%% Problem Definition
data=load('mydata');
R=data.R;
model.R=R;
model.method='cvar';
model.alpha=0.95;
CostFunction=@(x) PortMOC(x,model); % Cost Function
nVar=size(R,2); % Number of Decision Variables
VarSize=[1 nVar]; % Size of Decision Variables Matrix
VarMin=0; % Lower Bound of Variables
VarMax=1; % Upper Bound of Variables
% Number of Objective Functions
nObj=numel(CostFunction(unifrnd(VarMin,VarMax,VarSize)));
%% NSGA-II Parameters
MaxIt=100; % Maximum Number of Iterations
nPop=50; % Population Size
pCrossover=0.7; % Crossover Percentage
nCrossover=2round(pCrossovernPop/2); % Number of Parnets (Offsprings)
pMutation=0.4; % Mutation Percentage
nMutation=round(pMutation*nPop); % Number of Mutants
