- author = "Thi Thu Huong Dinh and Chu Thi Huong and Nguyen Quang Uy",
- title = "Transfer Learning in Genetic Programming",
- booktitle = "Proceedings of 2015 IEEE Congress on Evolutionary Computation (CEC 2015)",
- year = "2015",
- editor = "Yadahiko Murata",
- pages = "1145--1151",
- address = "Sendai, Japan",
- month = "25-28 " # may,
- publisher = "IEEE Press",
- keywords = "genetic algorithms, genetic programming",
- isbn13 = "978-1-4799-7491-7",
-
DOI = "
doi:10.1109/CEC.2015.7257018",
- abstract = "Transfer learning is a process in which a system can apply knowledge and skills learned in previous tasks to novel tasks. This technique has emerged as a new framework to enhance the performance of learning methods in machine learning. Surprisingly, transfer learning has not deservedly received the attention from the Genetic Programming research community. In this paper, we propose several transfer learning methods for Genetic Programming (GP). These methods were implemented by transferring a number of good individuals or sub-individuals from the source to the target problem. They were tested on two families of symbolic regression problems. The experimental results showed that transfer learning methods help GP to achieve better training errors. Importantly, the performance of GP on unseen data when implemented with transfer learning was also considerably improved. Furthermore, the impact of transfer learning to GP code bloat was examined that showed that limiting the size of transferred individuals helps to reduce the code growth problem in GP.",
-
notes = "1125 hrs 15457 CEC2015
Faculty of IT Thu Dau Mot University Binh Duong, Vietname",
