Genetic Programming: Difference between revisions
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* Evolve a selection function for example. |
* Evolve a selection function for example. |
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How do I avoid infinite loops |
How do I avoid infinite loops? Some hard limit? |
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Does uninitialised memory need to be avoided? |
Does uninitialised memory need to be avoided? |
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Random numbers. |
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* Need to be reproducible. Make a random number generator generator. |
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Revision as of 22:46, 1 November 2016
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.
- Evolve a selection function for example.
How do I avoid infinite loops? Some hard limit?
Does uninitialised memory need to be avoided?
Random numbers.
- Share the same random number for all programs in the same run?
- Need to be reproducible. Make a random number generator generator.