Re: DifferentialEvolution: custom mutation and recombination functions?

Thanks to everyone (incl. Andy!) who helped resolve my issues setting up
the development environment.  :)  I now have a pull request open for this
feature.

https://github.com/scipy/scipy/pull/19196

 - kv, Karen

þri., 22. ágú. 2023 kl. 05:40 skrifaði Andrew Nelson <[email protected]>:

> On Wed, Aug 16, 2023, 22:23 Karen Róbertsdóttir <
> [email protected]> wrote:
>
>>
>> Perfectly fine!  But, question:
>>
>> > The strategy_func would be
>> > responsible for mutating (blending) members of the population together,
>> > doing the crossover/recombination itself, and returning a trial vector
>> with
>> > shape (N,).
>>
>> So it wouldn't be told what other candidate to perform recombination with
>> - it should pick recombination targets itself?  I mean, that's workable,
>> just being clear on this.
>>
>>
> The strategy function would be solely responsible for creating a trial
> vector. It could do anything it wanted, so long as it returned a trial
> vector the same shape as the problem description. Whether the strategy
> function was sensible would be solely at the discretion of the user. The
> fitness of the trial function is determined outside the strategy function.
>
>
>> I can't speak for others, but I'm fine with receiving it in the [0, 1]
>> range and then scaling it myself, to avoid the need for the stock functions
>> to take that slight overhead hit.  But whatever your preference is works
>> for me.
>>
>
> On further reflection it makes sense to supply the population in the [
> bounds.lb, bounds.ub range]. It would be a copy of the population, so the
> original array wasn't overwritten by the user.
>
>
> (As a side note, Scipy's inability to save and resume the population
>> during differential_evolution optimization used to be really annoying,
>> given how long CFD optimization tasks take.  However, I did find a kind of
>> cheap hack that I've been using ever since - since the random number
>> generator is deterministic, I simply have the minimization function create
>> a hash value for the candidate, and store the results of the simulation in
>> a hash table, which I save to disk.  Then when I need to resume, I just
>> load up the hash table, and if a candidate has been encountered before, it
>> just immediately returns the previous run's simulation results rather than
>> re-running the simulation. It's an awkward hack, and wouldn't work on tasks
>> where the minimization function is really fast, but for slow tasks like
>> CFD, it works  :)  )
>>
>
> If you use the DifferentialEvolutionSolver (warning, it's private and
> subject to change), then you can step the solver very easily, it's an
> iterator. For a single run through it should be very rare that the fitness
> of a given vector is evaluated twice, only trial vectors are evaluated, and
> they're always novel. If you want to stop/restart then I understand the
> need to cache. TBH using very expensive objective functions doesn't sound
> great for differential_evolution though, there's always a lot of function
> evaluations.
>
> Custom strategy_funcs can be of course used for things that have nothing
>> to do with genes. For example: sometimes - as the docs note - a user may
>> want part of their candidates' data to be interpreted as integer data. How
>> do you mutate or crossbreed integers and have them make sense?  Well,
>> that's really going to be task-dependent. Maybe the integer means "number
>> of iterations" - if so, then perhaps simple interpolation is best.  But
>> maybe it's a category - in that case, interpolation is incoherent, and you
>> should either keep it the same or randomly pick a new category.  And if it
>> is a category, and that category influences some other values in the
>> candidate, then that may affect how you want to alter those values.  Maybe
>> if round(candidate[0]) is Category == 3, then you want the floating point
>> value at candidate[1] to be between 1.0 and 10.0, but if it's Category == 5
>> then maybe you want candidate[1] to be between 1.0 and 5.0.  Again, it's
>> task dependent.
>>
>
> `differential_evolution` already has an integrality keyword.
>
> w.r.t implementation, it just needs someone to do the programming and
> write tests. We always welcome new contributors for PRs.
>
> A.
>
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