Neural Network weight selection using Genetic Algorithm

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

Thank you

Your understanding is correct.

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Yes, you do create a new neural network for each chromosome (although you're considering the network structure as fixed, so technically you could reuse them by just resetting the weights anew for each chromosome.

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For the error, you're on the right track. For a GA, generally only relative differences in fitness are important, so it doesn't really matter if you divide by 100 or not -- that's just linearly scaling the fitness values. That might matter for some choices of genetic operators (e.g., roulette-wheel selection can be sensitive to absolute fitness values), but often it won't matter at all.

StackExchange Computer Science Q#7817, answer score: 2