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CSGSTest.m
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CSGSTest.m
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%**************************************************************************************************
% Developed by WANG, Xubin School of Artificial Intelligence, Jilin University,
% Changchun, China
% wangxb19@mails.jlu.edu.cn
% Reference:
% G. Wu, R. Mallipeddi, P. N. Suganthan, R. Wang, and H. Chen, "Differential evolution with multi-population based ensemble of mutation strategies," Information Sciences, vol. 329, pp. 329-345, 2016.
%**************************************************************************************************
clc;
clear all;
tic;
format long;
format compact;
'SaWDE'
% Objective function
fun=@jFitnessFunction1;
% fun=@fmeasure;
strParameterDescription = 'xxpopsize = 100 ';
prepopsize = 100;
xxpopsize = 100;%popsize
xxleastSelectionPro = 0.2;
selectedFeature = zeros(12, 1000);
AccResults = zeros(12, 2);
AccProcessionCSGS = zeros(100, 12);
SizeProcessionCSGS = zeros(100, 12);
for CSGS = 8 : 8
%load data
for n = 6 : 6
l = 1;
if n == 1
%Data 1
load('grammatical_facial_expression01.txt');
dataset=grammatical_facial_expression01;
elseif n == 2
%Data 2
load('SemeionHandwrittenDigit.txt');
dataset=SemeionHandwrittenDigit;
elseif n == 3
%Data 3
load('isolet5.txt');
dataset=isolet5;
elseif n == 4
%Data 4
load('MultipleFeaturesDigit.txt');
dataset=MultipleFeaturesDigit;
elseif n == 5
%Data 5
load('HAPTDataSet.txt');
dataset=HAPTDataSet;
elseif n == 6
%Data 6
load('har.txt');
dataset=har;
elseif n == 7
%Data 7
load('UJIIndoorLoc.txt');
dataset=UJIIndoorLoc;
elseif n == 8
%Data 8
load('MadelonValid.txt');
dataset=MadelonValid;
elseif n == 9
%Data 9
load('OpticalRecognitionofHandwritten.txt');
dataset=OpticalRecognitionofHandwritten;
elseif n == 10
%Data 10
load('ConnectionistBenchData.txt');
dataset=ConnectionistBenchData;
elseif n == 11
%Data 11
load('wdbc.txt');
dataset=wdbc;
elseif n == 12
%Data 12
load('LungCancer.txt');
dataset=LungCancer;
end
feat=dataset(:,1:end-1); labels=dataset(:,end);
D = size(feat,2);
countp = 0;
% lu: define the upper and lower bounds of the variables
lu = [-1* ones(1, D); 1 * ones(1, D)];
%fitness
premixVal = zeros(prepopsize,1);
mixVal0 = zeros(xxpopsize,1);
time = 1;
threshold = 0.6;
% The total number of runs
totalTime = 1;
while time <= totalTime
%% the values and indices of the best solutions
FES = 0;
leastSelectionPro = xxleastSelectionPro;
MaxFESpre = 1000000;
mixPopSizePre = prepopsize;
mixPopSize = xxpopsize;
MaxGen = round(MaxFESpre/mixPopSize);
%------Evaluate the best member after initialization------%
mixPop0 = zeros(mixPopSize,D);
% mixPop1 = repmat(lu(1, :), mixPopSizePre, 1) + rand(mixPopSizePre, D) .* (repmat(lu(2, :) - lu(1, :), mixPopSizePre, 1));
mixPop1 = rand(mixPopSizePre, D);
mixPop2 = mixPop1;
p1 = 0.6;
p2 = 0.6;
for i = 1 : mixPopSizePre
r1 = rand;
if r1 <= p1
for d = 1 : D
if mixPop2(i,d) >= p2
mixPop1(i,d) =1;
else
mixPop1(i,d) =0;
end
end
if size(find(mixPop1(i,:)==1),2)==D
r3=randperm(D);
r4=r3(1);
mixPop1(i,r4)=1;
mixPop2(i,r4) = p2 + (1 - p2) * rand;
end
if size(find(mixPop1(i,:)==0),2)==D
r3=randperm(D);
r4=r3(1);
mixPop1(i,r4)=0;
mixPop2(i,r4) = p2 * rand;
end
else
for d = 1:D
if mixPop2(i,d) <= p2
mixPop1(i,d) = 1;
mixPop2(i,d) = p2 + (1 - p2) * rand;
else
mixPop1(i,d) = 0;
mixPop2(i,d) = p2 * rand;
end
end
if size(find(mixPop1(i,:)==0),2)==D
r3 = randperm(D);
r4 = r3(1);
mixPop2(i,r4) = p2 + (1 - p2)*rand;
end
if size(find(mixPop1(i,:)==1),2)==D
r3 = randperm(D);
r4 = r3(1);
mixPop1(i,r4) = 0;
mixPop2(i,r4) = p2 *rand;
end
end
end
parfor i = 1 : mixPopSizePre
premixVal(i) = fun(dataset,mixPop1(i,:),1);
end
[~,I3]=sort(premixVal, 'descend');
parfor i = 1 : mixPopSize
mixPop0(i,:) = mixPop1(I3(i),:);
mixVal0(i) = fun(dataset,mixPop0(i,:),1);
end
[~,II]=sort(mixVal0, 'descend');
for i = 1:5
disp(i);
disp(mixVal0(II(i)));
end
mixPop = mixPop0;
mixVal = mixVal0;
overallBestVal = max(mixVal0);
c = 1/10;
pj = 0.1;
Afactor = 1;
archive.NP = mixPopSize; % the maximum size of the archive
archive.pop = zeros(0, D); % the solutions stored in te archive
archive.funvalues = zeros(0, 1); % the function value of the archived solutions
FESj = 0;
%% F & CR Initialization
% the five control parameter settings
F = [0.5 1 0.6 0.9 0.5 0.9 0.6 1];
CR = [0.1 0.2 0.9 0.8 0.9 0.1 0.8 0.2];
paraIndex = floor(rand(1, 8) * length(F)) + 1;
%% Choose the best results from good offspring to evolve(CBFG mechanism)
gen = 0;
MaxFES = MaxFESpre;
permutation = randperm(mixPopSize);
% dlmwrite('genChange.txt',n,'\t')
while FES < MaxFES%%gen < MaxGen
gen = gen + 1;
if CSGS == 1
arrayFirst = permutation(1:mixPopSize);
%% %% ===========================mutation 1=====================================%%%%
if ~isempty(arrayFirst)
pop1 = mixPop(arrayFirst,:); % the old population becomes the current population
valParents1 = mixVal(arrayFirst);
popsize = length(arrayFirst);
[~,I1]=sort(mixVal, 'descend');%I1:the indices of xxpopsize,big->small
[~,I2]=sort(valParents1, 'ascend');%I2:the indices of arrayFirst,small->big
for r = 1 : 3
pop1(I2(r),:) = mixPop(I1(r),:);%put the overall best into arrayFirst's smallest part
valParents1(I2(r)) = mixVal(I1(r));%put the overall best value into arrayFirst's smallest part
end
prevalParents1 = valParents1;
if FESj > 1 && ~isempty(goodCR) && sum(goodF) > 0 % If goodF and goodCR are empty, pause the update
CRm1 = (1 - c) * CRm1 + c * mean(goodCR);
Fm1 = (1 - c) * Fm1 + c * sum(goodF .^ 2) / sum(goodF); % Lehmer mean
else
CRm1 = CR(paraIndex(1));
Fm1 = F(paraIndex(1));
end
% Generate CR according to a normal distribution with mean CRm, and std 0.1
% Generate F according to a cauchy distribution with location parameter Fm, and scale parameter 0.1
[Fj, CRj] = randFCR(popsize, CRm1, 0.1, Fm1, 0.1);
r0 = [1 : popsize];
popAll = [pop1; archive.pop];
[r1, r2] = gnR1R2(popsize, size(popAll, 1), r0);
% Find the p-best solutions
[~, indBest] = sort(valParents1, 'descend');
pNP = max(round(pj * popsize), 5); % choose at least two best solutions
randindex = ceil(rand(1, popsize) * pNP); % select from [1, 2, 3, ..., pNP]
randindex = max(1, randindex); % to avoid the problem that rand = 0 and thus ceil(rand) = 0
pbest = pop1(indBest(randindex), :); % randomly choose one of the top 100p% solutions
% == == == == == == == == == == == == == == == Mutation == == == == == == == == == == == == ==
%DE/current to best/1
vi = pop1 + Fj(:, ones(1, D)) .* (pbest - pop1 + pop1(r1, :) - popAll(r2, :));
% == == == == = Crossover == == == == =
mask = rand(popsize, D) > CRj(:, ones(1, D)); % mask is used to indicate which elements of ui comes from the parent
rows = (1 : popsize)'; cols = floor(rand(popsize, 1) * D)+1; % choose one position where the element of ui doesn't come from the parent
jrand = sub2ind([popsize D], rows, cols); mask(jrand) = false;
ui = vi; ui(mask) = pop1(mask);
valOffspring1 = zeros(popsize,1);
for i = 1 : popsize
for j = 1 : D
if ui(i,j) >= threshold
ui(i,j) = 1;
else
ui(i,j) = 0;
end
if size(find(ui(i,:)==0),2)==D
r3 = randperm(D);
r4 = r3(1);
ui(i,r4) = 1;
end
if size(find(ui(i,:)==1),2)==D
r3 = randperm(D);
r4 = r3(1);
ui(i,r4) = 0;
end
end
end
parfor i = 1 : popsize
valOffspring1(i) = fun(dataset,ui(i,:),1);
end
FESj = FESj + popsize;
FES = FES + popsize;
% I == 1: the parent is better; I == 2: the offspring is better
[valParents1, I] = max([valParents1, valOffspring1], [], 2);
popold1 = pop1;
archive = updateArchive(archive, popold1(I == 2, :), valParents1(I == 2));
popold1(I == 2, :) = ui(I == 2, :);
valParents1(I == 2,:) = valOffspring1(I == 2, :);
goodCR = CRj(I == 2);
goodF = Fj(I == 2);
if max(valParents1) > overallBestVal
overallBestVal = max(valParents1);
end
% arrayGbestChange(1) = arrayGbestChange(1) + sum(prevalParents1- valParents1);
if prevalParents1(I2(1)) == valParents1(I2(1)) %if nothing changed at last,restore it.
for r = 1 : 3
popold1(I2(r),:) = mixPop(arrayFirst(I2(r)),:);
valParents1(I2(r)) = mixVal(arrayFirst(I2(r)));
end
end
mixPop(arrayFirst,:) = popold1;
mixVal(arrayFirst) = valParents1;
end
elseif CSGS == 2
arraySecond = permutation(1:mixPopSize);
%% ===========================mutation 2=====================================%%%%
if ~isempty(arraySecond)
pop2 = mixPop(arraySecond,:); % the old population becomes the current population
valParents2 = mixVal(arraySecond);
popsize2 = length(arraySecond);
[~,I1]=sort(mixVal, 'descend');
[~,I2]=sort(valParents2, 'ascend');
for r = 1 : 3
pop2(I2(r),:) = mixPop(I1(r),:);
valParents2(I2(r)) = mixVal(I1(r));
end
prevalParents2 = valParents2;
if gen > 1 && ~isempty(goodCR2) && sum(goodF2) > 0 % If goodF and goodCR are empty, pause the update
CRm2 = (1 - c) * CRm2 + c * mean(goodCR2);
Fm2 = (1 - c) * Fm2 + c * sum(goodF2 .^ 2) / sum(goodF2); % Lehmer mean
else
CRm2 = CR(paraIndex(2));
Fm2 = F(paraIndex(2));
end
% Generate CR according to a normal distribution with mean CRm, and std 0.1
% Generate F according to a cauchy distribution with location parameter Fm, and scale parameter 0.1
[F2, CR2] = randFCR(popsize2, CRm2, 0.1, Fm2, 0.1);
rot = (0:1:popsize2-1);
ind = randperm(2);
a1 = randperm(popsize2); % shuffle locations of vectors
rt = rem(rot+ind(1),popsize2); % rotate indices by ind(1) positions
a2 = a1(rt+1); % rotate vector locations
rt = rem(rot+ind(2),popsize2);
a3 = a2(rt+1);
pm1 = pop2(a1,:); % shuffled population 1
pm2 = pop2(a2,:); % shuffled population 2
pm3 = pop2(a3,:); % shuffled population 3
% ============================================mutation ===============================================
%DE/current to rand/1
vi =pop2 + repmat(rand(popsize2,1),1,D) .* (pm1 - pop2) + F2(:, ones(1, D)) .* (pm2 - pm3);
ui = vi;
valOffspring2 = zeros(popsize2,1);
for i = 1 : popsize2
for j = 1 : D
if ui(i,j) >= threshold
ui(i,j) = 1;
else
ui(i,j) = 0;
end
if size(find(ui(i,:)==0),2)==D
r3 = randperm(D);
r4 = r3(1);
ui(i,r4) = 1;
end
if size(find(ui(i,:)==1),2)==D
r3 = randperm(D);
r4 = r3(1);
ui(i,r4) = 0;
end
end
end
parfor i = 1 : popsize2
valOffspring2(i) = fun(dataset,ui(i,:),1);
end
FES = FES + popsize2;
% == == == == == == == == == == == == == == == Selection == == == == == == == == == == == == ==
% I == 1: the parent is better; I == 2: the offspring is better
[valParents2, I] = max([valParents2, valOffspring2], [], 2);
popold2 = pop2;
popold2(I == 2, :) = ui(I == 2, :);
valParents2(I == 2,:) = valOffspring2(I == 2, :);
goodCR2 = CR2(I == 2);
goodF2 = F2(I == 2);
if max(valParents2) > overallBestVal
overallBestVal = max(valParents2);
end
% arrayGbestChange(2) = arrayGbestChange(2) + sum(prevalParents2- valParents2);
if prevalParents2(I2(1)) == valParents2(I2(1))
for r = 1 : 3
popold2(I2(r),:) = mixPop(arraySecond(I2(r)),:);
valParents2(I2(r)) = mixVal(arraySecond(I2(r)));
end
end
mixPop(arraySecond,:) = popold2;
mixVal(arraySecond) = valParents2;
end
elseif CSGS == 3
arrayThird = permutation(1:mixPopSize);
%% ===========================mutation 3 =====================================%%%%
if ~isempty(arrayThird)
pop3 = mixPop(arrayThird,:); % the old population becomes the current population
valParents3 = mixVal(arrayThird);
popsize3 = length(arrayThird);
[~,I1]=sort(mixVal, 'descend');
[~,I2]=sort(valParents3, 'ascend');
for r = 1 : 3
pop3(I2(r),:) = mixPop(I1(r),:);
valParents3(I2(r)) = mixVal(I1(r));
end
prevalParents3 = valParents3;
if gen > 1 && ~isempty(goodCR3) && sum(goodF3) > 0 % If goodF and goodCR are empty, pause the update
CRm3 = (1 - c) * CRm3 + c * mean(goodCR3);
Fm3 = (1 - c) * Fm3 + c * sum(goodF3 .^ 2) / sum(goodF3); % Lehmer mean
else
CRm3 = CR(paraIndex(3));
Fm3 = F(paraIndex(3));
end
% Generate CR according to a normal distribution with mean CRm, and std 0.1
% Generate F according to a cauchy distribution with location parameter Fm, and scale parameter 0.1
[F3, CR3] = randFCR(popsize3, CRm3, 0.1, Fm3, 0.1);
rot = (0:1:popsize3-1);
ind = randperm(2);
a1 = randperm(popsize3); % shuffle locations of vectors
rt = rem(rot+ind(1),popsize3); % rotate indices by ind(1) positions
a2 = a1(rt+1); % rotate vector locations
rt = rem(rot+ind(2),popsize3);
a3 = a2(rt+1);
pm1 = pop3(a1,:); % shuffled population 1
pm2 = pop3(a2,:); % shuffled population 2
pm3 = pop3(a3,:); % shuffled population 3
%DE/rand/1
vi =pm1 + F3(:, ones(1, D)) .* repmat(rand(popsize3,1),1,D) .* (pm1 - pop3 + pm2 - pop3 - pm3 - pop3);
mask = rand(popsize3, D) > CR3(:, ones(1, D)); % mask is used to indicate which elements of ui comes from the parent
rows = (1 : popsize3)'; cols = floor(rand(popsize3, 1) * D)+1; % choose one position where the element of ui doesn't come from the parent
jrand = sub2ind([popsize3 D], rows, cols); mask(jrand) = false;
ui = vi; ui(mask) = pop3(mask);
valOffspring3 = zeros(popsize3,1);
for i = 1 : popsize3
for j = 1 : D
if ui(i,j) >= threshold
ui(i,j) = 1;
else
ui(i,j) = 0;
end
if size(find(ui(i,:)==0),2)==D
r3 = randperm(D);
r4 = r3(1);
ui(i,r4) = 1;
end
if size(find(ui(i,:)==1),2)==D
r3 = randperm(D);
r4 = r3(1);
ui(i,r4) = 0;
end
end
end
parfor i = 1 : popsize3
valOffspring3(i) = fun(dataset,ui(i,:),1);
end
FES = FES + popsize3;
% == == == == == == == == == == == == == == == Selection == == == == == == == == == == == == ==
% I == 1: the parent is better; I == 2: the offspring is better
[valParents3, I] = max([valParents3, valOffspring3], [], 2);
popold3 = pop3;
popold3(I == 2, :) = ui(I == 2, :);
valParents3(I == 2,:) = valOffspring3(I == 2, :);
goodCR3 = CR3(I == 2);
goodF3 = F3(I == 2);
if max(valParents3) > overallBestVal
overallBestVal = max(valParents3);
end
% arrayGbestChange(3) = arrayGbestChange(3) + sum(prevalParents3- valParents3);
if prevalParents3(I2(1)) == valParents3(I2(1))
for r = 1 : 3
popold3(I2(r),:) = mixPop(arrayThird(I2(r)),:);
valParents3(I2(r)) = mixVal(arrayThird(I2(r)));
end
end
mixPop(arrayThird,:) = popold3;
mixVal(arrayThird) = valParents3;
end
elseif CSGS == 4
arrayFourth = permutation(1:mixPopSize);
%% ===========================mutation 4 =====================================%%%%
if ~isempty(arrayFourth)
pop4 = mixPop(arrayFourth,:); % the old population becomes the current population
valParents4 = mixVal(arrayFourth);
popsize4 = length(arrayFourth);
[~,I1]=sort(mixVal, 'descend');
[~,I2]=sort(valParents4, 'ascend');
for r = 1 : 3
pop4(I2(r),:) = mixPop(I1(r),:);
valParents4(I2(r)) = mixVal(I1(r));
end
prevalParents4 = valParents4;
if gen > 1 && ~isempty(goodCR4) && sum(goodF4) > 0 % If goodF and goodCR are empty, pause the update
CRm4 = (1 - c) * CRm4 + c * mean(goodCR4);
Fm4 = (1 - c) * Fm4 + c * sum(goodF4 .^ 2) / sum(goodF4); % Lehmer mean
else
CRm4 = CR(paraIndex(4));
Fm4 = F(paraIndex(4));
end
% Generate CR according to a normal distribution with mean CRm, and std 0.1
% Generate F according to a cauchy distribution with location parameter Fm, and scale parameter 0.1
[F4, CR4] = randFCR(popsize4, CRm4, 0.1, Fm4, 0.1);
r0 = [1 : popsize4];
popAll = [pop4; archive.pop];
[r1, r2] = gnR1R2(popsize4, size(popAll, 1), r0);
% Find the p-best solutions
[~, indBest] = sort(valParents4, 'descend');
pNP = max(round(pj * popsize4), 5); % choose at least two best solutions
randindex = ceil(rand(1, popsize4) * pNP); % select from [1, 2, 3, ..., pNP]
randindex = max(1, randindex); % to avoid the problem that rand = 0 and thus ceil(rand) = 0
pbest = pop4(indBest(randindex), :); % randomly choose one of the top 100p% solutions
% == == == == == == == == == == == == == == == Mutation == == == == == == == == == == == == ==
%-----DE/best/1
vi = pbest + F4(:, ones(1, D)) .* (pop4(r1, :) - popAll(r2, :));
mask = rand(popsize4, D) > CR4(:, ones(1, D)); % mask is used to indicate which elements of ui comes from the parent
rows = (1 : popsize4)'; cols = floor(rand(popsize4, 1) * D)+1; % choose one position where the element of ui doesn't come from the parent
jrand = sub2ind([popsize4 D], rows, cols); mask(jrand) = false;
ui = vi; ui(mask) = pop4(mask);
valOffspring4 = zeros(popsize4,1);
for i = 1 : popsize4
for j = 1 : D
if ui(i,j) >= threshold
ui(i,j) = 1;
else
ui(i,j) = 0;
end
if size(find(ui(i,:)==0),2)==D
r3 = randperm(D);
r4 = r3(1);
ui(i,r4) = 1;
end
if size(find(ui(i,:)==1),2)==D
r3 = randperm(D);
r4 = r3(1);
ui(i,r4) = 0;
end
end
end
parfor i = 1 : popsize4
valOffspring4(i) = fun(dataset,ui(i,:),1);
end
FES = FES + popsize4;
% == == == == == == == == == == == == == == == Selection == == == == == == == == == == == == ==
% I == 1: the parent is better; I == 2: the offspring is better
[valParents4, I] = max([valParents4, valOffspring4], [], 2);
popold4 = pop4;
archive = updateArchive(archive, popold4(I == 2, :), valParents4(I == 2));
popold4(I == 2, :) = ui(I == 2, :);
valParents4(I == 2,:) = valOffspring4(I == 2, :);
goodCR4 = CR4(I == 2);
goodF4 = F4(I == 2);
if max(valParents4) > overallBestVal
overallBestVal = max(valParents4);
end
% arrayGbestChange(4) = arrayGbestChange(4) + sum(prevalParents4- valParents4);
if prevalParents4(I2(1)) == valParents4(I2(1))
for r = 1 : 3
popold4(I2(r),:) = mixPop(arrayFourth(I2(r)),:);
valParents4(I2(r)) = mixVal(arrayFourth(I2(r)));
end
end
mixPop(arrayFourth,:) = popold4;
mixVal(arrayFourth) = valParents4;
end
elseif CSGS == 5
arrayFifth = permutation(1:mixPopSize);
%% ============================mutation 5 =====================================%%%%
if ~isempty(arrayFifth)
pop5 = mixPop(arrayFifth,:); % the old population becomes the current population
valParents5 = mixVal(arrayFifth);
popsize5 = length(arrayFifth);
[~,I1]=sort(mixVal, 'descend');
[~,I2]=sort(valParents5, 'ascend');
for r = 1 : 3
pop5(I2(r),:) = mixPop(I1(r),:);
valParents5(I2(r)) = mixVal(I1(r));
end
prevalParents5 = valParents5;
if gen > 1 && ~isempty(goodCR5) && sum(goodF5) > 0 % If goodF and goodCR are empty, pause the update
CRm5 = (1 - c) * CRm5 + c * mean(goodCR5);
Fm5 = (1 - c) * Fm5 + c * sum(goodF5 .^ 2) / sum(goodF5); % Lehmer mean
else
CRm5 = CR(paraIndex(5));
Fm5 = F(paraIndex(5));
end
% Generate CR according to a normal distribution with mean CRm, and std 0.1
% Generate F according to a cauchy distribution with location parameter Fm, and scale parameter 0.1
[F5, CR5] = randFCR(popsize5, CRm5, 0.1, Fm5, 0.1);
r0 = [1 : popsize5];
popAll = [pop5; archive.pop];
[r1, r2] = gnR1R2(popsize5, size(popAll, 1), r0);
% Find the p-best solutions
[~, indBest] = sort(valParents5, 'descend');
pNP = max(round(pj * popsize5), 5); % choose at least two best solutions
randindex = ceil(rand(1, popsize5) * pNP); % select from [1, 2, 3, ..., pNP]
randindex = max(1, randindex); % to avoid the problem that rand = 0 and thus ceil(rand) = 0
pbest = pop5(indBest(randindex), :); % randomly choose one of the top 100p% solutions
rot = (0:1:popsize5-1);
ind = randperm(2);
a1 = randperm(popsize5); % shuffle locations of vectors
rt = rem(rot+ind(1),popsize5); % rotate indices by ind(1) positions
a2 = a1(rt+1); % rotate vector locations
rt = rem(rot+ind(2),popsize5);
a3 = a2(rt+1);
pm1 = pop5(a1,:); % shuffled population 1
pm2 = pop5(a2,:); % shuffled population 2
pm3 = pop5(a3,:); % shuffled population 3
% == == == == == == == == == == == == == == == Mutation == == == == == == == == == == == == ==
%DE/rand to best/1
vi =repmat(rand(popsize5,1),1,D) .* (pm1 - pop5) + F5(:, ones(1, D)) .* pop5(r1, :) - popAll(r2, :) + F5(:, ones(1, D)) .* ( pbest - repmat(rand(popsize5,1),1,D));
mask = rand(popsize5, D) > CR5(:, ones(1, D)); % mask is used to indicate which elements of ui comes from the parent
rows = (1 : popsize5)'; cols = floor(rand(popsize5, 1) * D)+1; % choose one position where the element of ui doesn't come from the parent
jrand = sub2ind([popsize5 D], rows, cols); mask(jrand) = false;
ui = vi; ui(mask) = pop5(mask);
valOffspring5 = zeros(popsize5,1);
for i = 1 : popsize5
for j = 1 : D
if ui(i,j) >= threshold
ui(i,j) = 1;
else
ui(i,j) = 0;
end
if size(find(ui(i,:)==0),2)==D
r3 = randperm(D);
r4 = r3(1);
ui(i,r4) = 1;
end
if size(find(ui(i,:)==1),2)==D
r3 = randperm(D);
r4 = r3(1);
ui(i,r4) = 0;
end
end
end
parfor i = 1 : popsize5
valOffspring5(i) = fun(dataset,ui(i,:),1);
end
FES = FES + popsize5;
% == == == == == == == == == == == == == == == Selection == == == == == == == == == == == == ==
% I == 1: the parent is better; I == 2: the offspring is better
[valParents5, I] = max([valParents5, valOffspring5], [], 2);
popold5 = pop5;
archive = updateArchive(archive, popold5(I == 2, :), valParents5(I == 2));
popold5(I == 2, :) = ui(I == 2, :);
valParents5(I == 2,:) = valOffspring5(I == 2, :);
goodCR5 = CR5(I == 2);
goodF5 = F5(I == 2);
if max(valParents5) > overallBestVal
overallBestVal = max(valParents5);
end
% arrayGbestChange(5) = arrayGbestChange(5) + sum(prevalParents5- valParents5);
if prevalParents5(I2(1)) == valParents5(I2(1))
for r = 1 : 3
popold5(I2(r),:) = mixPop(arrayFifth(I2(r)),:);
valParents5(I2(r)) = mixVal(arrayFifth(I2(r)));
end
end
mixPop(arrayFifth,:) = popold5;
mixVal(arrayFifth) = valParents5;
end
elseif CSGS == 6
arraySixth = permutation(1:mixPopSize);
%% ============================mutation 6 =====================================%%%%
if ~isempty(arraySixth)
pop6 = mixPop(arraySixth,:); % the old population becomes the current population
valParents6 = mixVal(arraySixth);
popsize6 = length(arraySixth);
[~,I1]=sort(mixVal, 'descend');
[~,I2]=sort(valParents6, 'ascend');
for r = 1 : 3
pop6(I2(r),:) = mixPop(I1(r),:);
valParents6(I2(r)) = mixVal(I1(r));
end
prevalParents6 = valParents6;
if gen > 1 && ~isempty(goodCR6) && sum(goodF6) > 0 % If goodF and goodCR are empty, pause the update
CRm6 = (1 - c) * CRm6 + c * mean(goodCR6);
Fm6 = (1 - c) * Fm6 + c * sum(goodF6 .^ 2) / sum(goodF6); % Lehmer mean
else
CRm6 = CR(paraIndex(6));
Fm6 = F(paraIndex(6));
end
% Generate CR according to a normal distribution with mean CRm, and std 0.1
% Generate F according to a cauchy distribution with location parameter Fm, and scale parameter 0.1
[F6, CR6] = randFCR(popsize6, CRm6, 0.1, Fm6, 0.1);
r0 = [1 : popsize6];
popAll = [pop6; archive.pop];
[r1, r2] = gnR1R2(popsize6, size(popAll, 1), r0);
% Find the p-best solutions
[~, indBest] = sort(valParents6, 'descend');
pNP = max(round(pj * popsize6), 5); % choose at least two best solutions
randindex = ceil(rand(1, popsize6) * pNP); % select from [1, 2, 3, ..., pNP]
randindex = max(1, randindex); % to avoid the problem that rand = 0 and thus ceil(rand) = 0
pbest = pop6(indBest(randindex), :); % randomly choose one of the top 100p% solutions
rot = (0:1:popsize6-1);
ind = randperm(2);
a1 = randperm(popsize6); % shuffle locations of vectors
rt = rem(rot+ind(1),popsize6); % rotate indices by ind(1) positions
a2 = a1(rt+1); % rotate vector locations
rt = rem(rot+ind(2),popsize6);
a3 = a2(rt+1);
pm1 = pop6(a1,:); % shuffled population 1
pm2 = pop6(a2,:); % shuffled population 2
pm3 = pop6(a3,:); % shuffled population 3
% == == == == == == == == == == == == == == == Mutation == == == == == == == == == == == == ==
%DE/rand/2
vi =pm1 + F6(:, ones(1, D)) .* repmat(rand(popsize6,1),1,D) .* ((pm2 - pm3) + (pop6(r1,:) - popAll(r2,:)));
mask = rand(popsize6, D) > CR6(:, ones(1, D)); % mask is used to indicate which elements of ui comes from the parent
rows = (1 : popsize6)'; cols = floor(rand(popsize6, 1) * D)+1; % choose one position where the element of ui doesn't come from the parent
jrand = sub2ind([popsize6 D], rows, cols); mask(jrand) = false;
ui = vi; ui(mask) = pop6(mask);
valOffspring6 = zeros(popsize6,1);
for i = 1 : popsize6
for j = 1 : D
if ui(i,j) >= threshold
ui(i,j) = 1;
else
ui(i,j) = 0;
end
if size(find(ui(i,:)==0),2)==D
r3 = randperm(D);
r4 = r3(1);
ui(i,r4) = 1;
end
if size(find(ui(i,:)==1),2)==D
r3 = randperm(D);
r4 = r3(1);
ui(i,r4) = 0;
end
end
end
parfor i = 1 : popsize6
valOffspring6(i) = fun(dataset,ui(i,:),1);
end
FES = FES + popsize6;
% == == == == == == == == == == == == == == == Selection == == == == == == == == == == == == ==
% I == 1: the parent is better; I == 2: the offspring is better
[valParents6, I] = max([valParents6, valOffspring6], [], 2);
popold6 = pop6;
archive = updateArchive(archive, popold6(I == 2, :), valParents6(I == 2));
popold6(I == 2, :) = ui(I == 2, :);
valParents6(I == 2,:) = valOffspring6(I == 2, :);
goodCR6 = CR6(I == 2);
goodF6 = F6(I == 2);
if max(valParents6) > overallBestVal
overallBestVal = max(valParents6);
end
% arrayGbestChange(6) = arrayGbestChange(6) + sum(prevalParents6- valParents6);
if prevalParents6(I2(1)) == valParents6(I2(1))
for r = 1 : 3
popold6(I2(r),:) = mixPop(arraySixth(I2(r)),:);
valParents6(I2(r)) = mixVal(arraySixth(I2(r)));
end
end
mixPop(arraySixth,:) = popold6;
mixVal(arraySixth) = valParents6;
end
elseif CSGS == 7
arraySeventh = permutation(1:mixPopSize);
%% ============================mutation 7 =====================================%%%%
if ~isempty(arraySeventh)
pop7 = mixPop(arraySeventh,:); % the old population becomes the current population
valParents7 = mixVal(arraySeventh);
popsize7 = length(arraySeventh);
[~,I1]=sort(mixVal, 'descend');
[~,I2]=sort(valParents7, 'ascend');
for r = 1 : 3
pop7(I2(r),:) = mixPop(I1(r),:);
valParents7(I2(r)) = mixVal(I1(r));
end
prevalParents7 = valParents7;
if gen > 1 && ~isempty(goodCR7) && sum(goodF7) > 0 % If goodF and goodCR are empty, pause the update
CRm7 = (1 - c) * CRm7 + c * mean(goodCR7);
Fm7 = (1 - c) * Fm7 + c * sum(goodF7 .^ 2) / sum(goodF7); % Lehmer mean
else
CRm7 = CR(paraIndex(7));
Fm7 = F(paraIndex(7));
end
% Generate CR according to a normal distribution with mean CRm, and std 0.1
% Generate F according to a cauchy distribution with location parameter Fm, and scale parameter 0.1
[F7, CR7] = randFCR(popsize7, CRm7, 0.1, Fm7, 0.1);
r0 = [1 : popsize7];
popAll = [pop7; archive.pop];
[r1, r2] = gnR1R2(popsize7, size(popAll, 1), r0);
% Find the p-best solutions
[~, indBest] = sort(valParents7, 'descend');
pNP = max(round(pj * popsize7), 5); % choose at least two best solutions
randindex = ceil(rand(1, popsize7) * pNP); % select from [1, 2, 3, ..., pNP]
randindex = max(1, randindex); % to avoid the problem that rand = 0 and thus ceil(rand) = 0
pbest = pop7(indBest(randindex), :); % randomly choose one of the top 100p% solutions
rot = (0:1:popsize7-1);
ind = randperm(2);
a1 = randperm(popsize7); % shuffle locations of vectors
rt = rem(rot+ind(1),popsize7); % rotate indices by ind(1) positions
a2 = a1(rt+1); % rotate vector locations
rt = rem(rot+ind(2),popsize7);
a3 = a2(rt+1);
pm1 = pop7(a1,:); % shuffled population 1
pm2 = pop7(a2,:); % shuffled population 2
pm3 = pop7(a3,:); % shuffled population 3
% == == == == == == == == == == == == == == == Mutation == == == == == == == == == == == == ==
%DE/best/2
vi = pbest + F7(:, ones(1, D)) .* ((pop7(r1, :) - popAll(r2, :)) + (pm3 - pm2));
mask = rand(popsize7, D) > CR7(:, ones(1, D)); % mask is used to indicate which elements of ui comes from the parent
rows = (1 : popsize7)'; cols = floor(rand(popsize7, 1) * D)+1; % choose one position where the element of ui doesn't come from the parent
jrand = sub2ind([popsize7 D], rows, cols); mask(jrand) = false;
ui = vi; ui(mask) = pop7(mask);
valOffspring7 = zeros(popsize7,1);
for i = 1 : popsize7
for j = 1 : D
if ui(i,j) >= threshold
ui(i,j) = 1;
else
ui(i,j) = 0;
end
if size(find(ui(i,:)==0),2)==D
r3 = randperm(D);
r4 = r3(1);
ui(i,r4) = 1;
end
if size(find(ui(i,:)==1),2)==D
r3 = randperm(D);
r4 = r3(1);
ui(i,r4) = 0;
end
end
end
parfor i = 1 : popsize7
valOffspring7(i) = fun(dataset,ui(i,:),1);
end
FES = FES + popsize7;
% == == == == == == == == == == == == == == == Selection == == == == == == == == == == == == ==
% I == 1: the parent is better; I == 2: the offspring is better
[valParents7, I] = max([valParents7, valOffspring7], [], 2);
popold7 = pop7;
archive = updateArchive(archive, popold7(I == 2, :), valParents7(I == 2));
popold7(I == 2, :) = ui(I == 2, :);
valParents7(I == 2,:) = valOffspring7(I == 2, :);
goodCR7 = CR7(I == 2);
goodF7 = F7(I == 2);
if max(valParents7) > overallBestVal
overallBestVal = max(valParents7);
end
% arrayGbestChange(7) = arrayGbestChange(7) + sum(prevalParents7- valParents7);
if prevalParents7(I2(1)) == valParents7(I2(1))
for r = 1 : 3
popold7(I2(r),:) = mixPop(arraySeventh(I2(r)),:);
valParents7(I2(r)) = mixVal(arraySeventh(I2(r)));
end
end
mixPop(arraySeventh,:) = popold7;
mixVal(arraySeventh) = valParents7;
end
elseif CSGS == 8
arrayEighth = permutation(1:mixPopSize);
%% ============================mutation 8 =====================================%%%%
if ~isempty(arrayEighth)
pop8 = mixPop(arrayEighth,:); % the old population becomes the current population
valParents8 = mixVal(arrayEighth);
popsize8 = length(arrayEighth);
[~,I1]=sort(mixVal, 'descend');
[~,I2]=sort(valParents8, 'ascend');
for r = 1 : 3
pop8(I2(r),:) = mixPop(I1(r),:);
valParents8(I2(r)) = mixVal(I1(r));
end
prevalParents8 = valParents8;
if gen > 1 && ~isempty(goodCR8) && sum(goodF8) > 0 % If goodF and goodCR are empty, pause the update
CRm8 = (1 - c) * CRm8 + c * mean(goodCR8);
Fm8 = (1 - c) * Fm8 + c * sum(goodF8 .^ 2) / sum(goodF8); % Lehmer mean
else
CRm8 = CR(paraIndex(8));
Fm8 = F(paraIndex(8));
end
% Generate CR according to a normal distribution with mean CRm, and std 0.1
% Generate F according to a cauchy distribution with location parameter Fm, and scale parameter 0.1
[F8, CR8] = randFCR(popsize8, CRm8, 0.1, Fm8, 0.1);
r0 = [1 : popsize8];
popAll = [pop8; archive.pop];
[r1, r2] = gnR1R2(popsize8, size(popAll, 1), r0);
% Find the p-best solutions
[~, indBest] = sort(valParents8, 'descend');
pNP = max(round(pj * popsize8), 5); % choose at least two best solutions
randindex = ceil(rand(1, popsize8) * pNP); % select from [1, 2, 3, ..., pNP]
randindex = max(1, randindex); % to avoid the problem that rand = 0 and thus ceil(rand) = 0
pbest = pop8(indBest(randindex), :); % randomly choose one of the top 100p% solutions
rot = (0:1:popsize8-1);
ind = randperm(2);
a1 = randperm(popsize8); % shuffle locations of vectors
rt = rem(rot+ind(1),popsize8); % rotate indices by ind(1) positions
a2 = a1(rt+1); % rotate vector locations
rt = rem(rot+ind(2),popsize8);
a3 = a2(rt+1);
pm1 = pop8(a1,:); % shuffled population 1
pm2 = pop8(a2,:); % shuffled population 2
pm3 = pop8(a3,:); % shuffled population 3
% == == == == == == == == == == == == == == == Mutation == == == == == == == == == == == == ==
%DE/best/3
vi = pbest + F8(:, ones(1, D)) .* ((pop8(r1, :) - popAll(r2, :)) + (pm3 - pm2)+(pop8 - pm1));
mask = rand(popsize8, D) > CR8(:, ones(1, D)); % mask is used to indicate which elements of ui comes from the parent
rows = (1 : popsize8)'; cols = floor(rand(popsize8, 1) * D)+1; % choose one position where the element of ui doesn't come from the parent
jrand = sub2ind([popsize8 D], rows, cols); mask(jrand) = false;
ui = vi; ui(mask) = pop8(mask);
valOffspring8 = zeros(popsize8,1);
for i = 1 : popsize8
for j = 1 : D
if ui(i,j) >= threshold
ui(i,j) = 1;
else
ui(i,j) = 0;
end
if size(find(ui(i,:)==0),2)==D
r3 = randperm(D);
r4 = r3(1);
ui(i,r4) = 1;
end
if size(find(ui(i,:)==1),2)==D
r3 = randperm(D);
r4 = r3(1);
ui(i,r4) = 0;
end
end
end
parfor i = 1 : popsize8
valOffspring8(i) = fun(dataset,ui(i,:),1);
end
FES = FES + popsize8;
% == == == == == == == == == == == == == == == Selection == == == == == == == == == == == == ==
% I == 1: the parent is better; I == 2: the offspring is better
[valParents8, I] = max([valParents8, valOffspring8], [], 2);
popold8 = pop8;
archive = updateArchive(archive, popold8(I == 2, :), valParents8(I == 2));
popold8(I == 2, :) = ui(I == 2, :);
valParents8(I == 2,:) = valOffspring8(I == 2, :);
goodCR8 = CR8(I == 2);
goodF8 = F8(I == 2);
if max(valParents8) > overallBestVal
overallBestVal = max(valParents8);
end
% arrayGbestChange(8) = arrayGbestChange(8) + sum(prevalParents8- valParents8);
if prevalParents8(I2(1)) == valParents8(I2(1))
for r = 1 : 3
popold8(I2(r),:) = mixPop(arrayEighth(I2(r)),:);
valParents8(I2(r)) = mixVal(arrayEighth(I2(r)));
end
end
mixPop(arrayEighth,:) = popold8;
mixVal(arrayEighth) = valParents8;
end
end
[outcome,best] = max(mixVal);
if mod(gen,100) == 0 %disp the results per 1w fes
% dlmwrite('genChange.txt',max(mixVal),'\t')
countp = countp + 1;
AccProcessionCSGS(countp, n) = max(mixVal);
SizeProcessionCSGS(countp, n) = size(find(mixPop(best, : )==1), 2);
disp(max(mixVal));
end
end
time = time + 1;
end
% figure(1); plot(1:MaxGen,curve); xlabel('Number of Iterations');
% ylabel('Fitness Value'); title('MP-SaCoDE'); grid on;
[DE_gbestval,ibest] = max(mixVal);
DE_gbest = mixPop(ibest, : );
CountSize = 0;
parfor i = 1 : D
if mixPop(ibest,i) == 1
CountSize = CountSize + 1;
selectedFeature(n, i) = 1;
end
end
% DE_Size(l) = CountSize;
% Size_rate(l) = CountSize/D;
overallBestVal = max(mixVal);
% results(l) = overallBestVal;
disp(n);
disp(CountSize);
% disp(DE_Size(l));
% disp(Size_rate(l));
disp(overallBestVal);
% selectedFeature(n, :) = DE_gbest;
AccResults(n,1) = overallBestVal;
AccResults(n,2) = CountSize;
% Feature = selectedFeature;
% if n == 1
% %Data 1
% load('C:\Users\qywxb\Desktop\MP_MSaCoDE\grammatical_facial_expression01.txt');
% dataset_train=grammatical_facial_expression01;
%
% load('C:\Users\qywxb\Desktop\MP_MSaCoDE\test\grammatical_facial_expression01.txt');
% dataset_test=grammatical_facial_expression01;
%
% elseif n == 2
% %Data 2
% load('C:\Users\qywxb\Desktop\MP_MSaCoDE\SemeionHandwrittenDigit.txt');
% dataset_train=SemeionHandwrittenDigit;
%
% load('C:\Users\qywxb\Desktop\MP_MSaCoDE\test\SemeionHandwrittenDigit.txt');
% dataset_test=SemeionHandwrittenDigit;
% %load('SemeionHandwrittenDigit.txt');
% %dataset=SemeionHandwrittenDigit;
% elseif n == 3
% %Data 3
% load('C:\Users\qywxb\Desktop\MP_MSaCoDE\isolet5.txt');
% dataset_train=isolet5;
%
% load('C:\Users\qywxb\Desktop\MP_MSaCoDE\test\isolet5.txt');
% dataset_test=isolet5;
% elseif n == 4
% %Data 4
% load('C:\Users\qywxb\Desktop\MP_MSaCoDE\MultipleFeaturesDigit.txt');
% dataset_train=MultipleFeaturesDigit;
%
% load('C:\Users\qywxb\Desktop\MP_MSaCoDE\test\MultipleFeaturesDigit.txt');