in article email@example.com, optionstraderjeff at firstname.lastname@example.org 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 > > email@example.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" <firstname.lastname@example.org> wrote in message >> news:email@example.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.