At ECAL 2015, Tim Taylor, Mark Bedau, and Alastair Channon organized a fascinating workshop on Open-Ended Evolution, which I presented at (you can watch the video here, but this post will basically cover the same points). Several of us in the Devolab have been thinking about this topic for a while; below is a collection of our thoughts for the sake of continuing this discussion. The question of open-ended evolution emerged from a practical place: organisms and ecosystems in computational evolutionary systems were far less diverse, complex, and interesting than those that seen in nature. The people studying these systems were concerned that this was the result of a fundamental limitation to the systems (although some have also argued that this is just an issue of scale). They began characterizing the dynamics of these systems in […]
I’ve recently published a paper on inferring the ruggedness of fitness landscapes by the clever use of spatial structure. We call this effect regarding the rates of adaptation, “The Tortoise-Hare Effect.” But instead of repeating myself, I’ll link to a couple summaries.
In a later post I’m going to introduce a new artificial life system which I’m working on. This new system is based on Markov Network Brains so I figured I’d take a little time to talk about them. Markov Brains use binary variables and arbitrary logic to implement deterministic or probabilistic finite state machines. They have been used to study behavior, character recognition and game theory among other topics. The majority of the work with Markov Brains has been done in Chris Adami’s lab. A Markov Brain consists of 3 parts : a set of binary variables called the Brain State a collection of logic gates connections between the variables and the gates
“What I cannot create, I do not understand” — Richard Feynman In the Devolab, we use artificial life systems to improve our understanding of evolutionary dynamics. Specifically, we perform experiments on populations of self-replicating digital organisms that evolve in a natural and unconstrained manner. But why did we decide to focus on artificial life? What are the advantages and drawbacks of using these relatively complex computational systems?
My research has long focused on understanding how simple processes can produce the amazing levels of complexity and diversity we see in nature. This past week, we had a paper appear in PLoS Biology that I am particularly pleased with. In it, we use digital organisms to explore how the interactions between hosts and parasites can promote the evolution of new complex traits, even when those traits would otherwise be costly. Zaman L, Meyer JR, Devangam S, Bryson DM, Lenski RE, and Ofria C (2014) Coevolution Drives the Emergence of Complex Traits and Promotes Evolvability. PLoS Biology 12(12): e1002023. doi:10.1371/journal.pbio.1002023. Researchers have long understood that coevolution produces rapid evolutionary changes: parasites race to find new mechanisms to infect hosts and in turn those hosts are pressed to keep evolving new defenses, just to survive. This effect […]