Genetic programming : on the programming of computers by means of natural selection

著者

• Koza, John R.

書誌事項

Genetic programming : on the programming of computers by means of natural selection

John R. Koza

（Complex adaptive systems）（Bradford book）

MIT Press, c1992-c1994

• [1]
• 2

タイトル別名

Genetic programming : automatic discovery of reusable programs

注記

Includes bibliographical references ([1]: p. [791]-804) and index

内容説明・目次

巻冊次

[1] ISBN 9780262111706

内容説明

In this ground-breaking book, John Koza shows how this remarkable paradigm works and provides substantial empirical evidence that solutions to a great variety of problems from many different fields can be found by genetically breeding populations of computer programs. Genetic programming may be more powerful than neural networks and other machine learning techniques, able to solve problems in a wider range of disciplines. In this ground-breaking book, John Koza shows how this remarkable paradigm works and provides substantial empirical evidence that solutions to a great variety of problems from many different fields can be found by genetically breeding populations of computer programs. Genetic Programming contains a great many worked examples and includes a sample computer code that will allow readers to run their own programs.In getting computers to solve problems without being explicitly programmed, Koza stresses two points: that seemingly different problems from a variety of fields can be reformulated as problems of program induction, and that the recently developed genetic programming paradigm provides a way to search the space of possible computer programs for a highly fit individual computer program to solve the problems of program induction. Good programs are found by evolving them in a computer against a fitness measure instead of by sitting down and writing them.

目次

• Pervasiveness of the problem of program induction
• introduction to genetic algorithms
• the representation problem for genetic algorithms
• overview of genetic programming
• detailed description of genetic programming
• four introductory examples of genetic programming
• amount of processing required to solve a problem
• non-randomness of genetic programming
• symbolic regression - error-driven evolution
• control - cost-driven evolution
• evolution of emergent behaviour
• evolution of subsumption
• entropy-driven evolution
• evolution of strategy
• co-evolution
• evolution of classification
• iteration, recursion, and setting
• evolution of constrained syntactic structures
• evolution of building blocks
• evolution of hierarchies of building blocks
• parallelization of genetic programming
• ruggedness of genetic programming
• extraneous variables and functions
• operational issues
• review of genetic programming
• comparison with other paradigms
• spontaneous emergence of self-replicating and evolutionarily self-improving computer programs. Appendices: computer implementation
• problem-specific part of simple LISP code
• kernel of the simple LISP code
• embellishments to the simple LISP code
• streamlined version of EVAL
• editor for simplifying S-expressions
• testing the simple LISP code
• time-saving techniques
• list of special symbols
• list of special functions.

巻冊次

2 ISBN 9780262111898

内容説明

Genetic Programming II extends the results of John Koza's ground-breaking work on programming by means of natural selection, described in his first book, Genetic Programming. Using a hierarchical approach, Koza 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. Koza 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. Koza shows 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.

目次

• 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.

「Nielsen BookData」 より

詳細情報

• NII書誌ID(NCID): BA1926018X
• ISBN:
  • 0262111705
  • 0262111896
• LCCN: 92025785
• LCCN: 94076375
• 出版国コード: us
• タイトル言語コード: eng
• 本文言語コード: eng
• 出版地: Cambridge, Mass.
• ページ数/冊数: 2 v.
• 大きさ: 27 cm
• 分類:
  • NDC8 : 007.64
  • LCC : QA76.6
  • DC20 : 006.3
• 件名:
  • LCSH : Electronic digital computers -- Programming
• 親書誌ID:
  • BA14399342
  • BA00245453