In smart cities, the use of intelligent automatic techniques to find efficient cycle programs of traffic lights is becoming an innovative
front for traffic flow management. However, this automatic programming of traffic lights requires a validation process of the
generated solutions, since they can affect the mobility (and security) of millions of citizens. In this paper, we propose a validation
strategy based on genetic algorithms and feature models for the automatic generation of different traffic scenarios checking the
robustness of traffic light cycle programs.We have concentrated on an extensive urban area in the city ofMalaga (in Spain), in which
we validate a set of candidate cycle programs generated bymeans of four optimization algorithms: Particle SwarmOptimization for
Traffic Lights, Differential Evolution for Traffic Lights, random search, and Sumo Cycle Program Generator.We can test the cycles
of traffic lights considering the different states of the city, weather, congestion, driver expertise, vehicle’s features, and so forth, but
prioritizing the most relevant scenarios among a large and varied set of them. The improvement achieved in solution quality is
remarkable, especially for CO2 emissions, in which we have obtained a reduction of 126.99% compared with the experts’ solutions.
