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Tuning module #109

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N-Wouda opened this issue Nov 11, 2022 · 2 comments
Open

Tuning module #109

N-Wouda opened this issue Nov 11, 2022 · 2 comments
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enhancement New feature or request

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N-Wouda commented Nov 11, 2022

See also #85. Most ALNS heuristics have a ton of (hyper)parameters. This happens more or less naturally, as may things can be tweaked - in operators, call-backs, but also in the core accept/stop/operator selection classes.

We can help users with tuning. The ML community has a lot of this already, with e.g. keras-tuner, ray.tune, etc. Those are used by a lot of people, apparently with some success. We could have a look at how these work, and take some of the good ideas for our own.
@N-Wouda N-Wouda added the enhancement New feature or request label Nov 11, 2022
@N-Wouda N-Wouda self-assigned this Nov 11, 2022
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N-Wouda commented Nov 11, 2022

Or should we just interface with one or more of those existing tuning packages?

@N-Wouda N-Wouda mentioned this issue Nov 19, 2022
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@N-Wouda N-Wouda mentioned this issue Feb 20, 2024
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N-Wouda commented May 12, 2024

For now at least document how to tune a bit better, and provide some pointers to places that I know did this reasonably well.

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