Evosim

A simplistic Evolution Simulator that generates random creatures and grades them based off their genetic fitness.

For one of my independent study projects in school, I chose to combine my interests in biology and computer programming by creating an Evolution SImulator. The idea stemmed from a conversation I had with my biology teacher. We discussed how when most people think of evolution, they incorrectly think of all creatures of a species as slowly striving towards a better version of themselves. When in reality, slight mutations may give a single creature superior genetic fitness, thus allowing it to have more offspring to pass on its genes. I believed this phenomenon could be easily illustrated by simulation, which is my I created my Evolution Simulator.

These images are screen captures of my Evolution Simulator that psuedo-randomly generates soft-bodied two-dimensional abstract creatures. A creature is comprised of a set of joints and muscles that each have specific values and attributes associated with them.

When hovering the mouse over a creature in the preview area, the user interface changes to show a simulation preview of that specific creature. The simulation preview shows the creature during its lifetime, and several values about the status of the creature.

When pressing Run Simulation after the first generation of creatures has been created, the simulator will elimate 500 of the creatures, and have the remaining 500 reproduce to bring the total back to 1000. Creatures will try to create a carbon copy of themselves, but there is a slight chance of mutation that can change some of the attributes of the joints or muscles.

In this picture I have let the simulation run for 100 generations, and you can clearly see the values rise in the Fitness chart, and how a specific species of creature dominated the population from the Diversity chart. In this particular seed, the creatures did not evolve very much from the beginning, but I have experimented with other seeds that have the creatures tripling or quadrupiling their initial fitness values.