Learning Fuzzy Systems by a Co-Evolutionary Artificial-Immune-Based Algorithm

Authors:
Luiz Lenarth Vermaas;Leonardo M. Honorio;Muriel Freire;Daniele Barbosa
Affiliations:
Institute of Technology and Electrical Engineering, UNIFEI, Centro, BR;Institute of Technology and Electrical Engineering, UNIFEI, Centro, BR;Institute of Technology and Electrical Engineering, UNIFEI, Centro, BR;Institute of Technology and Electrical Engineering, UNIFEI, Centro, BR
Venue:
WILF '09 Proceedings of the 8th International Workshop on Fuzzy Logic and Applications
Year:
2009

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Abstract

To create a Fuzzy System from a numerical data, it is necessary to generate rules and memberships representing the analyzed set. This goal demands to break the problem into two parts: one responsible for learning the rules and another responsible for optimizing the memberships. This paper uses a Gradient-based Artificial Immune System with a different population for each of these parts. By simultaneously co-evolving these two populations, it is possible to exchange information between them enhancing the fitness of the final generated system. To demonstrate this approach, a fuzzy system for autonomous vehicle maneuvering was developed by observing a human driver.