Genetic Programming II
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Home > Computing and Information Technology > Computer science > Artificial intelligence > Machine learning > Genetic Programming II: Automatic Discovery of Reusable Programs(Complex Adaptive Systems)
Genetic Programming II: Automatic Discovery of Reusable Programs(Complex Adaptive Systems)

Genetic Programming II: Automatic Discovery of Reusable Programs(Complex Adaptive Systems)


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About the Book

Using a hierarchical approach, the author shows that complex problems can be solved by breaking them down into smaller, simpler problems using the recently developed technique of automatic function definition in the context of genetic programming. Where conventional techniques of machine learning and artificial intelligence fail to provide an effective means for automatically handling the process of decomposing complex problems into smaller subsets, reassembling the solutions to these subsets, and applying an overall solution to the original problem, automatic function definition enables genetic programming to define useful and reusable subroutines dynamically. This text illustrates this new technique by showing how it solves (or approximately solves) a variety of problems in 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. It is shown that leverage accrues because genetic programming with automatic function definition repeatedly uses the solutions to the subproblems in the assembly of the solution to the overall problem. Moreover, genetic programming with automatic function definition produces solutions that are simpler and smaller than the solution obtained without automatic function definition.

Table of Contents:
Background on genetic algorithms, LISP, and genetic programming; hierarchical problem-solving; introduction to automatically-defined functions - the two-boxes problem; problems that straddle the breakeven point for computational effort; Boolean parity functions; determining the architecture of the program; the lawnmower problem; the bumblebee problem; the increasing benefits of ADFs as problems are scaled up; finding an impulse response function; artificial ant on the San Mateo trail; obstacle-avoiding robot; the minesweeper problem; automatic discovery of detectors for letter recognition; flushes and four-of-a-kinds in a pinochle deck; introduction to biochemistry and molecular biology; prediction of transmembrane domains in proteins; prediction of omega loops in proteins; lookahead version of the transmembrane problem; evolutionary selection of the architecture of the program; evolution of primitives and sufficiency; evolutionary selection of terminals; evolution of closure; simultaneous evolution of architecture, primitive functions, terminals, sufficiency, and closure; the role of representation and the lens effect. Appendices: list of special symbols; list of special functions; list of type fonts; default parameters; computer implementation; annotated bibliography of genetic programming; electronic mailing list and public repository.

About the Author :
John R. Koza is Consulting Associate Professor in the Computer Science Department at Stanford University.


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Product Details
  • ISBN-13: 9780262111898
  • Publisher: MIT Press Ltd
  • Publisher Imprint: MIT Press
  • Height: 259 mm
  • No of Pages: 768
  • Series Title: Complex Adaptive Systems
  • Spine Width: 43 mm
  • Weight: 1497 gr
  • ISBN-10: 0262111896
  • Publisher Date: 17 May 1994
  • Binding: Hardback
  • Language: English
  • Returnable: Y
  • Series Title: Genetic Programming II
  • Sub Title: Automatic Discovery of Reusable Programs
  • Width: 183 mm


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