基于遗传算法优化BP神经网络的数据分类预测

function [x, endPop, bPop, traceInfo] = ga(bounds, evalFN, evalOps, startPop, opts, ... termFN, termOps, selectFN, selectOps, xOverFNs, xOverOps, mutFNs, mutOps) % Output Arguments: % x - the best solution found during the course of the run % endPop - the final population % bPop - a trace of the best population % traceInfo - a matrix of best and means of the ga for each generation % % Input Arguments: % bounds - a matrix of upper and lower bounds on the variables % evalFN - the name of the evaluation .m function % evalOps - options to pass to the evaluation function ([NULL]) % startPop - a matrix of solutions that can be initialized % from initialize.m % opts - [epsilon prob_ops display] change required to consider two % solutions different, prob_ops 0 if you want to apply the % genetic operators probabilisticly to each solution, 1 if % you are supplying a deterministic number of operator % applications and display is 1 to output progress 0 for % quiet. ([1e-6 1 0]) % termFN - name of the .m termination function (['maxGenTerm']) % termOps - options string to be passed to the termination function % ([100]). % selectFN - name of the .m selection function (['normGeomSelect']) % selectOpts - options string to be passed to select after % select(pop,#,opts) ([0.08]) % xOverFNS - a string containing blank seperated names of Xover.m % files (['arithXover heuristicXover simpleXover']) % xOverOps - A matrix of options to pass to Xover.m files with the % first column being the number of that xOver to perform % similiarly for mutation ([2 0;2 3;2 0]) % mutFNs - a string containing blank seperated names of mutation.m % files (['boundaryMutation multiNonUnifMutation ... % nonUnifMutation unifMutation']) % mutOps - A matrix of options to pass to Xover.m files with the % first column being the number of that xOver to perform % similiarly for mutation ([4 0 0;6 100 3;4 100 3;4 0 0]) %% 初始化参数 n = nargin; if n < 2 || n == 6 || n == 10 || n == 12 disp('Insufficient arguements') end % 默认评估选项 if n < 3 evalOps = []; end % 默认参数 if n < 5 opts = [1e-6, 1, 0]; end % 默认参数 if isempty(opts) opts = [1e-6, 1, 0]; end %% 判断是否为m文件 if any(evalFN < 48) % 浮点数编码 if opts(2) == 1 e1str = ['x=c1; c1(xZomeLength)=', evalFN ';']; e2str = ['x=c2; c2(xZomeLength)=', evalFN ';']; % 二进制编码 else e1str = ['x=b2f(endPop(j,:),bounds,bits); endPop(j,xZomeLength)=', evalFN ';']; end else % 浮点数编码 if opts(2) == 1 e1str = ['[c1 c1(xZomeLength)]=' evalFN '(c1,[gen evalOps]);']; e2str = ['[c2 c2(xZomeLength)]=' evalFN '(c2,[gen evalOps]);']; % 二进制编码 else e1str=['x=b2f(endPop(j,:),bounds,bits);[x v]=' evalFN ... '(x,[gen evalOps]); endPop(j,:)=[f2b(x,bounds,bits) v];']; end end %% 默认终止信息 if n < 6 termOps = 100; termFN = 'maxGenTerm'; end %% 默认变异信息 if n < 12 % 浮点数编码 if opts(2) == 1 mutFNs = 'boundaryMutation multiNonUnifMutation nonUnifMutation unifMutation'; mutOps = [4, 0, 0; 6, termOps(1), 3; 4, termOps(1), 3;4, 0, 0]; % 二进制编码 else mutFNs = 'binaryMutation'; mutOps = 0.05; end end %% 默认交叉信息 if n < 10 % 浮点数编码 if opts(2) == 1 xOverFNs = 'arithXover heuristicXover simpleXover'; xOverOps = [2, 0; 2, 3; 2, 0]; % 二进制编码 else xOverFNs = 'simpleXover'; xOverOps = 0.6; end end %% 仅默认选择选项，即轮盘赌。 if n < 9 selectOps = []; end %% 默认选择信息 if n < 8 selectFN = 'normGeomSelect'; selectOps = 0.08; end %% 默认终止信息 if n < 6 termOps = 100; termFN = 'maxGenTerm'; end %% 没有定的初始种群 if n < 4 startPop = []; end %% 随机生成种群 if isempty(startPop) startPop = initializega(80, bounds, evalFN, evalOps, opts(1: 2)); end %% 二进制编码 if opts(2) == 0 bits = calcbits(bounds, opts(1)); end %% 参数设置 xOverFNs = parse(xOverFNs); mutFNs = parse(mutFNs); xZomeLength = size(startPop, 2); % xzome 的长度 numVar = xZomeLength - 1; % 变量数 popSize = size(startPop,1); % 种群人口个数 endPop = zeros(popSize, xZomeLength); % 第二种群矩阵 numXOvers = size(xOverFNs, 1); % Number of Crossover operators numMuts = size(mutFNs, 1); % Number of Mutation operators epsilon = opts(1); % Threshold for two fittness to differ oval = max(startPop(:, xZomeLength)); % Best value in start pop bFoundIn = 1; % Number of times best has changed done = 0; % Done with simulated evolution gen = 1; % Current Generation Number collectTrace = (nargout > 3); % Should we collect info every gen floatGA = opts(2) == 1; % Probabilistic application of ops display = opts(3); % Display progress %% 精英模型 while(~done) [bval, bindx] = max(startPop(:, xZomeLength)); % Best of current pop best = startPop(bindx, :); if collectTrace traceInfo(gen, 1) = gen; % current generation traceInfo(gen, 2) = startPop(bindx, xZomeLength); % Best fittness traceInfo(gen, 3) = mean(startPop(:, xZomeLength)); % Avg fittness traceInfo(gen, 4) = std(startPop(:, xZomeLength)); end %% 最佳解 if ( (abs(bval - oval) > epsilon) || (gen==1)) % 更新显示 if display fprintf(1, '\n%d %f\n', gen, bval); end % 更新种群矩阵 if floatGA bPop(bFoundIn, :) = [gen, startPop(bindx, :)]; else bPop(bFoundIn, :) = [gen, b2f(startPop(bindx, 1 : numVar), bounds, bits)... startPop(bindx, xZomeLength)]; end bFoundIn = bFoundIn + 1; % Update number of changes oval = bval; % Update the best val else if display fprintf(1,'%d ',gen); % Otherwise just update num gen end end %% 选择种群 endPop = feval(selectFN, startPop, [gen, selectOps]); % 以参数为操作数的模型运行 if floatGA for i = 1 : numXOvers for j = 1 : xOverOps(i, 1) a = round(rand * (popSize - 1) + 1); % Pick a parent b = round(rand * (popSize - 1) + 1); % Pick another parent xN = deblank(xOverFNs(i, :)); % Get the name of crossover function [c1, c2] = feval(xN, endPop(a, :), endPop(b, :), bounds, [gen, xOverOps(i, :)]); % Make sure we created a new if c1(1 : numVar) == endPop(a, (1 : numVar)) c1(xZomeLength) = endPop(a, xZomeLength); elseif c1(1:numVar) == endPop(b, (1 : numVar)) c1(xZomeLength) = endPop(b, xZomeLength); else eval(e1str); end if c2(1 : numVar) == endPop(a, (1 : numVar)) c2(xZomeLength) = endPop(a, xZomeLength); elseif c2(1 : numVar) == endPop(b, (1 : numVar)) c2(xZomeLength) = endPop(b, xZomeLength); else eval(e2str); end endPop(a, :) = c1; endPop(b, :) = c2; end end for i = 1 : numMuts for j = 1 : mutOps(i, 1) a = round(rand * (popSize - 1) + 1); c1 = feval(deblank(mutFNs(i, :)), endPop(a, :), bounds, [gen, mutOps(i, :)]); if c1(1 : numVar) == endPop(a, (1 : numVar))