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⛄ 内容介绍
为了得到性能优越的SVM预测 模型,实现失业率的准确预测,文中提出基于遗传算法优化支持向量机(GA-SVM)的失业率预测方法.其中通过遗传算法对SVM中的训练参数 进行优化处理,以得到优化的SVM预测模型.实验结果表明:用GA-SVM对失业率预测,预测精度远优于人工神经网络.
⛄ 部分代码
function NewChrom = recombin(REC_F, Chrom, RecOpt, SUBPOP)
%% 重组高级函数
% This function performs recombination between pairs of individuals
% and returns the new individuals after mating. The function handles
% multiple populations and calls the low-level recombination function
% for the actual recombination process.
%
% Input parameters:
% REC_F - String containing the name of the recombination or
% crossover function
% Chrom - Matrix containing the chromosomes of the old
% population. Each line corresponds to one individual
% RecOpt - (optional) Scalar containing the probability of
% recombination/crossover occurring between pairs
% of individuals.
% if omitted or NaN, 1 is assumed
% SUBPOP - (optional) Number of subpopulations
% if omitted or NaN, 1 subpopulation is assumed
%
% Output parameter:
% NewChrom - Matrix containing the chromosomes of the population
% after recombination in the same format as OldChrom.
%% 一致性检查
if nargin < 2
error('Not enough input parameter');
end
%% 确定种群规模 (Nind)
[Nind,~] = size(Chrom);
if nargin < 4
SUBPOP = 1;
end
if nargin > 3
if isempty(SUBPOP)
SUBPOP = 1;
elseif isnan(SUBPOP)
SUBPOP = 1;
elseif length(SUBPOP) ~= 1
error('SUBPOP must be a scalar');
end
end
if (Nind / SUBPOP) ~= fix(Nind / SUBPOP)
error('Chrom and SUBPOP disagree');
end
%% 计算每个亚群的个体数量
Nind = Nind / SUBPOP;
if nargin < 3
RecOpt = 0.7;
end
if nargin > 2
if isempty(RecOpt)
RecOpt = 0.7;
elseif isnan(RecOpt)
RecOpt = 0.7;
elseif length(RecOpt) ~= 1
error('RecOpt must be a scalar');
elseif (RecOpt < 0 || RecOpt > 1)
error('RecOpt must be a scalar in [0, 1]');
end
end
%% 选择一个亚群的个体并调用低级函数
NewChrom = [];
for irun = 1: SUBPOP
ChromSub = Chrom((irun - 1) * Nind + 1 : irun * Nind, :);
NewChromSub = feval(REC_F, ChromSub, RecOpt);
NewChrom = [NewChrom; NewChromSub];
end
⛄ 运行结果
⛄ 参考文献
[1] 孙超. 基于遗传算法优化支持向量机模型的变形预测分析[D]. 山东科技大学, 2016.
[2] 朱伟. 基于遗传算法优化支持向量机的铁路客运量预测[D]. 重庆交通大学, 2013.
[3] 高青. 基于遗传算法优化支持向量机的物流需求预测研究[J]. 宿州学院学报, 2016, 31(012):31-35.
