You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
In my application, this is a problem because I have to normalize some optimized probabilities before computing the fitness.
So if I have g1, g2, g3, g4 as genes that are optimized with the GA, and I need sum(g1, g2, g3, g4) = 1 for my fitness function, I most likely get an error.
The way I'm doing things with random mutation is that I normalize this in my on_mutation method.
Is there a way of using adaptive mutation with such restrictions?
The text was updated successfully, but these errors were encountered:
During the
adaptive
mutation, the fitness function is called inGeneticAlgorithmPython/pygad/utils/mutation.py
Line 473 in 0e8be9d
In my application, this is a problem because I have to normalize some optimized probabilities before computing the fitness.
So if I have
g1
,g2
,g3
,g4
as genes that are optimized with the GA, and I needsum(g1, g2, g3, g4) = 1
for my fitness function, I most likely get an error.The way I'm doing things with
random
mutation is that I normalize this in myon_mutation
method.Is there a way of using
adaptive
mutation with such restrictions?The text was updated successfully, but these errors were encountered: