(This month’s image is a slime mold growing on a log. It grows in a branching network of banana-yellow tendrils, some of which are engulfing plant debris they encountered. Source)
Later this month, I’ll be attending the Genetic and Evolutionary Computing Conference (GECCO) in Melbourne, Australia. I’m super excited to go, and to present my very first published academic paper as a poster. I’ll share more here when all is said and done, but unfortunately my paper isn’t really intended for a general audience, like this blog. It would probably be hard to understand for anyone outside of the fields of AI or ALife. So, for everybody else, I’d like to share what the paper means to me, and what I’m trying to say by publishing it.
My research is inspired by epigenetics, and new ways of thinking about evolution. I saw that life doesn’t just evolve by chance, it evolves to become more evolvable. It learns how to explore the range of possible forms and lifestyles more efficiently, and to nudge evolution down more fruitful paths. Life uses its intelligence to become more intelligent still. In my mind, this changes everything about evolution, and I was shocked it wasn’t more well known. Most discussions of evolution (and the programmer version: evolutionary computation, or EC) are too simple, and ignore these critical details. So, I figured I’d be the one to bring this up, and show people why it matters.
I started my first experiment before I even got to university. I was so excited by the idea, I just had to get it out of my head. I actually avoided looking for prior work, because I wanted to see how I would manifest this idea without being biased by other people’s thinking. Besides, I didn’t know of any research like mine, and I didn’t know how to find it, either. That’s why I applied to UVM. When I got here, my advisor and lab mates encouraged me to publish this project, and pointed me at the relevant literature. So I hit the books, reading all that had been done before in order to put my own work into context.
And, of course, I found I’m not the first to have this idea. There are many variations of EC inspired by biology, looking for the “secret sauce” that makes life more powerful than our computer models. In particular, how life evolves to be more evolvable is an active area of research, which has been building momentum in recent years. At first, I was disappointed. My idea was already taken! So much of what I thought made my project interesting had been tried before in some other context. But not exactly. Identifying those subtle differences has been tremendously helpful.
You see, it’s pretty well established now that “evolvability” is important. In our experiments, simulated life that’s more evolvable finds fitter solutions faster. It’s better at adapting to changing circumstances, too. It seems to be smarter and more creative. I find this exhilarating, yet these discoveries didn’t “change everything” like I had hoped. In the experiments so far, it feels like an incremental improvement. It helps, but not enough to draw much attention away from other areas of AI research, like deep learning, which is seen as much more powerful and more productive.
I think that’s because we still haven’t broken out of our old ways of thinking. Traditional EC is all about finding good solutions to a problem, but I would argue that evolution isn’t about problem solving. It’s about problem finding. Life explores the space of possible lifestyles to find and exploit opportunities. The evolution of life is a bit like a slime mold. It grows simultaneously in all directions, questing around obstacles to find resources, reinforcing the branches that get lucky, culling back the ones that don’t. It doesn’t have a top-down view of the world, but it’s still strategic and adaptive. When I look at most of the existing experiments in this space, I feel like we’re putting a slime mold into a narrow tunnel and measuring how fast it can get to the other end. We’re accidentally putting evolution in a straight jacket, and blinding ourselves to what makes it so interesting and powerful.
So, in my first experiment, I try to show a different perspective. I made a single algorithm that can adapt itself to solve many different tasks. Normally, an EC programmer picks one task to solve, then designs an evolutionary search strategy to suit that problem. They invent a genome language, a way of turning that into a solution, and ways of randomly tweaking the genome that might lead to better solutions. In my experiment, I evolved the search strategy, too. As the programmer, I designed a vast and open ended search domain, and many ways that the algorithm could restrict that space. But I wasn’t sure which restricted sub-spaces would work best, and, unlike traditional EC, I didn’t try to guess. I just let the algorithm figure that out for itself.
The way I did this is also interesting. It turns out, the algorithm I invented is strikingly similar to one that’s already popular: “compositional pattern-producing networks,” or CPPNs. Again, it was a little frustrating to be scooped, but I’m using this algorithm in a new way. Instead of evolving new “bodies” for simulated life, I’m evolving new ways of generating bodies. It’s a subtle difference, but an important one, I think. That extra level of indirection gives evolution more influence over its destiny, and the power to make more complex patterns in ways I couldn’t even anticipate. Now that I know how my idea is so similar to, yet different from, an existing algorithm, I’m teasing apart those differences, to measure the impact of each one.
I’m proud of my work, and excited to talk about it with other EC enthusiasts at GECCO. On the other hand, I’m still figuring out how to do science, and there’s a lot I don’t like about my first paper. This project was mostly my way of proving to myself that this crazy idea could work. The results are intriguing, but it’s not yet a clear example of what I want to show. It’s also complicated, unusual, and hard to explain, even to other EC researchers. If I want people to get excited about this, I need to simplify, make my work more relatable, and find better ways to demonstrate and measure the novel behavior I’m talking about here. There are no “obstacle courses for slime molds” in the EC literature that I know of, so perhaps I’ll need to design some.
Hopefully, I’ll get lots of inspiration and feedback at GECCO. As I learn more about the field of EC, I’m finding more and more examples of work similar, yet slightly different, from my own. This is great, because each of those differences is an opportunity for a new experiment, to see if my perspective can shed light on something new. I’m already dreaming up all sorts of new ways to explore my ideas. And that’s more or less how I hope to spend the next several years. Maybe that’s my PhD.
In any case, I hope that explanation was interesting, and not too vague. I’ll get more specific in a few weeks, when I post a follow up with the full GECCO paper, the poster I presented, a video summary of that poster, and links to some supplemental results and analysis. I bet I’ll have some fun things to report from my time in Melbourne, too! As always, I’d love to hear from you in the comments.
Thinking with Nate 