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 …

When learning from high-dimensional data for symbolic regression (SR), genetic
programming (GP) typically could not generalize well. Feature selection, as a data …

It is approximately 50 years since the first computational experiments were conducted in
what has become known today as the field of Genetic Programming (GP), twenty years since …

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 …

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

Dynamic flexible job shop scheduling is an important but difficult combinatorial optimisation
problem that has numerous real-world applications. Genetic programming has been widely …

Geometric semantic genetic programming (GP) has recently attracted much attention. The
key innovations are inducing a unimodal fitness landscape in the semantic space and …

Feature selection is a process that provides model extraction by specifying necessary or
related features and improves generalization. The Artificial Bee Colony (ABC) algorithm is …

One of the areas of Genetic Programming (GP) that, in comparison to other Machine
Learning methods, has seen fewer research efforts is that of generalization. Generalization …

Generalization ability, which reflects the prediction ability of a learned model, is an important
property in genetic programming (GP) for symbolic regression. Structural risk minimization …