Genetic programming is a method for getting a computer to solve a problem by telling it what needs to be done instead of how to do it. Koza, Bennett, Andre, and Keane present genetically evolved solutions to dozens of problems of design, optimal control, classification, system identification, function learning, and computational molecular biology. Among the solutions are 14 results competitive with human-produced results, including 10 rediscoveries of previously patented inventions.
Researchers in artificial intelligence, machine learning, evolutionary computation, and genetic algorithms will find this an essential reference to the most recent and most important results in the rapidly growing field of genetic programming.
Rezensionen / Stimmen
"Koza, Bennett, Andre, and Keane's evolutionary algorithm builds more complex and useful structures than the other approaches to computer learning that I have seen."
?John McCarthy, Stanford University
"John Koza and colleagues have demonstrated that genetic programming can be used to search highly discontinuous spaces and thereby find amazing solutions to practical engineering problems."
?Bernard Widrow, Stanford University
"In this impressive volume, the authors demonstrate that genetic programming is more than an intriguing idea-it is a practical synthesis method for solving hard problems."
?Nils J. Nilsson, Stanford University
"Through careful experiment, keen algorithmic intuition, and relentless application the authors deliver important results that rival those achieved by human designers. All readers in genetic and evolutionary computation and the related fields of artificial life, agents, and adaptive behavior will want this volume in their collections."
?David E. Goldberg, University of Illinois at Urbana-Champaign
"John Koza and his coauthors continue their relentless pursuit of a holy grail
in computer science: automatic programming."
?Moshe Sipper, Swiss Federal Institute of Technology (EPFL), Lausanne
Sprache
Verlagsort
Verlagsgruppe
Elsevier Science & Technology
Zielgruppe
Für Beruf und Forschung
AI researchers and engineers involved with circuit design
Gewicht
ISBN-13
978-1-55860-617-3 (9781558606173)
Copyright in bibliographic data is held by Nielsen Book Services Limited or its licensors: all rights reserved.
Schweitzer Klassifikation
John R. Koza is a consulting professor in the Section on Medical Informatics, Department of Medicine, School of Medicine at Stanford University. Forrest H Bennett III is chief scientist of Genetic Programming Inc., Los Altos, California. David Andre is a Ph.D. student in the Computer Science Division at the University of California at Berkeley. Martin A. Keane is chief scientist of Econometrics, Inc., Chicago. Scott Brave is a research assistant in the Tangible Media Group at the MIT Media Laboratory.
I. Introduction
II. Background
III. Architecture-Altering Operations
IV. Genetic Programming Problem Solver (GPPS)
V. Automated Synthesis of Analog Electrical Circuits
VI. Evolvable Hardware
VII. Discovery of Cellular Automata Rules
VIII. Discovery of Motifs and Programmatic Motifs for Molecular Biology
IX. Parallelization and Implementation Issues
X. Conclusion
