I had a thought the other day - I think Troy has done a really great job with the cracking the code content to demystify a lot of the different components of picking technique.
I think we’re almost at a point now where we could write a big long list of every variable we know about and start trying to model things in a computer.
An evolutionary algorithm is basically a name for a computer program that models a number of different parameters in a computer, and then tries to optimise for some heuristic by combining some of the principles of evolution.
It starts with some initial values, a way to mutate these values (like randomisation, for example), and a way of deciding which of a population is “doing well” or “closest” to what is being optimised for. It would then generate population after population after population, each population’s parameters being informed by the winners of prior populations.
This can result in some fairly amazing things…
This is one of my favourite examples, which even models muscles and neural delay, and optimises for a “natural gait” (it’s amazing to watch!):
It’s an interesting thought, right? I know for a fact I’m not in a position to do this type of research myself. But I wonder if it’s feasible?
Do you think a bright computer science student somewhere out there could uncover some interesting lessons for us?
e.g, imagine optimising for all sorts of different scenarios:
- 1 NPS, has to be alternate picking
- 3 NPS, no creative constraints (tweak constants like action of strings, flimsiness of pick, so on and see what happens to optimised solutions)
- Yngiwe malmsteen licks, does it come up with the yngiwe malmsteen picking system? (maybe you’d have to nudge it in the right direction when optimising but it would be fascinating if it derived it)