Genetic Programming II: Automatic Discovery of Reusable Programs

Genetic Programming II expands on Koza's pioneering work in evolutionary computation and automated program synthesis. The book presents advanced techniques for evolving computer programs through principles inspired by biological evolution and natural selection. This volume introduces automatic function definition and the architecture-altering operations that allow programs to dynamically modify their own structure. Through detailed examples and case studies, it demonstrates how genetic programming can discover reusable subroutines and automatically decompose problems into simpler components. The text covers key theoretical foundations while maintaining a focus on practical applications across fields like circuit design, signal processing, and control systems. Mathematics and algorithms are presented with accompanying empirical results from extensive experiments. The work represents a significant advancement in machine learning and artificial intelligence, exploring fundamental questions about automation and the nature of human programming. Its impact extends beyond computer science into broader discussions of computational creativity and problem-solving.

Reviewers emphasize the book's detail and comprehensiveness in explaining genetic programming techniques, with multiple readers noting the clear progression from basic concepts to complex applications. Liked: - Extensive code examples and implementation details - Strong mathematical foundations - Quality diagrams and illustrations - Practical applications across multiple domains Disliked: - Dense technical writing requires significant background knowledge - High price point ($65-85) - LISP-focused code examples limit accessibility - Some content overlaps with first volume Review Sources: Goodreads: 4.0/5 (8 ratings) Amazon: 4.2/5 (6 ratings) One reviewer on Amazon said "the mathematical rigor adds credibility but makes it hard to follow without advanced CS background." A Goodreads review noted "excellent reference but not for beginners." Limited review data exists online, likely due to the book's specialized academic focus and publication date (1994).

Introduction to Genetic Programming by Wolfgang Banzhaf, Peter Nordin, Robert E. Keller, and Frank D. Francone This book provides mathematical foundations and practical implementations of genetic programming algorithms for solving complex optimization problems.

Genetic Programming: An Introduction by William B. Langdon and Riccardo Poli The text presents core genetic programming concepts through mathematical formulas, algorithms, and code examples for implementation.

A Field Guide to Genetic Programming by Riccardo Poli, William B. Langdon, and Nicholas F. McPhee The book covers genetic programming theory and practice with source code examples and mathematical explanations of evolutionary computation methods.

Evolutionary Computation: A Unified Approach by Kenneth De Jong This work connects genetic programming with other evolutionary algorithms through mathematical frameworks and computational methods.

Essentials of Metaheuristics by Sean Luke The text explains genetic programming algorithms alongside other metaheuristic optimization methods with implementation details and mathematical foundations.

🧬 John Koza, before his work in genetic programming, invented the scratch-off lottery ticket system used worldwide and holds the original patent for this innovation. 🔬 The book introduced the concept of Automatically Defined Functions (ADFs), which revolutionized genetic programming by allowing the evolution of reusable subroutines. 💻 The research presented in the book required over 50 years of CPU time on a network of workstations - an enormous computational effort for 1994 when it was published. 🌳 The techniques described in the book have been used to create computer programs that have earned U.S. patents, marking some of the first AI-created inventions to receive patent protection. 🎓 The book builds on Koza's earlier work at Stanford University, where he studied under Nobel laureate Kenneth Arrow and helped establish genetic programming as a distinct field within machine learning.