2026-05-28T20:49:50Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/275322026-02-03T11:21:33Zcom_10630_2254col_10630_37953

00925njm 22002777a 4500 dc Cintrano López, Christian author Ferrer-Urbano, Francisco Javier author López-Ibáñez, Manuel author Alba-Torres, Enrique author 2023-03 In the traffic light scheduling problem, the evaluation of candidate solutions requires the simulation of a process under various (traffic) scenarios. Thus, good solutions should not only achieve good objective function values, but they must be robust (low variance) across all different scenarios. Previous work has shown that combining IRACE with evolutionary operators is effective for this task due to the power of evolutionary operators in numerical optimization. In this article, we further explore the hybridization of evolutionary operators and the elitist iterated racing of IRACE for the simulation–optimization of traffic light programs. We review previous works from the literature to find the evolutionary operators performing the best when facing this problem to propose new hybrid algorithms. We evaluate our approach over a realistic case study derived from the traffic network of Málaga (Spain) with 275 traffic lights that should be scheduled optimally. The experimental analysis reveals that the hybrid algorithm comprising IRACE plus differential evolution offers statistically better results than the other algorithms when the budget of simulations is low. In contrast, IRACE performs better than the hybrids for a high simulations budget, although the optimization time is much longer. Christian Cintrano, Javier Ferrer, Manuel López-Ibáñez, Enrique Alba; Hybridization of Evolutionary Operators with Elitist Iterated Racing for the Simulation Optimization of Traffic Lights Programs. Evol Comput 2023; 31 (1): 31–51. doi: https://doi.org/10.1162/evco_a_00314 https://hdl.handle.net/10630/27532 10.1162/evco_a_00314 Tráfico - Regulación - Métodos de simulación Hybridization of Evolutionary Operators with Elitist Iterated Racing for the Simulation Optimization of Traffic Lights Programs.