Genetic Programming

Possible targets

• LLVM - Can run as bytecode or compile to machine code.
• Web Assembly - New.
• JVM - I hate Java.
• MonoVM - Meh.
• SPIR-V - For the GPU. Can run naively (and fast), but would need to learn Vulkan/OpenCL at the same time. Problems with the GPU<->CPU differences.
• X86-64 - Native but CISC... Could use a subset...
• RISC-V - I love RISC-V (needs to be emulated).
• AVR - For a microcontroller (needs to be emulated).
• ARM - For a microcontroller (needs to be emulated).
• MIR - Rust intermediate level. Not sure if it would be any advantage over LLVM.
• BrainFuck - It's simplicity be good for genetic programming?
• GPTree - A Genetic Programming AST. Traditional.
• Maybe some made up IR that is then converted to X86-64, SPIPR-V or whatever (Isn't that LLVM bytecode?).

Ideas

Make the genetic program evolve genetic algorithms.

Break it up into functions. Evolve selection, crossover, mutation functions for example.

Maybe allow higher levels or program to evolve. Give some kind of weights to the chance of something evolving.

How do I avoid infinite loops? Some hard limit?

Does uninitialised memory need to be avoided? Or could that be useful.

Is is possible to evolve the algorithm of the program evolving the algorithms. Ie pick the current best? Or would that evolve itself into a corner?

Can it evolve new functions/subroutines? Should they be shared in some kind of pool? Is that some kind of 'crossover'? Unused functions get pruned?

Fitness

Can't evolve fitness functions...?

• Speed of evolution.
• Best solutions.
• Efficiency.
  • CPU Time?
  • Number of instructions?

Random numbers.

Share the same random number for all programs in the same run to prevent randomness messing with the evolutionary randomness?

Need to be reproducible. Make a random number generator generator.

How do I do this in ASM?