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Traffic signals play a significant role in the urban transportation system. They control the movement of traffic on urban streets by determining the appropriate signal timing settings. Due to the stochastic nature of the traffic flow, deciding on the best signal timing settings is a computationally complex problem, with the result that traditional analytical methods have been found to be inadequate in dealing with real world scenarios. This issue has already been tackled using computational intelligence algorithms such as the genetic algorithm (GA). However, despite good results, GA may experience slow convergence, especially when dealing with constrained optimisation problems. To address this issue, we propose an adaptive memetic algorithm (MA) for optimising signal timings in real world urban road networks using traffic volumes derived from induction loop detectors. The proposed MA combines the strengths of GA …