  Symbolic Regression searches for a function form that approximates a dataset
often using Genetic Programming.
  Since there is usually no restriction to what form the function can have,
Genetic Programming may return a hard to understand model due to non-linear
function chaining or long expressions. A novel representation called
Interaction-Transformation was recently proposed to alleviate this problem. In
this representation, the function form is restricted to an affine combination
of terms generated as the application of a single univariate function to the
interaction of selected variables. This representation obtained competing
solutions on standard benchmarks. Despite the initial success, a broader set of
benchmarking functions revealed the limitations of the constrained
representation.
  In this paper we propose an extension to this representation, called
Transformation-Interaction-Rational representation that defines a new function
form as the rational of two Interaction-Transformation functions. Additionally,
the target variable can also be transformed with an univariate function. The
main goal is to improve the approximation power while still constraining the
overall complexity of the expression.
  We tested this representation with a standard Genetic Programming with
crossover and mutation. The results show a great improvement when compared to
its predecessor and a state-of-the-art performance for a large benchmark.
