Genetic Programming II and the associated videotape are now available from the MIT Press. The MIT Press 55 Hayward Street Cambridge, MA 02142 USA Phone 1-800-356-0343 (toll-free); Phone to 617-625-8569; Fax to 617-625-6660; E-mail to mitpress-orders@mit.edu GENETIC PROGRAMMING II: AUTOMATIC DISCOVERY OF REUSABLE SUBPROGRAMS by John R. Koza Computer Science Department Stanford University It is often argued that the process of solving complex problems can be automated by first decomposing the problem into subproblems, then solving the presumably simpler subproblems, and then assembling the solutions to the subproblems into an overall solution to the original problem. The overall effort required to solve a problem can potentially be reduced to the extent that the decomposition process uncovers subproblems that are disproportionately easy to solve and to the extent that regularities in the problem environment permit multiple use of the solutions to the subproblems. Sadly, conventional techniques of machine learning and artificial intelligence provide no effective means for automatically executing this alluring three-step problem-solving process on a computer. GENETIC PROGRAMMING II describes a way to automatically implement this three-step problem-solving process by means the recently developed technique of automatically defined functions in the context of genetic programming. Automatically defined functions enable genetic programming to define useful and reusable subroutines dynamically during a run. This new technique is illustrated by solving, or approximately solving, example problems from the fields of Boolean function learning, symbolic regression, control, pattern recognition, robotics, classification, and molecular biology. In each example, the problem is automatically decomposed into subproblems; the subproblems are automatically solved; and the solutions to the subproblems are automatically assembled into a solution to the original problem. Leverage accrues because genetic programming with automatically defined functions repeatedly uses the solutions to the subproblems in the assembly of the solution to the overall problem. Moreover, genetic programming with automatically defined functionsn produces solutions that are simpler and smaller than the solutions obtained without automatically defined functions. CONTENTS... 1. Introduction 2. Background on Genetic Algorithms, LISP, and Genetic Programming 3. Hierarchical Problem-Solving 4. Introduction to Automatically Defined Functions Ð The Two-Boxes Problem 5. Problems that Straddle the Breakeven Point for Computational Effort 6. Boolean Parity Functions 7. Determining the Architecture of the Program 8. The Lawnmower Problem 9. The Bumblebee Problem 10. The Increasing Benefits of ADFs as Problems are Scaled Up 11. Finding an Impulse Response Function 12. Artificial Ant on the San Mateo Trail 13. Obstacle-Avoiding Robot 14. The Minesweeper Problem 15. Automatic Discovery of Detectors for Letter Recognition 16. Flushes and Four-of-a-Kinds in a Pinochle Deck 17. Introduction to Molecular Biology 18. Prediction of Transmembrane Domains in Proteins 19. Prediction of Omega Loops in Proteins 20. Lookahead Version of the Transmembrane Problem 21. Evolution of the Architecture of the Overall Program 22. Evolution of Primitive Functions 23. Evolutionary Selection of Terminals 24. Evolution of Closure 25. Simultaneous Evolution of Architecture, Primitive Functions, Terminals, Sufficiency, and Closure 26. The Role of Representation and the Lens Effect 27. Conclusion Bibliography Appendix A: List of Special Symbols Appendix B: List of Special Functions Appendix C: List of Type Fonts Appendix D: Default Parameters for Controlling Runs of Genetic Programming Appendix E: Computer Implementation of Automatically Defined Functions Appendix F: Annotated Bibliography of Genetic Programming Appendix G: Electronic Newsletter, Public Repository, and FTP Site Hardcover. 746 pages. ISBN 0-262-11189-6. ----------------------------------------------------------------------- Genetic Programming II Videotape: The Next Generation by John R. Koza This videotape provides an explanation of automatically defined functions, the hierarchical approach to problem solving by means of genetic programming with automatically defined functions, and a visualization of computer runs for many of the problems discussed in Genetic Programming II. These problems include symbolic regression, the parity problem, the lawnmower problem, the bumblebee problem, the artificial ant, the impulse response problem, the minesweeper problem. the letter recognition problem, the transmembrane problem, and the omega loop problem. VHS videotape. 62-Minutes. Available in VHS NTSC, PAL, and SECAM formats. NTSC ISBN 0-262-61099-X. PAL ISBN 0-262-61100-7. SECAM ISBN 0-262-61101-5. ----------------------------------------------------------------------- The following order form can be used to order copies of Genetic Programming I or II, videotapes I or II, and Kinnear's recent book. Order Form Send to The MIT Press 55 Hayward Street Cambridge, MA 02142 USA You may order by phone 1-800-356-0343 (toll-free); or by phone to 617-625-8569; or by Fax to 617-625-6660; or by-e-mail to mitpress-orders@mit.edu Please send the following: ___copies of book Genetic Programming: On the Programming of Computers by Means of Natural Selection by John R. 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