help me in Genetic algorithm

% Eng. Wael Mohamed Ahmed Sayed AboEl Dahab
% f(x)=cos(x)+sin(x)+2*tan(x)=y
% 0<=x<=400 with accuracy = 2
% intial population 20
% crossover Prob. = %25
% mutation Prob. = %8
% end when last four solution are equally with error = 0.1

clc 
clear 

x=0:2:400;
y=cos(x)+sin(x)+2*tan(x);                        % found 200 solution 
plot(x,y);
hold on ;
grid on ;

gen=round(400*rand(20,1))                        % gen === 1st generation of initial population
fit=cos(gen)+sin(gen)+2*tan(gen);                % fit ==== fitness function

for k=1:200

    npp(1)=max(gen)
total=sum(fit);
for i=1:19              
    p(i)=fit(i)/total;                           %calculate the probability of each fitness function                                       
end

% comulative probability

q(1)=p(1)
for i=2:19
    q(i)=q(i-1)+p(i);
end 


for t=2:20
    b(t)=rand;                                   % b====ball of roullete
    for i=1:19
        if b(t)<=q(i)
            npp(t)=gen(i);                       % npp--> new population 
            break;
        else
            continue
        end
    end
end

npp

for i=1:20
    npb(i,:)=dec2bin(npp(i),9)                   % npb --> new population in binary format
end

npb

x = npb(1,:)

% crossover operation 

n=round(20*0.25)                                 % n --> number Of gene That Applyed Crossover On Them

if(mod(n,2)~=0) 
    n=n+1
end


                                                % select the position of solution to make crossover operation 
flag=1;
for i=1:10
    b=round(20*rand);
    if b > 0 
        ppp(flag)=b                             % ppp -->position of solution 
        flag=flag+1;
    end
    if (flag > 6)
        break;
    end 
end


% 
for i=1:8
    nn=round(9*rand)
    if (nn>0 )
        nc=nn                                     %nc --> point of crossover 
        break;
    end
end 

x1=npb(ppp(1),:);
x2=npb(ppp(2),:);

temp=x1(1:nc);
x1(1:nc)=x2(1:nc);
x2(1:nc)=temp ;

npp(ppp(1))=bin2dec(x1);
npp(ppp(2))=bin2dec(x2);

x3=npb(ppp(3),:);
x4=npb(ppp(4),:);

temp=x3(1:nc);
x3(1:nc)=x4(1:nc);
x4(1:nc)=temp; 

npp(ppp(3))=bin2dec(x3);
npp(ppp(4))=bin2dec(x4);

x5=npb(ppp(5),:);
x6=npb(ppp(6),:);

temp=x5(1:nc);
x5(1:nc)=x6(1:nc);
x6(1:nc)=temp ;

npp(ppp(5))=bin2dec(x5);
npp(ppp(6))=bin2dec(x6);

                                                    % mutation 

mu=round(20*0.08);

                                                    % mutation point

for i=1:8
    nn=round(9*rand);
    if ( nn>0)
        nm=nn
        break;
    end
end

                                                    % solutions that applied mutation of them 

flag=1;
for i=1:8
    bbb=round(20*rand)
    if bbb>0
        pm(flag)=bbb                                 % pm --> position of solution 
        flag=flag+1
    end 
    if(flag > 2)
        break;
    end 
end 

xm1=npb(pm(1),:);
if xm1(nm) == '1' 
    xm1(nm)='0'
else 
    xm1(nm)='1'
end 

xm2=npb(pm(2),:);
if xm2(nm) == '1' 
    xm2(nm)='0'
else 
    xm2(nm)='1'
end 

npp(pm(1))=bin2dec(xm1);
npp(pm(2))=bin2dec(xm2);

fitness=cos(npp)+sin(npp)+2*tan(npp);

maxx=fitness(1);
for i=2:20
    if (fitness(i) >= maxx)
        maxx = fitness(i)
        id = i ;
    end 
end 

maxi(k)= maxx;
maxsolution=npp(i);
gen = npp ;

end 

 max=max(maxi);
 text(maxsolution,max,'max');