function [x,fval,exitFlag,output,population,scores] = galincon(FitnessFcn,GenomeLength, ...    Aineq,bineq,Aeq,beq,lb,ub,options,output,Iterate)%GALINCON Genetic algorithm linear constrained solver.%   GALINCON solves problems of the form:%           min F(X)    subject to:      A*x <= b%            X                          Aeq*x = beq%                                      LB <= X <= UB%   Private function to GA%   Copyright 2005-2007 The MathWorks, Inc.%   $Revision: 1.1.6.11 $  $Date: 2007/12/10 21:35:48 $% Initialize output argsx = []; fval = []; exitFlag = [];LinearConstr = options.LinearConstr;% Create initial state: population, scores, status datastate = makeState(GenomeLength,FitnessFcn,Iterate,output.problemtype,options);% Determine who is the callercallStack = dbstack;caller = callStack(2).file(1:end-2);% Set state for plot and output functions (only gacon will have% 'interrupt' state)if ~strcmp(caller,'gacon')    currentState = 'init';else    currentState = 'interrupt';end% Give the plot/output Fcns a chance to do any initialization they need.state = gadsplot(options,state,currentState,'Genetic Algorithm');[state,options] = gaoutput(FitnessFcn,options,state,currentState);% Setup display headerif  options.Verbosity > 1    fprintf('\n                               Best           Mean      Stall\n');    fprintf('Generation      f-count        f(x)           f(x)    Generations\n');end% Set state for plot and output functions (only gacon will have% 'interrupt' state)if ~strcmp(caller,'gacon')    currentState = 'iter';else    currentState = 'interrupt';end% Run the main loop until some termination condition becomes truewhile isempty(exitFlag)    state.Generation = state.Generation + 1;    % Repeat for each subpopulation (element of the populationSize vector)    offset = 0;    totalPop = options.PopulationSize;    % Each sub-population loop    for pop = 1:length(totalPop)        populationSize =  totalPop(pop);        thisPopulation = 1 + (offset:(offset + populationSize - 1));        population = state.Population(thisPopulation,:);        score = state.Score( thisPopulation );        % Empty population is also possible        if isempty(thisPopulation)            continue;        end        [score,population,state] = stepGA(score,population,options,state,GenomeLength,FitnessFcn);        % Store the results for this sub-population        state.Population(thisPopulation,:) = population;        state.Score(thisPopulation) = score;        offset = offset + populationSize;    end    % Remember the best score    best = min(state.Score);    generation = state.Generation;    state.Best(generation) = best;    % Keep track of improvement in the best    if((generation > 1) && isfinite(best))        if(state.Best(generation-1) > best)            state.LastImprovement = generation;            state.LastImprovementTime = cputime;        end    end    % Do any migration    state = migrate(FitnessFcn,GenomeLength,options,state);    % Update the Output    state = gadsplot(options,state,currentState,'Genetic Algorithm');    [state,options,optchanged] = gaoutput(FitnessFcn,options,state,currentState);    if optchanged        options.LinearConstr = LinearConstr;    end    % Check to see if any stopping criteria have been met    [exitFlag,output.message] = isItTimeToStop(options,state);end % End while loop% Find and return the best solution[fval,best] = min(state.Score);x = state.Population(best,:);% Update output structureoutput.generations = state.Generation;output.funccount   = state.FunEval;output.maxconstraint = 0.0;population = state.Population;scores = state.Score;% Call hybrid functionif ~isempty(options.HybridFcn)    if  strcmpi(options.PopulationType,'doubleVector')        [x,fval] = callHybridFunction;    else        warning('gads:galincon:notValidHybrid','''HybridFcn'' can only be used with ''doubleVector'' population; ignoring ''HybridFcn'' option');    endend% Set state for plot and output functions (only gacon will have% 'interrupt' state)if ~strcmp(caller,'gacon')    currentState = 'done';else    currentState = 'interrupt';end% give the Output functions a chance to finish upgadsplot(options,state,currentState,'Genetic Algorithm');gaoutput(FitnessFcn,options,state,currentState);%-----------------------------------------------------------------% Hybrid function    function [xhybrid,fhybrid] = callHybridFunction        xhybrid = x;        fhybrid = fval;        % Who is the hybrid function        if isa(options.HybridFcn,'function_handle')            hfunc = func2str(options.HybridFcn);        else            hfunc = options.HybridFcn;        end        % Inform about hybrid scheme        if   options.Verbosity > 1            fprintf('%s%s%s\n','Switching to the hybrid optimization algorithm (',upper(hfunc),').');        end        % Create functions handle to be passed to hybrid function        FitnessHybridFcn = @(x) FitnessFcn(x,options.FitnessFcnArgs{:});        ConstrHybridFcn = [];        if ~any(strcmpi(hfunc,{'fmincon', 'patternsearch'}))            msg = sprintf('%s is not a constrained solver',upper(hfunc));            msg = [msg, sprintf('\n%s',' using constrained solver FMINCON as hybrid function.')];            warning('gads:galincon:unconstrainedHybridFcn',msg);            hfunc = 'fmincon';        end        [x_temp,f_temp,funccount,message,e] = callHybrid(hfunc,FitnessHybridFcn,x,options.HybridFcnArgs,Aineq,bineq,Aeq,beq,lb,ub,ConstrHybridFcn);        output.funccount = output.funccount + funccount;        output.message   = sprintf([output.message '\n', message '\n']);                % Check for exitflag and fval        if f_temp < fhybrid && e > 0            fhybrid = f_temp;            xhybrid = x_temp;        end        % Inform about hybrid scheme termination        if  options.Verbosity > 1            fprintf('%s%s\n',upper(hfunc), ' terminated.');        end    end % End of callHybridFunctionend  % End of galincon