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Expected behavior
While the documentation for the decorator is nice and works as expected when calling the fitness function directly, the standard way to execute Pygmo is via algo.evolve(pop), which seems to render the decorator useless since the fitness function isn't triggered.
This should probably not be labeled as a bug - but certainly unexpected behaviour, as we would hope the decorator to be called as part of the algo evolving.
Alternatively, there is perhaps a way to call the fitness function directly to accomplish what algo.evolve() does, but I haven't seen a clear example in the docs.
Screenshots
NA
Environment (please complete the following information):
OS: Mac OS 10.15.7
Installation method: conda
Version: 2.16.1
Additional context
The text was updated successfully, but these errors were encountered:
means-to-meaning
changed the title
[BUG]
[BUG] Meta-problem decorator for the fitness function does not work with algo.evolve(pop)
May 18, 2021
In your example, you are trying to extract the log from the original problem instance. However, pygmo normally operates with deep copy semantics.
In the specific snippet you posted, pop = pg.population(drb, 10) deep-copies drb into the pop object. Then, when you do pop = algo.evolve(pop), pop will be a deep copy of the evolved population.
Describe the bug
Meta-problem decorator for the fitness function does not work with algo.evolve(pop)
To Reproduce
Below code mostly taken from: https://esa.github.io/pygmo2/tutorials/udp_meta_decorator.html
Expected behavior
While the documentation for the decorator is nice and works as expected when calling the fitness function directly, the standard way to execute Pygmo is via algo.evolve(pop), which seems to render the decorator useless since the fitness function isn't triggered.
This should probably not be labeled as a bug - but certainly unexpected behaviour, as we would hope the decorator to be called as part of the algo evolving.
Alternatively, there is perhaps a way to call the fitness function directly to accomplish what algo.evolve() does, but I haven't seen a clear example in the docs.
Screenshots
NA
Environment (please complete the following information):
Additional context
The text was updated successfully, but these errors were encountered: