How to optimize neural network by using genetic algorithm?

I'm quite new with this topic so any help would be great. What I need is to optimize a neural network in MATLAB by using GA. My network has [2x98] input and [1x98] target, I've tried consul开发者_StackOverflowting MATLAB help but I'm still kind of clueless about what to do :( so, any help would be appreciated. Thanks in advance.

Edit: I guess I didn't say what is there to be optimized as Dan said in the 1st answer. I guess most important thing is number of hidden neurons. And maybe number of hidden layers and training parameters like number of epochs or so. Sorry for not providing enough info, I'm still learning about this.

If this is a homework assignment, do whatever you were taught in class.

Otherwise, ditch the MLP entirely. Support vector regression ( http://www.csie.ntu.edu.tw/~cjlin/libsvm/ ) is much more reliably trainable across a broad swath of problems, and pretty much never runs into the stuck-in-a-local-minima problem often hit with back-propagation trained MLP which forces you to solve a network topography optimization problem just to find a network which will actually train.

well, you need to be more specific about what you are trying to optimize. Is it the size of the hidden layer? Do you have a hidden layer? Is it parameter optimization (learning rate, kernel parameters)?

I assume you have a set of parameters (# of hidden layers, # of neurons per layer...) that needs to be tuned, instead of brute-force searching all combinations to pick a good one, GA can help you "jump" from this combination to another one. So, you can "explore" the search space for potential candidates.

GA can help in selecting "helpful" features. Some features might appear redundant and you want to prune them. However, say, data has too many features to search for the best set of features by some approaches such as forward selection. Again, GA can "jump" from this set candidate to another one.

You will need to find away to encode the data (input parameters, features...) fed to GA. For finding a set of input paras or a good set of features, I think binary encoding should work. In addition, choosing operators for GA to reproduce offsprings is also important. Yet GA needs to be tuned, too (early stopping which can also be applied to ANN).

Here are just some ideas. You might want to search for more info about GA, feature selection, ANN pruning...

Since you're using MATLAB already I suggest you look into the Genetic Algorithms solver (known as GATool, part of the Global Optimization Toolbox) and the Neural Network Toolbox. Between those two you should be able to save quite a bit of figuring out.

You'll basically have to do 2 main tasks:

1. Come up with a representation (or encoding) for your candidate solutions
2. Code your fitness function (which basically tests candidate solutions) and pass it as a parameter to the GA solver.

If you need help in terms of coming up with a fitness function, or encoding of candidate solutions then you'll have to be more specific.

Hope it helps.

Matlab has a simple but great explanation for this problem here. It explains both the ANN and GA part.

For more info on using ANN in command line see this.

There is also plenty of litterature on the subject if you google it. It is however not related to MATLAB, but simply the results and the method.

Look up Matthew Settles on Google Scholar. He did some work in this area at the University of Idaho in the last 5-6 years. He should have citations relevant to your work.