Özer, Muhammed Mirac2025-03-172025-03-1720241868-4394https://doi.org/10.1007/978-3-031-66731-2_8https://hdl.handle.net/20.500.13099/1333In this study, the development of a hybrid genetic algorithm, integrating machine learning and function estimation, presents a novel approach to address the simultaneous intervention challenge involving unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs). The adaptability of this hybrid genetic algorithm confers a notable advantage in managing drone scenarios. Notably, this work constitutes the inaugural attempt in the literature to devise an exact solution for the concurrent intervention of a UGV and a UAV, with the added innovation of minimizing intervention time. This pioneering methodology holds promise for extending the problem domain to encompass more realistic scenarios, thereby bridging a significant gap in the literature and furnishing a foundational framework for future research endeavors. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.eninfo:eu-repo/semantics/closedAccessFitness function estimationHybrid genetic algorithmMachine learningTraveling salesman personVehicle routing problem with droneBook Part10.1007/978-3-031-66731-2_82601772162-s2.0-85205024908Q2