  The Interaction-Transformation (IT) is a new representation for Symbolic
Regression that restricts the search space into simpler, but expressive,
function forms. This representation has the advantage of creating a smoother
search space unlike the space generated by Expression Trees, the common
representation used in Genetic Programming. This paper introduces an
Evolutionary Algorithm capable of evolving a population of IT expressions
supported only by the mutation operator. The results show that this
representation is capable of finding better approximations to real-world data
sets when compared to traditional approaches and a state-of-the-art Genetic
Programming algorithm.
