A Study of Transfer Learning in an Ant-Based Generation Construction Hyper-Heuristic

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

@InProceedings{Singh:2022:CEC,

• author = "Emilio Singh and Nelishia Pillay",
• booktitle = "2022 IEEE Congress on Evolutionary Computation (CEC)",
• title = "A Study of Transfer Learning in an Ant-Based Generation Construction Hyper-Heuristic",
• year = "2022",
• editor = "Carlos A. Coello Coello and Sanaz Mostaghim",
• address = "Padua, Italy",
• month = "18-23 " # jul,
• isbn13 = "978-1-6654-6708-7",
• abstract = "Generation construction hyper-heuristics have proven to be effective in solving discrete optimization problems. Previous work has shown the effectiveness of an ant colony optimization hyper-heuristic for solving scheduling and packing problems. One of the challenges with generation construction hyper-heuristics is the high processing times associated with creating new construction heuristics. While there has been research into using transfer learning to reduce the computational cost of genetic programming generation constructive hyper-heuristics, this has not been investigated for ant colony optimization generation construction hyper-heuristics. In fact to the knowledge of the authors transfer learning has not previously been investigated for ant colony optimization. In this study the knowledge transferred is the pheromone map. The maps are transferred from the source domain to the target domain, with the target domain being more complicated problem instances and the source domain simpler problem instances, which do not take as long to solve. The approach was evaluated on the movie scene scheduling problem, the one dimensional bin packing problem and the quadratic assignment problem. The study has shown that the use of transfer learning has reduced the computational cost drastically while maintaining the same performance for the more complex problems for the movie scene scheduling problem and the quadratic assignment problem. However, for the one dimensional bin packing problem while there is a reduction in computational cost, the quality of the solutions is worse. Future research will investigate the reason for this and evaluate transferring different types of knowledge at various points in the life cycle of ant colony optimization generation construction hyper-heuristics.",
• keywords = "genetic algorithms, genetic programming, Ant colony optimization, Processor scheduling, Transfer learning, Evolutionary computation, Motion pictures, Computational efficiency, transfer learning, ant colony optimization, generation constructive hyper-heuristic, discrete optimization",
• DOI = "doi:10.1109/CEC55065.2022.9870415",
• notes = "Also known as \cite{9870415}",

}

Genetic Programming entries for Emilio Singh Nelishia Pillay

Citations