The GP-97 Late-Breaking Papers Book is available directly from the Stanford University Bookstore

This book containing 38 late-breaking papers and 16 one-page summaries of PhD thesis work in progress was distributed to all attendees of the Genetic Programming 1997 Conference (GP-97) held at Stanford University on July 13 - 16, 1997. This rapidly-printed book was distributed in addition to the peer-reviewed conference proceedings book published by Morgan Kaufmann Publishers of San Francisco.

The 38 late-breaking papers describe research that was initiated, enhanced, improved, or completed after the conference's original paper submission deadline of January 8, 1997. Late-breaking papers were briefly examined for minimum standards of acceptability and relevance, but were not peer reviewed or evaluated by the conference organizers. The statements and opinions contained in these papers are solely those of the authors and not those of the editor or Genetic Programming Conferences, Inc. (a California not-for-profit corporation), the AAAI, or the Stanford Bookstore. Late-breaking papers were presented as part of the poster session on the evening of Monday July 14, 1997.

The 16 one-page summaries of PhD thesis work in progress were provided by the 16 students who presented their work at the PhD Student Workshop held on Saturday July 12, 1997 (the day before the start of the conference).

This volume (ISBN 0-18-206995-8) may be purchased directly from the Custom Publishing Department of the Stanford University Bookstore by calling 415-329-1217 or 800-533-2670 or by writing Custom Publishing Department, Stanford Bookstore, Stanford University, Stanford, California 94305-3079 USA. The E-Mail address of the bookstore for mail orders is mailorder@bookstore.stanford.edu. The WWW URL for the Stanford Bookstore is http://bookstore.stanford.edu/. The price is about $l4.l4 plus shipping and handling charges and applicable sales tax.

The citation is...
Koza, John R. (editor). Late Breaking Papers at the Genetic Programming 1997 Conference, Stanford University, July 13-16, 1997. Stanford, CA: Stanford University Bookstore.

38 GP-97 Late-Breaking Papers...


Co-evolving Functions in Genetic Programming: An
Emergent Approach using ADFs and GLiB
Manu Ahluwalia, Larry Bull and Terence C. Fogarty


Controlling Exploration, Diversity and Escaping
Local Optima in GP: Adapting Weights of Training
Sets to Model Resource Consumption
Tommaso F. Bersano-Begey


A Discussion on Generality and Robustness and a
Framework for Fitness Set Construction in Genetic
Programming to Promote Robustness
Tommaso F. Bersano-Begey and Jason M. Daida


Reconstructing Incomplete Signals Using Nonlinear
Interpolation and Genetic Algorithms
Robert R. Bertram, Jason M. Daida, John F. Vesecky,
Guy A. Meadows, Christian Wolf


Constructivist AI with GP
K. Govinda Char


A Methodology for the Analysis of Complex Systems
based on Qualitative Reasoning, Stochastic
Complexity and Genetic Programming
Paolo Costa


Tagging as a Means for Self-Adaptive Hybridization
Jason M. Daida, Robert R. Bertram, Catherine S.
Grasso, Stephen A. Stanhope


Coevolving Classifier Systems to Control Traffic
Signals
Cathy Escazut and Terence C. Fogarty


Learning Schemes for Genetic Programming
Anna I. Esparcia-Alcazar and Ken Sharman


Genetic Nets
Charles Hand


Strongly Typed Genetic Programming To Promote
Hierarchy Through Explicit Syntactic Constraints
Christopher Harris


Distributed Genetic Programming In Java
Paul Hulse, Richard Gerber and Jenanne Price


Genetic Algorithm Optimization of Investment
Justification Theory
Zahir Irani and Amir Sharif


An Empirical Study of Facial Image Feature
Extraction by Genetic Programming
Satoru Isaka


Strings of Weights as Chromosomes in Genetic
Algorithms for the Traveling Salesman Problem
Bryant A. Julstrom


Implicitly Defined Functions as an alternative to GP-
schemata
Maarten Keijzer


Genetic Evolution of Shape-Altering Programs for
Supersonic Aerodynamics
Robert A. Kennelly, Jr.


Rapidly Reconfigurable Field-Programmable Gate
Arrays for Accelerating Fitness Evaluation in
Genetic Programming
John R. Koza, Forrest H Bennett III, Jeffrey L.
Hutchings, Stephen L. Bade, Martin A. Keane, and
David Andre


Fitness Causes Bloat: Mutation
W.B. Langdon and R. Poli


Using Co-Evolution to Produce Robust Control
Greg McNutt


Intelligent System for Customer Driven Design
A. Mousavi, A. Gunasekaran, and P. Adi


A General-Purpose AI Planning System Based on the
Genetic Programming Paradigm
Ion Muslea


Evolution Strategies to Improve Abstract
Interpretation Algorithms for Logic Programming
Kaninda Musumbu, Kablan Barbar, and Maroun
Nassif


Internet-Based Genetic Programming Platform
Phaderm Nansgue and Susan E. Conry


Using Genetic Programming to Predict the
Occurrence of Species in Ecological Communities
A. Darcie Neff, James W. Haefner and Raymond D.
Dueser


Evolutionary 3D Design of Complex Shapes and a
Vector Space Genetic Algorithm
Thang C. Nguyen and Thomas Huang


Using a Distance Metric on Genetic Programs to
Understand Genetic Operators
Una-May O'Reilly


Multiobjective Genetic Programming: A Nonlinear
System Identification Application
Katya Rodriguez-Vazquez, Carlos M. Fonseca, and
Peter J. Fleming


Self-organiztion Through Global and Local
Exchange of Information: A Schematic Model of
Bank Runs
Andres R. Schuschny, Roberto P.J. Perazzo and
Daniel Heymann


Finite Element Mesh Generation using Genetic
Algorithms
Amir Sharif and Robert Ettinger


Genetic Programming for Target Classification and
Identification in Synthetic Aperture Radar Imagery
Stephen A. Stanhope and Jason M. Daida


GP++ An Introduction
Borge Svingen


Using Genetic Programming for Document
Classification
Borge Svingen


A Framework for the Evolution of Autonomous
Agents
Adrian Trenaman


Investment Portfolio Optimization using Genetic
Algorithms
Ganesh Vedarajan, Louis Chi Chan and David
Goldberg


PolyGP: A Polymorphic Genetic Programming
System in Haskell
Tina Yu and Chris Clack


Application of Genetic Algorithm to Machining
Process Diagnostics with a DOE-Based GA
Validation Scheme
Rixin Zhu, Steven J. Skerlos, Richard E. DeVor and
Shiv G. Kapoor


CORE: Constrained Optimization by Random
Evolution
Sheela V.Belur

16 PhD Student Presentations ...


K. Govinda Char -University of Glasgow - Evolution
of Learning with Genetic Programming - Constructivist
AI with Genetic Programming


Anna I Esparcia-Alcazar - University of Glasgow - An
investigation into a Genetic Programming Technique for
Adaptive Signal Processing


Helen Gray - Aarhus University - Genetic
Programming for Classification of Medical Data


Christopher Harris - University College London -
Enforcing Hierarchy on Solutions with Strongly Typed
Genetic Programming


Thomas Haynes - University of Missouri - Competitive
Computational Agent Society


Frank W. Moore - Wright State University -A Genetic
Programming Methodology for Strategy Optimization
Under Uncertainty


Umur Ozkul - Bogazici University - Evolution of
Complex Systems


Simon Perkins - University of Edinburgh - Incremental
Acquisition of Visual Behaviour using Guided
Evolution


Simon Raik - Monash University - Parallel Program
Execution


Jamie Sherrah - University of Adelaide - Automatic
Feature Extraction using Genetic Programming


Astro Teller - Carnegie Mellon University - Algorithm
Evolution for Signal Understanding


Kanta Vekaria - University College London - Genetic
Programming With Gene Dominance


John Walker - Iowa State University - Methodologies
to the Design and Control of Virtual Agents


Andrew H. Watson - University of Plymouth -
Calibrating Gas Turbine Design Software using Genetic
Programming and Adaptive Search Techniques


Chi-Hsuan Yeh - National Chengchi University - From
Multi-Agent System to Macroeconomics: Applications
of Genetic Programming


Tina Yu - University College London - Functional
Genetic Programming

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