how to call my code to GA

Dear all,

I need to call my code to GA code, I have error message

     Output argument "output" (and maybe others) not assigned during call to "F:\myheader.m>myheader".
    Error in createAnonymousFcn>@(x)fcn(x,FcnArgs{:}) (line 11)
    fcn_handle = @(x) fcn(x,FcnArgs{:});
    Error in makeState (line 48)
                firstMemberScore = FitnessFcn(state.Population(initScoreProvided+1,:));
    Error in gaunc (line 41)
    state = makeState(GenomeLength,FitnessFcn,Iterate,output.problemtype,options);
    Error in ga (line 348)
                [x,fval,exitFlag,output,population,scores] = gaunc(FitnessFcn,nvars, ...
    Error in runsimple_fitnesss (line 16)
    [x,fval] =ga(FitnessFunction,numberOfVariables,[],[],[],[],[],[],[],options)
    Caused by:
        Failure in initial user-supplied fitness function evaluation. GA cannot continue.

I don't know what it means or how to fixed my code. Could you explain to me what it means and help me to fix the error?

        function [output]=myheader(i)
      format long;format compact;fontSize = 13;
             for i=1:4 
             p1=[25.8862 3.0912; 4.9390 44.5138; 25.3633 5.6858; 4.5108 48.9112]; 
             p2=[11.5684 5.9243; 1.6695 41.9517; 24.5223 29.9721; 32.6509 23.0566]; 
             d=sqrt((p1(i,1)-p1(i,2))^2+(p2(i,1)-p2(i,2))^2);
             a=d/2; b=a/3; g=b/5; h=g/11; k=a-b;
             results(i,1)=a;
             results(i,2)=b;
             results(i,3)=d;
             results(i,4)=g;
             results(i,5)=h;
             results(i,6)=k;
        end
      R=results;
      B=sortrows(R,-6);
      BB= B(:,1:5);
      for i=1:4                                                   
      l=regexprep(cellstr(num2str(BB(i,:),'%011.6f')),'[.\l]','');
      ll(i,1)=l;                                                                             
      end
      ll
      end

I want to call it to

    FitnessFunction =@myheader
    numberOfVariables =5;
    options =gaoptimset('display','iter','generations',200,'populationsize',4,'FitnessScalingFcn'
  {@fitscalingtop,0.4},'SelectionFcn',@selectiontournament,4},'crossoverfraction',0.65,'MutationFcn',{@mutationgaussian,0.25},'StallGenLimit',200)
    [x,fval] =ga(FitnessFunction,numberOfVariables,[],[],[],[],[],[],[],options)

My individuals here is the matrix {ll} which contains 4 individuals, I should apply the ga functions then change the final result from string to 5 separate numbers {a, b, d, g, h}.

Could you help me please?

thanks in advance

Answer 1

[Geoff Hayes]

imola - there are a couple of things that aren't quite correct with your fitness function, myheader. Note the signature

 function [output]=myheader(i)

The one input parameter i is, given that you have defined the GA to optimize your function 5 variables, a 1x5 vector of variables that are randomly created (you haven't defined the PopInitRange so I'm not sure what intervals the variables are initialized in). But note the name of this variable is i which is the same variable you are using to iterate in your for loop

 for i=1:4

What this means is that you end up NOT using the input data at all!

Going back to the signature of this function, the return variable is named output, but nowhere in the code is the output variable being set. So this is why you are seeing the error message Output argument "output" (and maybe others) not assigned during call to.

So the above two issues need to be addressed, but more importantly, you need to decide what your fitness function should be doing. What is the purpose of myheader? As you are passing it as the fitness function to the Genetic Algorithm (GA), the GA is assuming that this is the function that you wish to minimize i.e. find an optimal (or near-optimal) solution for. The GA (in your case) creates a population of size 4 (why so small?) where each member of the population is randomly assigned values for each of the five variables. The GA will then score each of these members using the fitness function by passing in the vector of five variables and getting some sort of result/fitness/score for each. That which the better fitness/score (where better means smaller since we are minimizing the fitness function) is then more likely going to be considered/selected for reproduction in creating "children" for the next generations (whose data, i.e. vector of five variables) will be a combination of the data from the two parents (the crossover part of the GA).

I know that from a couple of your previous posts, you have written code that converts numeric data into integer "strings" so that you can do some sort of combination on them (like crossover) but you don't have to do that since you are using the GA from the Global Optimization Toolbox.

So what is the problem that you are trying to optimize (i.e. minimize) and how can you write your fitness function to accomplish this?

[Geoff Hayes]

Imola - since you have decided to do the ga functionality from the Global Optimization Toolbox, then you don't have to worry about coding up the software to do the crossover or the mutation. The toolbox will do it for you. You don't have to convert the real data of

 p1=[25.8862 3.0912; 4.9390 44.5138; 25.3633 5.6858; 4.5108 48.9112]; 
 p2=[11.5684 5.9243; 1.6695 41.9517; 24.5223 29.9721; 32.6509 23.0566];

to string equivalents so that crossover is performed on these two parents (if that is what they are?). In fact, your fitness function doesn't know about parents or children or crossover or mutation. The sole purpose of the fitness function is to accept an input vector and return a fitness or score for that input. Since the GA is used to minimize the (fitness) function, then that input vector that leads to a smaller output is considered stronger (more fit) and better suited for reproduction.

From your comment I want to call the matrix of strings {ll}and apply crossover and mutation on it such that the string of 50 numbers represent 5 parameters{a,b,d,g,h} every parameter contains 10 numbers, you don't have to worry about this. The ga function will perform its own crossover (there are a number of methods that you can choose - one-point, two-point, heuristic, etc.) on two members of the population.

I think that the first step is to define your fitness function. You have stated that there are five variables. What do these represent? If I give you these five variables, what equation would you pass them into, or how would you evaluate all five together? What equation are you trying to minimize?

If the fitness function looked like the following

 function [score] = fitnessFunction(varData)

     % what gets filled in here so that score is set given the 
     % 1x5 vector of variable data, varData
     score = ????;
 end

What would the body of the above function be?

[imola]

 Dear Geoff,
     I'm sorry for confuse you with me, but this code is mimic my original code so {a, b, d, g, h} are represent parameters for a certain shape and because the code is very large I summarized for simple functions by wrote {b, d, g, h} in this way, because of that the shape is not clear and {k} also not clear.
     When I wrote the function,

      function [score] = fitnessFunction(varData)

       I wanted to generate 10 strings represent 10 shapes by using 10 points  {p1,p2} that I want to apply the GA on it to minimize {k}, and the 5 parameters represent the parameters of the shape that I want . if I write varData = 5 will be fault because I wrote this function to generate even 1000 shape not just 10 and minimize {k}, 

    when I apply the GA, I found that it takes the whole matrix {ll} as individual and even not apply the crossover on it, so how I call {ll} to the GA as initial population and {k} as fitness function which I wrote it in the middle of the code while I wrote [score] to generate the shapes.
    As I told you before when I put k in 

    function [k] = fitnessFunction(varData) 

    I got result but its absolutely mistake.

    thank you for everything, and forgive me again for confuse you, I will wait for your suggestions, you should know that this code is follow the original one step by step, so what I get here is exactly the same their.

[Geoff Hayes]

Imola - could you outline what your fitness function is supposed to do? It sounds like there are five parameters of a shape, and that these five parameters are the input to your fitness function. How does your fitness function evaluate these five inputs? What are you trying to minimize?

-------------------------------------------------------------------------

Here is a very simple example. Suppose we want to minimize the following function

 (x-1)^2 + (y-1)^2

The minimum for this function is zero, and if all of our variables are positive, then the optimal solution should be when (x,y)==(1,1).

If we wish to use a genetic algorithm, then we would create our fitness function as

 function [score] = fitnessFunction(varData)

     % get the variable data
     x = varData(1);
     y = varData(2);

     score = (x-1)^2 + (y-1)^2;

 end

Note how a 1x2 row vector varData is passed in to the function, and how we evaluate the two variables in the equation that produces the score. The genetic algorithm will use this function to determine the score for each of its randomly created members of the initial population. Those with the better score (where better implies smaller since we are minimizing our function) are more likely to be selected to create children for the next generation/iteration. Once the parents are selected, the crossover and mutation occurs to create the children. The cycle then repeats for each iteration.

-------------------------------------------------------------------------

Your fitness function must behave in a similar way. It accepts the 1x5 row vector, accesses each variable, and evaluates them all to produce a score for a single member of the population. Your population size may be 4, 10, 100, or even 1000. The fitness function doesn't care or even know about the population - it just needs to know how to handle the 1x5 row vector that represents (I think) in your case 5 shape parameters/variables.

[Image Analyst]

imola's "Answer" moved here since it's not an answer to her original question but a reply to Geoff:

Dear Geoff

Thanks for replying, I think you got the idea, the two set of points {p1,p2} I used to find the shapes every two points give one shape, {d} is the distance between the points p1 and p2, {a} is the major axis and {b} is the minor, {g} are the centre and {h} is the angle of the shape to rotate, and {k} is the area { k = a*b*h} I’m sorry because I wrote it as {k = a-b} for short and I use {k} to sort the strings to see which area is the best notes I’m in 2 dim. As you see I put the fife parameters in string so I can apply the crossover on it such that I can change the position of the centres and the length of the axis between the shapes like {h a g b d} , so that I can get the best shape with minimum area which is the fitness function. the code

Function [ ] = myheader(n)
   *** area
end

Is to generate the shapes, in every iteration the function draw one shape and compute the area for it. Because here I have four points that mean I will get four shapes, the {***} is the code of how I find all the parameters and the area which is the fitness function, my question, how I’m going to call the fitness function and the population, it suppose the matrix of the strings are my individual so how I’m going to call it. When I wrote {k} in

Function [k] = myheader(n)

and call, it was fault as I told you before. The GA code take default points for the population not my individuals and in the command window I got the matrix of the strings {ll} in every generation fixed not change

I hope you get now clear idea, every string represent a shape, in the end I want to get a best final shape with minimum area. I’m keeping search about GA examples and read, I may find some help, I’m really don’t know how to handle it. It is not straightway and easy.

Dear Geoff, if you don’t know how to handle, how I’m going to handle and I'm new in GA area and using MATLAB, could you please try to help me, I will appreciate that a lot. I will wait for your answer

regards

[Image Analyst]

geoff's reply moved here:

Imola - from your comment As you see I put the fife parameters in string so I can apply the crossover on it such that I can change the position of the centres and the length of the axis between the shapes like {h a g b d} , so that I can get the best shape with minimum area which is the fitness function.

If you are going to use ga functionality from the Global Optimization Toolbox, then you don't have to worry (for the time being) about doing the crossover yourself. The ga code will do this for you, and give you the opportunity to choose different crossover algorithms and (I think) use your own if needed.

Going back to your code,

 for i=1:10
     p1=[25.8862 3.0912; 4.9390 44.5138; 25.3633 5.6858; 4.5108 48.9112]; 
     p2=[11.5684 5.9243; 1.6695 41.9517; 24.5223 29.9721; 32.6509 23.0566]; 

     d=sqrt((p1(i,1)-p1(i,2))^2+(p2(i,1)-p2(i,2))^2);

    % other stuff
 end

The p1 and p2 matrices are 4x2, so you will get an Index exceeds matrix dimensions message once i reaches 5. Given the above, and what you have said in the previous comment, I think that I'm getting a better idea of how your fitness function should be organized.

On each iteration of the above loop, you use four variables - two from p1 and two from p2 - to compute some intermediate values and the k which you are trying to minimize. To me, this means that your fitness function should process a 1x4 vector of data as follows

 function [score] = myFitnessFunc(data)

     p11 = data(1);
     p12 = data(2);
     p21 = data(3);
     p22 = data(4);

     d=sqrt((p11-p12)^2+(p21-p22)^2);

     a=d/2; 
     b=a/3; 
     g=b/5; 
     h=g/11;
     k=a*b*h;

     score = k;
 end

In the above, we use the four variables (found in the input parameter/vector data) and calculate the parameters that you need to determine k.

The GA will create a random population of members and apply this function to each, so that all members are scored. It will then apply a selection algorithm to determine which members of the population should be parents and with whom. The crossover will follow (I recommend using the heuristic or other real-valued crossover functions), along with some mutation. The cycle will repeat itself for the next generation until the number of iterations has been reached.

With this, you don't have to be concerned with converting data to strings and performing the crossover yourself.

[Geoff Hayes]

Imola - I don't see how your above myFitnessFunc can work. What is G? What is p11, p12, p21 and p22? These last four are the variables that I described in my previous comment as functions of the input vector data, but you have not initialized them in any way, and you are not making use of data. So the above fitness function will fail with errors.

I also don't understand what you mean by about my initial population because I still need to regard the matrix of the strings as my population. Why must the matrix of strings be your initial population? Why are you insistent on using these strings?

Do you want your strings matrix to be the initial population for the GA? If so, then you have to convert these strings to a matrix of numbers where the rows represent each member in the population, and the columns represent each variable. If this new matrix is initPop then you would use add it to the options object as

 options = gaoptimset(options,'InitialPopulation',initPop);

But you still have to decide what a member of the population looks like. Does it have just four variables to define the shape? From your question way up top, the p1 and p2 could be used to create an initial population as

 initPop = [25.8862  3.0912 11.5684  5.9243; ...
             4.939  44.5138  1.6695 41.9517; ...
            25.3633  5.6858 24.5223 29.9721; ...
             4.5108 48.9112 32.6509 23.0566];

Here you have four members, each with four variables that when using the fitness function I described in my previous comment would be able to score each one according to the equation that you have defined.

Imola, unless there is some requirement to use strings, I would ignore that for the time being and just try to work with numbers. (Or just let the GA work these numbers...)

[Geoff Hayes]

Imola's answer pasted here

Dear Geoff, I'm really sorry because I wrote the code incorrect way, it should be like this

 function [k] = myheader()   %for example this is one string for short

    ll = [  '00282348210009411607005646964200018823210000171120'
            '00227125850007570862004542517000015141720000137652'
            '00117416770003913892002348335300007827780000071162'
            '00102091180003403039002041823500006806080000061873'];
    for i=1:4
        m=reshape(ll(i,:),10,[])'; 
        v=[str2num(m(:,1:4))+str2num(m(:,6:end))/1e6]'      
        %%%d=sqrt()p11-p12)^2+(p21-p22)^2);
        %%%a=d/2; b=a/3; g=b/5;  h=g/11;           

        % I don't need to find {a, b, d, g, h} now because
        % I have the parameters within the strings that I 
        % split so I will just compute {k} from the strings
        % so I wrote
        a=v(:,1);b=v(:,2); h=v(:,5);
        k=v(:,1)*v(:,2)*v(:,5);
        fitnessfunction = k;
    end
 end

with respect to the strings matrix, are you sure that the GA doesn't accept it as strings ?

I wanted like this so the functions mutation and crossover will applied directly, because when I spilt it, it gave a little bit different values. if so, then I called {k} to the GA, and the initial population I did exactly like you I copy the strings matrix after split it in this file above and pasted in GA file and wrote it as you

 options = gaoptimset(options,'InitialPopulation',initPop);

but it gave me error, too many input, I wrote it as before see

 FitnessFunction =@myheader
 numberOfVariables =5;   % the string has 5 parameters
 initpop=[ 28.0348    9.0116   56.0696    1.0823    0.0711
           22.0126    7.0709   45.0252    1.0142    0.0377
           11.0417    3.0139   23.0834    0.0828    0.0712
           10.0091    3.0030   20.0182    0.0806    0.0619];
 options = gaoptimset('display','iter','InitialPopulation',initPop, ...
                      'generations',2,'populationsize',4,...
                      'FitnessScalingFcn', ...
                      {@fitscalingtop,0.4},'SelectionFcn', ...
                       @selectiontournament,4}, ...
                       'crossoverfraction',0.65,'MutationFcn', ... 
                       {@mutationgaussian,0.25},'StallGenLimit',200);
 [x,fval] =ga(FitnessFunction,numberOfVariables,[],[],[],[],[],[],[],options);

I think I get contradiction if I use {v} to compute the fitness function, and I use the {p1,p2} as initial pop in GA, so I used the same matrix as pop. but I got error

 Too many input arguments.
 Error in createAnonymousFcn>@(x)fcn(x,FcnArgs{:}) (line 11)
 fcn_handle = @(x) fcn(x,FcnArgs{:});
 Error in makeState (line 48)
 firstMemberScore = FitnessFcn(state.Population(initScoreProvided+1,:));
 Error in gaunc (line 41)
 state = 
     makeState(GenomeLength,FitnessFcn,Iterate,output.problemtype,options);
 Error in ga (line 348)
   [x,fval,exitFlag,output,population,scores] = gaunc(FitnessFcn,nvars, ...
 Error in runfitcustomplot (line 18)
        [x,fval,output,exitflag,population,scores]=ga(FitnessFunction,numberOfVariables,[],[],[],[],[],[],[],options)
        Caused by:
            Failure in initial user-supplied fitness function evaluation. GA cannot continue.

do you see what I did correct if it will work I will need to draw the result to see how it is good

Thank you so much

[Geoff Hayes]

Imola - as we have already discussed, the fitness function must accept a row vector as its input. Your function

 function [k] = myheader()

doesn't do that so you will continually get the error message too many input arguments.

As well, you have now changed the number of variables from four to five.

Before going any further, you have to come up with a fitness function that conforms to what the ga is expecting.

[Geoff Hayes]

Imola's comment moved here

Dear Geoff, my fitness function use the vector {v} as inputs

 k=v(:,1)*v(:,2)*v(:,5);

so should I write

function [k] = myheader(v)

I got this result

                                 Best           Mean      Stall
  Generation      f-count        f(x)           f(x)    Generations
      1             8            1.86            1.86        0
      2            12            1.86            1.86        1
      3            16            1.86            1.86        2
      4            20            1.86            1.86        3
      5            24            1.86            1.86        4
      6            28            1.86            1.86        5
      7            32            1.86            1.86        6
      8            36            1.86            1.86        7
      9            40            1.86            1.86        8
     10            44            1.86            1.86        9
     11            48            1.86            1.86       10
     12            52            1.86            1.86       11
     13            56            1.86            1.86       12
     14            60            1.86            1.86       13
     15            64            1.86            1.86       14
     16            68            1.86            1.86       15
     17            72            1.86            1.86       16
     18            76            1.86            1.86       17
     19            80            1.86            1.86       18
     20            84            1.86            1.86       19
     21            88            1.86            1.86       20
     22            92            1.86            1.86       21
     23            96            1.86            1.86       22
     24           100            1.86            1.86       23
     25           104            1.86            1.86       24
     26           108            1.86            1.86       25
     27           112            1.86            1.86       26
     28           116            1.86            1.86       27
     29           120            1.86            1.86       28
     30           124            1.86            1.86       29
                                 Best           Mean      Stall
  Generation      f-count        f(x)           f(x)    Generations
     31           128            1.86            1.86       30
     32           132            1.86            1.86       31
     33           136            1.86            1.86       32
     34           140            1.86            1.86       33
     35           144            1.86            1.86       34
     36           148            1.86            1.86       35
     37           152            1.86            1.86       36
     38           156            1.86            1.86       37
     39           160            1.86            1.86       38
     40           164            1.86            1.86       39
  Optimization terminated: maximum number of generations exceeded.
  x =

     28.0348    9.0116   56.0696    1.0823    0.0711
  fval =

      1.8598
  output =

       0
  exitflag =

      problemtype: 'unconstrained'
         rngstate: [1x1 struct]
      generations: 40
        funccount: 164
          message: 'Optimization terminated: maximum number of generations exceeded.'
  population =

   28.0348    9.0116   56.0696    1.0823    0.0711
   22.0126    7.0709   45.0252    1.0142    0.0377
   28.0348    9.0116   45.0252    1.0823    0.0711
   22.0126    7.0709   45.0252    1.0142    0.0377
scores =

    1.8598
    1.8598
    1.8598
    1.8598
>>

this for 40 generation, as you see nothing happen

regards

[Geoff Hayes]

Imola - please continue the conversation here rather than creating answers that aren't answers to your original question. It will be less confusing for others who view this posting.

As for your above comment, I'm not sure what to say since you have changed the fitness function again and I do not know what it is. Is it simply

 function [k] = myheader(v) 

     k=v(:,1)*v(:,2)*v(:,5);

which doesn't make sense given that all scores are 1.8598 and none of the population members produce that kind of score.

As for "nothing happening" in 40 generations, yes, this does make sense since your population size is only 4! You will not get much (genetic) variability in the population with only four members. Also, many of the genes/variables for each member is the same so I suspect that you are using a two-point crossover method which will not vary the genes - it will just replace them. You should be using the crossoverheuristic with a ratio (say) of 1.2.

I would like you to do the following - rather than pasting code in comments or answers, please just attach the following to a comment:

- the fitness function that you are using (this can be in a file) myheader.m but you have to stop making changes to it

- the code that you are using to initialize the GA - what is the initial population, what are the options, etc.

Use the paperclip button to attach your code.

[Image Analyst]

imola's "Answer" moved here since it's not an answer to the original question but a reply to Geoff:

Dear Geoff,

I'm really sorry because I wrote the code incorrect way, it should be like this

function [k] = myheader()   %for example this is one string for short
         ll = [  '00282348210009411607005646964200018823210000171120'
                '00227125850007570862004542517000015141720000137652'
                  '00117416770003913892002348335300007827780000071162'
                  '00102091180003403039002041823500006806080000061873'];
              for i=1:4
           m=reshape(ll(i,:),10,[])'; 
           v=[str2num(m(:,1:4))+str2num(m(:,6:end))/1e6]'      
                   %%%d=sqrt()p11-p12)^2+(p21-p22)^2);
                   %%%a=d/2; b=a/3; g=b/5;  h=g/11;           
% I don't need to find {a, b, d, g, h} now because I have the parameters within the strings that I split so I will just compute {k} from the strings so I wrote
          a=v(:,1);b=v(:,2); h=v(:,5);
          k=v(:,1)*v(:,2)*v(:,5);
           fitnessfunction = k;
              end
              end

with respect to the strings matrix, are you sure that the GA doesn't accept it as strings ?

I wanted like this so the functions mutation and crossover will applied directly, because when I spilt it, it gave a little bit different values.

if so, then I called {k} to the GA, and the initial population I did exactly like you I copy the strings matrix after split it in this file above and pasted in GA file and wrote it as you

            options = gaoptimset(options,'InitialPopulation',initPop);

but it gave me error, too many input, I wrote it as before see

            FitnessFunction =@myheader
                numberOfVariables =5;   % the string has 5 parameters
        initpop=[ 28.0348    9.0116   56.0696    1.0823    0.0711
                  22.0126    7.0709   45.0252    1.0142    0.0377
                  11.0417    3.0139   23.0834    0.0828    0.0712
                  10.0091    3.0030   20.0182    0.0806    0.0619];

                options =gaoptimset('display','iter','InitialPopulation',initPop,'generations',2,'populationsize',4,'FitnessScalingFcn',    {@fitscalingtop,0.4},'SelectionFcn',@selectiontournament,4},'crossoverfraction',0.65,'MutationFcn',{@mutationgaussian,0.25},'StallGenLimit',200);
            [x,fval] =ga(FitnessFunction,numberOfVariables,[],[],[],[],[],[],[],options);

I think I get contradiction if I use {v} to compute the fitness function, and I use the {p1,p2} as initial pop in GA, so I used the same matrix as pop, but I got error

        Too many input arguments.

        Error in createAnonymousFcn>@(x)fcn(x,FcnArgs{:}) (line 11)
        fcn_handle = @(x) fcn(x,FcnArgs{:});

        Error in makeState (line 48)
                    firstMemberScore = FitnessFcn(state.Population(initScoreProvided+1,:));

        Error in gaunc (line 41)
        state = makeState(GenomeLength,FitnessFcn,Iterate,output.problemtype,options);

        Error in ga (line 348)
                    [x,fval,exitFlag,output,population,scores] = gaunc(FitnessFcn,nvars, ...

        Error in runfitcustomplot (line 18)
        [x,fval,output,exitflag,population,scores]=ga(FitnessFunction,numberOfVariables,[],[],[],[],[],[],[],options)
        Caused by:
            Failure in initial user-supplied fitness function evaluation. GA cannot continue.

Do you see what I did correct? If it will work I will need to draw the result to see how it is good

Thank you so much

[Image Analyst]

imola's second response moved here:

Dear Geoff,

My fitness function use the vector {v} as inputs

k=v(:,1)*v(:,2)*v(:,5);

so should I write

    function [k] = myheader(v)

I got this result

                               Best           Mean      Stall
Generation      f-count        f(x)           f(x)    Generations
    1             8            1.86            1.86        0
    2            12            1.86            1.86        1
    3            16            1.86            1.86        2
    4            20            1.86            1.86        3
    5            24            1.86            1.86        4
    6            28            1.86            1.86        5
    7            32            1.86            1.86        6
    8            36            1.86            1.86        7
    9            40            1.86            1.86        8
   10            44            1.86            1.86        9
   11            48            1.86            1.86       10
   12            52            1.86            1.86       11
   13            56            1.86            1.86       12
   14            60            1.86            1.86       13
   15            64            1.86            1.86       14
   16            68            1.86            1.86       15
   17            72            1.86            1.86       16
   18            76            1.86            1.86       17
   19            80            1.86            1.86       18
   20            84            1.86            1.86       19
   21            88            1.86            1.86       20
   22            92            1.86            1.86       21
   23            96            1.86            1.86       22
   24           100            1.86            1.86       23
   25           104            1.86            1.86       24
   26           108            1.86            1.86       25
   27           112            1.86            1.86       26
   28           116            1.86            1.86       27
   29           120            1.86            1.86       28
   30           124            1.86            1.86       29

                               Best           Mean      Stall
Generation      f-count        f(x)           f(x)    Generations
   31           128            1.86            1.86       30
   32           132            1.86            1.86       31
   33           136            1.86            1.86       32
   34           140            1.86            1.86       33
   35           144            1.86            1.86       34
   36           148            1.86            1.86       35
   37           152            1.86            1.86       36
   38           156            1.86            1.86       37
   39           160            1.86            1.86       38
   40           164            1.86            1.86       39
Optimization terminated: maximum number of generations exceeded.

x =

   28.0348    9.0116   56.0696    1.0823    0.0711

fval =

    1.8598

output =

     0

exitflag =

    problemtype: 'unconstrained'
       rngstate: [1x1 struct]
    generations: 40
      funccount: 164
        message: 'Optimization terminated: maximum number of generations exceeded.'

population =

   28.0348    9.0116   56.0696    1.0823    0.0711
   22.0126    7.0709   45.0252    1.0142    0.0377
   28.0348    9.0116   45.0252    1.0823    0.0711
   22.0126    7.0709   45.0252    1.0142    0.0377

scores =

    1.8598
    1.8598
    1.8598
    1.8598

>> this for 40 generation, as you see nothing happen

regards

[Geoff Hayes]

Imola - please continue the conversation here rather than creating answers that aren't answers to your original question. It will be less confusing for others who view this posting.

As for your above comment, I'm not sure what to say since you have changed the fitness function again and I do not know what it is. Is it simply

 function [k] = myheader(v) 

     k=v(:,1)*v(:,2)*v(:,5);

which doesn't make sense given that all scores are 1.8598 and none of the population members produce that kind of score.

As for "nothing happening" in 40 generations, yes, this does make sense since your population size is only 4! You will not get much (genetic) variability in the population with only four members. Also, many of the genes/variables for each member is the same so I suspect that you are using a two-point crossover method which will not vary the genes - it will just replace them. You should be using the crossoverheuristic with a ratio (say) of 1.2.

I would like you to do the following - rather than pasting code in comments or answers, please just attach the following to a comment:

- the fitness function that you are using (this can be in a file) myheader.m but you have to stop making changes to it

- the code that you are using to initialize the GA - what is the initial population, what are the options, etc.

Use the paperclip button to attach your code.

[Geoff Hayes]

Glad that I was able to help, Imola. I hope that the results were good!

[Geoff Hayes]

Imola - please post this as a new question as it doesn't follow from your original one. Also, format the code portions of your message by using the {}Code button so that it is readable and distinct from your question/comments. Right now, you have formatted everything in your comment as code.