Evolutionary algorithms are fun. I solved a laser ablation modeling problem with them. The idea was to improve the quality of the produced data, by applying wha

Evolutionary algorithms are fun. I solved a laser ablation modeling problem with them. The idea was to improve the quality of the produced data, by applying what knowledge of the process we had. Laser ablation measurements work something like this: Sample -> Ablation -> Measurement -> Data Since there are several distortions that happen in the middle two steps, you of course get a distorted measurement. You'd like to get the Sample from the Data. Since we know pretty well what happens in the middle two steps, I evolved my theoretical Sample by starting with random data, running it through the model of the middle two steps (ablation and measurement) and then comparing the modeled random data to the experimental data. The fitness function was exactly this difference between experimental and modeled data. I took the best ones of each generation and crossed them over. I then added some mutations and watched the magic happen. It ended up taking most of the night for a very simple set (our experimental sets are mostly orders of magnitude bigger). I took screenshots of every new generation, and made a movie of the whole process. When I played the movie in the morning I had one of those rare and special Eureka! moments, when all that work pays off and then some. I presented that movie at a conference and stole the show :) Long story short, evolutionary algorithms are fun, but I failed to apply them at the required scale.