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Re: grid search for global optimum, is there a way to decide
stepsize?
Posted:
Jul 3, 2006 11:12 AM
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in article 1151921688.604342.262620@m79g2000cwm.googlegroups.com,
optionstraderjeff at jeffkatz@scientific-consultants.com wrote on 7/3/06
5:14 AM:
> Hi,
>
> GAs get bad flames because they have often been used to fit a very
> large number of parameters (more than could be fit in a reasonable time
> using more traditional methods)with severe "curve fitting" being the
> inevitable result. The real problem, of course, is fitting an
> excessive number of parameters, not the fact that a GA was used to do
> the fitting.
>
> And yes, a GA is not guaranteed to find the global optimum; but, GAs
> appear to do very well when it comes to solving extremely difficult
> problems--"NP-Hard problems, as they say--like finding an optimal set
> of tracings for the lines on a printed circuit board, constructing
> complex molecules and "designer" drugs, and even creating and adapting
> (optimizing) all the various forms of life on earth!
>
> Stochastic processes may be "chancy", but chance, properly harnessed,
> can solve all kinds of problems.
>
> Finally, you do not need a "state of the art" GA. Any good, basic
> implementation will handle your problem. I can email you a GA class in
> C++ if you send me an email to
>
> jeffkatz@scientific-consultants.com
>
> mentioning this post.
>
> By the way, if you want a good laugh, read "The Dice Man" by Luke
> Reinhart--it is a really funny book. Couldn't help but mention it
> given the context.
>
> Jeff
>
> gino wrote:
>> "optionstraderjeff" <jeffkatz@scientific-consultants.com> wrote in message
>> news:1151670299.221764.45200@m73g2000cwd.googlegroups.com...
>>> Hi,
>>>
>>> Why not try genetic optimization. Genetic algorithms are amazingly
>>> effective when it comes to locating global optima; and, they can be
>>> orders of magnitude faster than other stochastic or brute-force methods
>>> when many parameters (>> 4) must be estimated. Depending on the
>>> precision required, you may want to combine a GA with a localized NR
>>> procedure (something you can do if your function is continuous and
>>> well-behaved).
>>>
>>> Jeff
>>
>>
>> Thanks a lot Jeff.
>>
>> But I have heard a lot about bad fames about these fancy stochastic
>> searching algorithms. They don't guarantee optimality, they are just
>> stochastic procedures, they are all about chance, that's the problem.
>>
>> They are only used when people are desperate.
>>
>> But for my 4-parameter small problem, I still hope some good global
>> searching algorithm can give me guaranteed global optimum within reasonable
>> time. I can wait for quite a few hours...or 1 day, etc. But the result
>> should be truely global optimum.
>>
>> okay, which Genetic algorithm is the current state-of-art?
>
Genetic algorithms are sometimes used because they are faster than other
methods, or more efficient in some other way. They are not guaranteed to
find a global optimum, but neither are more traditional methods such as
steepest descent.
A few examples of using GAs in real problems (a few years old, but not that
old):
B. P. Buckles, F. E. Petry, D. Prabhu, and M. Lybanon, Mesoscale Feature
Labeling from Satellite Images, in Genetic Algorithms for Pattern
Recognition, S. K. Pal and P. P. Wang, ed., CRC Press, 1996, ISBN
0-8493-9467-8, pp. 167-177.
M. Lybanon and K. C. Messa, Chapter 8: Genetic Algorithm Model Fitting, in
Practical Handbook of Genetic Algorithms, Complex Coding Systems, Volume
III, L. D. Chambers, ed., CRC Press LLC, 1999, ISBN 0-8493-2539-0.
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