We present an approach for regression problems that employs analytic continued fractions
as a novel representation. Comparative computational results using a memetic algorithm are …

This research aims to address the practical difficulties of computational heuristics for
symbolic regression, which models data with algebraic expressions. In particular we are …

Software modeling activities typically involve a tedious and time-consuming effort by
specially trained personnel. This lack of automation hampers the adoption of model-driven …

Recent research shows that incorporating semantic knowledge into the genetic
programming (GP) evolutionary process can improve its performance. This work proposes …

The definition of a concise and effective testbed for Genetic Programming (GP) is a recurrent
matter in the research community. This paper takes a new step in this direction, proposing a …

The error repair process in software systems is, historically, a resource-consuming task that
relies heavily in developer manual effort. Automatic program repair approaches enable the …

In this paper, we introduce the concept of semantics-based equivalence classes for symbolic
regression problems in genetic programming. The idea is implemented by means of two …

Animal and robot social interactions are interesting both for ethological studies and robotics.
On the one hand, the robots can be tools and models to analyse animal collective …

Symbolic regression (SR) is a function identification process, the task of which is to identify
and express the relationship between the input and output variables in mathematical …

Conciliating between multiple objectives involves managing partially sorted solutions. In this
regard, the main difficulties consist in finding proper ranking strategies able to preserve the …