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Raul Perez
Raul Perez
Professor of Computer Science and Artificial Intelligence, Universidad de Granada
Verified email at decsai.ugr.es
Title
Cited by
Cited by
Year
SLAVE: A genetic learning system based on an iterative approach
A González, R Pérez
Fuzzy Systems, IEEE Transactions on 7 (2), 176-191, 1999
3601999
Selection of relevant features in a fuzzy genetic learning algorithm
A González, R Pérez
IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 31 …, 2001
2062001
Completeness and consistency conditions for learning fuzzy rules
A Gonzalez, R Perez
Fuzzy Sets and Systems 96 (1), 37-51, 1998
1911998
Fuzzy control of HVAC systems optimized by genetic algorithms
R Alcalá, JM Benítez, J Casillas, O Cordón, R Pérez
Applied Intelligence 18, 155-177, 2003
1842003
Three new instance selection methods based on local sets: A comparative study with several approaches from a bi-objective perspective
E Leyva, A González, R Pérez
Pattern Recognition 48 (4), 1523-1537, 2015
1262015
Including a simplicity criterion in the selection of the best rule in a genetic fuzzy learning algorithm
L Castillo, A González, R Pérez
Fuzzy Sets and Systems 120 (2), 309-321, 2001
1152001
A Set of Complexity Measures Designed for Applying Meta-Learning to Instance Selection
E Leyva, A González, R Pérez
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2014
962014
Learning the structure of a fuzzy rule: a genetic approach
A González, R Pérez, JL Verdegay
Fuzzy Systems and Artificial Intelligence 3 (1), 57-70, 1994
761994
Improving the genetic algorithm of SLAVE
A González, R Pérez
Mathware & Soft Computing 16 (1), 59-70, 2009
572009
A learning system of fuzzy control rules based on genetic algorithms
A Gonzalez, R Perez
Genetic algorithms and soft computing, Physica-Verlag, 202-225, 1996
441996
Overview of the SLAVE learning algorithm: A review of its evolution and prospects
D García, A González, R Pérez
International Journal of Computational Intelligence Systems 7 (6), 1194-1221, 2014
392014
Preface: Special Issue on Genetic Fuzzy Systems and the Interpretability--Accuracy Trade-off
J Casillas, F Herrera, R Pérez, MJ del Jesus, P Villar
International Journal of Approximate Reasoning 44 (1), 1-3, 2007
372007
A study about the inclusion of linguistic hedges in a fuzzy rule learning algorithm
A González, R Pérez
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems …, 1999
361999
Combining instance selection methods based on data characterization: An approach to increase their effectiveness
Y Caises, A González, E Leyva, R Pérez
Information Sciences 181 (20), 4780-4798, 2011
342011
Knowledge-based instance selection: A compromise between efficiency and versatility
E Leyva, A González, R Pérez
Knowledge-Based Systems 47, 65-76, 2013
312013
On the use of meta-learning for instance selection: An architecture and an experimental study
E Leyva, Y Caises, A González, R Pérez
Information Sciences 266, 16-30, 2014
232014
An efficient inductive genetic learning algorithm for fuzzy relational rules
A González, R Pérez, Y Caises, E Leyva
International Journal of Computational Intelligence Systems 5 (2), 212-230, 2012
212012
Encouraging cooperation in the genetic iterative rule learning approach for qualitative modeling
O Cordon, A Gonzalez, F Herrera, R Perez
Computing with Words in Information/Intelligent Systems 2: Applications, 95-117, 1999
201999
Learning numerical action models from noisy and partially observable states by means of inductive rule learning techniques
JA Segura-Muros, R Pérez, J Fernández-Olivares
KEPS 2018 46, 2018
182018
SCIS: combining instance selection methods to increase their effectiveness over a wide range of domains
Y Caises, A González, E Leyva, R Pérez
Intelligent Data Engineering and Automated Learning-IDEAL 2009: 10th …, 2009
172009
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