Transfer Learning in Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Dinh:2015:CEC,
  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",
  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",
}

Genetic Programming entries for Thi Thu Huong Dinh Thi Huong Chu Quang Uy Nguyen

Citations