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@mati3230 mati3230 commented Jul 5, 2021

Dear Ahmed,
thanks for contributing this nice repository. After applying the framework to my problem, I realized that only a single core is utilized. This is fine if your fitness function calculation is fast. However, the calculation of the fitness in my problem takes very long so that I wished a speed up in terms of parallelization. Therefore, I added a parameter to assign a custom cal_pop_fitness function so that the fitness values can be calculated in parallel. You can find the code snippet that applies the parallelization in the here. It would also be possible to parallelize the fitness calculation over a cluster with this method.
Hope that this extension makes sense. So far it has helped me a lot and accelerated my calculations.
Best regards,
Marcel

@ahmedfgad
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@mati3230,

Thanks for this request but it was opened in a time where the library was still growing. It is outdated right now.
Your help is appreciated.

@ahmedfgad ahmedfgad closed this Jul 4, 2022
@ahmedfgad ahmedfgad added the enhancement New feature or request label Feb 25, 2023
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2 participants