[Solved]: Neural Network weight selection using Genetic Algorithm

Problem Detail: 

Hi I want to ask about weight selection in neural network using genetic algorithm.

Right now what I understand is

1. Initialize population
2. Encode the weight of the neural network to the chromosome
3. Calculating the error and fitness
4. crossover and mutation
5. looping until satisfy the condition

Is it the right thing?

if yes what I'm still not sure are :

1. If I have 50 chromosome in one population that means I must create 50 neural network?
2. 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

Asked By : Niko Yuwono

Answered By : deong

Your understanding is correct.

1. 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.

2. 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.

Best Answer from StackOverflow

Question Source : http://cs.stackexchange.com/questions/7817