J. David Schaffer

J. David Schaffer, Jin-Woo Park, Erin Barnes, Qiyi Lu, Xingye Qiao, Youping Deng, Yan Li, Walker H. Land Jr.: GRNN Ensemble Classifier for Lung Cancer Prognosis Using Only Demographic and TNM features. Procedia CS 12: 450-455 (2012)

Ravi Mathur, J. David Schaffer, Walker H. Land Jr., John J. Heine, Jonathan M. Hernandez, Timothy Yeatman: Perturbation and candidate analysis to combat overfitting of gene expression microarray data. I. J. Computational Biology and Drug Design 4(4): 307-315 (2011)

Ravi Mathur, J. David Schaffer, Walker H. Land Jr., John J. Heine, Steven Eschrich, Timothy Yeatman: Evolutionary computation with noise perturbation and cluster analysis to discover biomarker sets. Procedia CS 6: 153-158 (2011)

R. Batllori, Craig B. Laramee, Walker H. Land Jr., J. David Schaffer: Evolving spiking neural networks for robot control. Procedia CS 6: 329-334 (2011)

Heike Sichtig, J. David Schaffer, Alberto Riva: Evolving Spiking Neural Networks for predicting transcription factor binding sites. IJCNN 2010: 1-8

J. David Schaffer, Heike Sichtig, Craig B. Laramee: A series of failed and partially successful fitness functions for evolving spiking neural networks. GECCO (Companion) 2009: 2661-2664

Heike Sichtig, J. David Schaffer, Craig B. Laramee: SSNNS -: a suite of tools to explore spiking neural networks. GECCO (Companion) 2008: 1787-1790

Lalitha Agnihotri, Nevenka Dimitrova, Thomas McGee, Sylvie Jeannin, J. David Schaffer, Jan Nesvadba: Envolvable Visual Commercial Detector. CVPR (2) 2003: 79-84

J. David Schaffer, Lalitha Agnihotri, Nevenka Dimitrova, Thomas McGee, Sylvie Jeannin: Improving Digital Video Commercial Detectors With Genetic Algorithms. GECCO 2002: 1212-1218

Srinivas Gutta, Kaushal Kurapati, K. P. Lee, Jacquelyn Martino, John Milanski, J. David Schaffer, John Zimmerman: TV Content Recommender System. AAAI/IAAI 2000: 1121-1122

Keith E. Mathias, Larry J. Eshelman, J. David Schaffer: Niches in NK-Landscapes. FOGA 2000: 27-46

Keith E. Mathias, Larry J. Eshelman, J. David Schaffer, Lex Augusteijn, Paul F. Hoogendijk, Rik van de Wiel: Code Compaction Using Genetic Algorithms. GECCO 2000: 710-717

J. David Schaffer, Murali Mani, Larry J. Eshelman, Keith E. Mathias: The Effect of Incest Prevention on Genetic Drift. FOGA 1998: 235-244

Keith E. Mathias, J. David Schaffer, Larry J. Eshelman, Murali Mani: The Effects of Control Parameters and Restarts on Search Stagnation in Evolutionary Programming. PPSN 1998: 398-407

Larry J. Eshelman, Keith E. Mathias, J. David Schaffer: Crossover Operator Biases: Exploiting the Population Distribution. ICGA 1997: 354-361

Larry J. Eshelman, Keith E. Mathias, J. David Schaffer: Convergence Controlled Variation. FOGA 1996: 203-224

Larry J. Eshelman, J. David Schaffer: Productive Recombination and Propagating and Preserving Schemata. FOGA 1994: 299-313

Larry J. Eshelman, J. David Schaffer: Crossover's Niche. ICGA 1993: 9-14

J. David Schaffer, Larry J. Eshelman: Designing Multiplierless Digital Filters Using Genetic Algorithms. ICGA 1993: 439-444

Larry J. Eshelman, J. David Schaffer: Real-Coded Genetic Algorithms and Interval-Schemata. FOGA 1992: 187-202

J. David Schaffer, Larry J. Eshelman: On Crossover as an Evolutionarily Viable Strategy. ICGA 1991: 61-68

Larry J. Eshelman, J. David Schaffer: Preventing Premature Convergence in Genetic Algorithms by Preventing Incest. ICGA 1991: 115-122

J. David Schaffer, Larry J. Eshelman, Daniel Offutt: Spurious Correlations and Premature Convergence in Genetic Algorithms. FOGA 1990: 102-112

Larry J. Eshelman, Rich Caruana, J. David Schaffer: Biases in the Crossover Landscape. ICGA 1989: 10-19

J. David Schaffer, Rich Caruana, Larry J. Eshelman, Rajarshi Das: A Study of Control Parameters Affecting Online Performance of Genetic Algorithms for Function Optimization. ICGA 1989: 51-60

Rich Caruana, J. David Schaffer, Larry J. Eshelman: Using Multiple Representations to Improve Inductive Bias: Gray and Binary Coding for Genetic Algorithms. ML 1989: 375-378

Rich Caruana, Larry J. Eshelman, J. David Schaffer: Representation and Hidden Bias II: Eliminating Defining Length Bias in Genetic Search via Shuffle Crossover. IJCAI 1989: 750-755

J. David Schaffer (Ed.): Proceedings of the 3rd International Conference on Genetic Algorithms, George Mason University, Fairfax, Virginia, USA, June 1989. Morgan Kaufmann 1989, isbn 1-55860-066-3

Rich Caruana, J. David Schaffer: Representation and Hidden Bias: Gray vs. Binary Coding for Genetic Algorithms. ML 1988: 153-161

J. David Schaffer, Amy Morishima: An Adaptive Crossover Distribution Mechanism for Genetic Algorithms. ICGA 1987: 36-40

J. David Schaffer: Learning Multiclass Pattern Discrimination. ICGA 1985: 74-79

J. David Schaffer: Multiple Objective Optimization with Vector Evaluated Genetic Algorithms. ICGA 1985: 93-100

J. David Schaffer, John J. Grefenstette: Multi-Objective Learning via Genetic Algorithms. IJCAI 1985: 593-595