@conference {10.1007/978-3-319-44636-3_29,
	title = {Genetic Fuzzy Modelling of Li-Ion Batteries Through a Combination of Theta-DEA and Knowledge-Based Preference Ordering},
	booktitle = {Advances in Artificial Intelligence},
	year = {2016},
	pages = {310{\textendash}320},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {Learning semi-physical fuzzy models of rechargeable Li-Ion batteries from data involves solving a complex multicriteria optimization task where the accuracies of the approximations of the different observable variables are balanced. The fitness function of this problem depends on the recursive evaluation of a set of differential equations, where fuzzy rule-based systems are embedded as nonlinear blocks. Evaluating this function is a time consuming process, thus algorithms that efficiently promote diversity and hence demand a low number of evaluations of the fitness function are preferred. In this paper, a comparison is carried out between some recent genetic algorithms, whose performances are assessed in this particular modelling problem. It is concluded that the combination of the recent {\$}{\$}{\backslash}theta {\$}{\$}-Dominance Evolutionary Algorithm ({\$}{\$}{\backslash}theta {\$}{\$}-DEA) with a Knowledge-based precedence operator, that improves the selection, is a sensible choice. Dominance relations between the Pareto fronts are assessed in terms of binary additive {\$}{\$}{\backslash}epsilon {\$}{\$}-quality indicators.},
	isbn = {978-3-319-44636-3},
	author = {Echevarr{\'\i}a, Yuviny and S{\'a}nchez, Luciano and Blanco, Cecilio},
	editor = {Luaces , Oscar and G{\'a}mez, Jos{\'e} A. and Barrenechea, Edurne and Troncoso, Alicia and Galar, Mikel and Quinti{\'a}n, H{\'e}ctor and Corchado, Emilio}
}
@conference {10.1007/978-3-642-25274-7_27,
	title = {A Summary on the Study of the Medium-Term Forecasting of the Extra-Virgen Olive Oil Price},
	booktitle = {Advances in Artificial Intelligence},
	year = {2011},
	pages = {263{\textendash}272},
	publisher = {Springer Berlin Heidelberg},
	organization = {Springer Berlin Heidelberg},
	address = {Berlin, Heidelberg},
	abstract = {In this paper we present a summary of the application of CO2RBFN, a evolutionary cooperative-competitive algorithm for Radial Basis Function Networks design, to the medium-term forecasting of the extra-virgen olive price, carry out by the SIMIDAT research group. The forecast is about the price at source of the extra-virgin olive oil six months ahead. The influential of the feature selection algorithms over the forecasting of the extra-virgin olive oil price has been analysed in this study and the results obtained with CO2RBFN have been compared with those obtained by different soft computing methods.},
	isbn = {978-3-642-25274-7},
	author = {A.J. Rivera-Rivas and M.D. P{\'e}rez-Godoy and M. J. del Jesus and P{\'e}rez-Recuerda, Pedro and Fr{\'\i}as, Mar{\'\i}a Pilar and Parras, Manuel},
	editor = {Lozano, Jose A. and G{\'a}mez, Jos{\'e} A. and Moreno, Jos{\'e} A.}
}
