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I am reading through the NEAT paper here. On page 14 of the PDF, there is this quote about mutation:

There was an 80% chance of a genome having its connection weights mutated, in which case each weight had a 90% chance of being uniformly perturbed and a 10% chance of being assigned a new random value.

What exactly does it mean to perturb weights? What is uniform vs. nonuniform perturbation?

Is there an established method to do this? I am imagining the process as multiplying each connection weight by a random number, but I'm unfamiliar with the term.

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Perturbed here means adding a small random value to the weight. That random value comes from a uniform distribution or from a gaussian (or any distribution really). Imagine just nudging the weight by a little.

It’s done to overcome the problem of local minima where models can get stuck with a good set of weights but not the best set of weights. By perturbing the weights a little, the model has a chance of finding a better set of weights. Searching for methods that deal with local minima like stochastic gradient descent will give you a better intuition.

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