The COR methodology allows the learning of Linguistic Fuzzy Rule-Based Systems by considering cooperation among rules. In order to do that, COR firstly finds the set of candidate fuzzy rules that can be fired by the examples in the training set, and then uses a search algorithm to find the final set of rules. In the algorithms proposed so far, all candidate rules have the same number of antecedents, which is the number of input variables. However, these rules could be too specific, and rules more generic are not considered. In this paper we study the effect of considering all possible rules, regardless of their number of antecedents. Experiments show that the rule bases obtained use simpler rules, and the results for the error of prediction improve upon those obtained by using classical COR methods.