Generalized Genetic Algorithm Code

Hello,

I'm working on making a generalized code solving for optimization problems using the Genetic Algorithm method. I've stuck at the performing mutation and penalizing parts. Also, I'm not sure where to close my While loop. This is what I've got so far.

%%Genetic Algorith Code
clear all
clc
%%Defining Variables
num_var=2; % Number of Variables
x_min=[-2*pi; -2*pi];
x_max=[2*pi; 2*pi];
x_disc=[2048;2048]; % Discrete x-values

for i=1:num_var
    x_vals(i,:)=x_min(i,1):(x_max(i,1)-x_min(i,1))/(x_disc(:,1)-1):x_max(i,1);
end

for i=1:num_var
    num_bits(i,:)=ceil(log2(x_disc(i,:)));
end
%%Defining Parameters
pop_size=100; % Population Size
pen_val=5; % Penalty Parameter
ep=2e-7; % Fitness Function Permutation Value
pen_scale=1.5; % Penalty Parameter Scaling Value
pr_cross=0.5; % Probability of Performing Crossover
pr_mut=0.05; % Probability of Performing Mutation
num_mut=1; % Number of Points to Mutate
num_con_it=100; % Number of Iterations to Consider for Convergence
eps=0.001; % Convergence Tolerance
stop=0; % Convergence Switch
k=1; % Iteration Counter

%%Initial Population Definition
for i=1:num_var
    for j=1:pop_size
        x_int(j,i,1)=randi(x_disc(i

    if true
      % code
    end,:),1);
          x(j,i,1)=x_vals(i,x_int(j,i,1));
      end
  end
  %%Analyze Current Population Properties
  for j=1:pop_size
      pen(1,j)=sum(g_constraints(x(j,i,1)));
      f(:,j)=f_function(x(j,i,1) + pen_val*pen(1,j));
  end
  %%Fitness Function
  for j=1:pop_size
      F(:,j)=(1+ep)*max(f(1,:)-f(1,j));
  end
  for j=1:pop_size
      data_mat(j,:,1)=[ex(j,:,1) f(1,j)];
  end
  %%Sorting Data
  sorted_data_mat=sortrows(data_mat(:,:,1),num_var+1);
  x_best(1,:)=sorted_data_mat(1,[1:num_var]);
  y_best(1,:)=sorted_data_mat(1,num_var+1);
  %%V-Strings
  while stop ==0    % NOTE: while loop is still not ended with an end command
      for i=1:num_var
          for j=1:pop_size
              x_bin(j,i,k) = {dec2bin(x_int(j,i,k),num_buts(i,:))};
          end
      end

      x(pop_size,:,k+1) = x_best(k,:);
      x_int(pop_size,:,k+1)=x_int(index(1),:,k);
      x_bin(pop_size,:,k+1)=x_bin(index(1),:,k);

      %%Selecting Chromosome Preference
      Q(k,:)=sum(F(k,:));
      for j=1:pop_size
          P(k,j)= F(k,j)/Q(k,:);
          P_cum(k,j)=sum(P(k,:));
      end
      for j=1:(pop_size-1)
          num=rand;
          for jj=1:pop_size
              if num == P_cum(k,jj)
                  select=jj;
              end
          end
          x_bin(j,:,k+1)=x_bin(select,:,k);
          for j=1:((pop_size-1)/2)
              for i=1:num_var
                  cross=num;
                  if cross <= pr_cross
                      cross_loc = num_bits(i,:)-randi(num_bits(i,:)-1);
                      chromo1=char(x_bin(2*j-1,i,k+1));
                      chromo2=char(x_bin(2*j,i,k+1));
                      new_chromo1=strcat(chromo1(1:cross_loc),chromo2(cross_loc+1:num_bits(i,:)));
                      new_chromo2=strcat(chromo2(1:cross_loc),chromo1(cross_loc+1:num_bits(i,:)));

                      x_bin(2*k-1,i,k+1) = cellstr(new_chromo1);
                      x_bin(2*j,i,k+1) = cellstr(new+chromo2);
                  end
              end
          end
  %%Perform Mutation
  for j=1:(pop_size-1)
      for i=1:num_var
          for p=1:num_bits(i,:)
              mut=num;
              if mut <= pr_mut
                  chromo=char(x_bin(j,i,k+1));
                  if chromo(p)==0
                      chromo(p)=1;
                  elseif chromo(p)==1;
                      chromo(p)=0;
                  end
                  x_bin(j,i,k)=cellstr(chromo);
              end
              for j=1:(pop_size-1)
                  for i=1:num_var
                      x_int(j,i,k+1)=bin2dec(x_bin(j,i,k+1));
                      if x_int(j,i,k+1)==0
                          x_int(j,i,k+1)==1;
                      end
                      if x_int(j,i,k+1) == 1;
                          x_int(j,i,k+1)=round(x_disc(i,1))/(2^num_bits(i,:)); % NOTE: Not sure about this part
                      end
                      x(j,i,k+1)=x_vals(i,x_int(j,i,k+1));
                  end
              end
      % Next part should be the commented code but not completed
  %%Penalizing
  %for j = 1:pop_size
  %    pen(1,j) = sum(g_constraints 

% NOTE: Not complete here

%%Evaluate
    imp(k,:) = min(f(k,:)) - min(f(k+1),:);
    if norm(imp(k-num_con_it:k))<eps
        stop=1;
    end
end
k=k+1;

%%Determine New Fitness
% NOTE: Not sure if this is in the right place.
for j=1:pop_size
    F(k+1,j)=(1+eps)*max(f(k+1,:));
num_fun_eval=pop_size*k % Number of function Evaluations

%%x optimum point

%%y optimum point

I made notes to where I have problems and confused on how to code.

Thanks for your help in advance.