'Position' is a (68,6) array of doubles representing the population of Genetic algorithm. 'classGA1 ' is a (68,1) cell array of the type of population class ( inventory class A,B or C) . 'ClassDM1 ' is a (68,1) cell array of the inventory class(A,B, or C)given by the decision maker (materials manager).
[Fitness] = InvClassifyGAFitnessFunc(Position,classGA1,ClassDM1);
is the Fitness is a (68,1) double array determining the fitness of population.
I want to operate the GA in vectorized mode.
[Fitness] = @(Position)InvClassifyGAFitnessFunc(Position(':',1:6),classGA1{':',1},ClassDM1{':',1});
A=[0,0,0,0,-1,1];
b=[0];
Aeq=[1,1,1,1,0,0];
beq=[1];
lb=[0,0,0,0,0,0];
ub=[1,1,1,1,1,1];
options= gaoptimset('PlotFcn',@gaplotbestf,'Vectorized','on');
[x,fval]=ga(Fitness,6,A,b,Aeq,beq,lb,ub,[],options);
The genetic algorithm is giving the error message
Error using InvClassifyGAFitnessFunc
Too many input arguments.
How to run my genetic algorithm in vectorized mode.
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