How to link the outcome of an ANN model with GA or SA algorithms?

Dear Matlab Co-workers, I would like to link the Neural Network's fitting and modeling outcome to an optimization algorithm, in order to optimize a predicted dependent varible.

My problem consists of 3 independent variables and a dependent variable. The code for prediction using ANN is given below:

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%% Input - input data composed of three variables

% Output - target data.=Objective to minimize

inputs = [80 80 80 80 80 80 80 80 80 150 150 150 150 150 150 150 150 150 220 220 220 220 220 220 220 220 220; 0.05 0.05 0.05 0.1 0.1 0.1 0.15 0.15 0.15 0.05 0.05 0.05 0.1 0.1 0.1 0.15 0.15 0.15 0.05 0.05 0.05 0.1 0.1 0.1 0.15 0.15 0.15; 5 10 15 5 10 15 5 10 15 5 10 15 5 10 15 5 10 15 5 10 15 5 10 15 5 10 15]

targets = [4264 4127 3775 2872 3137 3218 2320 2537 2671 3808 4172 4290 2843 3085 3654 2497 2661 3131 4783 5037 5590 3126 3756 4386 2655 3134 3100]

% Create a Fitting Network

hiddenLayerSize = 10;

net = fitnet(hiddenLayerSize);

net.inputs{1}.processFcns = {'removeconstantrows','mapminmax'};

net.outputs{2}.processFcns = {'removeconstantrows','mapminmax'};

net.divideFcn = 'dividerand'; net.divideMode = 'sample'; % Divide up every sample

net.divideParam.trainRatio = 70/100;

net.divideParam.valRatio = 15/100;

net.divideParam.testRatio = 15/100;

net.trainFcn = 'trainlm';

net.performFcn = 'mse';

net.plotFcns = {'plotperform','plottrainstate','ploterrhist', ... 'plotregression', 'plotfit'};

[net,tr] = train(net,inputs,targets);

outputs = net(inputs);

errors = gsubtract(targets,outputs);

performance = perform(net,targets,outputs)

trainTargets = targets .* tr.trainMask{1};

valTargets = targets .* tr.valMask{1};

testTargets = targets .* tr.testMask{1};

trainPerformance = perform(net,trainTargets,outputs)

valPerformance = perform(net,valTargets,outputs)

testPerformance = perform(net,testTargets,outputs)

view(net) figure, plotperform(tr)

figure, plottrainstate(tr)

figure, plotfit(net,inputs,targets)

figure, plotregression(targets,outputs)

figure, ploterrhist(errors)

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The question: Anyone has an idea how to minimize the predicted above target with GA or any other optimization functions?

Thanks

W. Albrecht