Open olewhalehunter opened 7 years ago
https://jorgetavares.com/2017/05/03/gp-code-on-github/ is this yours?
"core-gp" looks promising, but it could do with better documentation, enough to where a beginner-level lisp programmer could get started with genetic programming.
Thinking about the "next big thing", having a good GP framework sounds increasingly beneficial. Cf. Neuroevolution, https://arxiv.org/abs/1703.01041
Related: Automated parameter tuning (e.g. ParamILS http://www.cs.ubc.ca/labs/beta/Projects/ParamILS/)
this is the literature I'm currently reading: http://www.informit.com/store/genetic-algorithms-for-vlsi-design-layout-and-test-9780130115669
some other considerations for a general framework are parallelism and distributed processing
parallelism and distributed processing
If you are talking about parallel GA then yeah, but is something that should be done later.
In terms of "parallel computation" in general, we already have several frameworks: For embarassingly parallel computation, lparallel and its distributed variant do the enough jobs. For HPC tasks requiring much better coordination I recommend CL-MPI. Personally I do not feel the need of new libraries in that field.
Do you think this is a good place to go? https://github.com/guicho271828/dynotune
Note: The old dynotune is renamed to dynotune-failed. I abandoned the idea and reuse the name for this new project.
this is some great stuff
generalized parameter optimization is a bit esoteric to me, and likely the average programmer, any resources you would recommend to understand this library's semantics better? perhaps a concise review of optimization explained using expressions from dynotune would help in the readme
oh it just tries to find the local/global minima of the function
There are many Lisp libraries for domain-specific genetic algorithms but no good generalized ones (genome definition, problem space mappings, performance profiling, fitness functions)