J. David Schaffer

Publication Activity (10 Years)

Years Active: 1985-2023
Publications (10 Years): 7

Top Topics

Confidence Values

Crossover Operator

Top Venues

Complex Adaptive Systems

Publications

Jacob Kiggins, J. David Schaffer, Cory E. Merkel
A Bio-Inspired Computational Astrocyte Model for Spiking Neural Networks. IJCNN (2023)
Roozbeh Sadeghian, J. David Schaffer, Stephen A. Zahorian
Towards an Automatic Speech-Based Diagnostic Test for Alzheimer's Disease. Frontiers Comput. Sci. 3 (2021)
J. David Schaffer
Evolving Spiking Neural Networks for Robot Sensory-motor Decision Tasks of Varying Difficulty. NICE (2020)
Roozbeh Sadeghian, J. David Schaffer, Stephen A. Zahorian
Speech Processing Approach for Diagnosing Dementia in an Early Stage. INTERSPEECH (2017)
Arnab Roy, J. David Schaffer, Craig B. Laramee
A novel approach to signal classification with an application to identifying the alcoholic brain. Appl. Soft Comput. 43 (2016)
Walker H. Land Jr., J. David Schaffer
Predicting with Confidence: Extensions to the GRNN Oracle Enabling Quantification of Confidence in Predictions. Complex Adaptive Systems (2015)
J. David Schaffer
Evolving spiking neural networks: A novel growth algorithm corrects the teacher. CISDA (2015)
Arnab Roy, J. David Schaffer, Craig B. Laramee
New crossover operators for multiple subset selection tasks. CoRR (2014)
Aaron S. Campbell, Walker H. Land Jr., Dan Margolis, Ravi Mathur, J. David Schaffer
Investigating the GRNN Oracle as a Method for Combining Multiple Predictive Models of Colon Cancer Recurrence from Gene Microarrays. Complex Adaptive Systems (2013)
Arnab Roy, J. David Schaffer, Craig B. Laramee
Evolving Spike Neural Network Sensors to Characterize the Alcoholic Brain Using Visually Evoked Response Potential. Complex Adaptive Systems (2013)
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. Complex Adaptive Systems (2012)
Thomas Raway, J. David Schaffer, Kenneth J. Kurtz, Hiroki Sayama
Evolving data sets to highlight the performance differences between machine learning classifiers. GECCO (Companion) (2012)
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. Complex Adaptive Systems (2011)
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. Int. J. Comput. Biol. Drug Des. 4 (4) (2011)
R. Batllori, Craig B. Laramee, Walker H. Land Jr., J. David Schaffer
Evolving spiking neural networks for robot control. Complex Adaptive Systems (2011)
Heike Sichtig, J. David Schaffer, Alberto Riva
Evolving Spiking Neural Networks for predicting transcription factor binding sites. IJCNN (2010)
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)
Heike Sichtig, J. David Schaffer, Craig B. Laramee
SSNNS -: a suite of tools to explore spiking neural networks. GECCO (Companion) (2008)
Lalitha Agnihotri, Nevenka Dimitrova, Thomas McGee, Sylvie Jeannin, J. David Schaffer, Jan Nesvadba
Envolvable Visual Commercial Detector. CVPR (2) (2003)
J. David Schaffer, Lalitha Agnihotri, Nevenka Dimitrova, Thomas McGee, Sylvie Jeannin
Improving Digital Video Commercial Detectors With Genetic Algorithms. GECCO (2002)
Srinivas Gutta, Kaushal Kurapati, K. P. Lee, Jacquelyn Martino, John Milanski, J. David Schaffer, John Zimmerman
TV Content Recommender System. AAAI/IAAI (2000)
Keith E. Mathias, Larry J. Eshelman, J. David Schaffer
Niches in NK-Landscapes. FOGA (2000)
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)
J. David Schaffer, Murali Mani, Larry J. Eshelman, Keith E. Mathias
The Effect of Incest Prevention on Genetic Drift. FOGA (1998)
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)
Larry J. Eshelman, Keith E. Mathias, J. David Schaffer
Crossover Operator Biases: Exploiting the Population Distribution. ICGA (1997)
Larry J. Eshelman, Keith E. Mathias, J. David Schaffer
Convergence Controlled Variation. FOGA (1996)
Larry J. Eshelman, J. David Schaffer
Productive Recombination and Propagating and Preserving Schemata. FOGA (1994)
J. David Schaffer, Larry J. Eshelman
Designing Multiplierless Digital Filters Using Genetic Algorithms. ICGA (1993)
Larry J. Eshelman, J. David Schaffer
Crossover's Niche. ICGA (1993)
Larry J. Eshelman, J. David Schaffer
Real-Coded Genetic Algorithms and Interval-Schemata. FOGA (1992)
Larry J. Eshelman, J. David Schaffer
Preventing Premature Convergence in Genetic Algorithms by Preventing Incest. ICGA (1991)
J. David Schaffer, Larry J. Eshelman
On Crossover as an Evolutionarily Viable Strategy. ICGA (1991)
J. David Schaffer, Larry J. Eshelman, Daniel Offutt
Spurious Correlations and Premature Convergence in Genetic Algorithms. FOGA (1990)
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)
Rich Caruana, J. David Schaffer, Larry J. Eshelman
Using Multiple Representations to Improve Inductive Bias: Gray and Binary Coding for Genetic Algorithms. ML (1989)
Larry J. Eshelman, Rich Caruana, J. David Schaffer
Biases in the Crossover Landscape. ICGA (1989)
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)
J. David Schaffer, Amy Morishima
Adaptive knowledge representation: A content sensitive recombination mechanism for genetic algorithms. Int. J. Intell. Syst. 3 (3) (1988)
Rich Caruana, J. David Schaffer
Representation and Hidden Bias: Gray vs. Binary Coding for Genetic Algorithms. ML (1988)
J. David Schaffer, Amy Morishima
An Adaptive Crossover Distribution Mechanism for Genetic Algorithms. ICGA (1987)
J. David Schaffer
Multiple Objective Optimization with Vector Evaluated Genetic Algorithms. ICGA (1985)
J. David Schaffer
Learning Multiclass Pattern Discrimination. ICGA (1985)
J. David Schaffer, John J. Grefenstette
Multi-Objective Learning via Genetic Algorithms. IJCAI (1985)