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Lumifer comments on Open thread, Jul. 11 - Jul. 17, 2016 - Less Wrong
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Evolution. Schedules are competing for being there. Every second 10000 or so are born and are mostly killed by the control program which let live only the top schedules according to the 30+ criteria set in the script.
Random (but perhaps clever) mutation and non-random selection, that's under the hood.
At first, the top schedule is a random one and not feasible at all. After a million (or a billion, that depends) generations the first feasible one appears and from there on, evolution produces more and more perfect schedules.
For every processor core, at least one evolution is going on. Each at least slightly different one. The program can spread across many computers and there may be as many as 100 or more parallel evolutions going on. They talk occasionally (via internet) and exchange their champions.
It has been 10 years long real life experiment, which went very well. A lot of schools involved, teachers and students and some academic papers published. Now it's time to spread it.
so, parallel genetic algorithm based scheduling app with (ranked?) constraints?
In what way is it more automatic than existing similar apps?
presumably you still need to give it a list of constraints (say a few thousand constraints), possibly in a spreadsheet, some soft, some hard and it spits out a few of the top solutions or presumably an error if the hard constraints cannot be met?
What can it do that, say, optaplanner can't do?
I wouldn't say it's "genetic algorithm", I prefer the term "evolution algorithm".
We did some testings. For example, we took some existing schedules and optimized them with our tool. The difference was substantial.
We also did some packings of circles inside a square and some spheres inside a cube, denser than it has been previously achieved.
We have built some 3D croswords 8 by 8 by 8 letters with no black field at all - field with English words.
I don't know if optalaner can do the same. I think not.
Every constrain has its own user specified weight. From 0 to 10^12 and every integer inside this interval. This is the measure of how soft or hard a constrain is.
If your algorithm is actually the best-of-class for this problem, there are serious applications for it outside of schools.
I know that. But my focus in this thread are North America's schools as a big market.
But yes - how good this algorithm really is? Where is its optimal domain?
I guess, evolving algorithms is the best usage. Either from a previous known algorithm, either from scratch, either from data. Like evolving Kepler's law from planetary data. I wrote a post about that here, a few years ago.
http://lesswrong.com/lw/9pl/automatic_programming_an_example/
The thing is, it's a very fragmented market. The US schools are local, basically run at the town level, so for you it is essentially a retail market with a large number of customers each of which buys little. I'm guessing that you'll need a large sales organization to break in.
Or possibly to find an existing company selling office/organization/planning software that's already got a big share of the market and selling them license to the tech.