Knowledge Transfer in Genetic Programming Hyper-heuristics

Created by W.Langdon from gp-bibliography.bib Revision:1.8120

@InCollection{mei:2021:ADMLSA,

• author = "Yi Mei and Mazhar {Ansari Ardeh} and Mengjie Zhang",
• title = "Knowledge Transfer in Genetic Programming Hyper-heuristics",
• booktitle = "Automated Design of Machine Learning and Search Algorithms",
• publisher = "Springer",
• year = "2021",
• editor = "Nelishia Pillay and Rong Qu",
• series = "Natural Computing Series",
• pages = "149--169",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-3-030-72068-1",
• URL = "http://link.springer.com/chapter/10.1007/978-3-030-72069-8_9",
• DOI = "doi:10.1007/978-3-030-72069-8_9",
• abstract = "Genetic Programming Hyper-heuristics (GPHHs) have been successfully applied in various problem domains for automatically designing heuristics such as dispatching rules in scheduling and routing policies in vehicle routing. In the real world, it is normal to encounter related problem domains, such as the vehicle routing problem with different objectives, constraints, and/or graph topology. On one hand, different heuristics are required for different problem domains. On the other hand, the knowledge learned from solving previous related problem domains can be helpful for solving the current one. Most existing studies solve different problem domains in isolation, and train/evolve the heuristic for each of them from scratch. we investigate different mechanisms to improve the effectiveness and efficiency of the heuristic retraining by employing knowledge transfer. Specifically, in the context of GPHH, we explored the following two transfer strategies: (1) useful subtrees and (2) importance of terminals, and verified their effectiveness in a case study of the uncertain capacitated arc routing problem.",

}

Genetic Programming entries for Yi Mei Mazhar Ansari Ardeh Mengjie Zhang

Citations