Does gamultiob/ga perfom differently depending on the Matlab version?

Hi,

I use gamultiobj with a personalized Creation Function, Crossover Function and Mutation Function. The random numbers that are used, are set to ensure reproducable results. This works fine in Matlab R2016a. I have to use a different computer for further evaluations. This computer has a different Matlab Version (R2015b). I changed how the options for gamultiobj are set (optimoptions to gaoptimset), but the option values stayed all the same. If I run gamulitobj with R2015b I get different results. Since I didn't find any changes mentioned in the release notes of the two versions, I was hoping somebody could help me to figure out why the results differ. Thank you for reading.

I think code for creation,crossover and muation function is not important, besides that the random numbers are set with:

global Zusatz
rng('default');
rng(Zusatz);

Function Call for Gamultiobj in R2016a:

% Set Options für gamultiobj
options = optimoptions('gamultiobj',...
    'UseParallel', true,...
    'UseVectorized', false,...
    'CreationFcn',@popFun,...
    'CrossoverFcn',@crossoverBinary,...
    'MutationFcn',@mutFun,...
    'PopulationSize',InitPop,...
    'MaxStallGenerations',MaxStallG); %

% Starte gamultiobj
[X,fval,exitflag] = gamultiobj(objFun,n,A,B,[],[],[],[],[],options);

Function call for Gamultiobj in R2015b:

options = gaoptimset(...
    'UseParallel', true,...
    'Vectorized', 'off',...
    'CreationFcn',@popFun,...
    'CrossoverFcn',@crossoverBinary,...
    'MutationFcn',@mutFun,...
    'PopulationSize',InitPop,...
    'StallGenLimit',MaxStallG); %

% Starte gamultiobj
[X,fval,exitflag] = gamultiobj(objFun,n,A,B,[],[],[],[],[],options);

The Creation Function:

function Population = popFun(GenomeLength,~,options)

global Zusatz 
Population=zeros(options.PopulationSize,GenomeLength);
% Sicherstellen, dass die randomnumbers sich wiederholen(nur für debugging;später entfernen)
rng('default');
rng(Zusatz);
intcon=1:GenomeLength;
A=xlsread('Linearconstraints.xlsx','A2:MD86');
B=ones(85,1);
B(36,1)=-1;
lb=zeros(1,GenomeLength);
ub=ones(1,GenomeLength);
opts =optimoptions('intlinprog','IntegerTolerance',1e-06,'Display','off');
for a=1:options.PopulationSize
    f= randn(GenomeLength,1);
    [x,fval,exitflag]= intlinprog(f,intcon,A,B,[],[],lb,ub,opts);
    Population(a,:)=x;
end
% Population = Population';

end

The Crossover Function:

function xoverKids  = crossoverBinary(parents,options,GenomeLength,~,~,thisPopulation)

% Sicherstellen, dass die randomnumbers sich wiederholen(nur für debugging;später entfernen)
global Zusatz
rng('default');
rng(Zusatz);

% Extract information about linear constraints, if any
linCon = options.LinearConstr;

nKids = length(parents)/2;
index = 1;

xoverKids = nan(nKids,GenomeLength);

for k = 1:nKids
    % Get the parents from the population
    parent1 = thisPopulation(parents(index),:);
    index = index + 1;
    parent2 = thisPopulation(parents(index),:);
    index = index+1;

      % find locations where parents have a different genome
      idx = randi(GenomeLength,1);

      % Where genome is the same, keep it the same in the kids
      xoverKids(k,1:idx) = parent1(1:idx);
      xoverKids(k,idx+1:end) = parent2(idx+1:end);

      % Ensure that kid astisfies constraints
      flag = any(linCon.Aineq*(xoverKids(k,:)') > linCon.bineq,1);

      while flag  % Does not satisfy constraints

          idx = randi(GenomeLength,1);

          xoverKids(k,1:idx) = parent1(1:idx);
          xoverKids(k,idx+1:end) = parent2(idx+1:end);

          flag = any(linCon.Aineq*(xoverKids(k,:)') > linCon.bineq,1);

      end

end

end

The Mutation Function:

        function mutationChildren = mutFun(parents, options, GenomeLength, ...
            ~, ~, ~, thisPopulation)

        % Extract information about linear constraints, if any
        linCon = options.LinearConstr;

        % Sicherstellen, dass die randomnumbers sich wiederholen(nur für debugging;später entfernen)
        global Zusatz
        rng('default');
        rng(Zusatz);

        % Initialize the output
        mutationChildren = nan(length(parents),GenomeLength);

        for k = 1:length(parents)
            % Get current sample
            mut = thisPopulation(parents(k),:)';
            idx = rand(GenomeLength,1) < 0.01; % 1% chance of mutating
            % Mutate by flipping the bits at the idx indice.
            mutated = mut;
            mutated(idx) = double(~mut(idx));

            % Check that constraints are satisfied.
            flag = any(linCon.Aineq*mutated > linCon.bineq,1);

            while flag %
                % Try the mutation again
                idx = rand(GenomeLength,1) < 0.01; % 1% chance of mutating
                % Mutate by flipping the bits at the idx indice.
                mutated = mut;
                mutated(idx) = double(~mut(idx));

                % Check that constraints are satisfied.
                flag = any(linCon.Aineq*mutated > linCon.bineq,1);

            end
            mutationChildren(k,:) = mutated';

        end

        end