Fitness Normalization

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Celso França

Oct 11, 2015, 4:10:46 PM10/11/15
to Watchmaker Framework for Evolutionary Computation

My fitness implementation, looks like:

fitness = surprise + efficiency

Efficiency is a value in an interval between 0 and 1. 
However, the surprise value can assume any value great than zero (actually very big values can occur). 
These two elements must to have the same weigh in equation and, to reach that, I have to normalize the surprise for current entire population before sum with efficiency value.

Do you have any idea to make this in the framework?

Thank you so much!
Live Long And Prosper
Celso França.


Oct 12, 2015, 1:34:52 PM10/12/15
to Watchmaker Framework for Evolutionary Computation
You could apply a nonlinear transformation to surprise. So, for instance, change fitness to f(surprise; lambda) + efficiency where f(x; lambda) = 1 - exp(-lambda * x). (I'm assuming here that higher surprise values should yield greater fitness.) Other functions are possible; you just want an increasing function with range [0, 1] (give or take the endpoints).

Celso França

Oct 12, 2015, 10:17:20 PM10/12/15
to Watchmaker Framework for Evolutionary Computation
Hello Paul,

Thank you so much for your answer. I've been very happy since I saw it, because this solution will work perfectly in my research.
Over again, thank you!

Live Long and Prosper.
Celso França.
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