@inproceedings{481,
  author = {Yuviny Echevarría and Luciano Sánchez and Cecilio Blanco},
  editor = {Oscar Luaces and José Gámez and Edurne Barrenechea and Alicia Troncoso and Mikel Galar and Héctor Quintián and Emilio Corchado},
  title = {Genetic Fuzzy Modelling of Li-Ion Batteries Through a Combination of Theta-DEA and Knowledge-Based Preference Ordering},
  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 $ \theta $-Dominance Evolutionary Algorithm ($ \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 $ \epsilon $ -quality indicators.},
  year = {2016},
  pages = {310-320},
  publisher = {Springer International Publishing},
  address = {Cham},
  isbn = {978-3-319-44636-3},
}