1. the complete title of one (or more) paper(s) published in the open literature describing the work that the author claims describes a human-competitive result; A structure-based genetic programming generation constructive hyper-heuristic with transfer learning for combinatorial optimisation 2. the name, complete physical mailing address, e-mail address, and phone number of EACH author of EACH paper(s); 1. Mr. Darius Scheepers, Liebergesellstrasse 11, 80802 Munich, dariusscheepers@gmail.com, +49 15734470631 2. Prof. Nelishia Pillay, Room 524, Department of Computer Science, Information Technology Building, University of Pretoria, Hatfield Campus, Lynwood Road, Pretoria, South Africa, nelishia.pillay@up.ac.za, +27834569195 3. the name of the corresponding author (i.e., the author to whom notices will be sent concerning the competition); Darius Scheepers 4. the abstract of the paper(s); 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. 5. a list containing one or more of the four letters (A, B, C, or D) that correspond to the criteria (see above) that the author claims that the work satisfies; (B) The result is published in a peer-reviewed scientific journal as a new scientific result independent of the fact that the result was created through evolutionary computation. (C) The result is better than the most recent human-created solution to a long-standing or previously unsolved problem of indisputable difficulty in its field. 6. a statement stating why the result satisfies the criteria that the contestant claims (see examples of statements of human-competitiveness as a guide to aid in constructing this part of the submission); (B) The paper presents a novel approach, structure-based genetic programming, a variation of genetic programming that directs the search using both fitness and structure, with transfer learning. This is the first study combining structure-based genetic programming and transfer learning in a generation construction hyper-heuristic. The proposed approach makes a contribution independent of the resulting automation of construction heuristics by an evolutionary algorithm approach. (C) The result is better than the most recent human-created solution to a long-standing or previously unsolved problem of indisputable difficulty in its field. The construction heuristics produced by the structured-based genetic programming approach with transfer learning outperforms a number of human-created construction heuristics for three combinatorial optimization problems, namely, packing, scheduling and routing, requiring much less time to derive than the human-created heuristics. 7. a full citation of the paper (that is, author names; title, publication date; name of journal, conference, or book in which article appeared; name of editors, if applicable, of the journal or edited book; publisher name; publisher city; page numbers, if applicable); Scheepers, D., Pillay, N. A structure-based genetic programming generation constructive hyper-heuristic with transfer learning for combinatorial optimisation. Genet Program Evolvable Mach 27, 2 (2026). https://doi.org/10.1007/s10710-025-09528-3 8. a statement either that "any prize money, if any, is to be divided equally among the co-authors" OR a specific percentage breakdown as to how the prize money, if any, is to be divided among the co-authors; Should be divided equally among the co-authors. 9. a statement stating why the authors expect that their entry would be the "best," and; Construction heuristics for combinatorial optimization problems have generally been human-derived and as such has been time-consuming. Different construction heuristics are more effective for different problems and different heuristics work better with different algorithms. To create different construction heuristics for different problems and heuristic-approach pairs is not feasible. This highlights the need for the creation of these heuristics to be automated so that they can be created on the fly. This structure-based genetic programming approach with transfer learning has produced heuristics that perform comparatively to, and better in a number of instances, than the human-derived heuristics. Furthermore, the time taken to produce these heuristics is much less than deriving these manually. By automating the process of creating these heuristics different heuristics can be created for different problems at any time. The approach can be incorporated into any metaheuristic to create initial points in the search from which the optimization can commence. The construction heuristics produced were also found to outperform a number of hyper-heuristics performing more optimization to solve the problem at hand. The effectiveness of the approach has been shown on three different types of industry-related problems, namely, scheduling, packing and routing and as such is valuable for solving industry problems and hence also contributing to attaining one of the United Nations Sustainable Development Goals(SDGs), namely, innovation in industry. 10. An indication of the general type of genetic or evolutionary computation used, such as GA (genetic algorithms), GP (genetic programming), ES (evolution strategies), EP (evolutionary programming), LCS (learning classifier systems), GI (genetic improvement), GE (grammatical evolution), GEP (gene expression programming), DE (differential evolution), etc.; Genetic programming 11. The date of publication of each paper. If the date of publication is not on or before the deadline for submission, but instead, the paper has been unconditionally accepted for publication and is “in press” by the deadline for this competition, the entry must include a copy of the documentation establishing that the paper meets the "in press" requirement; Published: 18 December 2025