Provides an in-depth and even treatment of the three pillars of computational intelligence
and how they relate to one another This book covers the three fundamental topics that form …

Most applications of genetic programming (GP) involve the creation of an entirely new
function, program or expression to solve a specific problem. In this paper, we propose a new …

Evolutionary algorithms are bio-inspired algorithms based on Darwin's theory of evolution.
They are expected to provide non-optimal but good quality solutions to problems whose …

Increasing the resolution of an image is an actual and extensively studied problem in image
processing. Recently, Regularization by Denoising (RED) showing that any inverse problem …

Most genetic programming systems use mutation and crossover operators to create child
programs from selected parent programs. Typically the mutation operator will replace a …

Software engineering and development is well-known to suffer from unplanned overtime,
which causes stress and illness in engineers and can lead to poor quality software with …

Intrusion detection on mobile ad hoc networks (MANETs) is difficult. This is because of their
dynamic nature, the lack of central points, and their highly resource-constrained nodes. In …

Abstract Cartesian Genetic Programming is often used with mutation as the sole genetic
operator. Compared to the fundamental knowledge about the effect and use of mutation in …

The goodness of the features extracted from the instances and the number of training
instances are two key components in machine learning, and building an effective model is …

Abstract While tree-based Genetic Programming is often used with crossover, Cartesian
Genetic Programming is mostly used only with mutation as genetic operator. In this paper, a …