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: CLIA.m
    path of file: ./repos/PlatEMO/PlatEMO/Algorithms/Multi-objective optimization/CLIA
    
    the code of the file until where you have to start completion: classdef CLIA < ALGORITHM
% <multi/many> <real/integer/label/binary/permutation>
% Evolutionary algorithm with cascade clustering and reference point incremental learning

%------------------------------- Reference --------------------------------
% H. Ge, M. Zhao, L. Sun, Z. Wang, G. Tan, Q. Zhang, and C. L. P. Chen, A
% many-objective evolutionary algorithm with two interacting processes:
% Cascade clustering and reference point i
    
function main(Algorithm,Problem)
            global stable_threshold delta crowding_pick_flag;
            [stable_threshold, delta] = Algorithm.ParameterSet([0, 0, 0], 2 * Problem.N);
            [Z, P, A, S, SVM] = initialize(Problem);
            while Algorithm.NotTerminated(S)
                MatingPool = TournamentSelection(2, Problem.N, sum(max(0, P.cons), 2)); 
                Offspring  = OperatorGA(Problem,P(MatingPool));
                A = update_archive(A, [P, Offspring], Z, ceil(0.33 * Problem.M * Problem.N), Problem);
                % SELECTION OF INDIVIDUALS
                [P, ICA, ICN] = cascade_cluster([P, Offspring], Z, 'PDM', Problem.N, Problem.FE < Problem.maxFE);
                % ADAPTATION OF REFERENCE VECTORS     
                [Z, SVM] = incremental_learn(Z, ICA, ICN, A, SVM, Problem);
                if Problem.FE >= Problem.maxFE && crowding_pick_flag
                    S = crowding_pick(update_archive(A, P, Z, [], Problem), Problem.N, 'precise');
                else
                    S = P;
                end