This book, "Cartesian Genetic Programming," is the first comprehensive resource on Cartesian Genetic Programming (CGP), a form of automatic evolution of computer programs and computational structures inspired by Darwin's theory of natural selection. The book is edited by Dr. Julian F. Miller and includes contributions from leading experts in the field.
The preface highlights the author's initial introduction to genetic algorithms and the transformative impact they had on his work, leading to a deep fascination with evolutionary algorithms. The book is designed for postgraduates, researchers, and academics, but also aims to provide a brief background for undergraduates interested in genetic programming.
The content covers various aspects of CGP, including its origins, general form, and applications in different domains such as digital circuits, mathematical equations, and artworks. It discusses the decoding of CGP genotypes, mutation, recombination, and parameter settings for effective evolutionary algorithms. The book also explores advanced topics like problem decomposition, self-modifying CGP, and the evolution of complex circuits.
Chapters delve into specific applications, such as image processing, hardware acceleration using field-programmable gate arrays (FPGAs) and graphics processing units (GPUs), and the design of artificial neural networks. The book concludes with a discussion on the medical applications of CGP, including the diagnosis of breast cancer, Parkinson's disease, and Alzheimer's disease.
Overall, the book provides a broad and in-depth look at the potential and capabilities of CGP, emphasizing its wide applicability and ongoing development.This book, "Cartesian Genetic Programming," is the first comprehensive resource on Cartesian Genetic Programming (CGP), a form of automatic evolution of computer programs and computational structures inspired by Darwin's theory of natural selection. The book is edited by Dr. Julian F. Miller and includes contributions from leading experts in the field.
The preface highlights the author's initial introduction to genetic algorithms and the transformative impact they had on his work, leading to a deep fascination with evolutionary algorithms. The book is designed for postgraduates, researchers, and academics, but also aims to provide a brief background for undergraduates interested in genetic programming.
The content covers various aspects of CGP, including its origins, general form, and applications in different domains such as digital circuits, mathematical equations, and artworks. It discusses the decoding of CGP genotypes, mutation, recombination, and parameter settings for effective evolutionary algorithms. The book also explores advanced topics like problem decomposition, self-modifying CGP, and the evolution of complex circuits.
Chapters delve into specific applications, such as image processing, hardware acceleration using field-programmable gate arrays (FPGAs) and graphics processing units (GPUs), and the design of artificial neural networks. The book concludes with a discussion on the medical applications of CGP, including the diagnosis of breast cancer, Parkinson's disease, and Alzheimer's disease.
Overall, the book provides a broad and in-depth look at the potential and capabilities of CGP, emphasizing its wide applicability and ongoing development.