Genetic Programming 1996

Proceedings of the First Annual Conference, July 28-31, 1996, Stanford University

Genetic programming is a domain-independent method for automatic programming that evolves computer programs that solve, or approximately solve, problems. Starting with a primordial ooze of thousands of randomly created computer programs composed of functions and terminals appropriate to a problem, a population of programs is progressively evolved over many generations using the Darwinian principle of survival of the fittest, a sexual recombination operation, and occasional mutation.

These proceedings of the first Genetic Programming Conference present the most recent research in the field of genetic programming as well as recent research results in the fields of genetic algorithms, evolutionary programming, and learning classifier systems.

Topics include: Applications of genetic programming. Theoretical foundations of genetic programming. Implementation issues. Technique extensions. Automated synthesis of analog electrical circuits. Automatic programming of cellular automata. Induction. System identification. Control. Evolution of machine language programs. Automatic programming of multi-agent strategies. Automated evolution of program architecture. Evolution of mental models. Implementations of memory and state. Cellular encoding. Evolvable hardware. Parallelization techniques. Relations to biology and cognitive systems. Genetic algorithms. Evolutionary programming. Evolution strategies. Learning classifier systems.

Complex Adaptive Systems series

A Bradford Book

Table of Contents

  1. Preface
  2. Chairs and Committees
  3. 1. Discovery by Genetic Programming of a Cellular Automata Rule that Is Better than any Known Rule for the Majority Classification Problem

    David Andre, Forrest H Bennett III and John R. Koza

  4. 2. A Study in Program Response and the Negative Effects of Introns in Genetic Programming

    David Andre and Astro Teller

  5. 3. An Investigation into the Sensitivity of Genetic Programming to the Frequency of Leaf Selection during Subtree Crossover

    Peter J. Angeline

  6. 4. Automatic Creation of an Efficient Multi-Agent Architecture Using Genetic Programming with Architecture-Altering Operations

    Forrest H Bennett III

  7. 5. Evolving Deterministic Finite Automata Using Cellular Encoding

    Scott Brave

  8. 6. Genetic Programming and the Efficient Market Hypothesis

    Shu-Heng Chen and Chia-Hsuan Yeh

  9. 7. Bargaining by Artificial Agents in Two Coalition Games: A Study in Genetic Programming for Electronic Commerce

    Garett Dworman, Steven O. Kinbrough and James D. Laing

  10. 8. Waveform Recognition Using Genetic Programmnig: The Myoelectric Signal Recognition Problem

    Jaime J. Fernandez, Kristin A. Farry and John B. Cheatham

  11. 9. Benchmarking the Generalization Capabilities of a Compiling Genetic Programming System Using Sparse Data Sets

    Frank D. Francone, Peter Nordin and Wolfgang Banzhaf

  12. 10. A Comparison between Cellular Encoding and Direct Encoding for Genetic Neural Networks

    Frédéric Gruau, Darrell Whitley and Larry Pyeatt

  13. 11. Entailment for Specification Refinement

    Thomas Haynes, Rose Gamble, Leslie Knight and Roger Wainwright

  14. 12. Genetic Programming of Near-Minimum-Time Spacecraft Attitude Maneuvers

    Brian Howley

  15. 13. Evolving Evolution Programs: Genetic Programming and L-Systems

    Christian Jacob

  16. 14. Genetic Programming Using Genotype-Phenotype Mapping from Linear Genomes into Linear Phenotypes

    Robert E. Keller and Wolfgang Banzhaf

  17. 15. Automated WYWIWYG Design of Both the Topology and Component Values of Electrical Circuits Using Genetic Programming

    John R. Koza, Forrest H Bennett III, David Andre and Martin A. Keane

  18. 16. Use of Automatically Defined Functions and Architecture-Altering Operations in Automated Circuit Synthesis with Genetic Programming

    John R. Koza, David Andre, Forrest H Bennett III and Martin A. Keane

  19. 17. Using Data Structures within Genetic Programming

    W. B. Langdon

  20. 18. Evolving Teamwork and Coordination with Genetic Programming

    Sean Luke and Lee Spector

  21. 19. Using Genetic Programming to Develop Inferential Estimation Algorithms

    Ben McKay, Mark Willis, Gary Montague and Geoffrey W. Barton

  22. 20. Dynamics of Genetic Programming and Chaotic Time Series Prediction

    Brian S. Mulloy, Rick L. Riolo and Robert S. Savit

  23. 21. Genetic Programming, the Reflection of Chaos, and the Bootstrap: Towards a Useful Test for Chaos

    E. Howard N. Oakley

  24. 22. Solving Facility Layout Problems Using Genetic Programming

    Jaime Garces-Perez, Dale A. Schoenefeld and Roger L. Wainwright

  25. 23. Variations in Evolution of Subsumption Architectures Using Genetic Programming: The Wall Following Robot Revisited

    Steven J. Ross, Jason M. Daida, Chau M. Doan, Tommaso F. Bersano-Begey and Jeffrey J. McClain

  26. 24. MASSON: Discovering Commonalities in Collection of Objects Using Genetic Programming

    Tae-Wan Ryu and Christoph F. Eick

  27. 25. Cultural Transmission of Information in Genetic Programming

    Lee Spector and Sean Luke

  28. 26. Code Growth in Genetic Programming

    Terrence Soule, James A. Foster and John Dickinson

  29. 27. High-Performance, Parallel, Stack-Based Genetic Programming

    Kilian Stoffel and Lee Spector

  30. 28. Search Bias, Language Bias, and Genetic Programming

    P. A. Whigham

  31. 29. Learning Recursive Functions from Noisy Examples Using Generic Genetic Programming

    Man Leung Wong and Kwong Sak Leung

  32. 30. Classification Using Cultural Co-Evolution and Genetic Programming

    Myriam Z. Abramson and Lawrence Hunter

  33. 31. Type-Constrained Genetic Programming for Rule-Base Definition in Fuzzy Logic Controllers

    Enrique Alba, Carlos Cotta and José M. Troya

  34. 32. The Evolution of Memory and Mental Models Using Genetic Programming

    Scott Brave

  35. 33. Automatic Generation of Object-Oriented Programs Using Genetic Programming

    Wilker Shane Bruce

  36. 34. Evolving Event-Driven Programs

    Mark Crosbie and Eugene H. Spafford

  37. 35. Computer-Assisted Design of Image Classification Algorithms: Dynamic and Static Fitness Evaluations in a Scaffolded Genetic Programming Environment

    Jason M. Daida, Tommaso F. Bersano-Begey, Steven J. Ross and John F. Vesecky

  38. 36. Improved Direct Acyclic Graph Evaluation and the Combine Operator in Genetic Programming

    Herman Ehrenburg

  39. 37. An Adverse Interaction between Crossover and Restricted Tree Depth in Genetic Programming

    Chris Gathercole and Peter Ross

  40. 38. The Prediction of the Degree of Exposure to Solvent of Amino Acid Residues via Genetic Programming

    Simon Handley

  41. 39. A New Class of Function Sets for Solving Sequence Problems

    Simon Handley

  42. 40. Evolving Edge Detectors with Genetic Programming

    Christopher Harris and Bernard Buxton

  43. 41. Toward Simulated Evolution of Machine Language Interaction

    Lorenz Huelsbergen

  44. 42. Robustness of Robot Programs Generated by Genetic Programming

    Takuya Ito, Hitoshi Iba and Masayuki Kimura

  45. 43. Signal Path Oriented Approach for Generation of Dynamic Process Models

    Peter Marenbach, Kurt D. Bettenhausen and Stephan Freyer

  46. 44. Evolving Control Laws for a Network of Traffic Signals

    David J. Montana and Steven Czerwinski

  47. 45. Distributed Genetic Programming: Empirical Study and Analysis

    Tatsuya Niwa and Hitoshi Iba

  48. 46. Programmatic Compression of Images and Sound

    Peter Nordin and Wolfgang Banzhaf

  49. 47. Investigating the Generality of Automatically Defined Functions

    Una-May O'Reilly

  50. 48. Parallel Genetic Programming: An Application to Trading Models Evolution

    Mouloud Oussaidène, Bastien Chopard, Olivier V. Pictet and Marco Tommassini

  51. 49. Genetic Programming for Image Analysis

    Riccardo Poli

  52. 50. Evolving Agents

    Adil Qureshi

  53. 51. Genetic Programming for Improved Data Mining: An Application to the Biochemistry of Protein Interactions

    M. I. Raymer, W. F. Punch, E. D. Goodman and L. A. Kuhn

  54. 52. Generality versus Size in Genetic Programming

    Justinian P. Rosca

  55. 53. Genetic Programming in Database Query Optimization

    Michael Stillger and Myra Spiliopoulou

  56. 54. Ontogenetic Programming

    Lee Spector and Kilian Stoffel

  57. 55. Using Genetic Programming to Approximate Maximum Clique

    Terence Soule, James A. Foster and John Dickinson

  58. 56. Paragen: A Novel Technique for the Autoparallelisation of Sequential Programs using Genetic Programming

    Paul Walsh and Conor Ryan

  59. 57. The Benefits of Computing with Introns

    Mark Wineberg and Franz Oppacher

  60. 58. Co-Evolving Hierarchical Programs Using Genetic Programming

    Manu Ahluwalia and Terence C. Fogarty

  61. 59. GP Tools Available on the Web: A First Encounter

    Anthony G. Deakin and Derek F. Yates

  62. 60. Speeding up Genetic Programming: A Parallel BSP Implementation

    Dimitris C. Dracopoulos and Simon Kent

  63. 61. Easy Inverse Kinematics Using Genetic Programming

    Jonathan Gibbs

  64. 62. Noisy Wall Following and Maze Navigation through Genetic Programming

    Andrew Goldish

  65. 63. Genetic Programming Classification of Magnetic Resonance Data

    H. F. Gray, R. J. Maxwell, I. Martínez-Perez, C. Arús and S. Cerdá

  66. 64. GP-COM: A Distributed Component-Based Genetic Programming System in C++

    Christopher Harris and Bernard Buxton

  67. 65. Clique Detection via Genetic Programming

    Thomas Haynes and Dale Schoenefeld

  68. 66. Functional Languages on Linear Chromosomes

    Paul Holmes and Peter J. Barclay

  69. 67. Improving the Accuracy and Robustness of Genetic Programming through Expression Simplification

    Dale C. Hooper and Nicholas S. Flann

  70. 68. COAST: An Approach to Robustness and Reusability in Genetic Programming

    Naohiro Hondo, Hitoshi Iba and Yukinori Kakazu

  71. 69. Recurrences with Fixed Base Cases in Genetic Programming

    Stefan J. Johansson

  72. 70. Evolutionary and Incremental Methods to Solve Hard Learning Problems

    Ibrahim Kuscu

  73. 71. Detection of Patterns in Radiographs using ANN Designed and Trained with the Genetic Algorithm

    Alejandro Pazos, Julian Dorado and Antonino Santos

  74. 72. The Logic-Grammars-Based Genetic Programming System

    Man Leung Wong and Kwong Sak Leung

  75. 73. Genetic Algorithms with Analytical Solution

    Erol Gelenbe

  76. 74. Silicon Evolution

    Adrian Thompson

  77. 75. On Sensor Evolution in Robotics

    Karthik Balakrishnan and Vasant Honavar

  78. 76. Testing Software Using Order-Based Genetic Algorithms

    Edward B. Boden and Gilford F. Martino

  79. 77. Optmizing Local Area Networks Using Genetic Algorithms

    Andy Choi

  80. 78. A Genetic Algorithm for the Construction of Small and Highly Testable OKFDD Circuits

    Rolf Dreschler, Bernd Becker and Nicole Göckel

  81. 79. Motion Planning and Design of CAM Mechanisms by Means of a Genetic Algorithm

    Rodolfo Faglia and David Vetturi

  82. 80. Evolving Strategies Based on the Nearest-Neighbor Rule and a Genetic Algorithm

    Matthias Fuchs

  83. 81. Recognition and Reconstruction of Visibility Graphs Using a Genetic Algorithm

    Marshall S. Veach

  84. 82. The Use of Genetic Algorithms in the Optimization of Competitive Neural Networks which Resolve the Stuck Vectors Problem

    Tin Ilakovac, Zeljka Perkovic and Strahil Ristov

  85. 83. An Extraction Method of a Car License Plate Using a Distributed Genetic Algorithm

    Dae Wook Kim, Sang Kyoon Kim and Hange Joon Kim

  86. 84. Evolving Fractal Movies

    Peter J. Angeline

  87. 85. Preliminary Experiments on Discriminating between Chaotic Signals and Noise Using Evolutionay Programming

    David B. Fogel and Lawrence J. Fogel

  88. 86. Discovering Patterns in Spatial Data Using Evolutionary Programming

    Adam Ghozeil and David. B. Fogel

  89. 87. Evolved Reduced Parameter Bilinear Models for Time Series Prediction Using Fast Evolutionary Programming

    Sathyanarayan S. Rao and Kumar Chellapilla

  90. 88. Three-Dimensional Shape Optimization Utilizing a Learning Classifier System

    Robert A. Richards and Sheri D. Sheppard

  91. 89. Classifier System Renaissance: New Analogies, New Directions

    H. Brown Cribbs, III and Robert E. Smith

  92. 90. Natural Niching for Evolving Cooperative Classifiers

    Jeffrey Horn and David E. Goldberg

  93. Author Index
  94. Subject Index