Тип публикации: доклад, тезисы доклада, статья из сборника материалов конференций
Конференция: Fourth International Conference on Optics, Computer Applications, and Materials Science (CMSD-IV 2024); Dushanbe, Tajikistan; Dushanbe, Tajikistan
Год издания: 2025
Идентификатор DOI: 10.1117/12.3060436
Ключевые слова: genetic algorithm, mutation, population problem solving, two-dimensional arrays, python
Аннотация: The paper implements a genetic algorithm for optimizing two-dimensional arrays using the NumPy library for Python. The main stages of the genetic algorithm, including selection, crossover, and mutation, are analyzed. The influence of various algorithm parameters on its efficiency and convergence rate is studied. It is revealed thatПоказать полностьюthe use of NumPy significantly accelerates computational processes due to the vectorization of operations. It is determined that the optimal settings of the algorithm parameters provide the best results when testing on various tasks. It is established that the developed algorithm demonstrates high performance and flexibility in application. The code structure is formed, allowing easy modification of the algorithm for various tasks. Recommendations for further improvement of the algorithm and its adaptation to more complex scenarios are proposed.
Журнал: Fourth International Conference on Optics, Computer Applications, and Materials Science (CMSD-IV 2024)
Выпуск журнала: 13651
Номера страниц: 136510
Место издания: Bellingham, Washington