Transfer Learning in Artificial Bee Colony Programming
Elif Bozoğullarından, Ceylan Bozogullarindan, Celal Öztürk · 2020 Innovations in Intelligent Systems and Applications Conference (ASYU) · 2020
Artificial Bee Colony Programming (ABCP) is a machine learning method based on Artificial Bee Colony (ABC) algorithm used for parametric and structured optimization problems. It is used for the solution of symbolic regression problems as Genetic Programming (GP). On the other hand, transfer learning is the approach of using the knowledge of a system trained for a particular problem in another problem having a similar distribution. There are a number of research studies in the literature reporting the successful applications of the transfer learning to machine learning and GP. In this study, the transfer learning approach is applied to ABCP for the first time and all of the new methods created this way are named as ABCP-T. As a result of the experiments conducted for the symbolic regression problems in the literature, it is observed that ABCP-T gives better results than the standard ABCP.