When learning the Neural Net optimizes towards all negative output neurons

[jellespijker]

The fitness routine has to be rewritten for the Genetic algorithms

This results in a optimization function that hangs on a vector of all -1 or 1

[jellespijker]

Extra bonuses added to stimulate the population members that show a complete intersect with the goal vector and a bonus when the positive neuron intersects with the goal. The Bonus is a multiplication of the number of output neurons.

[jellespijker]

Implement issue #86 A revolution against the elite upperclass

[jellespijker]

The problem probably lies in the fact that the NN currently predicts either a -1 or a 1 in each neuron. This makes it virtually impossible to move incrementally towards an optimum.

Let the neurons fire a continuous value not just a step function.

[jellespijker]

Tricky f..in' function...
It convergences, but then it get stuck on 30 % error. I will park this and continue to work on it when I got reliable learning data.

One possible solution is to have two NNś one for the roundness and one for angularity.

[jellespijker]

It could be that issue #92 is related to this bug

[jellespijker]

Not related to issue #92