Self-configuring genetic programming algorithm for solving symbolic regression problems : доклад, тезисы доклада

Тип публикации: доклад, тезисы доклада, статья из сборника материалов конференций

Конференция: 2nd International Conference on Modernization, Innovations, Progress: Advanced Technologies in Material Science, Mechanical and Automation Engineering, MIP: Engineering 2020; Krasnoyarsk, Russia; Krasnoyarsk, Russia

Год издания: 2020

Идентификатор DOI: 10.1088/1757-899X/862/5/052069

Аннотация: In most cases, the only way to study and solve practical problems is to investigate them with the help of models. However, this method is also to be a complicated problem where a significant part of the effort is aimed at finding the functional dependencies between input and output variables. But a great number of methods of numerical identification solve problems by developing a model that, in fact, is a black box. The reduction different types of problems to the problem of symbolic regression allow us to overcome the lack of these methods. The algorithm of genetic programming is applied for solving the problems of symbolic regression. The given paper considers an algorithmic complex that includes a genetic algorithm and an algorithm for genetic programming for solving symbolic regression problems. The uniform crossover operator is applied for these methods; it ensures the flexibility of the algorithm due to the greater diversity of structures resulting from crossover. The opportunity for selection two or more parents for recombination is realized. To automate the selection of the algorithms parameters a self-configuring procedure at the population level is realized and the efficiency of its application is proved for test problems of symbolic regression. The practical implementation of algorithms for solving classification problems and differential equations is carried out.

Ссылки на полный текст


Журнал: IOP Conference Series: Materials Science and Engineering

Выпуск журнала: Vol. 862, Is. 5

Номера страниц: 52069

Издатель: Institute of Physics Publishing


  • Karaseva T.S. (Krasnoyarsk%660041%Russian Federation)
  • Mitrofanov S.A. (Krasnoyarsk%660037%Russian Federation)
  • Kovalev I.V.Voroshilova A.A.

Вхождение в базы данных

Информация о публикациях загружается с сайта службы поддержки публикационной активности СФУ. Сообщите, если заметили неточности.

Вы можете отметить интересные фрагменты текста, которые будут доступны по уникальной ссылке в адресной строке браузера.