Different Approaches to Induce Cooperation in Fuzzy Linguistic Models Under the COR Methodology
Jorge Casillas, Óscar Cordón, Francisco Herrera · Studies in fuzziness and soft computing · 2002
Nowadays, Linguistic Modeling is considered to be one of the most important areas of application for Fuzzy Logic. It is accomplished by linguistic Fuzzy Rule-Based Systems, whose most interesting feature is the interpolative reasoning developed. This characteristic plays a key role in their high performance and is a consequence of the cooperation among the involved fuzzy rules. A new approach that makes good use of this aspect inducing cooperation among rules is introduced in this chapter: the Cooperative Rules methodology. One of its interesting advantages is its flexibility allowing it to be used with different combinatorial search techniques. Thus, four specific metaheuristics are considered: simulated annealing, tabu search, genetic algorithms and ant colony optimization. Their good performance is shown when solving a real-world problem. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.