Тип публикации: доклад, тезисы доклада, статья из сборника материалов конференций
Конференция: Hybrid Methods of Modeling and Optimization in Complex Systems (HMMOCS-III 2024); Krasnoyarsk; Krasnoyarsk
Год издания: 2025
Идентификатор DOI: 10.1051/itmconf/20257205008
Аннотация: This research explores a method of optimizing neural networks for vehicle control in a simulation environment using a real-coded genetic algorithm (RCGA). The study focuses on applying RCGA in conjunction with multiple genetic operators, including simulated binary crossover (SBX), power mutation (PM), and tournament selection, to eПоказать полностьюvolve neural network weights and biases, enhancing control performance for simulated vehicles. By utilizing RCGA to adjust neural network parameters, the approach enables adaptive and efficient vehicle control. The experiments demonstrate that combining sensor data with neuroevolutionary optimization in a simulation leads to a highly reliable control system, achieving performance metrics comparable to human operators. These findings suggest that RCGA-based optimization methods can be effectively applied to complex dynamic systems in various technical fields.
Журнал: ITM Web of Conferences
Номера страниц: 5008
Место издания: Krasnoyarsk