Genetic Programming IV: Routine Human-Competitive Machine Intelligence

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Book{koza:gp4,
  author =       "John R. Koza and Martin A. Keane and 
                 Matthew J. Streeter and William Mydlowec and Jessen Yu and 
                 Guido Lanza",
  title =        "Genetic Programming {IV}: Routine Human-Competitive
                 Machine Intelligence",
  publisher =    "Kluwer Academic Publishers",
  year =         "2003",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-4020-7446-8",
  URL =          "http://www.genetic-programming.org/gpbook4toc.html",
  URL =          "http://www.springer.com/computer/ai/book/978-0-387-25067-0",
  URL =          "http://www.amazon.com/Genetic-Programming-IV-Human-Competitive-Intelligence/dp/1402074468",
  abstract =     "Genetic programming (GP) is method for automatically
                 creating computer programs. It starts from a high-level
                 statement of what needs to be done and uses the
                 Darwinian principle of natural selection to breed a
                 population of improving programs over many
                 generations.

                 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.",
  notes =        "PDF of chapter 1 available. 42-minute DVD included in
                 Jaws-4

                 -------------------- 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

                 ",
  size =         "pages",
}

Genetic Programming entries for John Koza Martin A Keane Matthew J Streeter William J Mydlowec Jessen Yu Guido Lanza

Citations