PGE is essentially a BFS/DFS traversal of the space of all formulas, by using local enumeration of the AST. The biggest gains where from eliminating duplicate work (commutativity / associativity) and not going down bad branches (too much complexity added for no meaningful change to output). A lot of overlap in ideas here, and a lot of open research questions that could be worked on (like can use use RL to help guide the search like A*). There's definitely an exponential explosion or wall as the AST gets wider / deeper.
At one point I wrote the core algorithm in Haskell, which made it so much more concise and beautiful, but eventually landed on python https://github.com/verdverm/pypge
In all of Genetic Programming / Symbolic Regression, everyone starts by trying to generate computer code and then switches to just math formula. They are different classes of problems because code has more "genes" and is order sensitive, whereas math is not
https://seminars.math.binghamton.edu/ComboSem/worm-chiu.pge_...
PGE is essentially a BFS/DFS traversal of the space of all formulas, by using local enumeration of the AST. The biggest gains where from eliminating duplicate work (commutativity / associativity) and not going down bad branches (too much complexity added for no meaningful change to output). A lot of overlap in ideas here, and a lot of open research questions that could be worked on (like can use use RL to help guide the search like A*). There's definitely an exponential explosion or wall as the AST gets wider / deeper.
At one point I wrote the core algorithm in Haskell, which made it so much more concise and beautiful, but eventually landed on python https://github.com/verdverm/pypge
In all of Genetic Programming / Symbolic Regression, everyone starts by trying to generate computer code and then switches to just math formula. They are different classes of problems because code has more "genes" and is order sensitive, whereas math is not