Mutation function? about simple-neural-network HOT 2 OPEN

To mutate the weights depending on the probability you first need to convert the probability into delta error. then the rest is the same. However this one is just a simple example, so it's not so efficient in terms of performance. For better performance in the case of neuroevolution, you need to use relu and sigmoid both in different layers as activation functions and introduce dropout.

this concept comes from local minima. Our goal is to make the error minimum. So we try to calculate the derivative of the error with respect to the weights. This gives us the direction, i.e. whether to increase a certain weight or to decrease. now we decrease or increase the given weight by a small amount. this is considered as the learning weight. you need to have a basic knowledge of calculus to understand how to calculate partial derivatives or the direction of minima. You can search for gradient descent for more clear information.

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Oh ok, I have never properly learned calculus before but I’ll look into it, thanks!

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