On Nov 28, 2007 12:59 AM, Stefan van der Walt <stefan@sun.ac.za> wrote:

On Tue, Nov 27, 2007 at 11:07:30PM -0700, Charles R Harris wrote:Would this be a good candidate for a genetic algorithm? I haven't

> This is not a trivial problem, as you can see by googling mixed integer least

> squares (MILS). Much will depend on the nature of the parameters, the number of

> variables you are using in the fit, and how exact the solution needs to be. One

> approach would be to start by rounding the coefficients that must be integer

> and improve the solution using annealing or genetic algorithms to jig the

> integer coefficients while fitting the remainder in the usual least square way,

> but that wouldn't have the elegance of some of the specific methods used for

> this sort of problem. However, I don't know of a package in scipy that

> implements those more sophisticated algorithms, perhaps someone else on this

> list who knows more about these things than I can point you in the right

> direction.

used GA before, so I don't know the typical rate of convergence or its

applicability to optimization problems.

Regards

Stéfan

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If the number of terms is not huge and the function is well behaved; it might be worth trying the following simple and stupid approach:

- Find the floating point minimum.
- for each set of possible set of integer coefficients near the FP minimum:
- Solve for the floating point coefficients with the integer coefficients fixed.
- If the minimum is the best so far, stash it somewhere for later.
- Return the best set of coefficients.

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