Created by W.Langdon from gp-bibliography.bib Revision:1.5031
Genetic Programming IV: Routine Human-Competitive Machine Intelligence presents the application of GP to a wide variety of problems involving automated synthesis of controllers, circuits, antennas, genetic networks, and metabolic pathways.
The books describes 15 instances where GP has created an entity that either infringes or duplicates the functionality of a previously patented 20th-century invention, 6 instances where it has done the same with respect to post-2000 patented inventions, 2 instances where GP has created a patentable new invention, and 13 other human-competitive results.
The book additionally establishes:
GP now delivers routine human-competitive machine intelligence.
GP is an automated invention machine.
GP can create general solutions to problems in the form of parameterised topologies.
GP has delivered qualitatively more substantial results in synchrony with the relentless iteration of Moore's Law.",
-------------------- Comments on the Book -------------------- The research reported in this book is a tour de force. For the first time since the idea was bandied about in the 1940s and the early 1950s, we have a set of examples of human-competitive automatic programming. John H. Holland, University of Michigan
In 1992, John Koza published his first book on genetic programming and forever changed the world of computation. At the time, many researchers, myself included, were skeptical about whether the idea of using genetic algorithms directly to evolve programs would ever amount to much. But scores of conquered problems and three additional books makes the case utterly persuasive. The latest contribution, Genetic Programming IV: Routine Human-Competitive Machine Intelligence, demonstrates the everyday solution of such holy grail problems as the automatic synthesis of analog circuits, the design of automatic controllers, and the automated programming of computers. This would be impressive enough, but the book also shows how to evolve whole families of solutions to entire classes of problems in a single run. Such parametric GP is a significant achievement, and I believe it foreshadows generalised evolution of complex contingencies as an everyday matter. To artificial evolutionaries of all stripes, I recommend that you read this book and breath in its thoughtful mechanism and careful empirical method. To specialists in any of the fields covered by this books sample problem areas, I say read this book and discover the computer-augmented inventions that are your destiny. To remaining skeptics who doubt the inventive competence of genetics and evolution, I say read this book and change your mind or risk the strong possibility that your doubts will soon cause you significant intellectual embarrassment. David E. Goldberg, University of Illinois
The adaptive filters and neural networks that I have worked with over many years are self-optimising systems where the relationship between performance (usually mean-square-error) and parameter settings (weights) is continuous. Optimization by gradient methods works well for these systems. Now, this book describes a wider class of optimisation problems where the relationship between performance (fitness) and parameters is highly disjoint, and self-optimization is achieved by nature-inspired genetic algorithms involving random search (mutation) and crossover (sexual reproduction). John Koza and his colleagues have done remarkable work in advancing the development of genetic programming and applying this to practical problems such as electric circuit design and control system design. What is ingenious about their work is that they have found ways to approach design problems by parameterizing both physical and topological variables into a common code that can be subjected to genetic programming for optimisation. It is amazing how this approach finds optimised solutions that are not obvious to the best human experts. This fine book gives an accounting of the latest work in genetic programming, and it is must reading for those interested in adaptive and learning systems, neural networks, fuzzy systems, artificial intelligence, and neurobiology. I strongly recommend it. Bernard Widrow, Electrical Engineering Department, Stanford University
John Koza's genetic programming approach to machine discovery can invent solutions to more complex specifications than any other I have seen. John McCarthy, Computer Science Department, Stanford University
Genetic Programming entries for John Koza Martin A Keane Matthew J Streeter William J Mydlowec Jessen Yu Guido Lanza