Example: Simple & fast implementation of an Random Optimizer without Galapagos

Hello,

due to the closed "Samples & Examples" branch, I post this short code example in this section of this forum.

During the last months, several question came up on how to do things before or after Galapagos if some parameters changed or whatsoever. Most problem-solving tend to break the grasshopper logic somehow to do variable change. However, most people didn't think of implementing a simple solver by themselves.

The whole term of "genetics in the design process" tend to imply, that Galapagos is something extremely awesome and not reproducible in an easy way.

Don't get me wrong. I think Galapagos is a great and very abstract implementation of an evolutionary solver, but sometimes a simpler solution is good enough.

This implementation is very direct and not very abstract,

See in attached file: