abstract = "A PI is the range of values in which the real target
value of a supervised learning task is expected to fall
into, and it should combine two contrasting properties:
to be as narrow as possible, and to include as many
data observations as possible. This article presents an
study on modeling Prediction Intervals (PI) with two
Genetic Programming(GP) methods. The first proposed GP
method is called CWC-GP, and it evolves simultaneously
the lower and upper boundaries of the PI using a single
fitness measure. This measure is the Coverage
Width-based Criterion (CWC), which combines the width
and the probability coverage of the PI. The second
proposed GP method is called LUBE-GP, and it evolves
independently the lower and upper boundaries of the PI.
This method applies a multi-objective approach, in
which one fitness aims to minimise the width and the
other aims to maximise the probability coverage of the
PI. Both methods were applied both with the Direct and
the Sequential approaches. In the former, the PI is
assessed without the crisp prediction of the model. In
the latter, the method makes use of the crisp
prediction to find the PI boundaries. The proposed
methods showed to have good potential on assessing PIs
and the results pave the way to further
investigations.",