  Symbolic regression (SR) searches for parametric models that accurately fit a
dataset, prioritizing simplicity and interpretability. Despite this secondary
objective, studies point out that the models are often overly complex due to
redundant operations, introns, and bloat that arise during the iterative
process, and can hinder the search with repeated exploration of bloated
segments. Applying a fast heuristic algebraic simplification may not fully
simplify the expression and exact methods can be infeasible depending on size
or complexity of the expressions. We propose a novel agnostic simplification
and bloat control for SR employing an efficient memoization with
locality-sensitive hashing (LHS). The idea is that expressions and their
sub-expressions traversed during the iterative simplification process are
stored in a dictionary using LHS, enabling efficient retrieval of similar
structures. We iterate through the expression, replacing subtrees with others
of same hash if they result in a smaller expression. Empirical results shows
that applying this simplification during evolution performs equal or better
than without simplification in minimization of error, significantly reducing
the number of nonlinear functions. This technique can learn simplification
rules that work in general or for a specific problem, and improves convergence
while reducing model complexity.
