function [Population,Score] = PopAnneal(options,GenomeLength,FitnessFcn, ...    state,thisScore,thisPopulation)R=10;L=GenomeLength*100;%T0=0.1;alpha=0.8;%t = T0 * alpha^(state.Generation);nParents = size(thisPopulation,1);nAnneal = round(nParents / R /2);[Score,k] = sort(thisScore);Population  = thisPopulation(k,:);t = min(1/sqrt(eps),(Score(nAnneal*R)-Score(1))/(log(nAnneal*R)-log(nAnneal*R-1)));t = max(sqrt(eps),t);shrink=0.9;stepSize = max(sqrt(eps),shrink^(state.Generation));if isfield(options,'LinearConstr')    % Extract information about constraints    linCon = options.LinearConstr;    type = linCon.type;    % Sub-problem type is constrained?    if ~strcmpi(type,'unconstrained')        A = linCon.A;        LB = linCon.L;        UB = linCon.U;    endendLbnd = min(Population);%(1:nParents/2,:)Ubnd = max(Population);Bound = [Lbnd;Ubnd];PopAnneal = [];ScoreAnneal = [];for i = 1:nAnneal    x = Population(i,:);    fx = Score(i);    [X2,fX2] = Anneal(x,fx,FitnessFcn,Bound,stepSize^2,A,LB,UB,R,L,t*stepSize);    PopAnneal = [PopAnneal;X2];    ScoreAnneal = [ScoreAnneal;fX2];endPopulation = [PopAnneal;Population];Score = [ScoreAnneal;Score];[Score,k] = sort(Score);Population  = Population(k,:);Population = Population(1:nParents,:);Score = Score(1:nParents,:);