Path: news.mathworks.com!not-for-mail
From: <HIDDEN>
Newsgroups: comp.soft-sys.matlab
Subject: Re: Neural Networks and Genetic Algorithm
Date: Wed, 4 Mar 2009 10:59:02 +0000 (UTC)
Organization: San Diego State University
Lines: 13
Message-ID: <golmtm$1u3$1@fred.mathworks.com>
References: <gmv4l2$7ib$1@fred.mathworks.com>
Reply-To: <HIDDEN>
NNTP-Posting-Host: webapp-02-blr.mathworks.com
Content-Type: text/plain; charset="ISO-8859-1"
Content-Transfer-Encoding: 8bit
X-Trace: fred.mathworks.com 1236164342 1987 172.30.248.37 (4 Mar 2009 10:59:02 GMT)
X-Complaints-To: news@mathworks.com
NNTP-Posting-Date: Wed, 4 Mar 2009 10:59:02 +0000 (UTC)
X-Newsreader: MATLAB Central Newsreader 1500610
Xref: news.mathworks.com comp.soft-sys.matlab:522379


"zaheer ahmad" <ahmad.zaheer@yah00000.com> wrote in message <gmv4l2$7ib$1@fred.mathworks.com>...
> Hi All of You
> How to optimize Feed Forward Neural Network output using Genetic Algorithm instead of backpropagation Algorithm. i.e. to used GA for weights optimization.
> A small Matlab code will help me alot 
> thanks

That should be simple.

If you're dealing with a backprop NN with 1 hidden layer consisting 3 hidden nodes, and 2 input, then you have some (2x3)+(3x1) = 9 connection weights.  Using gatool, you can specify in the Problem section of the GUI, that you have 9 variables.  You can lower and/or upper bounds of these weights as you wish.

And then, gatool will do the rest.

Good luck!