title: How to avoid overfitting with genetic algorithm
body: <p>I am facing the following problem.
  I have a system able to produce a ranking of some operations according to their anomaly score. To improve the performance I implemented a genetic algorithm to perform a features selection, such that the most anomalous operations appears in the first positions. What I am doing is not exactly feature selection, because I am not using binary variables, rather float variables between 0-1, which sum is equal to 1.</p>
  
  <p>Currently, I have a population of 200 individuals for 50 generations. I am using as the evaluation function the system itself and I evaluate the quality of the solution by using the true positive rate, counting how many anomalous operations appears in the first N positions (where N is the number of anomalous operations). Then as operator the uniform crossover and I change a valueof a cell of the individual for the mutation. Of course, every time I make a check to fix the individual such that the sum is 1. Finally I use elitism to save the best-so-far solution over the time.</p>
  
  <p>I observed that one feature has a very high value, which is often important, but not always, and this causes very low values for the other features. I suspect that my GA is overfitting.
  Can you help me to find a good stop criteria?</p>