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Transfer learning in Genetic Programming

Dinh, T.T.H. and Chu, T.H. and Nguyen, Q.U. (2015) Transfer learning in Genetic Programming. In: IEEE Congress on Evolutionary Computation, CEC 2015, 25 May 2015 through 28 May 2015.

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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. © 2015 IEEE.

Item Type: Conference or Workshop Item (Paper)
Divisions: Faculties > Faculty of Information Technology
Identification Number: 10.1109/CEC.2015.7257018
Uncontrolled Keywords: Artificial intelligence; Genetic algorithms; Learning systems; Personnel training; Code bloats; Code growth; Learning methods; Research communities; Symbolic regression problems; Training errors; Transfer learning; Transfer learning methods; Genetic programming
Additional Information: Conference code: 118157. Language of original document: English.
URI: http://eprints.lqdtu.edu.vn/id/eprint/9905

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