Missing feature transformers and shallow pipelines

Hello, I want to use TPOT for feature engineering. Therefore, I chose a fixed model for TPOT like a linear regression model and the default configuration.

Having some features {x1,x2,…}, there are no feature transformation steps/operators in TPOT that could produce new features such as 5 * (x2 + log(x1))**3 or even just abs(x1 - x2), right?

Testing TPOT on synthetic data (where I know the target function) often results in many more and seemingly overly complex features. E.g. produced by a single RBFSampler operator and such.

Also, even if such non-linear feature transformation operators (|x|, exp(x), sin(x), cos(x), abs(x)) together with combination operators (+, −, ·) were part of TPOT, could a feature like 5 * (x2 + log(x1))**3 even be constructed? All my initial pipelines are very shallow and due to the multi objective optimisation and greedy evolutionary approach do not get bigger. What is with scenarios where multiple operators would need to be introduced at the same time to improve accuracy and to be part of the Pareto front?