Neuroevolution: A different kind of deep learning

Neuroevolution is making a comeback. Prominent artificial intelligence labs and researchers are experimenting with it, a string of new successes have bolstered enthusiasm, and new opportunities for impact in deep learning are emerging. Maybe you haven't heard of neuroevolution in the midst of all the excitement over deep learning, but it's been lurking just below the surface, the subject of study for a small, enthusiastic research community for decades. And it's starting to gain more attention as people recognize its potential. Put simply, neuroevolution is a subfield within artificial intelligence (AI) and machine learning (ML) that consists of trying to trigger an evolutionary process similar to the one that produced our brains, except inside a computer. In other words, neuroevolution seeks to develop the means of evolving neural networks through evolutionary algorithms. When I first waded into AI research in the late 1990s, the idea that brains could be evolved inside computers resonated with my sense of adventure. At that time, it was an unusual, even obscure field, but I felt a deep curiosity and affinity. The result has been 20 years of my life thinking about this subject, and a slew of algorithms developed with outstanding colleagues over the years, such as NEAT, HyperNEAT, and novelty search. In this article, I hope to convey some of the excitement of neuroevolution as well as provide insight into its issues, but without the opaque technical jargon of scientific articles. I have also taken, in part, an autobiographical perspective, reflecting my own deep involvement within the field.