https://homes.cs.washington.edu/~bornholt/post/synthesis-exp...
It's a much broader overview of the field so read that first if you need an intro!
https://www.microsoft.com/en-us/research/publication/program...
A lot of the example programs mentioned feel more like functions (stateless + deterministic mapping of a tuple of inputs to a single output).
In contrast, I think of a 'program' as having a longish lifecycle comprising a stream of inputs and outputs.
Relatedly, I'm not clear how well the approaches detailed here actually would scale with complexity - is this a meaningful building block in building more complex applications? Or is some higher-order framework needed that 'knows' how to apply this in a rich context?
[1] https://www.microsoft.com/en-us/research/video/flash-fill-fl...
[2] https://www.frontiersin.org/articles/10.3389/fbioe.2014.0007...
Most of the recent work in this field seems to be based around the idea of program "sketching", i.e. the programmer sketches a high-level specification, and the algorithms progressively synthesise and evaluate small functions / programs to efficiently implement the sketch.
No doubt machine learning researchers will have something to say about this though...
A big hall with computers, churning on calculations to end up with reusable modules/components for some predescribed purpose. These are combined and eventually larger systems emerge.
The inputs to the calculations were to be descriptions of what the program should do, no details on the "how". For instance, "make an input box for name and snailmail address, with cancel and ok buttons".
It was obviously warp engine level stuff when it comes to feasibility and I thought it would be very far away, like 100 years away... But maybe it is closer than that.
Tie generative methods in with solvers for hydrodynamics, motions, structures, stability, etc, and one can conceive of automating the ship hull design spiral, or sections of it anyway. That's not to say that some of this is not already out there in commercial software.
https://codon.com/hello-declarative-world#programming-paradi...
I’m not sure how the use of an SMT solver could be considered a genetic programming method.
https://homes.cs.washington.edu/~bornholt/post/synthesis-exp...
However they're using a SMT solver as part of the fitness function of the program.
You know - it needs the correct output, but you need to prevent overfitting, and if you have a formal proof of the program generated you can expend greater effort into reducing its complexity and optimising.
Again, it's an old new thing and possibly going to start getting hyped like neural networks have been of late.
