| Title | Linear Genetic Programming |
| Author(s) | Markus F. Brameier, Wolfgang Banzhaf |
| Publisher | Springer |
| Year | 2007 |
| Edition | 1st Edition |
| Language | English |
| Pages | 316 pages |
| ISBN | 9780387310299 |
| Genre / Domain | Computer Science, Artificial Intelligence |
| Series | Genetic and Evolutionary Computation |
| Size | 1.74 MB |
| Extension |
Summary
Linear Genetic Programming, authored by Markus F. Brameier and Wolfgang Banzhaf, is a definitive guide to the field of Linear Genetic Programming (LGP). Published in 2007 as part of Springer's Genetic and Evolutionary Computation series, this book systematically examines the evolution of imperative computer programs represented as linear sequences of instructions, a departure from the more traditional tree-based or functional approaches in Genetic Programming [citation:1][citation:4]. The book is designed to serve both as a solid introduction for newcomers and a comprehensive reference for experienced researchers, consolidating and extending material previously scattered across research papers [citation:2].
The text is structured into three distinct parts, each building upon the last. Part I, "Fundamental Analysis," introduces the basic concepts of LGP, detailing the characteristics of the linear representation and presenting efficient algorithms for runtime analysis and optimization. It also includes a comparative study between LGP and neural networks on classification tasks [citation:4][citation:6]. Part II, "Method Design," explores the systematic design of efficient LGP methods and genetic operators. It investigates various crossover and mutation techniques, analyzes the influence of control parameters, and presents a direct comparison between linear and tree-based GP, where LGP demonstrated superior performance across several benchmark problems [citation:2][citation:5][citation:9].
The book excels in its practical, example-driven approach. Each chapter in Parts II and III follows a uniform structure that first poses design questions and then answers them through well-designed experiments [citation:2]. This methodology allows readers to understand not just the "how" but the "why" behind the design choices in LGP. Part III, "Advanced Techniques and Phenomena," delves into more complex topics such as controlling diversity and variation step size, analyzing code growth and neutral variations (a common challenge in GP), and evolving program teams or classifier ensembles to solve problems collaboratively [citation:4][citation:6].
This book is an indispensable resource for researchers and postgraduate students in evolutionary computation, genetic programming, and machine learning [citation:5]. While it assumes a basic familiarity with evolutionary computing concepts, its clear writing and logical structure make it accessible to those new to the field. It is particularly valuable for anyone looking to implement or extend LGP systems, offering a deep understanding of both the theoretical foundations and practical considerations of the technique.
By providing a thorough and well-organized examination of LGP, this book has cemented its status as a cornerstone text. Its focus on empirical analysis and systematic method design provides a robust framework for understanding and advancing the field. The book's impact is evident in its continued relevance, and the subsequent publication of volumes like "Advances in Linear Genetic Programming" demonstrates the lasting influence of this foundational work [citation:3].
Key Features
- Provides a comprehensive and systematic examination of Linear Genetic Programming.
- Structured into three parts, each building on the previous to cover fundamental concepts, method design, and advanced techniques.
- Features a uniform, experiment-driven chapter structure that explains design choices through empirical results.
- Includes detailed comparisons of LGP with neural networks and tree-based genetic programming.
- Investigates typical GP phenomena such as non-effective code, neutral variations, and code growth from a linear perspective.
- Explores advanced topics including diversity control, step size management, and the evolution of program teams.
- Written by leading researchers with extensive experience in genetic programming and evolutionary computation.
- Serves as both an introduction for students and a detailed reference for researchers.
- Part of Springer's respected Genetic and Evolutionary Computation series.
- Offers a practical, code-focused approach, with discussions based on a register machine model.
About the Authors
Markus F. Brameier received his PhD degree in Computer Science from the University of Dortmund, Germany, in 2004. Following his doctorate, he was a postdoctoral fellow at the Stockholm Bioinformatics Center (SBC) in Sweden. He is currently an Assistant Professor at the Bioinformatics Research Center (BiRC) of the University of Aarhus in Denmark. His primary research interests are in bioinformatics and genetic programming [citation:1][citation:11].
Wolfgang Banzhaf is a Professor of Computer Science at the Department of Computer Science of Memorial University of Newfoundland, Canada, where he has also served as department head. He has held academic positions at the University of Dortmund, Germany, and has been a researcher at Mitsubishi Electric Corp. in Japan and the USA. He holds a PhD in Physics from the University of Karlsruhe, Germany. His research interests span artificial evolution, self-organization, and bioinformatics [citation:1][citation:11].
Related Books
- Genetic Programming: On the Programming of Computers by Means of Natural Selection — John R. Koza
- Foundations of Genetic Programming — William B. Langdon, Riccardo Poli
- Advances in Linear Genetic Programming — Markus F. Brameier, Wolfgang Banzhaf (Eds.)
- A Field Guide to Genetic Programming — Riccardo Poli, William B. Langdon, Nicholas F. McPhee
- Cartesian Genetic Programming — Julian F. Miller
- Evolutionary Algorithms in Theory and Practice — Thomas Bäck
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Frequently Asked Questions
Q : What is the main focus of "Linear Genetic Programming"?
R : The book provides a comprehensive examination of Linear Genetic Programming (LGP), a variant of Genetic Programming where evolved programs are represented as linear sequences of instructions rather than tree structures. It covers the fundamental concepts, method design, and advanced techniques in the field [citation:1][citation:4].
Q : Who are the authors and what are their credentials?
R : The book is authored by Markus F. Brameier and Wolfgang Banzhaf. Markus Brameier is a bioinformatics researcher with a PhD in Computer Science, while Wolfgang Banzhaf is a Professor of Computer Science at Memorial University of Newfoundland, known for his work in artificial evolution and bioinformatics [citation:1][citation:11].
Q : How is the book structured?
R : The book is divided into three parts. Part I covers fundamental concepts, Part II focuses on method design and genetic operators, and Part III explores advanced topics such as diversity control, code growth, and neutral variations [citation:2][citation:5].
Q : What is Linear Genetic Programming (LGP)?
R : LGP is a variant of Genetic Programming that evolves programs in an imperative, linear sequence of instructions, typically for a register-based machine. It contrasts with traditional GP, which often uses tree-based or functional representations. The linear structure allows for faster execution and unique types of analysis and optimization [citation:2][citation:5].
Q : What topics are covered in the advanced section of the book?
R : Part III covers advanced techniques and phenomena, including controlling diversity and variation step size, code growth, neutral variations, and the evolution of program teams (or classifier ensembles) [citation:6][citation:9].
Q : Is this book suitable for beginners?
R : Yes, the book is designed to provide a solid introduction to LGP. The first part is accessible to those new to the field, while the later parts delve into more advanced, research-level material [citation:1][citation:4].
Q : What is the significance of this book in the field?
R : This book is considered a cornerstone text on LGP. It consolidated a large body of research, providing a unified and comprehensive overview of the field, which has been influential in shaping subsequent research and applications [citation:3][citation:5].
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