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Advances in Fuzzy Systems
Volume 2015 (2015), Article ID 378156, 11 pages
Research Article

A Hybrid Fuzzy Genetic Algorithm for an Adaptive Traffic Signal System

1Department of Computer and Information System, Bethlehem University, Bethlehem, State of Palestine
2Department of Computer Architecture and Technology, University of Granada, Granada, Spain
3Department of Computing, Faculty of Sciences, University of Salamanca, Salamanca, Spain

Received 17 May 2015; Revised 21 July 2015; Accepted 2 August 2015

Academic Editor: Ning Xiong

Copyright © 2015 S. M. Odeh et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper presents a hybrid algorithm that combines Fuzzy Logic Controller (FLC) and Genetic Algorithms (GAs) and its application on a traffic signal system. FLCs have been widely used in many applications in diverse areas, such as control system, pattern recognition, signal processing, and forecasting. They are, essentially, rule-based systems, in which the definition of these rules and fuzzy membership functions is generally based on verbally formulated rules that overlap through the parameter space. They have a great influence over the performance of the system. On the other hand, the Genetic Algorithm is a metaheuristic that provides a robust search in complex spaces. In this work, it has been used to adapt the decision rules of FLCs that define an intelligent traffic signal system, obtaining a higher performance than a classical FLC-based control. The simulation results yielded by the hybrid algorithm show an improvement of up to 34% in the performance with respect to a standard traffic signal controller, Conventional Traffic Signal Controller (CTC), and up to 31% in the comparison with a traditional logic controller, FLC.