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

By Emily Dolson on February 10, 2015 in Review / 0 Comments
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. GECCO

Why use Artificial Life to Study Evolutionary Biology?

By Charles Ofria on February 3, 2015 in Research / 6 Comments
Why use Artificial Life to Study Evolutionary Biology?

“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?

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

By Anya Vostinar on January 27, 2015 in Review / 0 Comments

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 […]

Making Efficient Animations in Matplotlib with Blitting

By Emily Dolson on January 20, 2015 in Information / 2 Comments

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 […]

Spatial Co-evolution in Age-Layered Planes

By Emily Dolson on January 13, 2015 in Review / 0 Comments

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? […]