My main concern is whether the genetic programming is an active field of research, with some promising applications in practice. It seems like in field of machine learning, the neural networks are the main buzzword, with mentions in mainstream news today, but I have never heard of similar genetic programming "success story".

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    $\begingroup$ Possible duplicate of Why has research on genetic algorithms slowed? $\endgroup$ – whn Aug 4 '17 at 19:52
  • $\begingroup$ It's maybe ironic how you draw the connection between the popularity of genetic algorithms and machine learning. Given the history of genetic algorithms, maybe you are being prophetic for the field of machine learning? $\endgroup$ – Raphael Oct 7 '17 at 12:58

The research is well in progress, though not so much talked about in comparison to machine learning, deep learning in particular.

  • Genetic algorithms are applied in particular research areas (examples: "Genetic Programming for Estimation of Heat Flux between the Atmosphere and Sea Ice in Polar Regions" (link), "Genetic Programming with Alternative Search Drivers for Detection of Retinal Blood Vessels" (link), "Optimal placement of water-lubricated rubber bearings for vibration reduction of flexible multistage rotor systems" (link)).
  • If you are interested in a research of genetic algorithms themselves, see the papers by Claire Le Goues.
  • There is an annual conference dedicated to evolutionary computation.
  • Programming frameworks are far from dead as well.

Is genetic programming relevant today?

Not so long ago, in early 2000-s, people asked the same question about neural networks. Hugo Larochelle mentions how the neural networks papers have been reviewed back then. And we know how it turned out. So don't be so quick in your conclusions, it all can change one day.

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