Examines measures of diversity in genetic programming. The goal is to understand the
importance of such measures and their relationship with fitness. Diversity methods and …

The field of evolutionary computation (EC) can no longer be considered an esoteric one.
Today, after about thirty years of research, a rich corpus of theory exists and many …

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 …

Streaming data analysis potentially represents a significant shift in emphasis from schemes
historically pursued for offline (batch) approaches to the classification task. In particular, a …

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

We present an approach to genetic programming difficulty based on a statistical study of
program fitness landscapes. The fitness distance correlation is used as an indicator of …

This paper reports an improvement to genetic programming (GP) search for the symbolic
regression domain, based on an analysis of dissimilarity and mating. GP search is generally …

Abstract Genetic Programming (GP) based Symbolic Regression (SR) algorithms suffer from
the ineluctable effects over model bloat, blind search and diversity loss when determining …

Computer science is the science that deals with the treatment of information by means of
automatic procedures. It has multiple objectives, including the study of computation at a …

Job Scheduling on Computational Grids is gaining importance due to the need for efficient
large-scale Grid-enabled applications. Among different optimization techniques addressed …