@article {805,
	title = {An Internet of Things and Fuzzy Markup Language Based Approach to Prevent the Risk of Falling Object Accidents in the Execution Phase of Construction Projects},
	journal = {Sensors},
	volume = {21},
	number = {19},
	year = {2021},
	pages = {6461},
	keywords = {Fuzzy markup language},
	doi = {10.3390/s21196461},
	url = {http://hdl.handle.net/10481/71361},
	author = {Mart{\'\i}nez Rojas, Mar{\'\i}a and M. J. Gacto and Vitiello, Autilia and Acampora, Giovanni and Soto Hidalgo, Jos{\'e} Manuel}
}
@article {795,
	title = {Temporal association rule mining: An overview considering the time variable as an integral or implied component},
	journal = {WIREs Data Mining and Knowledge Discovery},
	volume = {10},
	year = {2020},
	note = {TIN2017-89517-P; P18-RT-2248},
	month = {04/2020},
	abstract = {Association rules are commonly used to provide decision-makers with knowledge that helps them to make good decisions. Most of the published proposals mine association rules without paying particular attention to temporal information. However, in real-life applications data usually change over time or presenting different temporal situations. Therefore, the extracted knowledge may not be useful, since we may not know whether the rules are currently applicable or whether they will be applicable in the future. For this reason, in recent years, many methods have been proposed in the literature for mining temporal association rules, which introduce a greater predictive and descriptive power providing an additional degree of interestingness. One of the main problems in this research field is the lack of visibility most works suffer since there is no standard terminology to refer to it, making it difficult to find and compare proposals and studies in the field. This contribution attempts to offer a well-defined framework that allows researchers both to easily locate the previous proposals and to propose well-grounded methods in the future. To accomplish both objectives, a two-level taxonomy is proposed according to whether the time variable is considered to provide order to the data collection and to locate some temporal constraints, or whether it is considered as an attribute within the learning process. Some recent applications, available software tools, and a bibliographical analysis in accordance with the Web of Science are also shown. Finally, some critical considerations and potential further directions are discussed.

This article is categorized under:

    Technologies > Association Rules
    Algorithmic Development > Association Rules
},
	keywords = {calendar association rules, cyclic association rules, data mining, sequential association rules, temporal association rules},
	doi = {https://www.doi.org/10.1002/widm.1367},
	author = {Alberto Segura-Delgado and M. J. Gacto and Alcal{\'a}, Rafael and J. Alcal{\'a}-Fdez}
}
@article {794,
	title = {Experimental Study on 164 Algorithms Available in Software Tools for Solving Standard Non-Linear Regression Problems},
	journal = {IEEE Access},
	volume = {7},
	year = {2019},
	month = {08/2019},
	pages = { 108916-108939},
	abstract = {In the specialized literature, researchers can find a large number of proposals for solving regression problems that come from different research areas. However, researchers tend to use only proposals from the area in which they are experts. This paper analyses the performance of a large number of the available regression algorithms from some of the most known and widely used software tools in order to help non-expert users from other areas to properly solve their own regression problems and to help specialized researchers developing well-founded future proposals by properly comparing and identifying algorithms that will enable them to focus on significant further developments. To sum up, we have analyzed 164 algorithms that come from 14 main different families available in 6 software tools (Neural Networks, Support Vector Machines, Regression Trees, Rule-Based Methods, Stacking, Random Forests, Model trees, Generalized Linear Models, Nearest Neighbor methods, Partial Least Squares and Principal Component Regression, Multivariate Adaptive Regression Splines, Bagging, Boosting, and other methods) over 52 datasets. A new measure has also been proposed to show the goodness of each algorithm with respect to the others. Finally, a statistical analysis by non-parametric tests has been carried out over all the algorithms and on the best 30 algorithms, both with and without bagging. Results show that the algorithms from Random Forest, Model Tree and Support Vector Machine families get the best positions in the rankings obtained by the statistical tests when bagging is not considered. In addition, the use of bagging techniques significantly improves the performance of the algorithms without excessive increase in computational times.},
	keywords = {data mining, experimental study., regression algorithms, supervised learning},
	doi = {https://doi.org/10.1109/ACCESS.2019.2933261},
	author = {M. J. Gacto and Jose Manuel Soto-Hidalgo and J. Alcal{\'a}-Fdez and Alcal{\'a}, Rafael}
}
@article {270,
	title = {Evolutionary Data Mining and Applications: A Revision on the Most Cited Papers from the Last 10 Years (2007-2017)},
	journal = {WIREs Data Mining and Knowledge Discover},
	volume = {8},
	year = {2018},
	note = {TIN2014-57251-P, TIN2015-68454-R, P11-TIC-7765},
	pages = {1-17},
	doi = {https://doi.org/10.1002/widm.1239},
	author = {R. Alcal{\'a} and M. J. Gacto and J. Alcal{\'a}-Fdez}
}
@article {546,
	title = {Comparison and Design of Interpretable Linguistic vs. Scatter FRBSs: GM3M Generalization and New Rule Meaning Index (RMI) for Global Assessment and Local Pseudo-Linguistic Representation},
	journal = {Information Sciences},
	volume = {282},
	year = {2014},
	note = {314031,DPI2012-39381-C02-02,TIN2012-33856,P10-TIC-6858},
	pages = {190{\textendash}213},
	abstract = {This work is devoted to defining more general interpretability indexes to be applied to any scatter or linguistic model implemented by any type of membership functions. They are based on metrics that should take into account the semantic and inference issues: the semantic issue in order to preserve the meaning of the linguistic labels and the inference issue since this can influence the behavior of the rules. On the other hand, these metrics have been designed to be intuitive in order to support the analysis or selection of a final model and to favor a low computational cost within an optimization process. In order to check their usefulness, a multi-objective evolutionary algorithm, simultaneously performing a rule selection and an adjustment of the fuzzy partitions, is guided by the proposed indexes on several benchmark data sets to obtain models with different degrees of accuracy and interpretability. In addition, using these metrics, a local analysis can be carried out between models of a different nature. This local analysis through the model components, gives support to the user to make the best choice from among the models.},
	doi = {10.1016/j.ins.2014.05.023},
	author = {M. Galende and M. J. Gacto and G. Sainz and R. Alcal{\'a}}
}
@article {545,
	title = {METSK-HDe: A Multiobjective Evolutionary Algorithm to learn accurate TSK-fuzzy Systems in High-Dimensional and Large-Scale Regression Problems},
	journal = {Information Sciences},
	volume = {276},
	year = {2014},
	note = {P10-TIC-6858,TIN2011-28488, DPI2012-39381-C02-02,TIN2012-33856},
	pages = {63{\textendash}79},
	abstract = {In this contribution, we propose a two-stage method for Accurate Fuzzy Modeling in High-Dimensional Regression Problems using Approximate Takagi{\textendash}Sugeno{\textendash}Kang Fuzzy Rule-Based Systems. In the first stage, an evolutionary data base learning is performed (involving variables, granularities and slight fuzzy partition displacements) together with an inductive rule base learning within the same process. The second stage is a post-processing process to perform a rule selection and a scatter-based tuning of the membership functions for further refinement of the learned solutions. Moreover, the second stage incorporates an efficient Kalman filter to learn the coefficients of the consequent polynomial function in the Takagi{\textendash}Sugeno{\textendash}Kang rules. Both stages include mechanisms that significantly improve the accuracy of the model and ensure a fast convergence in high-dimensional and large-scale regression datasets. We tested our approach on 28 real-world datasets with different numbers of variables and instances. Five well-known methods have been executed as references. We compared the different approaches by applying non-parametric statistical tests for pair-wise and multiple comparisons. The results confirm the effectiveness of the proposed method, showing better results in accuracy within a reasonable computing time.},
	doi = {10.1016/j.ins.2014.02.047},
	author = {M. J. Gacto and M. Galende and R. Alcal{\'a} and F. Herrera}
}
@conference {544,
	title = {Obtaining accurate TSK Fuzzy Rule-Based Systems by Multi-Objective Evolutionary Learning in high-dimensional regression problems},
	booktitle = {IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)},
	year = {2013},
	note = {TIN2011-28488, DPI2009-14410-C02-02,P10-TIC-6858},
	month = {07/2013},
	pages = {1-7},
	abstract = {Abstract{\textemdash}This paper addresses the challenging problem of fuzzy modeling in high-dimensional and large scale regression datasets. To this end, we propose a scalable two-stage method for obtaining accurate fuzzy models in high-dimensional regression problems using approximate Takagi-Sugeno-Kang Fuzzy Rule-Based Systems. In the first stage, we propose an effective Multi-Objective Evolutionary Algorithm, based on an embedded genetic Data Base learning (involved variables, granularities and a slight lateral displacement of fuzzy partitions) together with an inductive rule base learning within the same process. The second stage is a post-processing process based on a second MOEA to perform a rule selection and a fine scatter-based tuning of the Membership Functions. Moreover, it incorporates an efficient Kalman filter to estimate the coefficients of the consequent polynomial functions in the Takagi-Sugeno-Kang rules. In both stages, we include mechanisms in order to significantly improve the accuracy of the model and to ensure a fast convergence in high-dimensional regression problems. The proposed method is compared to the classical ANFIS method and to a well-known evolutionary learning algorithm for obtaining accurate TSK systems in 8 datasets with different sizes and dimensions, obtaining better results.},
	author = {M. J. Gacto and M. Galende and R. Alcal{\'a} and F. Herrera}
}
@article {simidat193,
	title = {A Multi-Objective Evolutionary Algorithm for an Effective Tuning of Fuzzy Logic Controllers in Heating, Ventilating and Air Conditioning Systems},
	journal = {Applied Intelligence},
	volume = {36},
	number = {2},
	year = {2012},
	pages = {330-347},
	doi = {10.1007/s10489-010-0264-x},
	author = {M. J. Gacto and R. Alcal{\'a} and F. Herrera}
}
@conference {simidat224,
	title = {Ajuste gen{\'e}tico lateral de las etiquetas ling{\"u}{\'\i}sticas en descubrimiento de subgrupos},
	booktitle = {XVI Congreso Espa{\~n}ol sobre Tecnolog{\'\i}as y L{\'o}gica Fuzzy (ESTYLF)},
	year = {2012},
	note = {TIN-2008-06681-C06-02,TIC-3928},
	month = {February},
	pages = {271-276},
	address = {Valladolid (Spain)},
	author = {C. J. Carmona and P. Gonz{\'a}lez and M. J. Gacto and M. J. del Jesus}
}
@conference {simidat223,
	title = {Aprendizaje Evolutivo De Sistemas Aproximativos De Tipo TSK Para Problemas De Alta Dimensionalidad},
	booktitle = {XVI Congreso Espa{\~n}ol sobre Tecnolog{\'\i}as y L{\'o}gica Fuzzy (ESTYLF)},
	year = {2012},
	month = {February},
	pages = {295-300},
	address = {Valladolid (Spain)},
	author = {M. J. Gacto and R. Alcal{\'a} and F. Herrera}
}
@article {simidat243,
	title = {Automatic Laser Pointer Detection Algorithm for Environment Control Device Systems Based on Template Matching and Genetic Tuning of Fuzzy Rule-Based Systems},
	journal = {International Journal of Computational Intelligence Systems},
	volume = {5},
	number = {2},
	year = {2012},
	pages = {368-386},
	doi = {10.1080/18756891.2012.685327},
	author = {F. Ch{\'a}vez and A. Fern{\'a}ndez and M. J. Gacto and R. Alcal{\'a}}
}
@article {Cggd12,
	title = {Genetic lateral tuning for subgroup discovery with fuzzy rules using the algorithm NMEEF-SD},
	journal = {International Journal of Computational Intelligence Systems},
	volume = {5},
	number = {2},
	year = {2012},
	note = {TIN-2008-06681-C06-02,TIC-3928},
	pages = {355-367},
	doi = {10.1080/18756891.2012.685323},
	author = {C. J. Carmona and P. Gonz{\'a}lez and M. J. Gacto and M. J. del Jesus}
}
@article {simidat242,
	title = {Mining Fuzzy Association Rules from Low Quality Data},
	journal = {Soft Computing},
	volume = {16},
	number = {5},
	year = {2012},
	pages = {883-901},
	doi = {10.1007/s00500-011-0775-3},
	author = {A. Palacios and M. J. Gacto and J. Alcal{\'a}-Fdez}
}
@conference {simidat217,
	title = {A double axis classi},
	booktitle = {9th International Workshop on Fuzzy Logic and Applications (WILF)},
	year = {2011},
	author = {M. J. Gacto and R. Alcal{\'a} and F. Herrera}
}
@conference {10.1007/978-3-642-23713-3_13,
	title = {A Double Axis Classification of Interpretability Measures for Linguistic Fuzzy Rule-Based Systems},
	booktitle = {Fuzzy Logic and Applications},
	year = {2011},
	pages = {99{\textendash}106},
	publisher = {Springer Berlin Heidelberg},
	organization = {Springer Berlin Heidelberg},
	address = {Berlin, Heidelberg},
	abstract = {In this paper, we present a simple classification of the papers devoted to interpretability of Linguistic Fuzzy Rule-Based Systems attending to the type of interpretability measures and the part of the system for which they are applied, i.e., a double axis classification. A taxonomy considering this double axis is used to easily categorize the proposals in the existing literature. In this way, this work also represents a simple summary of the current state-of-the-art to assess the interpretability of Linguistic Fuzzy Rule-Based Systems.},
	isbn = {978-3-642-23713-3},
	author = {M. J. Gacto and Alcal{\'a}, R. and F. Herrera},
	editor = {Fanelli, Anna Maria and Pedrycz, Witold and Petrosino, Alfredo}
}
@article {simidat215,
	title = {A Fast and Scalable Multi-Objective Genetic Fuzzy System for Linguistic Fuzzy Modeling in High-Dimensional Regression Problems},
	journal = {IEEE Transactions on Fuzzy Systems},
	volume = {19},
	number = {4},
	year = {2011},
	pages = {666-681},
	doi = {10.1109/TFUZZ.2011.2131657 COMPLEMENTARY MATERIAL to the paper here: dataset partitions, results, figures, etc..},
	author = {R. Alcal{\'a} and M. J. Gacto and F. Herrera}
}
@conference {simidat205,
	title = {Evolutionary Multi-Objective Algorithm to Effectively Improve the Performance of the Classic Tuning of Fuzzy Logic Controllers for a Heating, Ventilating and Air Conditioning System},
	booktitle = {5th International Workshop On Genetic And Evolutionary Fuzzy Systems (GEFS)},
	year = {2011},
	month = {April},
	author = {M. J. Gacto and R. Alcal{\'a} and F. Herrera}
}
@article {simidat216,
	title = {Interpretability of Linguistic Fuzzy Rule-Based Systems: An Overview of Interpretability Measures},
	journal = {Information Sciences},
	volume = {181},
	number = {20},
	year = {2011},
	pages = {4340{\textendash}4360},
	doi = {10.1016/j.ins.2011.02.021 COMPLEMENTARY MATERIAL to the paper here: links to the papers with doi, new contributions, etc..},
	author = {M. J. Gacto and R. Alcal{\'a} and F. Herrera}
}
@conference {simidat192,
	title = {Analysis of the Performance of a Semantic Interpretability-Based Tuning and Rule Selection of Fuzzy Rule-Based Systems by Means of a Multi-Objective Evolutionary Algorithm},
	booktitle = {LNAI 6097},
	year = {2010},
	month = {June},
	pages = {228-238},
	address = {C{\'o}rdoba},
	author = {M. J. Gacto and R. Alcal{\'a} and F. Herrera}
}
@conference {simidat174,
	title = {Indice de Interpretabilidad Sem{\'a}ntica para el Ajuste de Sistemas Basados en Reglas Difusas y Selecci{\'o}n de Reglas Mediante un Algoritmo Evolutivo Multi-Objetivo},
	booktitle = {XV Edici{\'o}n del Congreso Espa{\~n}ol sobre Tecnolog{\'\i}as y L{\'o}gica Fuzzy (ESTYLF)},
	year = {2010},
	month = {February},
	pages = {73-78},
	address = {Huelva},
	author = {M. J. Gacto and R. Alcal{\'a} and F. Herrera}
}
@article {simidat173,
	title = {Integration of an Index to Preserve the Semantic Interpretability in the Multi-Objective Evolutionary Rule Selection and Tuning of Linguistic Fuzzy Systems},
	journal = {IEEE Transactions on Fuzzy Systems},
	volume = {18},
	number = {3},
	year = {2010},
	pages = {515-531},
	doi = {10.1109/TFUZZ.2010.2041008},
	author = {M. J. Gacto and R. Alcal{\'a} and F. Herrera}
}
@conference {simidat151,
	title = {A Multiobjective Evolutionary Algorithm for Tuning Fuzzy Rule Based Systems with Measures for Preserving Interpretability},
	booktitle = {Proceedings of the Joint International Fuzzy Systems Association World Congress and the European Society for Fuzzy Logic and Technology Conference (IFSA)},
	year = {2009},
	month = {July},
	pages = {1146-1151},
	publisher = {IFSA/EUSFLAT 2009},
	organization = {IFSA/EUSFLAT 2009},
	address = {Lisbon, Portugal},
	author = {M. J. Gacto and R. Alcal{\'a} and F. Herrera}
}
@article {simidat98,
	title = {Adaptation and Application of Multi-Objective Evolutionary Algorithms for Rule Reduction and Parameter Tuning of Fuzzy Rule-Based Systems},
	journal = {Soft Computing},
	volume = {13},
	number = {5},
	year = {2009},
	pages = {419-436},
	doi = {10.1007/s00500-008-0359-z},
	author = {M. J. Gacto and R. Alcal{\'a} and F. Herrera}
}
@conference {simidat113,
	title = {Algoritmo Gen{\'e}tico Multi-Objetivo Avanzado para el ajuste de un sistema difuso aplicado al Control de Sistemas de Ventilaci{\'o}n, Calefacci{\'o}n y Aire Acondicionado},
	booktitle = {Proceedings of the Congreso Espa{\~n}ol sobre Metaheur{\'\i}sticas, Algoritmos Evolutivos y Bioinspirados (MAEB)},
	year = {2009},
	month = {February},
	pages = {595-602},
	author = {M. J. Gacto and R. Alcal{\'a} and F. Herrera}
}
@conference {simidat170,
	title = {Handling High-Dimensional Regression Problems by Means of an Efficient Multi-Objective Evolutionary Algorithm},
	booktitle = {9th International Conference on Intelligent Systems Design and Applications (ISDA)},
	year = {2009},
	month = {November},
	pages = {109-114},
	address = {Pisa (Italy)},
	author = {M. J. Gacto and R. Alcal{\'a} and F. Herrera}
}
@article {simidat100,
	title = {Improving Fuzzy Logic Controllers Obtained by Experts: A Case Study in HVAC Systems},
	journal = {Applied Intelligence},
	volume = {31},
	number = {1},
	year = {2009},
	pages = {15-30},
	doi = {10.1007/s10489-007-0107-6},
	author = {R. Alcal{\'a} and J. Alcal{\'a}-Fdez and M. J. Gacto and F. Herrera}
}
@article {simidat99,
	title = {Learning the Membership Function Contexts for Mining Fuzzy Association Rules by Using Genetic Algorithms},
	journal = {Fuzzy Sets and Systems},
	volume = {160},
	number = {7},
	year = {2009},
	pages = {905-921},
	doi = {10.1016/j.fss.2008.05.012},
	author = {J. Alcal{\'a}-Fdez and R. Alcal{\'a} and M. J. Gacto and F. Herrera}
}
@conference {simidat111,
	title = {An Improved Multi-Objective Genetic Algorithm for Tuning Linguistic Fuzzy System},
	booktitle = {Proceedings of the 2008 International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU)},
	year = {2008},
	month = {June},
	pages = {1121-1128},
	author = {M. J. Gacto and R. Alcal{\'a} and F. Herrera}
}
@conference {simidat112,
	title = {Multi-Objective Genetic Fuzzy Systems: On the Necessity of Including Expert Knowledge in the MOEA Design Process},
	booktitle = {Proceedings of the 2008 International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU)},
	year = {2008},
	month = {June},
	pages = {1446-1453},
	author = {M. J. Gacto and R. Alcal{\'a} and F. Herrera}
}
@inbook {simidat110,
	title = {On the use of Multiobjective Genetic Algorithms to Improve the Accuracy-Interpretability Trade-Off of Fuzzy Rule-Based Systems},
	booktitle = {Multi-objective Evolutionary Algorithms for Knowledge Discovery from Data Bases},
	volume = {98},
	year = {2008},
	isbn = {978-3-540-77466-2},
	author = {R. Alcal{\'a} and J. Alcal{\'a}-Fdez and M. J. Gacto and F. Herrera},
	editor = {A. Ghosh and S. Dehuri and S. Ghosh}
}
@inbook {Alcal{\'a}2008,
	title = {On the Usefulness of MOEAs for Getting Compact FRBSs Under Parameter Tuning and Rule Selection},
	booktitle = {Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases},
	year = {2008},
	pages = {91{\textendash}107},
	publisher = {Springer Berlin Heidelberg},
	organization = {Springer Berlin Heidelberg},
	address = {Berlin, Heidelberg},
	abstract = {In the last years, multi-objective genetic algorithms have been successfully applied to obtain Fuzzy Rule-Based Systems satisfying different objectives, usually different performance measures.},
	isbn = {978-3-540-77467-9},
	doi = {10.1007/978-3-540-77467-9_5},
	url = {https://doi.org/10.1007/978-3-540-77467-9_5},
	author = {Alcal{\'a}, R. and Alcal{\'a}-Fdez, J. and M. J. Gacto and F. Herrera},
	editor = {Ghosh, Ashish and Dehuri, Satchidananda and Ghosh, Susmita}
}
@conference {simidat108,
	title = {A Multi-Objective Evolutionary Algorithm for Rule Selection and Tuning on Fuzzy Rule-Based Systems},
	booktitle = {Proceedings of the 16th IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)},
	year = {2007},
	month = {July},
	pages = {1367-1372},
	address = {London (United Kingdom)},
	author = {R. Alcal{\'a} and J. Alcal{\'a}-Fdez and M. J. Gacto}
}
@article {simidat97,
	title = {A Multi-objectiveGenetic Algorithm for Tuning and Rule Selection to Obtain Accurate and Compact Linguistic Fuzzy Rule-Based Systems},
	journal = {International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems},
	volume = {15},
	number = {5},
	year = {2007},
	pages = {539{\textendash}557},
	doi = {10.1142/S0218488507004868},
	author = {R. Alcal{\'a} and M. J. Gacto and F. Herrera and J. Alcal{\'a}-Fdez}
}
@conference {simidat109,
	title = {Aprendizaje Evolutivo de los Contextos de las Funciones de Pertenencia para Extraer Reglas de Asociaci{\'o}n Difusas},
	booktitle = {Proceedings of the II Congreso Espa{\~n}ol de Inform{\'a}tica (CEDI 2007). II Simposio sobre L{\'o}gica Fuzzy y Soft Computing (LFSC)},
	year = {2007},
	month = {September},
	pages = {25-32},
	author = {J. Alcal{\'a}-Fdez and R. Alcal{\'a} and M. J. Gacto and F. Herrera}
}
@conference {simidat107,
	title = {Genetic Learning of Membership Functions for Mining Fuzzy Association Rules},
	booktitle = {Proceedings of the 16th IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)},
	year = {2007},
	month = {July},
	pages = {1538-1543},
	address = {London (United Kingdom)},
	author = {R. Alcal{\'a} and J. Alcal{\'a}-Fdez and M. J. Gacto and F. Herrera}
}
@article {simidat96,
	title = {Rule Base Reduction and Genetic Tuning of Fuzzy Systems based on the Linguistic 3-Tuples Representation},
	journal = {Soft Computing},
	volume = {11},
	number = {5},
	year = {2007},
	pages = {401-419},
	doi = {10.1007/s00500-006-0106-2},
	author = {R. Alcal{\'a} and J. Alcal{\'a}-Fdez and M. J. Gacto and F. Herrera}
}
@conference {simidat37,
	title = {A Genetic-Programming-Based Approach for the Learning of Compact Fuzzy Rule-Based Classification Systems},
	booktitle = {The Eighth International Conference on Artificial Intelligence and Soft Computing (ICAISC)},
	series = {LNCS},
	volume = {4029},
	year = {2006},
	pages = {182-191},
	publisher = {Springer},
	organization = {Springer},
	address = {Zakopane (Poland)},
	author = {F. J. Berlanga and M. J. del Jesus and M. J. Gacto and F. Herrera}
}
@inbook {simidat104,
	title = {Fuzzy Rule Reduction and Tuning of Fuzzy Logic Controllers for a HVAC System},
	booktitle = {Fuzzy Applications in Industrial Engineering, Studies in Fuzziness and Soft Computing},
	volume = {201},
	year = {2006},
	pages = {89-117},
	isbn = {978-3-540-33516-0},
	author = {R. Alcal{\'a} and J. Alcal{\'a}-Fdez and M. J. Gacto and F. Herrera},
	editor = {Cengiz Kahraman}
}
@conference {simidat38,
	title = {Genetic Lateral and Amplitude Tuning with Rule Selection for Fuzzy Control of Heating, Ventilating and Air Conditioning Systems},
	booktitle = {19th International Conference on Industrial \& Engineering Applications of Artificial Intelligence \& Expert Systems (IEA/AIE)},
	series = {LNCS},
	volume = {4031},
	year = {2006},
	pages = {452-461},
	publisher = {Springer-Verlag},
	organization = {Springer-Verlag},
	address = {Annecy (France)},
	author = {R. Alcal{\'a} and J. Alcal{\'a}-Fdez and F. J. Berlanga and M. J. Gacto and F. Herrera}
}
@conference {simidat39,
	title = {Improving Fuzzy Rule-Based Decision Models by Means of a Genetic 2-Tuples Based Tuning and the Rule Selection},
	booktitle = {Modeling Decisions for Artificial Intelligence (MDAI)},
	series = {LNCS},
	volume = {3885},
	year = {2006},
	pages = {317-328},
	publisher = {Springer},
	organization = {Springer},
	address = {Tarragona (Spain)},
	author = {R. Alcal{\'a} and J. Alcal{\'a}-Fdez and F. J. Berlanga and M. J. Gacto and F. Herrera}
}
@conference {simidat105,
	title = {Obtaining Compact and Still Accurate Linguistic Fuzzy Rule-Based Systems by Using Multi-Objetive Genetic Algorithms},
	booktitle = {Proceedings of the Symposium on Fuzzy Systems in Computer Science (FSCS)},
	year = {2006},
	pages = {53-62},
	author = {R. Alcal{\'a} and J. Alcal{\'a}-Fdez and M. J. Gacto and F. Herrera}
}
@conference {simidat106,
	title = {Obtenci{\'o}n de Sistemas Basados en Reglas Difusas Precisos y Compactos Mediante Algoritmos Gen{\'e}ticos Multiobjetivo},
	booktitle = {Proceedings of the XIII Congreso Espa{\~n}ol sobre Tecnolog{\'\i}as y L{\'o}gica Fuzzy (ESTYLF)},
	year = {2006},
	pages = {1-6},
	author = {R. Alcal{\'a} and J. Alcal{\'a}-Fdez and M. J. Gacto}
}
@conference {simidat102,
	title = {Ajuste Evolutivo Lateral y de Amplitud de etiquetas para Sistemas Basados en Reglas Difusas},
	booktitle = {Simposio de Inteligencia Computacional (SICO)},
	year = {2005},
	pages = {481-488},
	address = {Granada (Spain)},
	author = {R. Alcal{\'a} and J. Alcal{\'a}-Fdez and M. J. Gacto and F. Herrera}
}
@conference {simidat103,
	title = {Genetic Lateral and Amplitude Tuning of Membership Functions for Fuzzy Systems},
	booktitle = {Proceedings of the 2nd International Conference on Machine Intelligence (ACIDCA-ICMI)},
	year = {2005},
	pages = {589-595},
	author = {R. Alcal{\'a} and J. Alcal{\'a}-Fdez and M. J. Gacto and F. Herrera}
}
@conference {simidat101,
	title = {IN-RECS: {\'I}ndice de Impacto de las Revistas Espa{\~n}olas de Ciencias Sociales},
	booktitle = {Proceedings of the IX Jornadas Espa{\~n}olas de Documentaci{\'o}n},
	year = {2005},
	author = {E. Jim{\'e}nez and E. Delgado and R. Ruiz-Ba{\~n}os and A.G. L{\'o}pez-Herrera and M. J. Gacto and D. Torres and M. Moneda and R. Ruiz-Ba{\~n}os and J.M. P{\'e}rez and R. Bail{\'o}n and E. Poyatos and M. J. Rodr{\'\i}guez}
}
