A quick introduction to Markov Network Brains

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 :

  1. a set of binary variables called the Brain State
  2. a collection of logic gates
  3. connections between the variables and the gates

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Playing With Evolution In Artificial Life

One of my stepping stones towards becoming an evolutionary biologist was playing with engaging programs that combine evolution and artificial life. Although these “games” are often neither intended for educational or research purposes, I found them instrumental in developing an appreciation for the power and creativity of evolution by natural selection. Here is a collection of my favorites in no particular order:


The body plan for an organism from the Bitozoa program.

The body plan for an organism from the Bitozoa program.

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Fun with GECCO 2014, ALIFE 2014, and Zotero Paper Machines

I recently discovered the Paper Machines add-on to Zotero, which allows you to perform visualizations and topic modeling analyses on papers in your Zotero collection. I just so happened to have the complete proceedings of both GECCO 2014 and ALife 2014 kicking around in my Zotero database, so I decided to try comparing them. As a quick background, GECCO, which focuses on Genetic and Evolutionary Computation, and ALife, which focuses on Artificial Life, are the two main computer science* conferences that we in the Devolab tend to go to. There is substantial overlap between these conferences (GECCO has an Artificial Life track, after all), but there are also some fundamental differences in approach and focus.


Word cloud of the most commonly used words in papers presented at GECCO 2014.

Word cloud of the most commonly used words in papers presented at GECCO 2014.

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Using Artificial Life to Test Evolutionary Hypotheses – Part 1: Why Artificial Life?

“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?

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7 Things I Learned From “Evolutionary limits to cooperation in microbial communities”

Back in December PNAS published a paper from Oliveira et al. called “Evolutionary limits to cooperation in microbial communities” [1]. My research interests lie right at the intersection of evolution and cooperation so I was fascinated by the idea that evolution imposes limits on cooperation. In this paper, Oliveira et al. examine the evolutionary dynamics of a community of microbes that can exchange a number of valuable secretions between different strains. This experimental setup enables the evolution of cooperation if one genotype focuses on producing one secretion and shares that secretion with a different strain while also gaining access to that other strain’s secretions. Ultimately, however, they found that cooperation only evolved under specific and limited conditions because of the fitness decrease that occurs when an individual isn’t close enough to receive secretions from another strain.

Because I enjoy reading papers that are not in my immediate specialty, I frequently track the interesting information I learn in both the background literature review section as well as the main results of the study. In this feature, I discuss those bits of information that I found relevant to my interests and just generally cool.

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Making Efficient Animations in Matplotlib with Blitting

I flip-flop between Python, R, and D3 for my data visualizations depending on what exactly I’m doing. My default, though, is definitely Python. One of the most well-established data visualization libraries in Python is Matplotlib. If you dig deep enough in it, you can find a wide variety of features beyond standard graphs. One of the less well-documented of these features is the animation library. The FuncAnimation class in particular is quite powerful, allowing you to programmatically generate the frames for your animation and compile them together. Jake VanderPlas has a great tutorial on using FuncAnimation which I’m not going to try to duplicate. Here, I’m just going to focus on a small but critical aspect of using FuncAnimation that is glossed over elsewhere: blitting.

Here’s how critical blitting is: My first attempted Matplotlib animation took around an hour to render. That wasn’t going to work. Thanks to blitting, I can now render the same animation in under a minute.
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Spatial Co-evolution in Age-Layered Planes

I recently read a cool paper from GECCO 2012 called “Spatial Co-Evolution: Quicker, Fitter and Less Bloated” [1] by Robin Harper. This paper explores some benefits of the Spatial Co-evolution in Age-Layered Planes (SCALP) algorithm, originally described by Harper in a previous paper (paywall) [2]. In SCALP, the population lives in a three-dimensional grid of cells. Each cell is inhabited by both a solver (the “host”) and a test case (the “parasite”). Solvers try to find the right answer for each test case that is either next to or below them. They gain fitness by more closely approximating the desired answer. Test cases, on the other hand, gain fitness by being hard to get right – fitter test cases are those for which solvers have higher error.

That explains the “spatial co-evolution” part of the algorithm, but what about the age-layered planes? That’s what the z-axis in the three-dimensional grid controls. Just like in Hornby’s ALPS algorithm [3], the age of the lineage controls which XY plane a given solver or test case lives on. As lineages get older, their members move up. The bottom layer is periodically randomly restarted. This ensures that diversity keeps getting added to the population, and that new solutions aren’t immediately wiped out by well established ones.
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7 Grad School Survival Tips

As many of us are starting the winter semester, it seemed an opportune time to discuss surviving not just the winter weather (here’s hoping Michigan’s winter isn’t as bad as last year!), but the rather mentally and emotionally trying process that is graduate school. I’m currently in my third year at MSU and am just finishing up classes, so these tips are only guaranteed to be even partially relevant for your first few years, but hopefully they continue to help me and you in our final years as well!
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Happy Holidays from the Devolab!

Happy Holidays!

We hope that you’re taking at least a bit of a break from pipetting and typing sometime last week or this week. In the mean time, here’s a pretty graph to add to the flashing lights:

Flame Graph of Organism Length in Avida

Genome lengths of all organisms at each time point in a run of Avida with limited resources.


An Academic Christmas Carol: Part 7

Missed the beginning? Parts 1, 2, 34, 5, and 6.

Part 7

Scrooge awoke with a start. He was in his own bed. And seemingly alive! What a relief! He bounced out of bed and ran to his window. Looking down, he saw a student walking by on the street outside and called down to her “You there! What day is it?”

“It’s Christmas day, sir!” the student called back, looking a bit perplexed.

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