| Genetic
Programming: Papers
Genetic
Programming for Agricultural Purposes
(Page 783)
C. Chion, L. E. Da Costa, J.-A. Landry (École de Technologie
Superiéure)
Improving
Cooperative GP Ensemble with Clustering and Pruning for Pattern Classification
(Page 791)
G. Folino, C. Pizzuti, G. Spezzano (ICAR-CNR)
Characterizing
the Dynamics of Symmetry Breaking in Genetic Programming
(Page 799)
J. M. Daida (The University of Michigan)
ORDERTREE:
A New Test Problem for Genetic Programming
(Page 807)
T.-H. Hoang (University of New South Wales)
N. X. Hoai, N. T. Hien (Vietnamese Military Technical
Academy)
RI. McKay (Seoul National University)
D. Essam (University of New South Wales)
ALPS:
The Age-Layered Population Structure for Reducing the Problem of Premature
Convergence
(Page 815)
G. S. Hornby (University Affiliated Research Center)
Automated
Synthesis of a Human-Competitive Solution to the Challenge Problem of
the 2002 International Optical Design Conference by Means of Genetic
Programming and a Multi-Dimensional Mutation Operation
(Page 823)
L. W. Jones, S. H. Al-Sakran (Genetic Programming
Inc.)
J. R. Koza (Stanford University)
Genetically
Programmed Strategies for Chess Endgame
(Page 831)
N. Lassabe, S. Sanchez, H. Luga, Y. Duthen (IRIT/UT1)
A
Hybridized Genetic Parallel Programming based Logic Circuit Synthesizer
(Page 839)
W. S. Lau, K. H. Lee, K. S. Leung (The Chinese University
of Hong Kong)
Using
Context-aware Crossover to Improve the Performance of GP
(Page 847)
H. Majeed, C. Ryan (University of Limerick)
Canonical
Form Functions as a Simple Means for Genetic Programming to Evolve Human-Interpretable
Functions
(Page 855)
T. McConaghy, G. Gielen (K.U. Leuven)
MOGE:
GP Classification Problem Decomposition using Multi-objective Optimization
(Page 863)
A. McIntyre, M. Heywood (Dalhousie University)
Dynamics
of Evolutionary Robustness
(Page 871)
A. Piszcz, T. Soule (University of Idaho)
Convergence
to Global Optima for Genetic Programming Systems with Dynamically Scaled
Operators (Page 879)
L. M. Schmitt , S. Droste (Universität Dortmund)
Synthesis
of Interest Point Detectors Through Genetic Programming
(Page 887)
L. Trujillo, G. Olabue (Proyecto EvoVisión)
A
Quantitative Study of Neutrality in GP Boolean Landscapes
(Page 895)
L. Vanneschi , Y. Pirola (University of Milano-Bicocca)
P. Collard (University of Nice-Sophia Antipolis)
M. Tomassini (University of Lausanne)
S. Verel (University of Nice-Sophia Antipolis)
G. Mauri (University of Milano-Bicocca)
A
Multi-chromosome Approach to Standard and Embedded Cartesian Genetic
Programming (Page 903)
J. A. Walker, J. F. Miller, R. Cavill (University
of York)
Embedded
Cartesian Genetic Programming and the Lawnmower and Hierarchical-if-and-only-if
Problems (Page 911)
J. A. Walker, J. F. Miller (University of York)
Alternative
Evolutionary Algorithms for Evolving Programs: Evolution Strategies
and Steady State GP (Page
919)
D. Whitley, M. Richards, R. Beveridge (Colorado State
University)
A. da Motta Salles Baretto (Universidade Federal do
Rio de Janeiro)
Algebraic
Simplification of GP Programs During Evolution
(Page 927)
P. Wong (Victoria University of Wellington)
M. Zhang (M&E College)
Genetic
Programming: Posters
Relaxed
Genetic Programming (Page
937)
L. E. Da Costa , J.-A. Landry (École de Technology
Supérieure)
Improving
GP Classifier Generalization Using a Cluster Separation Metric
(Page 939)
A. George, M. I. Heywood (Dalhousie University)
Genetic
Programming with Primitive Recursion
(Page 941)
S. Kahrs (University of Kent at Canterbury)
Nonlinear
Parametric Regression in Genetic Programming
(Page 943)
Y.-K. Kwon, S.-S. Choi, B.-R. Moon (Seoul National
University)
Pareto-coevolutionary
Genetic Programming Classifier
(Page 945)
M. Lemczyk, M. Heywood (Dalhousie University)
Alternative
Cross-Over Strategies and Selection Techniques for Grammatical Evolution
Optimized Neural Networks
(Page 947)
A. A. Motsinger, L. W. Hahn, S. M. Dudek, K. K. Ryckman,
M. D. Ritchie (Vanderbilt University)
Investigation
on Artificial Ant using Analytic Programming
(Page 949)
Z. Oplatková, I. Zelinka (Tomas Bata University in
Zlin)
A
Survey of Mutation Techniques in Genetic Programming
(Page 951)
A. Piszcz, T. Soule (University of Idaho)
Genetic
Programming: Optimal Population Size for Varying Complexity Problems
(Page 953)
A. Piszcz, T. Soule (University of Idaho)
Predicting
Currency Exchange Rates by Genetic Programming with Trigonometric Functions
and High-Order Statistics
(Page 955)
R. Schwaerzel (The University of Texas at San Antonio)
(Page 955)
T. Bylander (The University of Texas at San Antonio)
When
Lisp is Faster than C (Page
957)
B. Svingen (Fast Search & Transfer)
Redundant
Genes and the Evolution of Robustness
(Page 959)
R. Thomason, T. Soule (University of Idaho)
|
