Parallel Distributed Two-Level Evolutionary Multiobjective Methodology for Granularity Learning and Membership Functions Tuning in Linguistic Fuzzy Systems
Miguel A. De Vega, Juan M. Bardallo, Francisco A. Márquez, Antonio Peregrín · 2009
This paper deals with the learning of the membership functions for Mamdani Fuzzy Systems – the number of labels of the variables and the tuning of them – in order to obtain a set of Linguistic Fuzzy Systems with different trade-offs between accuracy and complexity, through the use of a two-level evolutionary multi-objective algorithm. The presented methodology employs a high level main evolutionary multi-objective heuristic searching the number of labels, and some distributed low level ones, also evolutionary, tuning the membership functions of the candidate variable partitions.