> > Mitchell, > > Well as I explained if you penalise those chrome which performs not > > similar for all the 32 patterns and encourage those whose devation > > from the mean is less that is the performance is similar and falls > > within a range of values over all the 32 patterns than the results may > > be better.... > > You may try this.... in next few evolution.... > > > > Algo may be something like this.... > > > > Store all the fittness(Performance) values for all 32 patterns in a > > sorted array.... > > The middle of the array is the mean.... > > Now find the largest difference with the mean of any of the above > > elements in the array... > > If the largest value is < than a speciefied constant.... than the > > fittness is inversely proproiotnal to the largest diffrence.... > > > I don't really understand what you are suggesting, but you should know > that there are about 500 patterns. The 32 patterns was when we were > using exactly 5 steps. There are 32 patterns for 5 steps. Now, we are > using all path lengths from 3 to 8 steps. 8 steps has 256 patterns, 7 > has 128, etc. > > *** Yes I know that there are near about 500 patterns... I am bad in counting.... What I was saying is: Let me take an example: The fittness pass of Chromosome i of Generation G will be something like.... test all patterns of lentgh 3 and find the mean devation.... test for length 4.... 5... ... 8.... Now than again take the mean devation of above 5 results.... mean devation for simplicity.... actually it's the largest difference of any run in that tuple with the mean.... The main goal is to get ANN perform in similar manner for all the i/p patterns.... if performs 90% for one particular pattern of length 5 than it should perform ~ 90% (approx) for all other pattern of length 5.... Similary this same equation can be extended over all lengths and among lengths.... ...................... But the excution may be slower..... Well I hopw now it's a bit clear!
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