Evolve Your Own Army of Drunken Walkers and Other Educational Evolution Simulators

Want to understand evolution better, or explain it handily to others? Evolution simulators can be a great way to explain the concepts in an easy-to-understand way. I’m hoping to run a short discussion series on different Christian perspectives on evolution and human origins at my university this spring, and the first goal will have to be making sure everyone understands what evolution actually is. Given the huge differences in understanding and acceptance of evolution between life scientists and members of conservative Protestant churches in North America, this is an important conversation to have.

To that end, I’ve collected a few neat evolution simulators to help explain some concepts. These won’t give you a full understanding of how evolution works, but they are handy and fun ways to understand some of the concepts better.

Rednuht Genetic Walkers
This is definitely the most silly/fun evolution simulator I’ve found. The premise is that a bunch boxy humanoids start in a standing position, and more-or-less randomly move their limbs to walk. They are very bad at walking. The most successful walker is the one who manages to get the farthest. The race ends when all of the drunken walkers have fallen over, and the next generation spawns. The winner is cloned a bunch of times in the new generation, and also randomly mutates in some cases to act differently. Over time, more successful mutants develop and get less terrible at walking. Essentially, this is a computer learning to play QWOP by using brute force problem solving.

Some of my evolved walkers. The guy on the left is about 1 second from a faceplant.

Besides the delight of watching hundreds of boxy humans fall on their faces, this also demonstrates a couple important points of how evolution works:

1. Optimization based on “success”: Here “success” means getting chosen by the program after walking the farthest. In biology, “success” means reproducing. Whoever reproduces the most for the next generation “wins” and individuals like the winners end up more represented in the future generation.

2. Brute-force problem solving: Mutations are the random outcomes of chemistry, so evolving a way to get more “success” is a brute-force process of making guesses at a solution. As a result, the solutions aren’t always very elegant, but they do work. This is a bit more apparent in the Rednuht Genetic Cars program. The best cars can be pretty awful, but manage to limp along the farthest and win the race.


Bacteria MEGA Plate Experiment

Researchers over at Harvard set up this experiment to show how evolution occurs over space and time. It’s pretty spectacular! The set-up is a huge agar gel (sort of like Jello, only for bacteria to grow on). Stronger doses of an antibiotic are in the gel towards the center. Each time the bacteria reach the frontier of a stronger antibiotic dose there is a pause in growth until some mutant can grow in the stronger dosed area. There are different solutions to the same problem (as seen in evolving cars or walkers above). Eventually some mutants always manage to find a way to adapt.

I think this is my favorite tool for explaining evolution some concepts of, because it shows so much so simply:

1. Brute-force problem solving: The bacteria were genetically analyzed after the experiment, and different solutions were mutated to solve the same problems. Some of these mutated solutions let some strains grow faster than others, while other solutions led to slower growth but were a quicker solution when faced with a stronger dose of antibiotic.

2. Biogeography and niche filling: Getting to the center of the plate isn’t the only way to “win” for the bacteria here. It’s just one way of winning. Some bacteria lived happily on the edges with no antibiotic around, and that’s a win for them. The wide-open frontier of unused space (niche) in the middle is an opportunity for mutants, and that’s a win for them. The end result is different bacteria filling different spaces in the environment. We see the same thing everywhere in the world, with life finding different ways to live in pretty much all the different environments found on Earth.

Genetic and physical map of the different bacteria strains evolved during the experiment.
Genetic and physical map of the different bacteria strains evolved during the experiment.

3. Cladistic nested hierarchy: At the end of the experiment, lines are drawn in the video to show the different branches of mutations that showed up. This branching pattern is called a nested hierarchy, and it’s the result of multiple speciation events from a common ancestor. We see the same pattern when comparing genes from many different types of creatures. That makes common descent a pretty obvious conclusion.

A nested hierarchy of the gene for making vitamin C. Figure adapted from Biologos at: http://biologos.org/common-questions/scientific-evidence/evolution-evidence
A nested hierarchy of the gene for making vitamin C among some mammals. Figure adapted from Biologos at: http://biologos.org/common-questions/scientific-evidence/evolution-evidence

Red Lynx Population Genetics Simulator
This is the most powerful evolution simulator I’ve found, but it’s unfortunately also the least entertaining. Red Lynx considers how an allele (version of a genes) behaves in a population over time. It lets you specify mutation rate, population size, # of generations, migration, allele dominance, allele starting frequency, and selection strength. Once set, the program calculates the outcome and gives you a tidy graph. If you’re familiar with the Hardy-Weinberg Equilibrium Law, you can set it to evolve the way you want. Or you can just experiment with the settings and see how different factors affect evolution.

Here are some examples:
1. Genetic Drift: With a low population, even completely neutral alleles can end up completely dominant (fixed) or get cleaned out of the population entirely. This random fluctuation of allele frequency in a population is called genetic drift. Small populations drift more than larger populations.

Three runs of Red Lynx with a low population (800). The allele has no advantage, but becomes the only allele in the population 2 out of 3 times.
Three runs of Red Lynx with a low population (800). The allele has no advantage, but becomes the only allele in the population 2 out of 3 times.

2: Selective forces: Since it’s inception, selection has been the name of the game in evolution. All else being equal, even a small level of selection can drastically shape a population over time.


Those are the best evolution simulators I’ve found so far, but I’m always keen to find new ones. Evolution might be the most misunderstood scientific concept in North America, so there is still a long way for science education to go here. Easy to understand examples are a great way to address the problem. If any readers find other cool evolution simulators, do let me know!

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