function ind=genetic_op(subproblems, index, domain, params)%GENETICOP function implemented the DE operation to generate a new%individual from a subproblems and its neighbours.%   subproblems: is all the subproblems.%   index: the index of the subproblem need to handle.%   domain: the domain of the origional multiobjective problem.%   ind: is an individual structure.    neighbourindex = subproblems(index).neighbour;        %The random draw from the neighbours.    nsize = length(neighbourindex);    si = ones(1,3)*index;        si(1)=neighbourindex(ceil(rand*nsize));    while si(1)==index        si(1)=neighbourindex(ceil(rand*nsize));    end        si(2)=neighbourindex(ceil(rand*nsize));    while si(2)==index || si(2)==si(1)        si(2)=neighbourindex(ceil(rand*nsize));    end        si(3)=neighbourindex(ceil(rand*nsize));    while si(3)==index || si(3)==si(2) || si(3)==si(1)        si(3)=neighbourindex(ceil(rand*nsize));    end         %retrieve the individuals.    points = [subproblems(si).curpoint];    selectpoints = [points.parameter];        oldpoint = subproblems(index).curpoint.parameter;    parDim = size(domain, 1);        jrandom = ceil(rand*parDim);        randomarray = rand(parDim, 1);    deselect = randomarray<params.CR;    deselect(jrandom)=true;    newpoint = selectpoints(:,1)+params.F*(selectpoints(:,2)-selectpoints(:,3));    newpoint(~deselect)=oldpoint(~deselect);        %repair the new value.    newpoint=max(newpoint, domain(:,1));    newpoint=min(newpoint, domain(:,2));        ind = struct('parameter',newpoint,'objective',[], 'estimation',[]);    %ind.parameter = newpoint;    %ind = realmutate(ind, domain, 1/parDim);    ind = gaussian_mutate(ind, 1/parDim, domain);        %clear points selectpoints oldpoint randomarray deselect newpoint neighbourindex si;end