Scheduling seminar – Machine Learning meets Selection Hyper-heuristics


Datum / čas
Date(s) - 12.06.
15:00 - 17:00


Presenter – Ender Ozcan (Uni of Nottingham)       Invited by – Zdeněk Hanzálek (CTU in Prague)

Machine Learning meets Selection Hyper-heuristics

June 12, 2024 at 15 CET

Join online or offline on our Youtube channel: Scheduling seminar – YouTube


Hyper-heuristics are powerful search methodologies that operate on low level heuristics or heuristic components to tackle computationally hard optimisation problems. The current state-of-the-art in hyper-heuristic research contains classes of algorithms that focus on intelligently selecting or generating a suitable heuristic for a given situation. Hence, there are two main types of hyper-heuristics: selection and generation hyper-heuristics. A typical selection hyper-heuristic chooses a low-level heuristic and applies it to the current solution at each step of a search, before deciding whether to accept or reject the newly created solution. Generation hyper-heuristics, in contrast, automatically build heuristics or heuristic components during the search process. Machine learning is revolutionising various fields, and its integration with hyper-heuristics holds immense potential. This talk will first offer a concise overview of hyper-heuristics, followed by illustrative case studies demonstrating how we have successfully applied machine learning to automatically design more effective selection hyper-heuristics.

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