  Very recently new genetic operators, called geometric semantic operators,
have been defined for genetic programming. Contrarily to standard genetic
operators, which are uniquely based on the syntax of the individuals, these new
operators are based on their semantics, meaning with it the set of input-output
pairs on training data. Furthermore, these operators present the interesting
property of inducing a unimodal fitness landscape for every problem that
consists in finding a match between given input and output data (for instance
regression and classification). Nevertheless, the current definition of these
operators has a serious limitation: they impose an exponential growth in the
size of the individuals in the population, so their use is impossible in
practice. This paper is intended to overcome this limitation, presenting a new
genetic programming system that implements geometric semantic operators in an
extremely efficient way. To demonstrate the power of the proposed system, we
use it to solve a complex real-life application in the field of
pharmacokinetic: the prediction of the human oral bioavailability of potential
new drugs. Besides the excellent performances on training data, which were
expected because the fitness landscape is unimodal, we also report an excellent
generalization ability of the proposed system, at least for the studied
application. In fact, it outperforms standard genetic programming and a wide
set of other well-known machine learning methods.
