Thomas Ray's Tierra is a computer program which simulates life.
In the linked paper, he argues how this simulation may have real-world applications, showing how his digital organisms (computer programs) evolve in an interesting way: they develop novel ways of replicating themselves and become faster at it (he argues that the evolved organisms employ an algorithm which is 5 times faster than the original one he wrote).
Tierra's approach is different from standard GAs:
- While in GAs usually there is a set of genomes manipulated, copied and mutated by the program, in Tierra everything is done by the programs themselves: they self-replicate.
- There is no explicit fitness function: instead, digital organisms compete for energy resources (CPU time) and space resources (memory).
- Organisms which take a long time to replicate reproduce less frequently, and organisms who create many errors are penalized (they die out faster).
- Tierran machine language is extremely small: operands included, it only has 32 instructions. Oftentimes, so called RISC instruction sets have a limited set of opcodes, but if you consider the operands, you get billions of possible instructions.
- Consequentially, Tierran code is less brittle, and you can mutate it without breaking the code. In contrast, usually, if you mutate randomly some machine code, you get a broken program.
I was wondering if we could use this approach to optimize machine code. For instance, let's assume we have some assembly-like program which computes a certain function $f$. We could link reproduction time with efficiently computing $f$, and life-span with correctly computing it. This could motivate programs to find novel and faster ways to compute $f$.
Has anything similar ever been tried? Could it work? Where should I look into?
