|29 noviembre, 2019|
Título: How machine learning can help optimization
Ponente: El-ghazali Talbi (Polytech’Lille – University of Lille)
Organizador: Juan Aparicio
Fecha: Viernes 29 de noviembre de 2019, 11:00 h.
Lugar: Sala de Seminarios del CIO , Instituto Universitario de Investigación CIO, Edificio Torretamarit, Universidad Miguel Hernández (Campus de Elche)
Resumen: During the last years, research in applying machine learning (ML) in designing efficient, effective and robust metaheuristics become increasingly popular. Many of those data driven metaheuristics have generated high quality results and represent state-of-the-art optimization algorithms. Although various appproaches have been proposed, there is a lack of a comprehensive survey and taxonomy on this research topic. In this talk we will investigate the different opportunities for using ML into metaheuristics. We define in a unified way the various ways synergies that may be achieved. A detailed taxonomy is proposed according to the concerned search component: target optimization problem, low-level and high-level components of metaheuristics. Our goal is also to motivate researchers in optimization to include ideas from ML into metaheuristics. We identify some open research issues in this topic which needs further in-depth investigations.