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Re: grid search for global optimum, is there a way to decide stepsize?
Posted:
Jul 3, 2006 11:12 AM


in article 1151921688.604342.262620@m79g2000cwm.googlegroups.com, optionstraderjeff at jeffkatz@scientificconsultants.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"NPHard problems, as they saylike 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@scientificconsultants.com > > mentioning this post. > > By the way, if you want a good laugh, read "The Dice Man" by Luke > Reinhartit is a really funny book. Couldn't help but mention it > given the context. > > Jeff > > gino wrote: >> "optionstraderjeff" <jeffkatz@scientificconsultants.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 bruteforce 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 >>> wellbehaved). >>> >>> 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 4parameter 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 stateofart? >
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 0849394678, pp. 167177.
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 0849325390.



