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Think this way: if there are easy ways to improve, it should be already within every nano-sized pattern matcher.

And, most changes are making things worse simply means you may be already at some local minimum, which is discovered by the predecessors. The "improvements" you easily think of may be already tried by predecessors as well, and the ones that work are already applied.

So, to push the bar further, just go the "hard" way. Record some data of every opponent you need to face, encode some behavior of your gun into genes, and use genetic algorithms to find a way out!

Ah, I was hoping there were some simple ways that took a bit more codesize. Since the nano-sized pattern matcher I am using is not in a NanoBot, I can afford better performance for more code. I have quite a few bytes to spare in Basilite and I was hoping for changes that were easy to put in and gave small gains, like energy management for example.

Then don't look at pattern matchers in nanobots, look at those in microbots, minibots and even megabots. And energy management is mostly independent of gun type I think, as long as they are learning. So learn from gf targeting bots' energy management as well.

OK. I'll check it out ;)