I dabble in this stuff on occasion and enjoy reading about it. My first favorite was Danny Hillis' story [1] about designing sorting algorithms using genetic algorithms on a massively parallel machine. He also showed the power of co-evolution: both solutions and tests evolve. Later, I saw the HUMIE awards [2] show off what people have accomplished. Then, there was the nice article [3] on John Koza's Invention Machine. Plenty to get a person interested in the stuff.<p>Yet, most of it happens in academia and paid industry with a lot of good information not easily accessible to non-experts. There's not as much momentum in developing easy to use tools and frameworks for most use cases like we see with, say, web applications. This limits the field to people willing to put in significant time in understanding the subject, the methods, their strengths/limitations, and the various implementations out there.<p>Nonetheless, I at least enjoy reading the abstracts and know I could contract a specialist for a certain applications.<p>[1] <a href="http://kk.org/mt-files/outofcontrol/ch15-d.html" rel="nofollow">http://kk.org/mt-files/outofcontrol/ch15-d.html</a><p>[2] <a href="http://www.genetic-programming.org/combined.html" rel="nofollow">http://www.genetic-programming.org/combined.html</a><p>[3] <a href="http://www.popsci.com/scitech/article/2006-04/john-koza-has-built-invention-machine" rel="nofollow">http://www.popsci.com/scitech/article/2006-04/john-koza-has-...</a>