42 genetic traits or rules have been designed to extract all the information those bars can provide. Of course, to be valid, data has to be prepared therefore normalized, and then classified into categories. At that stage, two options were offered. The first one was to analyse statistically, the other one was to use fuzzy logic. We know that fuzzy logic sounds very smart, and is a killer from a marketing viewpoint.
Many people rave about it, but it is really no big deal to implement it.
Our rule classification can easily be turned into membership functions, and from a programming point of view, rule selection is in itself only a variation on the probability computations we currently use in our alpha version. Neural nets are already pretty neat fuzzy operators, and besides the marketing bonus, we are not sure fuzzy logic would actually improve our models. This further technique analysis is the main cause for delay. I said, so I'd better keep it short. The genetic process not only optimises the best values for the 42 genetic traits, but also drops genetic traits which do not contribute to good solutions.