A structure-based genetic programming generation constructive hyper-heuristic with transfer learning for combinatorial optimisation

Created by W.Langdon from gp-bibliography.bib Revision:1.8688

@Article{Scheepers:2026:GPEM,

• author = "Darius Scheepers and Nelishia Pillay",
• title = "A structure-based genetic programming generation constructive hyper-heuristic with transfer learning for combinatorial optimisation",
• journal = "Genetic Programming and Evolvable Machines",
• year = "2026",
• volume = "27",
• pages = "Article no 2",
• note = "Online first",
• keywords = "genetic algorithms, genetic programming, Generation constructive hyper-heuristic, Structure-based genetic programming, Transfer learning in generation constructive hyper-heuristics",
• ISSN = "1389-2576",
• DOI = "10.1007/s10710-025-09528-3",
• abstract = "Generation constructive hyper-heuristics have proven to be very effective at creating construction heuristics for combinatorial optimization problems, with the heuristics derived by these hyper-heuristics often outperforming human-derived heuristics. Genetic programming has been predominantly used by generation constructive hyper-heuristics. This study investigates the effectiveness of two emerging technologies in genetic programming, namely, transfer learning and structure-based genetic programming, in genetic programming constructive generation hyper-heuristics. The study investigates whether structure-based genetic programming in generation constructive hyper-heuristics with (SBGP-HH-TL) and without transfer learning (SBGP-HH). The hyper-heuristics were evaluated on three problem domains, namely, the examination timetabling problem, the one-dimensional bin packing problem and the capacitated vehicle routing problem. Both SBGP-HH-TL and SBGP-HH outperformed the generational hyper-heuristic employing canonical genetic programming (CGP-HH) on a majority of the problem instances for the three problem domains, with SBGP-HH-TL outperforming SBGP-HH. Hence, the study has revealed that both transfer learning and structure-based genetic programming have resulted in performance improvements in genetic programming generation constructive hyper-heuristics for combinatorial optimization.",
• notes = "Published 18 Dec 2025

  MSc 2024 University of Pretoria http://hdl.handle.net/2263/100614

  Department of Computer Science, University of Pretoria, Lynnwood Rd., 0028, Pretoria, Gauteng, South Africa",

}

Genetic Programming entries for Darius Scheepers Nelishia Pillay

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