos del Frente de Pareto en Algoritmos Evolutivos Multi-objetivo Utilizando la Técnica {TOPSIS
• Operadores de cruce basados en intervalos de confianza en algoritmos genéticos con codificacion real