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Plot gaplotdistance in one plot for multiple runs of genetic algorithm

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Tessa Kol
Tessa Kol on 28 Oct 2020
Edited: Mario Malic on 31 Oct 2020
Dear all,
With the code below I managed to run the genetic algorithm multiple times.
gprMdl2 = fitrgp(X,Y1,'KernelFunction','squaredexponential','OptimizeHyperparameters','auto','HyperparameterOptimizationOptions',struct('AcquisitionFunctionName','expected-improvement-plus'));
for i = 1:3
options = optimoptions('ga','CrossoverFrac',0.9,'PopulationSize',50,'StallGen',50,'Generations',70,'PlotFcn', {'gaplotbestf','gaplotdistance'});
fun = @(X) [abs(((predict(gprMdl2,X)-MFR_exp)/MFR_exp))];
[x_opt, Obj, exitflag,output] = ga(fun,2,[],[],[],[],[0.1 0.1], [0.9 0.9],[],[],options);
end
With PlotFcn I will get the plot of the fitness value vs genration and the average distance vs generation (as shown below). How can I combine the plots of each run into one plot?
  7 Comments
Mario Malic
Mario Malic on 31 Oct 2020
Great to hear it works! Actually, I put the distance first, and then I thought, but it doesn't show every single distance out there, so I set it back to d.

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Accepted Answer

Mario Malic
Mario Malic on 31 Oct 2020
Edited: Mario Malic on 31 Oct 2020
Edited according to the change of testdist. More details in comments.
function state = customgaplotdistance(options,state,flag)
%GAPLOTDISTANCE Averages several samples of distances between individuals.
% STATE = GAPLOTDISTANCE(OPTIONS,STATE,FLAG) plots an averaged distance
% between individuals.
%
% Example:
% Create an options structure that uses GAPLOTDISTANCE
% as the plot function
% options = optimoptions('ga','PlotFcn',@gaplotdistance);
%
% (Note: If calling gamultiobj, replace 'ga' with 'gamultiobj')
% Copyright 2003-2015 The MathWorks, Inc.
persistent testdist % change number 1
testdist(1,:) = [0 0]; % initialising the value
samples = 20;
choices = ceil(sum(options.PopulationSize) * rand(samples,2));
switch flag
case 'init'
population = state.Population;
distance = 0;
for i = 1:samples
d = population(choices(i,1),:) - population(choices(i,2),:);
distance = distance + sqrt( sum ( d.* d));
testdist(end+1,:) = distance; % change number 2
end
plotDist = plot(state.Generation,distance/samples,'.');
set(gca,'xlimmode','manual','zlimmode','manual', ...
'alimmode','manual')
set(gca,'xlim',[1,options.MaxGenerations]);
set(plotDist,'Tag','gaplotdistance');
xlabel('Generation','interp','none');
ylabel('Average Distance');
title('Average Distance Between Individuals','interp','none')
case 'iter'
population = state.Population;
distance = 0;
for i = 1:samples
d = population(choices(i,1),:) - population(choices(i,2),:);
distance = distance + sqrt( sum ( d.* d));
testdist(end+1,:) = distance; % change number 3
assignin('base', 'testdist', testdist) % it might be better to assign it and save it from the main file
% save('testdist.mat', 'testdist') % as it will save the file 3000+ times
end
plotDist = findobj(get(gca,'Children'),'Tag','gaplotdistance');
newX = [get(plotDist,'Xdata') state.Generation];
newY = [get(plotDist,'Ydata') distance/samples];
set(plotDist,'Xdata',newX,'Ydata',newY);
end
Filtering out each ga run is done by lines below
A1 = testdis(:,1)/20;
A2 = zeros(1281,3);
A2(1:1281,1)=A1(1:1281,1);
A2(2:1081,2)=A1(1282:2361,1);
A2(2:1221,3)=A1(2362:3581,1);
A3 = A2(41:20:1221,3);
A4 = A2(41:20:1081,2);
A5 = A2(41:20:1281,1);

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