Many of the "human-competitive" results that have received "Humies" awards (http://www.genetic-programming.org/combined.php http://www.genetic-programming.org/c

Many of the "human-competitive" results that have received "Humies" awards (http://www.genetic-programming.org/combined.php http://www.genetic-programming.org/combined.php) used GP. Some colleagues and I surveyed the first 10 years of Humies results in terms of techniques used and other factors; preprint here: https://www.dropbox.com/s/tjpa6afxqibwnno/Anaylzing_the_HUMIES.pdf https://www.dropbox.com/s/tjpa6afxqibwnno/Anaylzing_the_HUMI.... In what I think is another exciting development (although I'm biased -- I was his advisor), Tom Helmuth just finished his PhD at UMass using GP (and Clojush) for automatic programming, automatically synthesizing programs for first-year computer science assignments (abstract: https://www.cics.umass.edu/speakers/thomas-helmuth/2015/jul/27 https://www.cics.umass.edu/speakers/thomas-helmuth/2015/jul/...). This is just the tip of an iceberg, but it's a big iceberg. As for why GP is rarely mentioned when people talk about machine learning, I think there are a couple of reasons, but one important one is that GP usually shines the brightest on different kinds of problems than other ML techniques are applied to. If you put them head to head on a problem to which other ML techniques can be easily applied then GP will probably lose. But GP can succeed on problems for which it's not at all clear how one would even begin to apply most other ML techniques.