We are delighted to announce the talk given by Ender Ozcan (Uni of
Nottingham).
The title is "Machine Learning meets Selection Hyper-heuristics". The
seminar will take place on Zoom on Wednesday, June 12 at 13:00 UTC.
Join Zoom Meeting
https://cesnet.zoom.us/j/91588200395?pwd=sySE3R77BKsPZfaOOQ8O7EyeuhfLT3.1
Meeting ID: 915 8820 0395
Passcode: 557258
You can follow the seminar online or offline on our Youtube channel as
well:
https://www.youtube.com/channel/UCUoCNnaAfw5NAntItILFn4A
The abstract follows.
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.
The next talk in our series will be:
For more details, please visit https://schedulingseminar.com/
With kind regards
Zdenek, Mike and Guohua
--
Zdenek Hanzalek
Industrial Informatics Department,
Czech Institute of Informatics, Robotics and Cybernetics,
Czech Technical University in Prague,
Jugoslavskych partyzanu 1580/3, 160 00 Prague 6, Czech Republic
https://rtime.ciirc.cvut.cz/~hanzalek/
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