I am looking up some material for my thesis in CS (development of a module to integrate a genetic algorithm in a system developed by other students).

My actual current task is to make a comparative analysis of several libraries for genetic algorithms. After this comparison I will choose one of these libraries and an algorithm.

I want to know if you can give me any tips or best practice to perform such comparison. Which approach to the task would you recommend?

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    $\begingroup$ The core question here seems to be about best practice for comparing libraries -- it doesn't seem particularly important that the libraries are written in Python or that they implement genetic algorithms. Indeed, it doesn't even seem very important that the code is in a library. $\endgroup$ – David Richerby Sep 8 '15 at 15:53
  • $\begingroup$ I agree. Editing the function to be language-agnostic. $\endgroup$ – Raphael Sep 8 '15 at 16:44
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    $\begingroup$ The question is underspecified. Compare with respect to what? $\endgroup$ – Raphael Sep 8 '15 at 16:45

Your first step, when doing a comparative analysis, is to identify your requirements. We can't tell you what the requirements should be; that's something you'll need to figure out on your own.

Then, you can try to evaluate the libraries to determine how well they meet your requirements. The particular style of evaluation depends upon what kind of requirement you want to evaluate. For instance, evaluating performance is very different from evaluating reliability.

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