MATLAB: Stopping criterion in GA

gastopping criterion

hi everyone I have written a genetic algorithm in matlab and I need to define a stop criterion in which :
if after 100 iteration the produced answer is the same as previous answers, the algorithm must be stopped
how can I write this? can someone help?

Best Answer

alexaa - what you can do is compare, at each generation, the best found solution with the best one from the previous generation/iteration. If the absolute difference between the two is less than some tolerance, then they can be considered equal, and you just increment a counter. When that counter hits 100, exit the loop (I'm guessing that you will be using a for or while loop).
Try the following
 % initialize a local variable to keep track of the previous generation's best fitness
 prevGenBestFitness     = [];
 % initialize our counter
 numGensWithSameFitness = 0;
 % define a tolerance (note that this assumes real fitness scores, if
 % your fitness scores are integer, then this should be set to one or 
 % something similar)
 tol = 0.00001;
 % do the GA; this will be your for or while loop
 while true
     % do the GA stuff
     % save the best fitness from the current generation
     bestFitness = …;
     % compare this best with the previous best (we use ~empty to capture
     % the first iteration when there is nothing to compare)
     if ~isempty(prevGenBestFitness)
         % we can compare
         % NOTE I'm assuming that the best fitness from the current generation is
         % always as good or better than the fitness from the previous generation
         if abs(prevGenBestFitness-bestFitness)<tol
             % the two fitnesses are considered identical so increment the
             % counter
             numGensWithSameFitness = numGensWithSameFitness + 1;
             if numGensWithSameFitness==100
                 % 100 generations with same fitness, so exit
                 break;
             end
         else
             % the two fitnesses are not identical so reset the counter
             numGensWithSameFitness = 0;
         end
     end
     prevGenBestFitness = bestFitness;
 end
Note that the above does make certain assumptions about the fitness/score for your population and so may have to be adjusted to take into account a different scoring scheme (or data type).
Try the above and see what happens!
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