abstract = "From a recent study, we know that if we are able to
find two optimally aligned individuals, then we can
reconstruct a globally optimal solution analytically
for any regression problem. With this knowledge in
mind, the objective of this chapter is to discuss two
Genetic Programming (GP) models aimed at finding pairs
of optimally aligned individuals. The first one of
these models, already introduced in a previous
publication, is SGP-1. The second model, discussed for
the first time here, is called Pair Optimisation GP
(POGO). The main difference between these two models is
that, while SGP-1 represents solutions in a traditional
way, as single expressions (as in standard GP), in POGO
individuals are pairs of expressions, that evolution
should push towards the optimal alignment. The results
we report for both these models are extremely
encouraging. In particular, ESAGP-1 outperforms
standard GP and geometric semantic GP on two complex
real-life applications. At the same time, a preliminary
set of results obtained on a set of symbolic regression
benchmarks indicate that POGP, although rather new and
still in need of improvement, is a very promising
model, that deserves future developments and
investigation.",