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Advances in genetic programming

Author: Kenneth E Kinnear; M.I.T. Press.
Publisher: Cambridge, Mass. : MIT Press, ©1994.
Series: Complex adaptive systems.; Bradford book.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Database:WorldCat
Summary:
There is increasing interest in genetic programming by both researchers and professional software developers. These twenty-two invited contributions show how a wide variety of problems across disciplines can be solved using this new paradigm. Advances in Genetic Programming reports significant results in improving the power of genetic programming, presenting techniques that can be employed immediately in the  Read more...
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Details

Genre/Form: Electronic books
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Kenneth E Kinnear; M.I.T. Press.
ISBN: 0262111888 9780262111881
OCLC Number: 61168016
Notes: Title from title screen (viewed July 12th, 2005).
Reproduction Notes: Electronic reproduction. Cambridge, Mass. : MIT CogNet, [2004?].
Description: 518 pages ; 25 cm.
Details: Mode of access: World Wide Web.
Contents: Contributors --
Preface --
Acknowledgments --
I. Introduction --
1. A Perspective on the Work in this Book / Kenneth E. Kinnear, Jr. --
2. Introduction to Genetic Programming / John R. Koza --
II. Increasing the Power of Genetic Programming --
3. The Evolution of Evolvability in Genetic Programming / Lee Altenberg --
4. Genetic Programming and Emergent Intelligence / Peter J. Angelino --
5. Scalable Learning in Genetic Programming using Automatic Function Definition / John R. Koza --
6. Alternatives in Automatic Function Definition: A Comparison of Performance / Kenneth E. Kinnear, Jr. --
7. The Donut Problem: Scalability, Generalization and Breeding Policies in Genetic Programming / Walter Alden Tackett and Aviram Carmi --
8. Effects of Locality in Individual and Population Evolution / Patrik D'haeseleer and Jason Bluming --
9. The Evolution of Mental Models / Astro Teller --
10. Evolution of Obstacle Avoidance Behavior: Using Noise to Promote Robust Solutions / Craig W. Reynolds --
11. Pygmies and Civil Servants / Conor Ryan --
12. Genetic Programming Using a Minimum Description Length Principle / Hitoshi Iba, Hugo de Garis and Taisuke Sato --
13. Genetic Programming in C++: Implementation Issues / Mike J. Keith and Martin C. Martin --
14. A Compiling Genetic Programming System that Directly Manipulates the Machine Code / Peter Nordin --
III. Innovative Applications of Genetic Programming --
15. Automatic Generation of Programs for Crawling and Walking / Graham Spencer --
16. Genetic Programming for the Acquisition of Double Auction Market Strategies / Martin Andrews and Richard Prager --
17. Two Scientific Applications of Genetic Programming: Stack Filters and Non-Linear Equation Fitting to Chaotic Data / Howard Oakley --
18. The Automatic Generation of Plans for a Mobile Robot via Genetic Programming with Automatically Defined Functions / Simon G. Handley --
19. Competitively Evolving Decision Trees Against Fixed Training Cases for Natural Language Processing / Eric V. Siegel --
20. Cracking and Co-Evolving Randomizers / Jan Jannink --
21. Optimizing Confidence of Text Classification by Evolution of Symbolic Expressions / Birj Massand --
22. Evolvable 3D Modeling for Model-Based Object Recognition Systems / Thang Nguyen and Thomas Huang --
23. Automatically Defined Features: The Simultaneous Evolution of 2-Dimensional Feature Detectors and an Algorithm for Using Them / David Andre --
24. Genetic Micro Programming of Neural Networks / Frederic Gruau --
Author Index.
Series Title: Complex adaptive systems.; Bradford book.
Other Titles: MIT CogNet.
Responsibility: edited by Kenneth E. Kinnear.

Abstract:

There is increasing interest in genetic programming by both researchers and professional software developers. These twenty-two invited contributions show how a wide variety of problems across disciplines can be solved using this new paradigm. Advances in Genetic Programming reports significant results in improving the power of genetic programming, presenting techniques that can be employed immediately in the solution of complex problems in many areas, including machine learning and the simulation of autonomous behavior. Popular languages such as C and C++ are used in many of the applications and experiments, illustrating how genetic programming is not restricted to symbolic computing languages such as LISP. Researchers interested in getting started in genetic programming will find information on how to begin, on what public domain code is available, and on how to become part of the active genetic programming community via electronic mail. A major focus of the book is on improving the power of genetic programming. Experimental results are presented in a variety of areas, including adding memory to genetic programming, using locality and "demes" to maintain evolutionary diversity, avoiding the traps of local optima by using coevolution, using noise to increase generality, and limiting the size of evolved solutions to improve generality. Significant theoretical results in the understanding of the processes underlying genetic programming are presented, as are several results in the area of automatic function definition. Performance increases are demonstrated by directly evolving machine code, and implementation and design issues for genetic programming in C++ are discussed.

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