Above: Phenotypes from 4 different runs of the same evolution. The goal was to reach a fixed target number of nodes in the colony, in this case 13. The genomes for each shape are sub-programs composed of primitive functions (see genetic programming). This was possible because an important error was removed from the code. See below.
Evolution runs prior to this post were excruciatingly slow. I attributed this, naively, to each growth simulation taking a long time. I even made the simulation runs take half as long, by making the collision computation more efficient. But the evolution runs were still frustrating me. On some level I knew, perhaps intuitively, that they shouldn’t be quite so slow. Individuals simulation runs were pretty quick. So it was frustrating and bewildering that the evolution took so long!
Looking Under the Hood with Dots
At this point I recalled a conversation with my friend Amiti, who is a software developer, about running scripts that take a long time. She mentioned that when dealing with these long computations, she’s gotta see something happening, even if it is just printing out periods or dashes. So I had the evolution script print a “.” each time it did a simulation run. At first I saw the dots appear quickly, like the pattering of rain in a puddle, but then the intervals got longer and longer. Each dot succeeded the next with increasing sluggishness! This definitely wasn’t right. For a sanity check I added a timer and verified that the simulation runs got progressively longer during the course of an evolution.
From there it was pretty easy to deduce that some sort of memory-leak phenomena was occuring. Turns out I was re-using an instance of an object which ‘grows’ the colony. I switched the code to re-instantiate the object each time a simulation ran, and the problem went away. I’m not even sure exactly why this was happening, but for the time being it doesn’t matter. The evolution runs are fast enough now to have a decent population (50-200) and have multiple simulation runs per genome. This is necessary since there is some random variation between each simulation run for the same genotype.
So lesson learned: The lowest cost change that gives any indication of what is going on is highly valuable. Printing dots was more helpful than a fancy tool like profiling, which led me down a bit of a rabbit hole trying to figure out how to use the tool. I am sure profiler’s have their place for me, but only after the simpler methods have been exhausted.