I Prerequisites of Genetic Programming
1 Genetic Programming as Machine Learning 1.1 Motivation 1.2 A Brief History of Machine Learning 1.3 Machine Learning as a Process 1.4 Major Issues in Machine Learning 1.5 Representing the Problem 1.6 Transforming Solutions with Search Operators 1.7 The Strategy of Search 1.8 Learning 1.9 Conclusion
2 Genetic Programming and Biology 2.1 Minimal Requirements for Evolution to Occur 2.2 Test Tube Evolution—A Study in Minimalist Evolution 2.3 The Genetic Code—DNA as a Computer Program 2.4 Genomes, Phenomes, and Ontogeny 2.5 Stability and Variability of Genetic Transmission 2.6 Species and Sex
3 Computer Science and Mathematical Basics 3.1 The Importance of Randomness in Evolutionary Learning 3.2 Mathmatical Basics 3.3 Computer Science Background and Terminology 3.4 Computer Hardware 3.5 Computer Software
4 Genetic Programming as Evolutionary Computation 4.1 The Dawn of Genetic Programming—Setting the Stage 4.2 Evolutionary Algorithms: The General View 4.3 Flavors of Evolutionary Algorithms 4.4 Summary of Evolutionary Algorithms
II Genetic Programming Fundamentals
5 Basic Concepts—The Foundation 5.1 Terminals and Functions—The Primitives of Genetic Programs 5.2 Executable Program Structures 5.3 Initializing a GP Population 5.4 Genetic Operators 5.5 Fitness and Selection 5.6 Basic GP Algorithm 5.7 An Example Run
Since the early 1990s, genetic programming (GP)—a discipline whose goal is to enable the automatic generation of computer programs—has emerged as one of the most promising paradigms for fast, productive software development. GP combines biological metaphors gleaned from Darwin's theory of evolution with computer-science approaches drawn from the field of machine learning to create programs that are capable of adapting or recreating themselves for open-ended tasks.
This unique introduction to GP provides a detailed overview of the subject and its antecedents, with extensive references to the published and online literature. In addition to explaining the fundamental theory and important algorithms, the text includes practical discussions covering a wealth of potential applications and real-world implementation techniques. Software professionals needing to understand and apply GP concepts will find this book an invaluable practical and theoretical guide.
- No. of pages:
- © Morgan Kaufmann 1998
- 1st December 1997
- Morgan Kaufmann
- eBook ISBN:
- Hardcover ISBN:
"[The authors] have performed a remarkable double service with this excellent book on genetic programming. First, they give an up-to-date view of the rapidly growing field of automatic creation of computer programs by means of evolution and, second, they bring together their own innovative and formidable work on evolution of assembly language machine code and linear genomes." --John R. Koza