Andrei Alexandrescu Wrote: > On 04/24/2010 04:30 PM, strtr wrote: > > Andrei Alexandrescu Wrote: > > > >> On 04/24/2010 12:52 PM, strtr wrote: > >>> Walter Bright Wrote: > >>> > >>>> strtr wrote: > >>>>> Portability will become more important as evo algos get used > >>>>> more. Especially in combination with threshold functions. > >>>>> The computer will generate/optimize all input/intermediate > >>>>> values itself and executing the program on higher precision > >>>>> machines might give totally different outputs. > >>>> > >>>> > >>>> You've got a bad algorithm if increasing the precision breaks > >>>> it. > >>> > >>> No, I don't. All algorithms using threshold functions which have > >>> been generated using evolutionary algorithms will break by > >>> changing the precision. That is, you will need to retrain them. > >>> The point of most of these algorithms(eg. neural networks) is > >>> that you don't know what is happening in it. > >> > >> I'm not an expert in GA, but I can tell that a neural network that > >> is dependent on precision is badly broken. > > How can you tell? > > > >> Any NN's transfer function must be smooth. > > http://www.doc.ic.ac.uk/~nd/surprise_96/journal/vol4/cs11/report.html#Transfer%20Function > > > > It wasn't for nothing I mentioned threshold functions > > > > Especially in the more complex spiking neural networks bases on > > dynamical systems, thresholds are kind of important. > > Meh. You can't train using a gradient method unless the output is smooth > (infinitely derivable).
Which was exactly why I mentioned evolutionary algorithms.
