A Hybrid Fuzzy Genetic Algorithm for an Adaptive Traffic Signal System

Abstract EN

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.