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From: Givemore on 13 Jul 2010 18:49
Hi Yeo,
Did you find a solution to your problem. I am looking at solving a similar issue, and will be happy if you can share the solution to this problem with me.
Thanks
"Yeo " <yeocr88(a)gmail.com> wrote in message <ho43au$mu7$1(a)fred.mathworks.com>...
> Hello all,
>
> I have a NN built using the MATLAB toolbox to predict a yield stress of a metal given its composition and conditions. I predicted the outputs. Now I have to employ Genetic Algorithm (GA) to optimise my inputs to reach my desired output. I understand there is a toolbox to do so and uses a function [x , fval] = ga(@fitnessfcn,no.ofvars).
>
> My thinking now is to put in my output into the GA so it can determine the best input to obtain the output.
>
> However, I have trouble determining my fitness function. They should be derived from my outputs and its derivations. Should it be just:
>
> function f = fitnessfcn(DesiredTargets,PredictedTargets)
>
> f = Desired Targets - Predicted i.e. the residuals since I want to minimize the residuals
>
> I believe my no.ofvars should be 2 since I have Desired and Predicted targets in my function. And my desired targets have a dimension of 1x100.
>
> Is my approach i.e. minimizing the residuals correct in order to optimize my inputs? I am a bit doubtful because I want to optimize the process not the NN model.
>
> Any advice would be helpful.
>
> Thanks.
From: Yeo on 14 Jul 2010 17:34
Hello Mr Givemore,

I did find a solution to the problem. The things you want to take note of are:

(i) What you are trying to optimise? There is a big difference between optimising your model and optimising the inputs.

(ii) What defines your fitness function?

(iii) Are you doing single or multiple objective optimisation?

I would recommend you to read on GA to understand its operation parameters and how they affect the optimisation process.

Please come back with more specific questions.



"Givemore " <givesak(a)gmail.com> wrote in message <i1iqh1$bj5$1(a)fred.mathworks.com>...
> Hi Yeo,
> Did you find a solution to your problem. I am looking at solving a similar issue, and will be happy if you can share the solution to this problem with me.
> Thanks
> "Yeo " <yeocr88(a)gmail.com> wrote in message <ho43au$mu7$1(a)fred.mathworks.com>...
> > Hello all,
> >
> > I have a NN built using the MATLAB toolbox to predict a yield stress of a metal given its composition and conditions. I predicted the outputs. Now I have to employ Genetic Algorithm (GA) to optimise my inputs to reach my desired output. I understand there is a toolbox to do so and uses a function [x , fval] = ga(@fitnessfcn,no.ofvars).
> >
> > My thinking now is to put in my output into the GA so it can determine the best input to obtain the output.
> >
> > However, I have trouble determining my fitness function. They should be derived from my outputs and its derivations. Should it be just:
> >
> > function f = fitnessfcn(DesiredTargets,PredictedTargets)
> >
> > f = Desired Targets - Predicted i.e. the residuals since I want to minimize the residuals
> >
> > I believe my no.ofvars should be 2 since I have Desired and Predicted targets in my function. And my desired targets have a dimension of 1x100.
> >
> > Is my approach i.e. minimizing the residuals correct in order to optimize my inputs? I am a bit doubtful because I want to optimize the process not the NN model.
> >
> > Any advice would be helpful.
> >
> > Thanks.
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