Evolution Is the New Deep Learning
Having studied this extensively back when they were called Genetic Algorithms, I would like to offer a few insights.<p>1) One of the biggest reasons they fell out of favor for more &quot;mathematical&quot; approaches was that no one could really explain why exactly they worked.  It makes sense on the surface that &quot;survival of the fittest&quot; and doing something akin to multiple stochastic gradient descents would work, but no one has really been able to produce a mathematical proof as to why.<p>Since other folks are producing good examples of &quot;explainable AI&quot;, I don&#x27;t know how Genetic Algorithms&#x2F;programming could be made &#x27;explainable&#x27; as to why they achieved an optimal solution other than hand-waving to how evolution works in nature.<p>2) The most important thing to define is the fitness function, this defines what the search space looks like and how easily a globally optimal solution can be derived.  For a good example of an interesting search space that a genetic program would have a difficult time with, see Schwefel functions [0]. Back when I researched these things closely, my intuition was that reality rarely fits neatly into good fitness functions and I felt that at the point you are understanding the problem, you may just be better off with a direct approach, which leads to<p>3) Genetic programming should only really be considered when there are no known alternatives or they are way too computationally expensive.<p>In either case, I would welcome a resurgence in a topic I once knew quite well, though I haven&#x27;t been in that field for a few years now.<p>[0] <a href="https:&#x2F;&#x2F;jamesmccaffrey.files.wordpress.com&#x2F;2011&#x2F;12&#x2F;schwefelsfunction.jpg" rel="nofollow">https:&#x2F;&#x2F;jamesmccaffrey.files.wordpress.com&#x2F;2011&#x2F;12&#x2F;schwefels...</a>