(I took this post’s photo at the Star Trek Original Series Set Tour in Ticonderoga, New York. It’s a view of the warp core of the USS Enterprise, which is only a few feet deep but looks much larger thanks to forced perspective. The room is filled with structures with complicated geometric shapes, technical looking panels, and dramatic lighting in red, blue, and purple.)
In my last post, I wrote about my latest research project and why I was so excited to present it at GECCO, the premier conference for evolutionary computation. I promised a follow-up, and here it is! Unfortunately, I didn’t make it to Melbourne. Instead, I had a very complicated and protracted battle with my University’s travel planning system, United Airlines, and the Australian visa office, all from the comfort of my home in Vermont. I couldn’t even participate in the event remotely, because of the time zone difference. This is all very disappointing, but I tried to make the best of it. I’ve been busy with the next iteration of this project, and enjoying a bit of “staycation” time here in New England (hence this month’s cover photo).
In any case, my paper did get published, and I’d still like to share the materials I presented virtually at the conference. It’s mostly intended for a technical audience, but I hope at least some of my readers will find it interesting. The paper is titled A Meta-Evolutionary Algorithm for Co-evolving Genotypes and Genotype / Phenotype Maps. I had to cut it down to just four pages for the official publication, since it was accepted as a poster, but the full length version is available here, and I wrote up an overview of my algorithm’s implementation for those who want to go deeper. There’s also a digital version of my poster and a short video overview of my experiment.
I continue to work on this idea, and it is starting to evolve beyond what I presented in that paper. Right now, I’m actively deconstructing and rebuilding the algorithm. CPPNs are an important and well known part of the AI field, so I’m trying to describe precisely how my algorithm is different, and which of those differences account for the remarkable results I found. Originally I thought of this research as being about epigenetics specifically, but as I try to generalize and simplify, what I’m left with looks like straight-up endosymbiosis. I’ve been thinking of this algorithm as a metaphor for a cell and its genes / nucleus, but it could just as easily be a metaphor for an animal and its community of microbes. This is exciting, since I’d love to do more research on endosymbiosis, and I really like the idea that perhaps symbiosis is the driving force behind intelligence as we know it, fundamentally changing the dynamics of evolution.
Anyway, that’s how I see it for the moment, and where I hope my research will lead in the near future. For now, though, I’m wrapping up my summer with a few more fun outings, and preparing for the start of classes later this month. I’ll be diving deep into both evolutionary computation and deep learning, which I’m really looking forward to.
Thinking with Nate 