@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}
}