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GA/optimizer frameworks with automatic adjustment of mut/cross extent?

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I'm looking for an efficient optimizer on a noisy high-dimensional and costly function.

My own GA hack seems to be somewhat stiff and I find me trying too much around with different cooling speeds and other non-systematic screwing ...

There are some GA frameworks and recipes around for Python (that one of scipy ( disapeared?). Which can be recommended?

The searching in my use cases is mainly on fixed length parameter vectors (float and integer ranges).
Just a few use cases on chromosome-bits and complex variable length structure building.

My main concern is about a smart, general and robust population alteration - an automatic selection of mutation/crossover-random-step-ranges and mutation/crossover-ratio. And possibly a dynamic population/selection scheme also.
The mutation function accepts a vector with step-width's 0..inf (default 1.0) for each parameter/gene. And the crossover function accepts a scalar 0 .. 1.0 controlling the extent of crossover.


def mutate(obj, astepstd=[1.0,1.0,1.0,...]):
def crossover(obj, other, extent=0.5):

The good optimizer alg which I think of should be smart enough to dynamically auto-adjust based the optimization history (independently of the task):
* astepstd's
* extent
* crossover/mutate rate
(* dynamic population size and selection-scheme)

Any recommendations or hints?

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