MATLAB: Please help me to using genetic algorithm

genetic algorithmOptimization Toolbox

I write this code but I want to solve this problem with 'ga' not with 'intlinprog' solver!
Can anyone guide me?
costprob = optimproblem;
% Indices
k = 15;
j = 2;
f = 10;
l = 5;
r0 = 6;
r = 6;
% Parameters
cr0 = 0 + 1*rand(1,r0);
dr0f = 0 + 1*rand(r0,f);
csl = 0 + 1*rand(1,l);
DE = 200 + 100*rand(1,1);
csur0f = 2000 + 1000*rand(r0,f);
ctl = 1000 + 1000*rand(1,l);
cvl = 10 + 10*rand(1,l);
cpjk = 0 + 1*rand(j,k);
corj = 0 + 1*rand(r,j);
pr0f = 0 + 1*rand(r0,f);
vjk = 0 + 1*rand(j,k);
cvjrk = 0 + 1*rand(j,r,k);
M = 10000000000000;
% Variables
xl = optimvar('xl',1,l,'LowerBound',0);
yr0f = optimvar('yr0f',r0,f,'Type','integer','LowerBound',0,'UpperBound',1);
xx1r0f = optimvar('xx1r0f',r0,f,'LowerBound',0);
xx2r0f = optimvar('xx2r0f',r0,f,'LowerBound',0);
yjk1 = optimvar('yjk1',j,k,'Type','integer','LowerBound',0,'UpperBound',1);
yl2 = optimvar('yl2',1,l,'Type','integer','LowerBound',0,'UpperBound',1);
zjkr = optimvar('zjkr',j,k,r,'LowerBound',0);
wrj = optimvar('wrj',r,j,'LowerBound',0);
% Objective function
objfun1 = sum(sum(dr0f.*xx1r0f,2).*cr0',1);
objfun2 = sum(sum(corj.*wrj,2),1);
objfun3 = sum(sum(pr0f.*xx1r0f,2),1);
objfun4 = sum(sum(cpjk.*yjk1,2),1);
objfun5 = sum(csl.*xl,2);
costprob.Objective = objfun1 + objfun2 + objfun3 + objfun4 + objfun5;
% Constraints
cons1 = sum(xl,2) >= DE;
cons2 = sum(xl,2)*ones(j,1,r) == sum(zjkr,2);
cons3 = xx1r0f <= csur0f.*yr0f;
cons4 = xl <= ctl.*yl2;
cons5 = xl >= cvl.*yl2;
cons6 = sum(yjk1,2) == ones(j,1);
cons7 = squeeze(sum(zjkr,3)) <= M*yjk1;
cons8 = (1-dr0f).*xx1r0f == xx2r0f;
costprob.Constraints.cons1 = cons1;
costprob.Constraints.cons2 = cons2;
costprob.Constraints.cons3 = cons3;
costprob.Constraints.cons4 = cons4;
costprob.Constraints.cons5 = cons5;
costprob.Constraints.cons6 = cons6;
costprob.Constraints.cons7 = cons7;
costprob.Constraints.cons8 = cons8;

Best Answer

You can use prob2struct to obtain most of your problem parameters in solver form,
problem=prob2struct(costprob);
problem=rmfield(problem,'solver');
problem.nvars=numel(problem.lb);
problem.fitnessfcn=@(x) dot(problem.f,x);
 x=ga(problem);
However your problem, as currently formulated, has both integer and equality constraints, which ga cannot handle. See here, for guidelines on how to rewrite the problem without equality constraints:
https://www.mathworks.com/matlabcentral/answers/167216-genetic-algorithm-ga-with-integer-equality-and-inequality-constraint
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