@article {752,
	title = {Evolutionary Fuzzy Sistems for Explainable Artificial Intelligence: Why, When, What for, and Where to ?},
	journal = {IEEE Computational Intelligence},
	volume = {1},
	number = {14},
	year = {2019},
	note = {TIN2015-68454-R; TIN2015-67661-P; TIN2017-89517-P},
	pages = {69-81},
	abstract = {Evolutionary fuzzy systems are one of the greatest advances within the area of computational intelligence. They consist of evolutionary algorithms applied to the design of fuzzy systems. Thanks to this hybridization, superb abilities are provided to fuzzy modeling in many different data science scenarios. This contribution is intended to comprise a position paper developing a comprehensive analysis of the evolutionary fuzzy systems research field. To this end, the "4 W" questions are posed and addressed with the aim of understanding the current context of this topic and its significance. Specifically, it will be pointed out why evolutionary fuzzy systems are important from an explainable point of view, when they began, what they are used for, and where the attention of researchers should be directed to in the near future in this area. They must play an important role for the emerging area of eXplainable Artificial Intelligence (XAI) learning from data.},
	issn = {1556-603X},
	doi = {10.1109/TFUZZ.2018.2814577},
	author = {A. Fern{\'a}ndez and M. J. del Jesus and O. Cord{\'o}n and F. Marcelloni and F. Herrera}
}
@article {article,
	title = {Cost-Sensitive Learning of Fuzzy Rules for Imbalanced Classification Problems Using FURIA},
	journal = {International Journal of Uncertainty Fuzziness and Knowledge-Based Systems},
	volume = {22},
	year = {2014},
	month = {10},
	pages = {643-675},
	doi = {10.1142/S0218488514500330},
	author = {Palacios, Ana and Trawinski, Krzysztof and O. Cord{\'o}n and S{\'a}nchez, Luciano}
}
@article {585,
	title = {A Genetic Fuzzy Linguistic Combination Method for Fuzzy Rule-based Multiclassifiers Revista: IEEE Transactions on Fuzzy Systems},
	journal = {IEEE Transactions on Fuzzy Systems},
	volume = {21},
	number = {5},
	year = {2013},
	month = {01},
	pages = {950-965},
	issn = {1063-6706},
	doi = {10.1109/TFUZZ.2012.2236844},
	author = {O. Cord{\'o}n and S{\'a}nchez, Luciano and Arnaud Quirin and Trawinski, Krzysztof}
}
@article {TRAWINSKI20133,
	title = {Multiobjective genetic classifier selection for random oracles fuzzy rule-based classifier ensembles: How beneficial is the additional diversity?},
	journal = {Knowledge-Based Systems},
	volume = {54},
	year = {2013},
	pages = {3 - 21},
	abstract = {Recently we proposed the use of the Random Linear Oracles classical classifier ensemble (CE) design methodology in a fuzzy environment. It derived fuzzy rule-based CEs obtaining an outstanding performance. Random Oracles introduce an additional diversity into the base classifiers improving the accuracy of the entire CE. Meanwhile, the overproduce-and-choose strategy leads to a good accuracy-complexity trade-off. It is based on the generation of a large number of component classifiers and a subsequent selection of the best cooperating subset of them. The current contribution has a twofold aim: (1) Introduce a new Random Oracles approach into the fuzzy rule-based CEs design; (2) Incorporate an evolutionary multi-objective overproduce-and-choose strategy to our approach analyzing the influence of this additional diversity in the final CE performance (focusing on the accuracy). To do so, firstly, we incorporate the two Random Oracle variants into the fuzzy rule-based CE framework. Then, we use NSGA-II to provide a specific component classifier selection driven by three different criteria. Exhaustive experiments are carried out over 29 UCI and KEEL datasets with high complexity (considering both the number of attributes as well as the number of examples) showing the good performance of the proposed approach.},
	keywords = {Bagging, Diversity measures, Evolutionary multiobjective optimization, Fuzzy rule-based classifier ensembles, Genetic classifier selection, High complexity datasets, NSGA-II, Random oracles},
	issn = {0950-7051},
	doi = {https://doi.org/10.1016/j.knosys.2013.08.006},
	url = {http://www.sciencedirect.com/science/article/pii/S0950705113002360},
	author = {Krzysztof Trawi{\'n}ski and O. Cord{\'o}n and Arnaud Quirin and Luciano S{\'a}nchez}
}
@conference {5454152,
	title = {Introducing a genetic fuzzy linguistic combination method for bagging fuzzy rule-based multiclassification systems},
	booktitle = {2010 4th International Workshop on Genetic and Evolutionary Fuzzy Systems (GEFS)},
	year = {2010},
	month = {March},
	pages = {75-80},
	abstract = {Many different fuzzy aggregation operators have been successfully used to combine the outputs provided by the individual classifiers in a multiclassification system. However, up to our knowledge, the use of fuzzy combination methods composed of a fuzzy system is less extended. By using a fuzzy linguistic rule-based classification system as a combination method, the resulting classifier ensemble would show a hierarchical structure and the operation of the latter component would be transparent to the user. Moreover, for the specific case of fuzzy multiclassification systems, the new approach could also become a smart way to allow fuzzy classifiers to deal with high dimensional problems avoiding the curse of dimensionality. The present contribution establishes the first basis in this direction by introducing a genetic fuzzy system-based framework to build the fuzzy linguistic combination method for a bagging fuzzy multiclassification system.},
	keywords = {Bagging, bagging fuzzy multiclassification system, Classification tree analysis, classifier ensemble, Computer science, Decision trees, fuzzy aggregation operators, fuzzy linguistic combination methods, fuzzy linguistic rule-based system, Fuzzy reasoning, fuzzy set theory, Fuzzy systems, genetic algorithms, genetic fuzzy-based system, Genetics, knowledge based systems, learning (artificial intelligence), machine learning, Neural networks, pattern classification},
	doi = {10.1109/GEFS.2010.5454152},
	author = {L. S{\'a}nchez and O. Cord{\'o}n and A. Quirin and K. Trawinski}
}
@conference {4484560,
	title = {A first study on bagging fuzzy rule-based classification systems with multicriteria genetic selection of the component classifiers},
	booktitle = {2008 3rd International Workshop on Genetic and Evolving Systems},
	year = {2008},
	month = {March},
	pages = {11-16},
	keywords = {Bagging, bagging fuzzy rule-based classification system, Boosting, component classifier, Design methodology, evolutionary computation, fuzzy set theory, Fuzzy systems, genetic algorithms, heuristic fuzzy classification rule generation method, Humans, knowledge based systems, learning (artificial intelligence), machine learning, multicriteria genetic algorithm, pattern classification, Proposals, Scalability},
	doi = {10.1109/GEFS.2008.4484560},
	author = {O. Cord{\'o}n and A. Quirin and L. S{\'a}nchez}
}
@conference {4626687,
	title = {On the Use of Bagging, Mutual Information-Based Feature Selection and Multicriteria Genetic Algorithms to Design Fuzzy Rule-Based Classification Ensembles},
	booktitle = {2008 Eighth International Conference on Hybrid Intelligent Systems},
	year = {2008},
	month = {Sep.},
	pages = {549-554},
	abstract = {In this contribution we explore the combination of bagging with random subspace and two variants of Battiti{\textquoteright}s mutual information feature selection methods to design fuzzy rule-based classification system ensembles. Besides, we consider a multicriteria genetic algorithm guided by the training error to select the component classifiers, in order to look for appropriate accuracy-complexity trade-offs in the final multiclassifier.},
	keywords = {Bagging, classification, Classification algorithms, fuzzy rule-based classification ensembles, fuzzy set theory, Gallium, genetic algorithms, Glass, multicriteria genetic algorithms, mutual information-based feature selection, Sonar, Training, Vehicles},
	doi = {10.1109/HIS.2008.147},
	author = {O. Cord{\'o}n and A. Quirin and L. S{\'a}nchez}
}
@article {simidat23,
	title = {Genetic tuning of fuzzy rule deep structures preserving interpretability for linguistic modeling},
	journal = {IEEE Transactions on Fuzzy Systems},
	volume = {13},
	number = {1},
	year = {2005},
	pages = {13-29},
	author = {J. Casillas and O. Cord{\'o}n and M. J. del Jesus and F. Herrera}
}
@inbook {CORDON2003315,
	title = {- A Multiobjective Genetic Algorithm for Feature Selection and Data Base Learning in Fuzzy-Rule Based Classification Systems},
	booktitle = {Intelligent Systems for Information Processing},
	year = {2003},
	pages = {315 - 326},
	publisher = {Elsevier Science},
	organization = {Elsevier Science},
	address = {Amsterdam},
	abstract = {Publisher Summary This chapter illustrates a multiobjective genetic algorithm for feature selection and database learning in Fuzzy Rule-Based Classification System (FRBCS). An FRBCS presents two main components{\textemdash}the Inference System and the Knowledge Base (KB). The KB is composed of the Rule Base (RB) constituted by the collection of fuzzy rules, and of the Data Base (DB), containing the membership functions of the fuzzy partitions associated to the linguistic variables. The composition of the KB of an FRBCS directly depends on the problem being solved. If there is no expert information about the problem under solving, an automatic learning process must be used to derive the KB from examples. This contribution proposes a multiobjective genetic process for jointly performing feature selection and DB components learning that is combined with an efficient fuzzy classification rule generation method to obtain the complete KB for a descriptive FRBCS. This method achieves an important reduction of the relevant variables selected for the final system and adapts the fuzzy partition of each variable to the problem being solved. Therefore, the conclusion is that the proposed method allows for enhancing interpretability, accuracy, and performance of the FRBCS method.},
	isbn = {978-0-444-51379-3},
	doi = {https://doi.org/10.1016/B978-044451379-3/50026-1},
	url = {http://www.sciencedirect.com/science/article/pii/B9780444513793500261},
	author = {O. Cord{\'o}n and F. Herrera and M. J. del Jesus and L. Magdalena and A.M. S{\'a}nchez and P. Villar},
	editor = {Bernadette Bouchon-Meunier and Laurent Foulloy and Ronald R. Yager}
}
@inbook {Cord{\'o}n2003,
	title = {A Multiobjective Genetic Learning Process for joint Feature Selection and Granularity and Contexts Learning in Fuzzy Rule-Based Classification Systems},
	booktitle = {Interpretability Issues in Fuzzy Modeling},
	year = {2003},
	pages = {79{\textendash}99},
	publisher = {Springer Berlin Heidelberg},
	organization = {Springer Berlin Heidelberg},
	address = {Berlin, Heidelberg},
	abstract = {In this contribution, we propose a genetic process to select an appropiate set of features in a Fuzzy Rule-Based Classification System (FRBCS) and to automatically learn the whole Data Base definition using a non linear scaling function to adapt the fuzzy partition contexts and determining an appropiate granularity for each of them. An ad-hoc data covering learning method is considered to obtain the Rule Base. The method uses a multiobjective genetic algorithm in order to obtain a good trade-off between accuracy and interpretability.},
	isbn = {978-3-540-37057-4},
	doi = {10.1007/978-3-540-37057-4_4},
	url = {https://doi.org/10.1007/978-3-540-37057-4_4},
	author = {O. Cord{\'o}n and M. J. del Jesus and F. Herrera and Magdalena, Luis and Villar, Pedro},
	editor = {Casillas, Jorge and O. Cord{\'o}n and F. Herrera and Magdalena, Luis}
}
@inbook {702,
	title = {GA-P Based Search of Structures and Parameters of Dynamital Process Models},
	booktitle = {Advances in Soft Computing},
	year = {2003},
	pages = {371-380},
	publisher = {Springer-Verlag},
	organization = {Springer-Verlag},
	issn = {1-85233-755-9},
	author = {Ben{\'\i}tez, Jos{\'e} Manuel and O. Cord{\'o}n and Hoffmann, Frank and Rajkumar, Roy}
}
@article {simidat5,
	title = {Linguistic Modeling with Hierarchical Systems of Weighted Linguistic Rules},
	journal = {International Journal of Approximate Reasoning},
	volume = {32},
	number = {2-3},
	year = {2003},
	pages = {187-215},
	author = {R. Alcal{\'a} and J. R. Cano and O. Cord{\'o}n and F. Herrera and P. Villar and I. Zwir}
}
@inbook {S{\'a}nchez2003,
	title = {Tuning fuzzy partitions or assigning weights to fuzzy rules: which is better?},
	booktitle = {Accuracy Improvements in Linguistic Fuzzy Modeling},
	year = {2003},
	pages = {366{\textendash}385},
	publisher = {Springer Berlin Heidelberg},
	organization = {Springer Berlin Heidelberg},
	address = {Berlin, Heidelberg},
	abstract = {The accuracy of linguistic classifiers can be improved with several techniques, but they all compromise the interpretability of the rule base up to a certain degree. Assigning weights to fuzzy rules and tuning the memberships associated to linguistic variables are two of the most common methods. In this work we study whether tuning the membership functions in a linguistic classifier is better or not than adjusting rule weights, in terms of the interpretability of the rule base and the complexity of the output.},
	isbn = {978-3-540-37058-1},
	doi = {10.1007/978-3-540-37058-1_15},
	url = {https://doi.org/10.1007/978-3-540-37058-1_15},
	author = {S{\'a}nchez, Luciano and Otero, Jos{\'e}},
	editor = {Casillas, Jorge and O. Cord{\'o}n and F. Herrera and Magdalena, Luis}
}
@conference {CanoCHS02,
	title = {A GRASP Algorithm for Clustering},
	booktitle = {Proceedings of the 8th Ibero-American Conference on Artifical Intelligence, Seville, Spain, November 12-15, 2002,},
	year = {2002},
	pages = {214{\textendash}223},
	author = {J. R. Cano and O. Cord{\'o}n and F. Herrera and Luciano S{\'a}nchez}
}
@article {CanoCHS02,
	title = {A greedy randomized adaptive search procedure applied to the clustering problem as an initialization process using K-Means as a local search procedure},
	journal = {Journal of Intelligent and Fuzzy Systems},
	volume = {12},
	number = {3-4},
	year = {2002},
	pages = {235{\textendash}242},
	author = {J. R. Cano and O. Cord{\'o}n and F. Herrera and Luciano S{\'a}nchez}
}
@article {SANCHEZ2002175,
	title = {Some relationships between fuzzy and random set-based classifiers and models},
	journal = {International Journal of Approximate Reasoning},
	volume = {29},
	number = {2},
	year = {2002},
	pages = {175 - 213},
	abstract = {When designing rule-based models and classifiers, some precision is sacrificed to obtain linguistic interpretability. Understandable models are not expected to outperform black boxes, but usually fuzzy learning algorithms are statistically validated by contrasting them with black-box models. Unless performance of both approaches is equivalent, it is difficult to judge whether the fuzzy one is doing its best, because the precision gap between the best understandable model and the best black-box model is not known. In this paper we discuss how to generate probabilistic rule-based models and classifiers with the same structure as fuzzy rule-based ones. Fuzzy models, in which features are partitioned into linguistic terms, will be compared to probabilistic rule-based models with the same number of terms in every linguistic partition. We propose to use these probabilistic models to estimate a lower precision limit which fuzzy rule learning algorithms should surpass.},
	keywords = {Fuzzy classifiers, fuzzy models, Random set-based classifiers, Random set-based models},
	issn = {0888-613X},
	doi = {https://doi.org/10.1016/S0888-613X(01)00063-9},
	url = {http://www.sciencedirect.com/science/article/pii/S0888613X01000639},
	author = {Luciano S{\'a}nchez and Jorge Casillas and O. Cord{\'o}n and M. J. del Jesus}
}
@conference {741,
	title = {Utilizaci{\'o}n de Algoritmos Gen{\'e}ticos Multiobjetivos para la Selecci{\'o}n de Caracter{\'\i}sticas y Dise{\~n}o de la Base de Conocimiento de un Sistema de Clasificaci{\'o}n Basado en Reglas Difusas},
	booktitle = {Congreso Espa{\~n}ol sobre Tecnolog{\'\i}as y L{\'o}gica Fuzzy},
	year = {2002},
	month = {01},
	address = {Le{\'o}n (Espa{\~n}a)},
	author = {O. Cord{\'o}n and F. Herrera and del Jesus D{\'\i}az, Mar{\'\i}a Jos{\'e} and Magdalena, Luis and A.M. S{\'a}nchez and Villar, Pedro}
}
@conference {inproceedings,
	title = {A multiobjective genetic algorithm for feature selection and granularity learning in fuzzy-rule based classification systems},
	volume = {3},
	year = {2001},
	month = {08},
	pages = {1253 - 1258 vol.3},
	isbn = {0-7803-7078-3},
	doi = {10.1109/NAFIPS.2001.943727},
	author = {O. Cord{\'o}n and F. Herrera and M. J. del Jesus and Villar, P}
}
@article {CASILLAS2001135,
	title = {Genetic feature selection in a fuzzy rule-based classification system learning process for high-dimensional problems},
	journal = {Information Sciences},
	volume = {136},
	number = {1},
	year = {2001},
	note = {Recent Advances in Genetic Fuzzy Systems},
	pages = {135 - 157},
	abstract = {The inductive learning of a fuzzy rule-based classification system (FRBCS) is made difficult by the presence of a large number of features that increases the dimensionality of the problem being solved. The difficulty comes from the exponential growth of the fuzzy rule search space with the increase in the number of features considered in the learning process. In this work, we present a genetic feature selection process that can be integrated in a multistage genetic learning method to obtain, in a more efficient way, FRBCSs composed of a set of comprehensible fuzzy rules with high-classification ability. The proposed process fixes, a priori, the number of selected features, and therefore, the size of the search space of candidate fuzzy rules. The experimentation carried out, using Sonar example base, shows a significant improvement on simplicity, precision and efficiency achieved by adding the proposed feature selection processes to the multistage genetic learning method or to other learning methods.},
	keywords = {feature selection, Fuzzy reasoning methods, fuzzy rule-based classification systems, Inductive learning},
	issn = {0020-0255},
	doi = {https://doi.org/10.1016/S0020-0255(01)00147-5},
	url = {http://www.sciencedirect.com/science/article/pii/S0020025501001475},
	author = {J Casillas and O. Cord{\'o}n and M. J. del Jesus and F. Herrera}
}
@article {simidat161,
	title = {Genetic Feature Selection in a Fuzzy Rule-Based Classification System Learning Process},
	journal = {Information Sciences},
	volume = {136},
	year = {2001},
	pages = {135-157},
	author = {J. Casillas and O. Cord{\'o}n and M. J. del Jesus and F. Herrera}
}
@conference {943783,
	title = {Genetic tuning of fuzzy rule-based systems integrating linguistic hedges},
	booktitle = {Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569)},
	volume = {3},
	year = {2001},
	month = {July},
	pages = {1570-1574 vol.3},
	abstract = {Tuning fuzzy rule-based systems for linguistic modeling is an interesting and widely developed task. It involves adjusting the membership functions composing the knowledge base. To do that, changing the parameters defining each membership function as using linguistic hedges to slightly modify them may be considered. This paper introduces a genetic tuning process for jointly making these two tuning approaches. The experimental results show that our method obtains accurate linguistic models in both approximation and generalization aspects.},
	keywords = {Computer science, experimental results, fuzzy logic, Fuzzy rule-based systems, Fuzzy sets, Fuzzy systems, generalisation (artificial intelligence), generalization, genetic algorithms, genetic tuning process, knowledge base, knowledge based systems, linguistic hedges, linguistic modeling, membership functions, Proposals, Shape, Takagi-Sugeno model, Timing, uncertainty handling},
	doi = {10.1109/NAFIPS.2001.943783},
	author = {J. Casillas and O. Cord{\'o}n and F. Herrera and M. J. del Jesus}
}
@conference {735,
	title = {Selecting Fuzzy-Ruled Based Classification System with Specific Reasoning Methods Using Genetical Algorithms},
	booktitle = {Joint 9th IFSA World congres and 20th Nafips International},
	year = {2001},
	month = {07},
	address = {Praga},
	author = {O. Cord{\'o}n and del Jesus D{\'\i}az, Mar{\'\i}a Jos{\'e} and F. Herrera}
}
@inbook {Cord{\'o}n2000,
	title = {Different Proposals to Improve the Accuracy of Fuzzy Linguistic Modeling},
	booktitle = {Fuzzy If-Then Rules in Computational Intelligence: Theory and Applications},
	year = {2000},
	pages = {189{\textendash}221},
	publisher = {Springer US},
	organization = {Springer US},
	address = {Boston, MA},
	abstract = {Nowadays, Linguistic Modeling is considered as one of the most important applications of Fuzzy Set Theory, along with Fuzzy Control. Linguistic models have the advantage of providing a human-readable description of the system modeled in the form of a set of linguistic rules. In this contribution, we will analyze several approaches to improve the accuracy of linguistic models while maintaining their descriptive power. All these approaches will share the common idea of improving the way in which the Fuzzy Rule-Based System performs interpolative reasoning by improving the cooperation between the rules in the linguistic model Knowledge Base.},
	isbn = {978-1-4615-4513-2},
	doi = {10.1007/978-1-4615-4513-2_9},
	url = {https://doi.org/10.1007/978-1-4615-4513-2_9},
	author = {O. Cord{\'o}n and F. Herrera and M. J. del Jesus and Villar, Pedro and Zwir, Igor},
	editor = {Ruan, Da and Kerre, Etienne E.}
}
@article {simidat162,
	title = {A proposal on Reasoning Methods in Fuzzy Rule-Based Classification Systems},
	journal = {International Journal of Approximate Reasoning},
	volume = {20},
	year = {1999},
	pages = {21-45},
	author = {O. Cord{\'o}n and M. J. del Jesus and F. Herrera}
}
@article {simidat164,
	title = {Analyzing the Reasoning Mechanisms in Fuzzy Rule-Based Classification Systems},
	journal = {Mathware \& Soft Computing},
	volume = {5},
	year = {1999},
	pages = {321-332},
	author = {O. Cord{\'o}n and M. J. del Jesus and F. Herrera}
}
@conference {Cordn1999EvolutionaryAT,
	title = {Evolutionary approaches to the learning of fuzzy rule-based classification systems},
	year = {1999},
	author = {O. Cord{\'o}n and F. Herrera and M. J. del Jesus}
}
@article {simidat163,
	title = {MOGUL: A Methodology to Obtain Genetic fuzzy rule-based systems Ander the iterative rule Learning Approach},
	journal = {International Journal of Intelligent Systems},
	volume = {14},
	year = {1999},
	pages = {1123-1153},
	author = {O. Cord{\'o}n and M. J. del Jesus and F. Herrera}
}
@article {Cord{\'o}n1999,
	title = {Solving Electrical Distribution Problems Using Hybrid Evolutionary Data Analysis Techniques},
	journal = {Applied Intelligence},
	volume = {10},
	number = {1},
	year = {1999},
	month = {Jan},
	pages = {5{\textendash}24},
	abstract = {Real-world electrical engineering problems can take advantage of the last Data Analysis methodologies. In this paper we will show that Genetic Fuzzy Rule-Based Systems and Genetic Programming techniques are good choices for tackling with some practical modeling problems. We claim that both evolutionary processes may produce good numerical results while providing us with a model that can be interpreted by a human being. We will analyze in detail the characteristics of these two methods and we will compare them to the some of the most popular classical statistical modeling methods and neural networks.},
	issn = {1573-7497},
	doi = {10.1023/A:1008384630089},
	url = {https://doi.org/10.1023/A:1008384630089},
	author = {O. Cord{\'o}n and F. Herrera and S{\'a}nchez, Luciano}
}
@article {758,
	title = {Analyzing the Reasoning Mechanism in Fuzzy Rule-Based Classification Systems},
	journal = {Mathware \& Soft Computing},
	volume = {5},
	year = {1998},
	pages = {321-332},
	issn = {1134-5632},
	author = {O. Cord{\'o}n and M. J. del Jesus and F. Herrera}
}
@conference {10.1007/3-540-64582-9_778,
	title = {Computing the spanish medium electrical line maintenance costs by means of evolution-based learning processes},
	booktitle = {Methodology and Tools in Knowledge-Based Systems},
	year = {1998},
	pages = {478{\textendash}486},
	publisher = {Springer Berlin Heidelberg},
	organization = {Springer Berlin Heidelberg},
	address = {Berlin, Heidelberg},
	abstract = {In this paper, we deal with the problem of computing the maintenance costs of electrical medium line in spanish towns. To do so, we present two Data Analysis tools taking as a base Evolutionary Algorithms, the Interval Genetic Algorithm-Programming method to perform symbolic regression and Genetic Fuzzy Rule-Based Systems to design fuzzy models, and use them to solve the said problem. Results obtained are compared with other kind of techniques: classical regression and neural modeling.},
	isbn = {978-3-540-69348-2},
	author = {O. Cord{\'o}n and F. Herrera and S{\'a}nchez, Luciano},
	editor = {Mira, Jos{\'e} and del Pobil, Angel Pasqual and Ali, Moonis}
}
@conference {711,
	title = {Estimaci{\'o}n de la Longitud de L{\'\i}nea de Baja Tensi{\'o}n Mediante T{\'e}cnicas Evolutivas de An{\'a}lisis de Datos},
	booktitle = {8{\textordfeminine} Reuni{\'o}n Nacional de Grupos de Investigaci{\'o}n en Ingenier{\'\i}a El{\'e}ctrica},
	year = {1998},
	author = {O. Cord{\'o}n and Sp{\'\i}n, Antonio and Fajardo, Waldo and F. Herrera and S{\'a}nchez, Luciano}
}
@article {simidat165,
	title = {Genetic Learning of Fuzzy Rule-Based Classification Systems Cooperating with Fuzzy Reasoning Methods},
	journal = {International Journal of Intelligent Systems},
	volume = {13},
	number = {10/11},
	year = {1998},
	pages = {1025-1053},
	author = {O. Cord{\'o}n and M. J. del Jesus and F. Herrera}
}
@conference {713,
	title = {M{\'e}todos de razonamiento aproximado basados en el concepto de mayor{\'\i}a difusa para sistemas de clasificaci{\'o}n},
	booktitle = {Congreso espa{\~n}ol sobre tecnolog{\'\i}a y l{\'o}gica fuzzy},
	year = {1998},
	month = {09},
	address = {Pamplona (Espa{\~n}a)},
	author = {O. Cord{\'o}n and del Jesus D{\'\i}az, Mar{\'\i}a Jos{\'e} and F. Herrera}
}
@article {simidat166,
	title = {Modelado cualitativo utilizando una metodolog{\'\i}a evolutiva de aprendizaje iterativo de bases de reglas difusas},
	journal = {Revista Iberoamericana de Inteligencia Artificial},
	volume = {50},
	year = {1998},
	pages = {56-61},
	author = {O. Cord{\'o}n and M. J. del Jesus and F. Herrera and M. Lozano}
}
@conference {inproceedings,
	title = {An evolutionary paradigm for designing fuzzy rule-based systems from examples},
	year = {1997},
	month = {10},
	pages = {139 - 144},
	isbn = {0-85296-693-8},
	doi = {10.1049/cp:19971170},
	author = {O. Cord{\'o}n and M. J. del Jesus and F. Herrera and Lozano, Manuel}
}
@conference {708,
	title = {Nuevos m{\'e}todos de razonamiento en sistemas de clasificaci{\'o}n basados en reglas difusas},
	booktitle = {Congreso espa{\~n}ol sobre tecnolog{\'\i}as y l{\'o}gica fuzzy.},
	year = {1997},
	address = {Tarragona},
	author = {O. Cord{\'o}n and del Jesus D{\'\i}az, Mar{\'\i}a Jos{\'e} and F. Herrera}
}
@conference {707,
	title = {Sistema de Clasificaci{\'o}n con Reglas Difusas Utilizando Algoritmos Gen{\'e}ticos},
	booktitle = {VI Congreso Espa{\~n}ol sobre Tecnolog{\'\i}as y L{\'o}gica Fuzzy (ESTYLF{\textquoteright}96)},
	year = {1996},
	address = {Oviedo},
	author = {O. Cord{\'o}n and del Jesus D{\'\i}az, Mar{\'\i}a Jos{\'e} and F. Herrera}
}
