So, we are looking for volunteers to be discussants for each long and short paper to be presented at the GP-97 conference to be held on July 13-16, 1997 at Stanford University.
At the conference, each paper will be presented in a 15-minute period by one of the paper's authors. After the author orally presents the paper, the discussant will present his comments on the paper and/or discuss the paper with the presenting author during a 5-minute period. The discussant's comments may be in the form of prepared probing questions, comments on what's good or bad about the paper, or any other comments designed to stimulate the audience concerning the paper. The focus should stay on the presenter's paper. The discussant will be followed by a conventional 5-minute period in which the audience asks questions of the presenting author. The discussant should have some back-up questions ready in case that the audience doesn't ask a lot of questions. In event that the conference is videotaped, the discussant will have to sign a release form permitting the videotaping.
To become the discussant for for one or two papers:
John Koza GP-97 General Chair
Angeline, Peter J. Subtree Crossover: Building Block Engine or Macromutation?
Ashlock, Dan GP-Automata for Dividing the Dollar
Ashlock, Dan, and Charles Richter The Effect of Splitting Populations on Bidding Strategies
Banzhaf, Wolfgang, Peter Nordin, and Markus Olmer Generating Adaptive Behavior for a Real Robot using Function Regression within Genetic Programming
Bennett III, Forrest H A Multi-Skilled Robot that Recognizes and Responds to Different Problem Environments
Bruce, Wilker Shane The Lawnmower Problem Revisited: Stack-Based Genetic Programming and Automatically Defined Functions
Chen, Shu-Heng, and Chia-Hsuan Yeh Using Genetic Programming to Model Volatility in Financial Time Series
Daida, Jason, Steven Ross, Jeffrey McClain, Derrick Ampy, and Michael Holczer Challenges with Verification, Repeatability, and Meaningful Comparisons in Genetic Programming
Deakin, Anthony G., and Derek F. Yates Economical Solutions with Genetic Programming: the Non- Hamstrung Squadcar Problem, FvM and EHP
Dracopoulos, Dimitris C. Evolutionary Control of a Satellite
Droste, Stefan Efficient Genetic Programming for Finding Good Generalizing Boolean Functions
Esparcia-Alcazar, Anna J., and Ken Sharman Evolving Recurrent Neural Network Architectures by Genetic Programming
Freitas, Alex A. A Genetic Programming Framework for Two Data Mining Tasks: Classification and Generalized Rule Induction
Fuchs, Matthias, Dirk Fuchs, and Marc Fuchs Solving Problems of Combinatory Logic with Genetic Programming
Gathercole, Chris, and Peter Ross Small Populations over Many Generations can beat Large Populations over Few Generations in Genetic Programming
Gathercole, Chris, and Peter Ross Tackling the Boolean Even N Parity Problem with Genetic Programming and Limited-Error Fitness
Geyer-Schulz, Andreas The Next 700 Programming Languages for Genetic Programming
Gritz, Larry, and James K. Hahn Genetic Programming Evolution of Controllers for 3-D Character Animation
Harries, Kim, and Peter Smith Exploring Alternative Operators and Search Strategies in Genetic Programming
Haynes, Thomas On-line Adaptation of Search via Knowledge Reuse
Haynes, Thomas, and Sandip Sen Crossover Operators for Evolving A Team
Hiden, Hugo, Mark Willis, Ben McKay, and Gary Montague Non-Linear And Direction Dependent Dynamic Modelling Using Genetic Programming
Hooper, Dale C., Nicholas S. Flann, and Stephanie R. Fuller Recombinative Hill-Climbing: A Stronger Search Method for Genetic Programming
Howley, Brian Genetic Programming and Parametric Sensitivity: a Case Study In Dynamic Control of a Two Link Manipulator
Huelsbergen, Lorenz Learning Recursive Sequences via Evolution of Machine- Language Programs
Iba, Hitoshi Multiple-Agent Learning for a Robot Navigation Task by Genetic Programming
Jaske, Harri On code reuse in genetic programming
Koza, John R., Forest H. Bennett III, Martin A. Keane, and David Andre Evolution of a Time-Optimal Fly-To Controller Circuit using Genetic Programming
Koza, John R., Forest Bennett III, Jason Lohn, Frank Dunlap, Martin A. Keane, and David Andre Use of Architecture-Altering Operations to Dynamically Adapt a Three-Way Analog Source Identification Circuit to Accommodate a New Source
Langdon, W. B., and R. Poli An Analysis of the MAX Problem in Genetic Programming
Lensberg, Terje A Genetic Programming Experiment on Investment Behavior under Knightian Uncertainty
Luke, Sean, and Lee Spector A Comparison of Crossover and Mutation in Genetic Programming
Moore, Frank W., and Dr. Oscar N. Garcia A Genetic Programming Approach to Strategy Optimization in the Extended Two-Dimensional Pursuer/Evader Problem
Nordin, Peter, and Wolfgang Banzhaf Genetic Reasoning Evolving Proofs with Genetic Search
Paterson, Norman, and Mike Livesey Evolving caching algorithms in C by genetic programming
Poli, Riccardo, and Stefano Cagnoni Genetic Programming with User-Driven Selection: Experiments on the Evolution of Algorithms for Image Enhancement
Poli, R., and W. B. Langdon A New Schema Theory for Genetic Programming with One- point Crossover and Point Mutation
Rosca, Justinian P. Analysis of Complexity Drift in Genetic Programming
Ryan, Conor, and Paul Walsh The Evolution of Provable Parallel Programs
Sherrah, Jamie R., Robert E. Bogner, and Abdesselam Bouzerdoum The Evolutionary Pre-Processor: Automatic Feature Extraction for Supervised Classification using Genetic Programming
Soule, Terence, and James A. Foster Code Size and Depth Flows in Genetic Programming
Teller, Astro, and David Andre Automatically Choosing the Number of Fitness Cases: The Rational Allocation of Trials
Winkeler, Jay F., and B. S. Manjunath Genetic Programming for Object Detection
Zhang, Byoung-Tak, and Je-Gun Joung Enhancing Robustness of Genetic Programming at the Species Level
Cantu-Paz, Erick, an David E. Goldberg Modeling Idealized Bounding Cases of Parallel Genetic Algorithms
Kargupta, Hillol, David E. Goldberg, and Liwei Wang Extending The Class of Order-k Delineable Problems For The Gene Expression Messy Genetic Algorithm
Lathrop, James I. Compression Depth and Genetic Programs
Yang, Jihoon, and Vasant Honavar Feature Subset Selection Using A Genetic Algorithm
Floreano, Dario, and Stefano Nolfi God Save the Red Queen! Competition in Co-Evolutionary Robotics
Hasegawa, Yasuhisa and Toshio Fukuda Motion Generation of Two-link Brachiation Robot
Ray, Thomas S. Selecting Naturally for Differentiation
Angeline, Peter J. An Alternative to Indexed Memory for Evolving Programs with Explicit State Representations
Chellapilla, Kumar Evolutionary Programming with Tree Mutations: Evolving Computer Programs without Crossover
Greenwood, Garrison W. Experimental Observation of Chaos in Evolution Strategies
Longshaw, Tom Evolutionary learning of large Grammars
Arita, Masanori, Akira Suyama, and Masami Hagiya A Heuristic Approach for Hamiltonian Path Problem with Molecules
Deaton, R, M. Garzon, R. C. Murphy, D. R. Francschetti, J. A. Rose, and S. E. Stevens Jr. Information Transfer through Hybridization Reactions in DNA based Computing
Garzon, M., P. Neathery, R. Deaton, R. C. Murphy, D. R. Franschetti, S. E. Stevens Jr. A New Metric for DNA Computing
Rose, J. A., Y. Gao, M. Garzon, and R. C. Murphy DNA Implementation of Finite-State Machines
Dreschler, Rolf, Nicole Gockel, Elke Mackensen, and Bernd Becker BEA: Specialized Hardware for Implementation of Evolutionary Algorithms
Kazimierczak, Jan An Approach to Evolvable Hardware representing the Knowledge Base in an Automatic Programming System
Michael Korkin, Hugo de Garis, Felix Gers, and Hitoshi Hemmi ``CBM (CAM-Brain Machine)'': A Hardware Tool which Evolves a Neural Net Module in a Fraction of a Second and Runs a Million Neuron Artificial Brain in Real Time
Liu, Weixin, Masahiro Murakawa, and Tetsuya Higuchi Evolvable Hardware for On-line Adaptive Traffic Control in ATM Networks
Sipper, Moshe, Eduardo Sanchez, Daniel Mange, Marco Tomassini, Andres Perez-Uribe, and Andre Stauffer The POE Model of Bio-Inspired Hardware Systems: A Short Introduction
Nagasaka, Ichiro, and Toshiharu Taura Geometic Representation for Shape Generation using Classifier System
Spohn, Bryan G., and Philip H. Crowley Complexity of Strategies and the Evolution of Cooperation
Westerdale, T. H. Classifier Systems--No Wonder They Don't Work
Koza, John R., Deb, Kalyanmoy, Dorigo, Marco, Fogel, David B., Garzon, Max, Iba, Hitoshi, and Riolo, Rick L. (editors). 1997. Genetic Programming 1997: Proceedings of the Second Annual Conference, July 13P16, 1997, Stanford University. San Francisco, CA: Morgan Kaufmann.