The reason I am asking this is that for a multi-parameter function optimization, the evolving rules (crossover+mutation+selection...) are critical to obtain a good, fast, and robust results. I have already used genetic optimization for a 7 variables function optimization and had good stable results. One great thing is that it is possible to parallelize this optimizer pretty easily (so is for brute force and D&C)
On Oct 27, 2:06 pm, "Eugene Kononov" <[EMAIL PROTECTED]> wrote: > > How did you 'code' your optimization problem and what evolving rules > > did you use ? > > I didn't have to code much, JGAP did the work. All I had to do was to > implement a cost function, which was returning the performance result of a > strategy, given a particular set of parameters. --~--~---------~--~----~------------~-------~--~----~ You received this message because you are subscribed to the Google Groups "JBookTrader" group. To post to this group, send email to [email protected] To unsubscribe from this group, send email to [EMAIL PROTECTED] For more options, visit this group at http://groups.google.com/group/jbooktrader?hl=en -~----------~----~----~----~------~----~------~--~---
