It is also appropriate at this point to address the relationship between OO
and Genetic Algorithms (GA) and to Fuzzy Logic (FL). In a sentence, OO is
completely complementary to GA and FL. While OO deals with softening the goal
of optimization, FL can further blur the definitions used for softening. For
example, we may say that the top-5% represents the "good enough".
But what about the top-6%? Are they necessarily bad? A more reasonable
definition of good enough can be defined using the membership function of
fuzzy logic permitting a gradual degradation. Similarly, high probability
need not be defined by a sharp boundary of >0.95. Instead, a linearly
declining membership from 1 to 0 for P=0.95 to 0.9 can be used. The point is
that all the material we have discussed w.r.t. OO can be and probably should
be fuzzified. There is no conflict with FL.