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J. David Schaffer
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2020 – today
- 2024
- [i2]Andreas Duus Pape, J. David Schaffer, Hiroki Sayama, Christopher Zosh:
On the Preservation of Input/Output Directed Graph Informativeness under Crossover. CoRR abs/2406.10369 (2024) - 2023
- [c39]Jacob Kiggins, J. David Schaffer, Cory E. Merkel:
A Bio-Inspired Computational Astrocyte Model for Spiking Neural Networks. IJCNN 2023: 1-8 - 2021
- [j4]Roozbeh Sadeghian
, J. David Schaffer, Stephen A. Zahorian:
Towards an Automatic Speech-Based Diagnostic Test for Alzheimer's Disease. Frontiers Comput. Sci. 3: 624594 (2021) - 2020
- [c38]J. David Schaffer:
Evolving Spiking Neural Networks for Robot Sensory-motor Decision Tasks of Varying Difficulty. NICE 2020: 1:1-1:7
2010 – 2019
- 2017
- [c37]Roozbeh Sadeghian
Speech Processing Approach for Diagnosing Dementia in an Early Stage. INTERSPEECH 2017: 2705-2709 - 2016
- [j3]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: 406-414 (2016) - 2015
- [c36]J. David Schaffer:
Evolving spiking neural networks: A novel growth algorithm corrects the teacher. CISDA 2015: 1-8 - [c35]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: 381-387 - 2014
- [i1]Arnab Roy, J. David Schaffer, Craig B. Laramee:
New crossover operators for multiple subset selection tasks. CoRR abs/1408.1297 (2014) - 2013
- [c34]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: 27-32 - [c33]Aaron S. Campbell, Walker H. Land Jr., Dan Margolis, Ravi Mathur
Investigating the GRNN Oracle as a Method for Combining Multiple Predictive Models of Colon Cancer Recurrence from Gene Microarrays. Complex Adaptive Systems 2013: 374-378 - 2012
- [c32]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: 657-658 - [c31]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: 450-455 - 2011
- [j2]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): 307-315 (2011) - [c30]Ravi Mathur
Evolutionary computation with noise perturbation and cluster analysis to discover biomarker sets. Complex Adaptive Systems 2011: 153-158 - [c29]R. Batllori, Craig B. Laramee, Walker H. Land Jr., J. David Schaffer:
Evolving spiking neural networks for robot control. Complex Adaptive Systems 2011: 329-334 - 2010
- [c28]Heike Sichtig, J. David Schaffer, Alberto Riva
Evolving Spiking Neural Networks for predicting transcription factor binding sites. IJCNN 2010: 1-8
2000 – 2009
- 2009
- [c27]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 - 2008
- [c26]Heike Sichtig, J. David Schaffer, Craig B. Laramee:
SSNNS -: a suite of tools to explore spiking neural networks. GECCO (Companion) 2008: 1787-1790 - 2003
- [c25]Lalitha Agnihotri, Nevenka Dimitrova, Thomas McGee, Sylvie Jeannin, J. David Schaffer, Jan Nesvadba:
Envolvable Visual Commercial Detector. CVPR (2) 2003: 79-84 - 2002
- [c24]J. David Schaffer, Lalitha Agnihotri, Nevenka Dimitrova, Thomas McGee, Sylvie Jeannin:
Improving Digital Video Commercial Detectors With Genetic Algorithms. GECCO 2002: 1212-1218 - 2000
- [c23]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 - [c22]Keith E. Mathias, Larry J. Eshelman, J. David Schaffer:
Niches in NK-Landscapes. FOGA 2000: 27-46 - [c21]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
1990 – 1999
- 1998
- [c20]J. David Schaffer, Murali Mani, Larry J. Eshelman, Keith E. Mathias:
The Effect of Incest Prevention on Genetic Drift. FOGA 1998: 235-244 - [c19]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 - 1997
- [c18]Larry J. Eshelman, Keith E. Mathias, J. David Schaffer:
Crossover Operator Biases: Exploiting the Population Distribution. ICGA 1997: 354-361 - 1996
- [c17]Larry J. Eshelman, Keith E. Mathias, J. David Schaffer:
Convergence Controlled Variation. FOGA 1996: 203-224 - 1994
- [c16]Larry J. Eshelman, J. David Schaffer:
Productive Recombination and Propagating and Preserving Schemata. FOGA 1994: 299-313 - 1993
- [c15]Larry J. Eshelman, J. David Schaffer:
Crossover's Niche. ICGA 1993: 9-14 - [c14]J. David Schaffer, Larry J. Eshelman:
Designing Multiplierless Digital Filters Using Genetic Algorithms. ICGA 1993: 439-444 - 1992
- [c13]Larry J. Eshelman, J. David Schaffer:
Real-Coded Genetic Algorithms and Interval-Schemata. FOGA 1992: 187-202 - 1991
- [c12]J. David Schaffer, Larry J. Eshelman:
On Crossover as an Evolutionarily Viable Strategy. ICGA 1991: 61-68 - [c11]Larry J. Eshelman, J. David Schaffer:
Preventing Premature Convergence in Genetic Algorithms by Preventing Incest. ICGA 1991: 115-122 - 1990
- [c10]J. David Schaffer, Larry J. Eshelman, Daniel Offutt:
Spurious Correlations and Premature Convergence in Genetic Algorithms. FOGA 1990: 102-112
1980 – 1989
- 1989
- [c9]Larry J. Eshelman, Rich Caruana, J. David Schaffer:
Biases in the Crossover Landscape. ICGA 1989: 10-19 - [c8]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 - [c7]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 - [c6]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 - [e1]J. David Schaffer:
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 [contents] - 1988
- [j1]J. David Schaffer, Amy Morishima:
Adaptive knowledge representation: A content sensitive recombination mechanism for genetic algorithms. Int. J. Intell. Syst. 3(3): 229-246 (1988) - [c5]Rich Caruana, J. David Schaffer:
Representation and Hidden Bias: Gray vs. Binary Coding for Genetic Algorithms. ML 1988: 153-161 - 1987
- [c4]J. David Schaffer, Amy Morishima:
An Adaptive Crossover Distribution Mechanism for Genetic Algorithms. ICGA 1987: 36-40 - 1985
- [c3]J. David Schaffer:
Learning Multiclass Pattern Discrimination. ICGA 1985: 74-79 - [c2]J. David Schaffer:
Multiple Objective Optimization with Vector Evaluated Genetic Algorithms. ICGA 1985: 93-100 - [c1]J. David Schaffer, John J. Grefenstette:
Multi-Objective Learning via Genetic Algorithms. IJCAI 1985: 593-595
Coauthor Index
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