You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: TiGE2.m
    path of file: ./repos/PlatEMO/PlatEMO/Algorithms/Multi-objective optimization/TiGE-2
    
    the code of the file until where you have to start completion: classdef TiGE2 < ALGORITHM
% <many> <real/integer/label/binary/permutation> <constrained/none>
% Tri-Goal Evolution Framework for CMaOPs

%------------------------------- Reference --------------------------------
% Y. Zhou, Z. Min, J. Wang, Z. Zhang, and J.Zhang, Tri-goal evolution
% framework for constrained many-objective optimization, IEEE Transactions
% on Systems Man and Cybernetics Systems, 2020, 50(8): 3086-3099.
%------------------------------- Copyright --------------------------------
% Copyright (c) 2023 BIMK Group. You are free to use the PlatEMO for
% research purposes. All publications which use this platform or any code
% in the platf
    
function main(Algorithm,Problem)
            %% Parameter setting
            [Epsilon0,row] = Algorithm.ParameterSet(0.05,1.01);
            
            %% Generate random population
            Population = Problem.Initialization();
            [fpr,fcd]  = Estimation(Population.objs,1/Problem.N^(1/Problem.M));
            fcv        = Calculate_fcv(Population); 
            Epsilon    = Epsilon0;
            PopObj_1   = [fpr,fcd]; 
            [fm,~]     = NDSort(PopObj_1,Problem.N);
            PopObj     = [fm' + Epsilon * fcv,fcv];
            [frank,~]  = NDSort(PopObj,Problem.N);
            fitness    = frank' + fcv./(fcv+1);
            
            %% Optimization
            while Algorithm.NotTerminated(Population)
                MatingPool = TournamentSelection(2,Problem.N,fitness);
                Offspring  = OperatorGA(Problem,Population(MatingPool));
                [fpr,fcd]  = Estimation(Offspring.objs,1/Problem.N^(1/Problem.M));
                fcv = Calculate_fcv(Offspring); 
                OffObj_1   = [fpr,fcd]; 
                [fm,~] = NDSort(OffObj_1,Problem.N);
                OffObj = [fm' + Epsilon * fcv,fcv];
                [Population,fitness] = EnvironmentalSelection([Population,Offspring],PopObj,OffObj,Problem.N);
                [fpr,fcd] = Estimation(Population.objs,1/Problem.N^(1/Problem.M));
                fcv = Calculate_fcv(Population);
                PopObj_1 = [fpr,fcd]; 
                [fm,~]   = NDSort(PopObj_1,Problem.N);
                PopObj   = [fm' + Epsilon * fcv,fcv];
                Epsilon  = row * Epsilon;
            end