A Global and Local Surrogate-Assisted Genetic Programming Approach to Image Classification

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{Qinglan_Fan:ieeeTEC2,

• author = "Qinglan Fan and Ying Bi and Bing Xue and Mengjie Zhang",
• title = "A Global and Local Surrogate-Assisted Genetic Programming Approach to Image Classification",
• journal = "IEEE Transactions on Evolutionary Computation",
• year = "2024",
• volume = "28",
• number = "3",
• pages = "718--732",
• month = jun,
• keywords = "genetic algorithms, genetic programming, Image Classification, Fitness Evaluations, Surrogate Models, Feature extraction, Computational modeling, Training, Evolutionary computation, Predictive models, Computational efficiency",
• ISSN = "1089-778X",
• URL = "https://ieeexplore.ieee.org/abstract/document/9919269/",
• DOI = "doi:10.1109/TEVC.2022.3214607",
• size = "15 pages",
• abstract = "Genetic programming (GP) has achieved promising performance in image classification. However, GP-based methods usually require a long computation time for fitness evaluations, posing a challenge to real-world applications. Surrogate models can be efficiently computable approximations of expensive fitness evaluations. However, most existing surrogate methods are designed for evolutionary computation techniques with a vector-based representation consisting of numerical values, thus cannot be directly used for GP with a tree-based representation consisting of functions/operators. The variable sizes of GP trees further increase the difficulty of building the surrogate model for fitness approximations. To address these limitations, we propose a new surrogate-assisted GP approach including global and local surrogate models, which can accelerate the evolutionary learning process and achieve competitive classification performance simultaneously. The global surrogate model can assist GP in exp",
• notes = "also known as \cite{9919269}",

}

Genetic Programming entries for Qinglan Fan Ying Bi Bing Xue Mengjie Zhang

Citations