changed the fitness functions, added some clocking modified the NN sigmoid fixed the total input count
tried making the fitness function a minimized, that was unsuccessful, put sigmoid threshold at .3, this made it impossible for the value of 0 to occur, which is indicative of a biasing issue
increasing the bias had no affect other than to allow the full range of values to be hit again, still tuning to one side or the other
fitness based soully on the discrepancy between the sigmoid output and the actual classification summed to be minimized for all images showed no improvement
tied the error directly to the sigmoid output error with respect to the actual classification. still tunes to 50%, watching the sigmoid output for all of the attempts shows that the  input data may be just to similare.
implemented roullete wheel and tested on cancer data, trains fine with cancer. maybe try another simple data set...
it is learning