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Comment by strags | Hacker News Reader

From personal experience, I've always had better results from Simulated Annealing than Genetic Programming. I hasten to point out that it's entirely possible I've been doing GP poorly - but part of the problem seems to me that GP has so many more knobs/dials/parameters that need to be tweaked correctly in order to yield good results.

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Are you referring to GA or GP? GP is not comparable to simulated annealing, as it's kind of non-parametric.

Many ideas of GP are getting reused in Bayesian program induction, in conjunction with differentiable programming, SAT solvers, etc [1]. IMHO a very promising route to AGI.

[1] https://web.mit.edu/ellisk/www/documents/dreamcoder_with_sup...

These are orthogonal things. Simulated annealing is an algorithm. Genetic Programming is pursuit, which can (among other things) use simulated annealing to achieve its goal. Are you sure you're not confusing this with something else? Did you mean Koza-style GP perhaps? Or did you mean a genetic algorithm (GA)?

Results on what? The effectiveness of an algorithm depends on the problem you target.

Edit: Also, if you broaden the definition just a bit, you could call simulated annealing a kind of genetic program.

Do you have any good references for SA? Thanks!

Just read wikipedia. [0] is also a good high-level overview of the different algorithms in metaheuristics.

[0]: https://cs.gmu.edu/~sean/book/metaheuristics/Essentials.pdf

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