R
Roberto Santana
Researcher at University of the Basque Country
Publications - 207
Citations - 3320
Roberto Santana is an academic researcher from University of the Basque Country. The author has contributed to research in topics: Estimation of distribution algorithm & Evolutionary algorithm. The author has an hindex of 25, co-authored 191 publications receiving 2855 citations. Previous affiliations of Roberto Santana include Technical University of Madrid & Polytechnic University of Valencia.
Papers
More filters
Journal ArticleDOI
Machine learning in bioinformatics
Pedro Larrañaga,Borja Calvo,Roberto Santana,Concha Bielza,Josu Galdiano,Iñaki Inza,Jose A. Lozano,Rubén Armañanzas,Guzmán Santafé,Aritz Pérez,Víctor Robles +10 more
TL;DR: Modelling methods, such as supervised classification, clustering and probabilistic graphical models for knowledge discovery, as well as deterministic and stochastic heuristics for optimization, are presented.
Journal ArticleDOI
A review on evolutionary algorithms in Bayesian network learning and inference tasks
TL;DR: This paper reviews the application of evolutionary algorithms for solving some NP-hard optimization tasks in Bayesian network inference and learning.
Journal ArticleDOI
Protein Folding in Simplified Models With Estimation of Distribution Algorithms
TL;DR: It is argued that EDAs are an efficient alternative for many instances of the protein structure prediction problem and are indeed appropriate for a theoretical analysis of search procedures in lattice models.
Journal ArticleDOI
A review of estimation of distribution algorithms in bioinformatics
Rubén Armañanzas,Iñaki Inza,Roberto Santana,Yvan Saeys,Jose Luis Flores,Jose A. Lozano,Yves Van de Peer,Rosa Blanco,Víctor Robles,Concha Bielza,Pedro Larrañaga +10 more
TL;DR: A basic taxonomy of EDA techniques is set out, underlining the nature and complexity of the probabilistic model of each EDA variant, and emphasizing the EDA paradigm's potential for further research in this domain.
Journal ArticleDOI
Estimation of Distribution Algorithms with Kikuchi Approximations
TL;DR: The method combines a reformulation of a probability approximation procedure known in statistical physics as the Kikuchi approximation of energy, with a novel approach for finding graph decompositions and can outperform other EDAs that use traditional methods of probability approximation in the optimization of functions with strong interactions among their variables.