- 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",
- keywords = "genetic algorithms, genetic programming, Generation constructive hyper-heuristic, Structure-based genetic programming, Transfer learning in generation constructive hyper-heuristics",
- ISSN = "1389-2576",
-
URL = "
https://www.egr.msu.edu/~goodman/HumiesEntries2026/Scheepers/Paper.pdf",
-
URL = "
https://dl.acm.org/doi/abs/10.1007/s10710-025-09528-3",
-
DOI = "
10.1007/s10710-025-09528-3",
- size = "49 pages",
- 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 = "Entered HUMIES 2026
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",
