"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
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
