Abstract Traditional Genetic Programming (GP) searches the space of functions/programs by
using search operators that manipulate their syntactic representation, regardless of their …

We investigate the effects of semantically-based crossover operators in genetic
programming, applied to real-valued symbolic regression problems. We propose two new …

Several methods to incorporate semantic awareness in genetic programming have been
proposed in the last few years. These methods cover fundamental parts of the evolutionary …

The field of natural computing has been the focus of a substantial research effort in recent
decades. One particular strand of this concerns the development of computational …

Concrete is a composite construction material made primarily with aggregate, cement, and
water. In addition to the basic ingredients used in conventional concrete, high-performance …

Geometric semantic operators are new and promising genetic operators for genetic
programming. They have the property of inducing a unimodal error surface for any …

Forest fires importantly influence our environment and lives. The ability of accurately
predicting the area that may be involved in a forest fire event may help in optimizing fire …

Moraglio et al. have recently introduced new genetic operators for genetic programming,
called geometric semantic operators. These operators induce a unimodal fitness landscape …

In genetic programming, a search algorithm is expected to produce a program that achieves
the desired final computation state (desired output). To reach that state, an executing …

In this paper, a new genetic programming (GP) algorithm for symbolic regression problems
is proposed. The algorithm, named statistical genetic programming (SGP), uses statistical …