Meeting Notes
next week we need to have a meeting, ONLINE!!!!
Gecco deadline is January we should have results and bits of text by next week
“Man will do anything in order to avoid the true difficulty of thinking” -proverb by Lee
Go Zeke’s publication for Chemistry!
Options with Ranjan
Do linear regression guided by Ranjan on problems in chemistry that we have no idea about
or we could try and evolve a material (organic or inorganic), which is evolved with fabrication and physical testing
– takes about two days to do each generation
– we need to focus on making each result good, rather then a hodgepodge of garbage with gems in it
– Fitness prediction would be nice!
– in between: simulate enzymes and run GP on them, no physical testing
Ranjan’s STUFFFFF
-mechanistic deterministic molding of biological and chemical systems
– has a teammate (Chris Corneleus) who can do polymers and stuff, but Ranjan doesn’t have supplies to do that
– there’s a big initiative to do materials stuff called “materials genome”
– Weird stuff happens when you mix polymers, it is not always the geometric mean, definitely non-intuitive and non-linear
– It is mostly explored with intuition
– applications range: water filtration, fuel stuff,
– we want problems where “the proof is in the pudding”, so this is bawler
– we would be making recipes for these polymers
– the throughput is hella low throughput, so we need to make sure that it is unusually likely to land you in good places of a searchspace, and mutates/crosses you over towards that
– note we are evolving every part of the recipe
– based on experience manipleting the temperature matters a lot
– the starting components and processing are both incredibly important (particularly looking at water filtration)
– the tests would be assessment of water purity, flow output (diffusion of the polymer), swelling of polymers
– so non-linear that regression sucks like hell, the systems are multidimensional and non-linear
– ok, not nessisairily the case that symbolic regression has not been tested with it
– there are weird metrics like torchuosity, which is a metric of how strangely a molecule moves
– potentially be useful to run grammatical evolution or developmental evolution (so the programs can be weird, by changing stuff and such things, but the starting seed is always good)
– the seed formula is take two material mix them, heat them, cool them
– getting the max yield from a small sample size is very, very, very important
– check out evolutionary robotics field
– simple story there is a shit ton of fitness cases, and then they look at situations in which the population varies significantly and then only looks at those fitness cases
– unfortunately that is not a perfect match
– josh bongaurd used a simulation for most of the tests, but would occasionally do a real life test which they would use to improve the simulation
– we could try to evolve the simulator using fitness predictors
-yay, there’s a lot of data already!
– there is a lot of proprietary going into parrot front
– there’s some data already
-probs at least on the scale of a few hundred
– that’s enough to at least to start
– making something to predict efficacy of future recipes based on current recipes and their results
– what step was x or y
– try lots of heuristics
– want to make sure we give the real test predicted solutions which are distinct, not just successful ones
– do we keep individuals from the last test in the new test
– Solubility is unpredictable, simulated spectra would be nice, we could do it, there’s also a shit ton of data on it already
– we could do attempt something to do with enzymes and protein folding and doing predictions (sounds scary)
– what if we could make something sufficiently good to do a denovo
– in protiens there is a sequence space, a shape space and a function space, they’re hella weird to predict and are not always super successful attempts
– they’re a bunch of stuff which suggests that shapespace may not even exist
-would this be from data or simulation
– it’s not got much data, but if you choose the right problem it could work
Lee’s writing on the board: zee’s writing – we start with data that goes like { recepe: measurement }, …
we have a population of R:M pairs
we evolve a function that given an R gives an M
we need to figure out how to chew up R
that fitness predictor is used in another found of evolution
where we evolve programs that change a recipe to maximize it’s fitness in measurements
then we physically test a hundred of them, selected somehow
now we have a new (larger) set of RM pairs
-a shit ton of ideas about how this should work
– fitness predictors (how many, do we do lexicase selection, do we do multiple runs)
– should we choose a diverse set of programs from the fitness predictor set (to clarify one’s that are distinct and will produce interesting darts on the search space)
what are scopes good for?
gsxo? kata bowling?
when you don’t have scopes it seems clearly like a bad idea to do something that operates not the whole stack.
code map does crazy shit
could also be used for strings
Pre-Meeting Agenda
- Ranjan Srivastava will be joining us
