Goldberg D.E. Genetic Algorithms in Search, Optimization, and Machine Learning

Addison-Wesley, 1989, -432 p.This book is about genetic algorithms (GAs)—search procedures based on the mechanics of natural selection and natural genetics. In writing it, I have tried to bring together the computer techniques, mathematical tools, and research results that will enable you to apply genetic algorithms to problems in your field. If you choose to do so, you will join a growing group of researchers and practitioners who have come to appreciate the natural analogues, mathematical analyses, and computer techniques comprised by the genetic algorithm methodology.
The book is designed to be a textbook and a self-study guide. I have tested the draft text in a one semester, senior-level undergraduate/first-year graduate course devoted to genetic algorithms. Although the students came from different backgrounds (biochemistry, chemical engineering, computer science, electrical engineering, engineering mechanics, English, mathematics, mechanical engineering, and physics) and had wide differences in mathematical and computational maturity, they all acquired an understanding of the basic algorithm and its theory of operation. To reach such a diverse audience, the tone of the book is intentionally casual, and rigor has almost always been sacrificed in the interest of building intuition and understanding. Worked out examples illustrate major topics, and computer assignments are available at the end of each chapter.
I have minimized the mathematics, genetics, and computer background required to read this book. An understanding of introductory college-level mathematics (algebra and a little calculus) is assumed. Elementary notions of counting and finite probability are used, and Appendix A summarizes the important concepts briefly. I assume no particular knowledge of genetics and define all required genetic terminology and concepts within the text. Last, some computer programming ability is necessary. If you have programmed a computer in any language, you should be able to follow the computer examples I present. All computer code in this book is written in Pascal, and Appendix B presents a brief introduction to the essentials of that language.
Although I have not explicitly subdivided the book into separate parts, the chapters may be grouped in two major categories: those dealing with search and optimization and those dealing with machine learning.
The first five chapters are devoted to genetic algorithms in search and optimization. Chapter 1 introduces the topic of genetic search; it also describes a simple genetic algorithm and illustrates the GA's application through a hand calculation. Chapter 2 introduces the essential theoretical basis of GAs, covering topics including schemata, the fundamental theorem, and extended analysis. If you dislike theory, you can safely skip Chapter 2 without excessive loss of continuity; however, before doing so, I suggest you try reading it anyway. The mathematical underpinnings of GAs are not difficult to follow, but their ramifications are subtle; some attention to analysis early in the study of GAs promotes fuller understanding of algorithm power. Chapter 3 introduces computer implementation of genetic algorithms through example. Specifically, a Pascal code called the simple genetic algorithm (SGA) is presented along with a number of extensions. Chapter 4 presents a historical account of early genetic algorithms together with a potpourri of current applications. Chapter 5 examines more advanced genetic operators and presents a number of applications illustrating their use. These include applications of micro- and macro-level operators as well as hybrid techniques.
Chapters 6 and 7 present the application of genetic algorithms in machine learning systems. Chapter 6 gives a generic description of one type of genetics- based machine learning (GBML) system, a classifier system. The theory of operation of such a system is briefly reviewed, and one Pascal implementation called the simple classifier system (SCS) is presented and applied to the learning of a Boolean function. Chapter 7 rounds out the picture of GBML by presenting a historical review of early GBML systems together with a selective survey of other current systems and topics.A Gentle Introduction to Genetic Algorithms
Genetic Algorithms Revisited: Mathematical Foundations
Computer Implementation of a Genetic Algorithm
Some Applications of Genetic Algorithms
Advanced Operators and Techniques in Genetic Search
Introduction to Genetics-Based Machine Learning
Applications of Genetics-Based Machine Learning
A Look Back, a Glance Ahead
A: Review of Combinatorics and Elementary Probability
B: Pascal with Random Number Generation for Fortran, Basic, and Cobol Programmers
C: A Simple Genetic Algorithm (SGA) in Pascal
D: A Simple Classifier System (SCS) in Pascal
E: Partition Coefficient Transforms for Problem-Coding Analysis