mighty biomorphin’ power rangers

It’s been a while since my last post, mainly because I’ve seen a lot of impressive work in various places and I’ve felt very small and insignificant in comparison. I also went on a training course recently which claimed that 80% of people achieve only 20% of their potential. As someone who likes to potter about and explore different things, I know that I fit into this category. Perhaps I ought to buckle down and concentrate on one particular thing. But the use of the word ‘ought’ is enough to put me off. Like most people, I’m not good at persevering at something I’m not been interested in.

I’d been feeling this despondency to such an extent that I was beginning to wonder whether I’d reached the end of my little experiment in genetic algorithms. For various reasons I’d not had a session with Ollie on Skype for several weeks until tonight , and I was starting to wonder whether it was worth chasing down every little aspect of code. I know now how to implement these algorithms, so perhaps it was time to move onto something else.

Well, how foolish was that thought? Very.

Tonight’s session was stimulating. Ollie suggested a technique to overcome a particular coding problem (showing me that I needed to convert in one place from an Integer object to a primitive int), as well as getting me to think of a way to speed up the routine that checks whether each fly has been trapped by a web. Those are both very useful for the sketch, but what really got me interested was the discussion afterwards about where we could take this next.

I was getting bogged down in the appearance of the sketch and in the limited number of characteristics included in the coding. So far I’ve been using ellipses as a quick and dirty representation of webs. These are both boring and limited, but because the webs are a class, it would be easy to replace the ellipses with something else, such as Richard Dawkins’ biomorphs. Rather than use human intervention to select which creatures breed, they could be ranked on their success in trapping flies. This opens up a lot of possibilities. If a fly successfully moves past the outer edge of the biomorph, then an inner arrangement would be useful to add another layer to trap it. This means that sheer size isn’t the only aspect of a successful predator.

The more successful webs could have more energy to use in the next generation, so some might evolve to require lots of energy but therefore need to trap a lot of flies, while others might need less, and therefore survive on very few flies. It would be possible to reach a point where some creatures have evolved so far from each other that they can’t breed with each other. The flies could evolve too. Very soon there could be enough complexity that it would be difficult to predict the results. Suddenly I’m very interested again in this exercise.

Tags: algorithm, genetic, processing