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During the adaptive mutation, the fitness function is called in
| def adaptive_mutation_population_fitness(self, offspring): |
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?
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