Hi I want to ask about weight selection in neural network using genetic algorithm.
Right now what I understand is
- Initialize population
- Encode the weight of the neural network to the chromosome
- Calculating the error and fitness
- crossover and mutation
- looping until satisfy the condition
Is it the right thing?
if yes what I'm still not sure are :
- If I have 50 chromosome in one population that means I must create 50 neural network?
- Let's say I have 100 different input and I want the network to learn it by using weight selection only (not using backpropagation) and how I calculate the error? Testing and calculating the error of every input(using MSE) and divide it by 100?
I think that's all for now