title: Genetic Algorithm for test suite generation using python
body: <p>I am looking for a <em>Test suite</em> optimization using a Genetic Algorithm(GA). There are multiple IEEE research papers suggesting this, but I need help on implementing the same (using python).</p>
  
  <p><strong>These are my GA steps:</strong></p>
  
  <ol>
  <li>Representation: How to represent test case features in GA?</li>
  <li>Initialize and evaluate (Fitness Function): How to create a Fitness function - formula? </li>
  <li>Parent selection, Crossover and Mutation: Running though the GA </li>
  <li>Output: Inferring from the output </li>
  </ol>
  
  <p>I've done a lot of research on this, but I could not get on to the phase where I can implement. </p>
  
  <p>For test case optimization (selecting the best test case) I use <a href="http://pyeasyga.readthedocs.io/en/latest/usage.html" rel="nofollow noreferrer">GA python packages</a>.<br>
  Any help is much appreciated.</p>