How can i combine the two coding

first code (OFDM)

clc;
clear all;
close all;

L=1;%input('Enter the L factor(1 to 1.5)= '); %oversampling factor
M=16;%input('Enter the alphabet size(Power of 2 and less than number of Symbols)(preferably<32)=');
N=64;%input('Enter the number of transmitted symbols(Power of 2)(preferably>32)=');
ran=floor(M*rand(N,1));                          %Generating the random data for transmission
a=qammod(ran,M);                                 %QAM modulating the data.

                                                 %Inserting zeros
  LN=floor(L*N);
  at=a';
  aa=[at(1:N) zeros(1,LN-N)]';

x=ifft(aa); %Generating OFDM signal and calculating PAPR
xmag=abs(x);
papr=max(xmag.^2)/mean(xmag.^2);
paprdb=10*log(papr);
subplot(3,1,1),stem(xmag),title('Normal OFDM signal');
xlabel('Time')
ylabel('Amplitude')
xlim([0 LN]);

2nd code (GA)

%%Start of Program
clc
clear
close all

tic

%%Algorithm Parameters
SelMethod = 1;
CrossMethod = 1;

PopSize = 1000;
MaxIteration = 50;

CrossPercent = 65;
MutatPercent = 10;
ElitPercent = 100 - CrossPercent - MutatPercent;

CrossNum = round(CrossPercent/100*PopSize); 

if mod(CrossNum,2)~=0;Percent 
    CrossNum = CrossNum - 1; 
end

MutatNum = round(MutatPercent/100*PopSize);
ElitNum = PopSize - CrossNum - MutatNum;

%%Problem Satement

VarMin = [1 2 3 4 5 2];
VarMax =[3 5 5 9 7 3];
b=repmat(VarMin,1000,1);
s=repmat(VarMax,1000,1);
DimNum = 6;
CostFuncName = @rastriginsfcn;

%%Initial Population
Pop = rand(PopSize,DimNum).* (s - b) +b;
Cost = feval(CostFuncName,Pop);
[Cost, Indx] = sort(Cost);
Pop = Pop(Indx,:);

%%Main Loop
MeanMat = [];
MinMat = [];

for Iter = 1:MaxIteration
    %%Elitism
    ElitPop = Pop(1:ElitNum,:);

      %%Cross Over
      CrossPop = [];
      ParentIndexes = SelectParents_Fcn(Cost,CrossNum,SelMethod);

      for ii = 1:CrossNum/2
          Par1Indx = ParentIndexes(ii*2-1);
          Par2Indx = ParentIndexes(ii*2);

          Par1 = Pop(Par1Indx,:);
          Par2 = Pop(Par2Indx,:);

          [Off1 Off2] = MyCrossOver_Fcn(Par1,Par2,CrossMethod);
          CrossPop = [CrossPop ; Off1 ; Off2];
      end

      %%Mutation
      MutatPop = rand(MutatNum,DimNum).*(s(1:100,:) - b(1:100,:)) + b(1:100,:);

      %%New Population
      Pop = [ElitPop ; CrossPop ; MutatPop];
      Cost = feval(CostFuncName,Pop);
      [Cost Indx] = sort(Cost);
      Pop = Pop(Indx,:);

      %%Algorithm Progress
      disp('----------------------------------------------')
      BestP = Pop(1,:)
      BestC = Cost(1)
      MinMat(Iter) = Cost(1);
      MeanMat(Iter) = mean(Cost);

      plot(MinMat,'--r','linewidth',2);
      hold on
  %     plot(MeanMat,'--k','linewidth',2);
  %     hold off
      pause(.5)
  end
  % ylim([0 5])
  %%Results
  BestSolution = Pop(1,:)
  BestCost = Cost(1,:)

%%End of Program
toc

[Walter Roberson]

What do you want the combined program to do ?

[Fatin Najwa Nursyadza Mohd Ramdan]

To find the PAPR

[Walter Roberson]

So why would finding the PAPR involve a genetic algorithm?

[Fatin Najwa Nursyadza Mohd Ramdan]

i need to investigate if genetic algorithm can decrease the PAPR

[Walter Roberson]

No, it cannot.

[Fatin Najwa Nursyadza Mohd Ramdan]

i still need to prove it. SO, can i combine the two coding?. Thank you for the information

[Fatin Najwa Nursyadza Mohd Ramdan]

Then, should i add another method before genetic algorithm to reduce the PAPR?

[Walter Roberson]

Genetic algorithms are only meaningful if you are changing something and measuring how "good" the resulting configuration is. You have nothing to change.

Be careful, by the way, that you do not optimize against one particular sequence of random numbers in a way that might not be good for a different sequence of random numbers.

[Fatin Najwa Nursyadza Mohd Ramdan]

thank you.