Pillay, Nelishia2025-02-072025-02-072025-052024-12*A2025http://hdl.handle.net/2263/100614Dissertation (MSc (Computer Science))--University of Pretoria, 2024.Genetic programming and variants of genetic programming such as grammar-based genetic program ming have predominately been used in generation construction hyper-heuristics (GC-HH). Previous work has also shown the effectiveness of transfer learning in genetic programming generation hyper heuristics. Structure-based genetic programming (SBGP) uses both the fitness of an individual and its structure to direct the search in a search space. This study investigates the use of a structure-based genetic programming hyper-heuristic (SBGP-HH) in generation construction hyper-heuristics. The use of SBGP-HH with transfer learning (SBGP-HH-TL) is also investigated. The proposed approaches were evaluated on the examination timetabling, one dimensional bin-packing and capacitated vehicle routing problems. SBGP-HH was found to outperform the canonical genetic programming hyper-heuristic (CGP-HH) for the selected problem domains. SBGP-HH-TL produced better results than SBGP-HH with statistical significance on most problem instances. These results were found to be statistically significant at the 90% level of confidence. SBGP-HH-TL was found to outperform CGP-HH with transfer learning (CGP-HH-TL) for the selected problem domains.en© 2023 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria.UCTDTransfer learning in generation constructive hyper-heuristicsGeneration constructive hyper-heuristicGenetic programmingStructure-based genetic programmingDissertationu16006250-