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Dr. Antonio Berlanga
Applied Artificial Intelligence Group, Universidad Carlos III de Madrid, Colmenarejo (Madrid), Spain
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(Calculated once Scilit publication data is synced: current year − year of first publication + 1)
Representative Publications
View All Publications7 May 2008 in IEEE Transactions on Evolutionary Computation
Journal Article
JHJesus Garcia Herrero
Jesus Garcia Herrero
ABAntonio Berlanga
Antonio Berlanga
Jose M. MolinaThis paper addresses a real-world engineering design requiring the application of effective and global optimization techniques. The problem it deals with is the design of nonlinear tracking filters under up to several hundreds of performance specifications. The suitability of different evolutionary computation techniques for solving multiobjective problems is explored, contrasting the performance achieved with recent multiobjective evolutionary algorithm (MOEAs) proposals and different aggregation schemes. In particular, a new scheme is proposed to build a fitness function based on an operator that selects worst cases of multiple specifications in different situations. They have been evaluated in the design of an air traffic control (ATC) tracking filter that should accomplish a specific normative with 264 specifications. Results show their performance in terms of effectiveness and computational load, comparing their capability to scale the problem with respect to problem size.
1 May 2009
Conference Paper
LMLuis Martí
Luis Martí
Jesus GarciaABAntonio Berlanga
Antonio BerlangaJMJose M. Molina
Jose M. Molina
In this work we put forward a comprehensive study on the design of global stopping criteria for multi-objective optimization. We describe a novel stopping criterion, denominated MGBM criterion that combines the mutual domination rate (MDR) improvement indicator with a simplified Kalman filter that is used for evidence gathering process. The MDR indicator, which is introduced along, is a special purpose solution meant for the stopping task. It is capable of gauging the progress of the optimization with a low computational cost and therefore suitable for solving complex or many-objective problems. The viability of the proposal is established by comparing it with some other possible alternatives. It should be noted that, although the criteria discussed here are meant for MOPs and MOEAs, they could be easily adapted to other softcomputing or numerical methods by substituting the local improvement metric with a suitable one.
1 May 2005 in Applied Intelligence
Journal Article
ABAntonio Berlanga
Antonio BerlangaJesus GarciaJose M. MolinaJCJosé R. Casar
José R. Casar
1 June 2000 in Engineering Applications of Artificial Intelligence
Journal Article
JMJ.M. Molina
J.M. Molina
PIPedro Isasi
Pedro IsasiABAntonio Berlanga
Antonio Berlanga
Araceli Sanchis de Miguel1 February 2011 in Operations Research Letters
Journal Article
LMLuis Martí
Luis MartíJesus GarciaABAntonio Berlanga
Antonio Berlanga
Carlos CoelloJose M. Molina1 January 2012 in Neurocomputing
Journal Article
RCRodrigo Cilla
Rodrigo CillaMPMiguel A. Patricio
Miguel A. Patricio
ABAntonio Berlanga
Antonio BerlangaJose M. MolinaAbout Me
Dr. Antonio Berlanga de Jesús is an Associate Professor at the Department of Computer Science and Engineering, Universidad Carlos III de Madrid. He completed his PhD studies in Informatics Engineering at Universidad Carlos III de Madrid (also holding a degree in Physics), and is a member of the Applied Artificial Intelligence Group (GIAA) and the Gregorio Peces-Barba Institute of Human Rights. He recently led research projects on automated business process optimization (2024–2026) and applied-AI initiatives with industry (e.g., XFERA Móviles, 2025–2029), serving as principal investigator on more than 20 projects. He has published 30+ papers in various journals, along with several book chapters and 25+ conference contributions, holds two registered patents, has supervised four doctoral theses, and collaborates in numerous national and international research projects. His teaching disciplines include machine learning and artificial intelligence, and his interests include machine learning, evolutionary algorithms, data fusion, video analysis, and IoT, with applications to medicine, robotics, telecommunications, agriculture, and law.