Computers thatprogram themselves' has long been an aim of computer scientists. Recently
genetic programming (GP) has started to show its promise by automatically evolving …

The latest book on Genetic Programming, Poli, Langdon and McPhee's (with contributions
from John R. Koza) A Field Guide to Genetic Programming represents an exciting landmark …

In this paper we analyze the effects of using nonlinear least squares for parameter
identification of symbolic regression models and integrate it as local search mechanism in …

Genetic Programming is an optimisation procedure which may be applied to the
identification of the nonlinear structure of a dynamic model from experimental data. In such …

Evolution through natural selection has been going on for a very long time. Evolution
through artificial selection has been practiced by humans for a large part of our history, in the …

The goal of having computers automatically solve problems is central to artificial
intelligence, machine learning, and the broad area encompassed by what Turing called …

The aim of this paper is to provide an introduction to the rapidly developing field of genetic
programming (GP). Particular emphasis is placed on the application of GP to engineering …

Pattern recognition methods rely on maximum-information, minimum-dimension feature sets
to reliably perform classification and regression tasks. Many methods exist to reduce feature …

Although various nonlinear control theories, such as sliding mode control, have proved
sound and successful, there is a serious lack of effective or tractable design methodologies …

In this publication a constant optimization approach for symbolic regression is introduced to
separate the task of finding the correct model structure from the necessity to evolve the …