Sure..and put the rest of us out of business? : ) Although it is interesting to think about what the make-up of the genetic material would be. Just a string of 0s and 1s? CPU opcodes? Or maybe some higher level sub-units.
These have been tried. Historically Genetic Algorithms have represented their genotypes as bit strings. Some Genetic Programming systems use CPU instruction strings (pros: fast as heck, cons: can produce indecipherable spaghetti code that breaks when you remove those 400 NOOPs because it's adapted to the cache size of this particular CPU).
> Or maybe some higher level sub-units.
These too. Sometimes as trees of instructions, dataflow diagrams, FSMs and so on.
It would be a really interesting challenge to get broad enough coverage in the fitness function that things like the previously-mentioned "400 NOPS" don't cause problems.
Besides unit (or otherwise) tests, it should also be possible to use static analysis to measure fitness and/or use a static type system to restrict the possibilities. I'd think this would have some of the same problems, though- anybody know of any work on that?
All the branches of evolutionary computing agree that evolved systems are really good at finding flaws in your problem statements. Most of the evolved systems you see in production are not the product of the first attempt to evolve the system.
> Besides unit (or otherwise) tests, it should also be possible to use static analysis to measure fitness and/or use a static type system to restrict the possibilities. I'd think this would have some of the same problems, though- anybody know of any work on that?
Type consistency for GPs is a whole subfield in itself. See the textbook I referred to, it has a chapter on it from memory.