Choosing Fitness function for float output in Genetic algorithm I have a NN that has ten outputs. The output values range between 0 and 1. The elements in the target array are all zeros except one element, which is "one".

I am searching for a Fitness Function that will correctly evaluate (score) the Neural Networks.

Currently, I am trying to calculate the distance between the target and the output arrays. The issue is that at the beginning all my NNs return results that are very close to each other and I can't properly choose the fittest individuals. 

For Example:

NN1 -&gt; FF score:0.10030096 -&gt; Rank?
NN2 -&gt; FF score:0.09996143 -&gt; Rank?
NN3 -&gt; FF score:0.10015215 -&gt; Rank?


Any suggestions?
The standard solution is: Use a softmax layer at the output of your neural network, and use the cross-entropy loss as your fitness function.