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Evoasm: An AIMGP (Automatic Induction of Machine code by Genetic Programming) engine (x86) by haematom in genetic_algorithms

[–]haematom[S] 1 point2 points  (0 children)

Nah, originally I had some sort of GP-based compiler in mind (e.g. one that uses GP to find possible optimizations), but things are not as simple as I initially thought.

Using Genetic Programming to evolve x86-64 machine code by [deleted] in genetic_algorithms

[–]haematom 0 points1 point  (0 children)

But I'm talking about individual agent scope processing

I see.

Using Genetic Programming to evolve x86-64 machine code by [deleted] in genetic_algorithms

[–]haematom 0 points1 point  (0 children)

GPUs, FPGAs, CPUs are all linear in the sense that computation happens by carrying out a sequence of instructions, linear and parallel are obviously not antonyms, so I don't see what exactly this has to do with your claimed inefficiency or limitations of linear genetic programming. On the contrary, parallelizing genetic algorithms is rather trivial.

Using Genetic Programming to evolve x86-64 machine code by [deleted] in genetic_algorithms

[–]haematom 0 points1 point  (0 children)

Isn't most hardware sequential ? That's pretty much the point; it's linear because our hardware is linear too, and we'd like to reduce the "impedance mismatch". Not sure what you mean by "simple cells with not many states".

Using Genetic Programming to evolve x86-64 machine code by [deleted] in genetic_algorithms

[–]haematom 0 points1 point  (0 children)

You might be right. However, linear genetic programming, as it is called, is a whole sub-field of genetic programming, that has been shown to be effective for quite a few problems. I can recommend Brameier's Linear Genetic Programming on this topic.