1000s, there is a whole field and set of conferences. You can find more by searching "Genetic Programming" or "Symbolic Regression"
KAN, with the library of variables and math operators, very much resembles this family of algos, problems, and limitations. The lowest hanging fruit they usually leave on the proverbial tree is that you can use fast regression techniques for the constants and coefficients. No need to leave it up to random perturbations or gradient descent. What you really need to figure out is the form or shape of the model, rather than leaving it up to the human (in KAN)
KAN, with the library of variables and math operators, very much resembles this family of algos, problems, and limitations. The lowest hanging fruit they usually leave on the proverbial tree is that you can use fast regression techniques for the constants and coefficients. No need to leave it up to random perturbations or gradient descent. What you really need to figure out is the form or shape of the model, rather than leaving it up to the human (in KAN)