Improving Classification on Images by Extracting and Transferring Knowledge in Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{Iqbal:2016:CEC,

• author = "Muhammad Iqbal and Mengjie Zhang and Bing Xue",
• title = "Improving Classification on Images by Extracting and Transferring Knowledge in Genetic Programming",
• booktitle = "Proceedings of 2016 IEEE Congress on Evolutionary Computation (CEC 2016)",
• year = "2016",
• editor = "Yew-Soon Ong",
• pages = "3582--3589",
• address = "Vancouver",
• month = "24-29 " # jul,
• publisher = "IEEE Press",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-1-5090-0623-6",
• DOI = "doi:10.1109/CEC.2016.7744243",
• abstract = "Genetic programming (GP) is a well established evolutionary computation technique that automatically generates a computer program to solve a given problem. GP has been successfully used to solve optimization, symbolic regression and classification problems. Transfer learning in GP has been investigated to learn various Boolean and symbolic regression problems. However, there has been not much work on transfer learning in GP for image classification problems. In this paper, we propose a new technique to use transfer learning in GP to learn image classification problems. The developed method has been compared with the baseline GP method on three image classification benchmarks. The obtained results indicate that transfer learning has significantly improved the classification accuracy in learning various rotated and noisy versions of the tested image classification problems.",
• notes = "WCCI2016",

}

Genetic Programming entries for Muhammad Iqbal Mengjie Zhang Bing Xue

Citations