J. David Schaffer
Jacob Kiggins
J. David Schaffer
Cory E. Merkel
A Bio-Inspired Computational Astrocyte Model for Spiking Neural Networks.
1-8
2023
IJCNN
https://doi.org/10.1109/IJCNN54540.2023.10191572
conf/ijcnn/2023
db/conf/ijcnn/ijcnn2023.html#KigginsSM23
Roozbeh Sadeghian
J. David Schaffer
Stephen A. Zahorian
Towards an Automatic Speech-Based Diagnostic Test for Alzheimer's Disease.
624594
2021
3
Frontiers Comput. Sci.
https://doi.org/10.3389/fcomp.2021.624594
db/journals/fcomp/fcomp3.html#SadeghianSZ21
J. David Schaffer
Evolving Spiking Neural Networks for Robot Sensory-motor Decision Tasks of Varying Difficulty.
1:1-1:7
2020
NICE
https://doi.org/10.1145/3381755.3381757
conf/nice/2020
db/conf/nice/nice2020.html#Schaffer20
Roozbeh Sadeghian
J. David Schaffer
Stephen A. Zahorian
Speech Processing Approach for Diagnosing Dementia in an Early Stage.
2705-2709
2017
INTERSPEECH
https://doi.org/10.21437/Interspeech.2017-1712
conf/interspeech/2017
db/conf/interspeech/interspeech2017.html#SadeghianSZ17
Arnab Roy 0004
J. David Schaffer
Craig B. Laramee
A novel approach to signal classification with an application to identifying the alcoholic brain.
406-414
2016
43
Appl. Soft Comput.
https://doi.org/10.1016/j.asoc.2016.02.048
db/journals/asc/asc43.html#RoySL16
J. David Schaffer
Evolving spiking neural networks: A novel growth algorithm corrects the teacher.
1-8
2015
CISDA
https://doi.org/10.1109/CISDA.2015.7208630
conf/cisda/2015
db/conf/cisda/cisda2015.html#Schaffer15
Walker H. Land Jr.
J. David Schaffer
Predicting with Confidence: Extensions to the GRNN Oracle Enabling Quantification of Confidence in Predictions.
381-387
2015
Complex Adaptive Systems
https://doi.org/10.1016/j.procs.2015.09.164
conf/complexsystems/2015
db/conf/complexsystems/complexsystems2015.html#LandS15
Arnab Roy 0004
J. David Schaffer
Craig B. Laramee
New crossover operators for multiple subset selection tasks.
2014
abs/1408.1297
CoRR
http://arxiv.org/abs/1408.1297
db/journals/corr/corr1408.html#RoySL14
Arnab Roy 0004
J. David Schaffer
Craig B. Laramee
Evolving Spike Neural Network Sensors to Characterize the Alcoholic Brain Using Visually Evoked Response Potential.
27-32
2013
Complex Adaptive Systems
https://doi.org/10.1016/j.procs.2013.09.234
conf/complexsystems/2013
db/conf/complexsystems/complexsystems2013.html#RoySL13
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.
374-378
2013
Complex Adaptive Systems
https://doi.org/10.1016/j.procs.2013.09.289
https://www.wikidata.org/entity/Q57428389
conf/complexsystems/2013
db/conf/complexsystems/complexsystems2013.html#CampbellLMMS13
Thomas Raway
J. David Schaffer
Kenneth J. Kurtz
Hiroki Sayama
Evolving data sets to highlight the performance differences between machine learning classifiers.
657-658
2012
GECCO (Companion)
https://doi.org/10.1145/2330784.2330907
conf/gecco/2012c
db/conf/gecco/gecco2012c.html#RawaySKS12
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.
450-455
2012
conf/complexsystems/2012
Complex Adaptive Systems
https://doi.org/10.1016/j.procs.2012.09.103
db/journals/procedia/procedia12.html#SchafferPBLQDLL12
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.
307-315
2011
4
Int. J. Comput. Biol. Drug Des.
4
https://doi.org/10.1504/IJCBDD.2011.044443
https://www.wikidata.org/entity/Q34112009
db/journals/ijcbdd/ijcbdd4.html#MathurSLHHY11
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.
153-158
2011
conf/complexsystems/2011
Complex Adaptive Systems
https://doi.org/10.1016/j.procs.2011.08.030
db/journals/procedia/procedia6.html#MathurSLHEY11
R. Batllori
Craig B. Laramee
Walker H. Land Jr.
J. David Schaffer
Evolving spiking neural networks for robot control.
329-334
2011
conf/complexsystems/2011
Complex Adaptive Systems
https://doi.org/10.1016/j.procs.2011.08.060
db/journals/procedia/procedia6.html#BatlloriLLS11
Heike Sichtig
J. David Schaffer
Alberto Riva
Evolving Spiking Neural Networks for predicting transcription factor binding sites.
1-8
2010
IJCNN
https://doi.org/10.1109/IJCNN.2010.5596642
https://www.wikidata.org/entity/Q58072186
conf/ijcnn/2010
db/conf/ijcnn/ijcnn2010.html#SichtigSR10
J. David Schaffer
Heike Sichtig
Craig B. Laramee
A series of failed and partially successful fitness functions for evolving spiking neural networks.
2661-2664
2009
GECCO (Companion)
https://doi.org/10.1145/1570256.1570378
conf/gecco/2009c
db/conf/gecco/gecco2009c.html#SchafferSL09
Heike Sichtig
J. David Schaffer
Craig B. Laramee
SSNNS -: a suite of tools to explore spiking neural networks.
1787-1790
2008
GECCO (Companion)
https://doi.org/10.1145/1388969.1388971
conf/gecco/2008c
db/conf/gecco/gecco2008c.html#SichtigSL08
Lalitha Agnihotri
Nevenka Dimitrova
Thomas McGee
Sylvie Jeannin
J. David Schaffer
Jan Nesvadba
Envolvable Visual Commercial Detector.
79-84
2003
conf/cvpr/2003
CVPR (2)
https://doi.org/10.1109/CVPR.2003.1211455
https://doi.ieeecomputersociety.org/10.1109/CVPR.2003.1211455
db/conf/cvpr/cvpr2003-2.html#AgnihotriDMJSN03
J. David Schaffer
Lalitha Agnihotri
Nevenka Dimitrova
Thomas McGee
Sylvie Jeannin
Improving Digital Video Commercial Detectors With Genetic Algorithms.
1212-1218
2002
conf/gecco/2002
GECCO
db/conf/gecco/gecco2002.html#SchafferADMJ02
Srinivas Gutta
Kaushal Kurapati
K. P. Lee
Jacquelyn Martino
John Milanski
J. David Schaffer
John Zimmerman
TV Content Recommender System.
1121-1122
2000
conf/aaai/2000
AAAI/IAAI
db/conf/aaai/aaai2000.html#GuttaKLMMSZ00
http://www.aaai.org/Library/AAAI/2000/aaai00-197.php
Keith E. Mathias
Larry J. Eshelman
J. David Schaffer
Niches in NK-Landscapes.
27-46
2000
FOGA
https://doi.org/10.1016/B978-155860734-7/50085-8
conf/foga/2000
db/conf/foga/foga2000.html#MathiasES00
Keith E. Mathias
Larry J. Eshelman
J. David Schaffer
Lex Augusteijn
Paul F. Hoogendijk
Rik van de Wiel
Code Compaction Using Genetic Algorithms.
710-717
2000
conf/gecco/2000
GECCO
db/conf/gecco/gecco2000.html#MathiasESAHW00
J. David Schaffer
Murali Mani
Larry J. Eshelman
Keith E. Mathias
The Effect of Incest Prevention on Genetic Drift.
235-244
1998
conf/foga/1998
FOGA
db/conf/foga/foga1998.html#SchafferMEM98
Keith E. Mathias
J. David Schaffer
Larry J. Eshelman
Murali Mani
The Effects of Control Parameters and Restarts on Search Stagnation in Evolutionary Programming.
398-407
1998
conf/ppsn/1998
PPSN
https://doi.org/10.1007/BFb0056882
db/conf/ppsn/ppsn1998.html#MathiasSEM98
Larry J. Eshelman
Keith E. Mathias
J. David Schaffer
Crossover Operator Biases: Exploiting the Population Distribution.
354-361
1997
conf/icga/1997
ICGA
db/conf/icga/icga1997.html#EshelmanMS97
Larry J. Eshelman
Keith E. Mathias
J. David Schaffer
Convergence Controlled Variation.
203-224
1996
conf/foga/1996
FOGA
db/conf/foga/foga1996.html#EshelmanMS96
Larry J. Eshelman
J. David Schaffer
Productive Recombination and Propagating and Preserving Schemata.
299-313
1994
conf/foga/1994
FOGA
db/conf/foga/foga1994.html#EshelmanS94
https://doi.org/10.1016/b978-1-55860-356-1.50018-2
Larry J. Eshelman
J. David Schaffer
Crossover's Niche.
9-14
1993
conf/icga/1993
ICGA
db/conf/icga/icga1993.html#EshelmanS93
J. David Schaffer
Larry J. Eshelman
Designing Multiplierless Digital Filters Using Genetic Algorithms.
439-444
1993
conf/icga/1993
ICGA
db/conf/icga/icga1993.html#SchafferE93
Larry J. Eshelman
J. David Schaffer
Real-Coded Genetic Algorithms and Interval-Schemata.
187-202
1992
conf/foga/1992
FOGA
db/conf/foga/foga1992.html#EshelmanS92
https://doi.org/10.1016/b978-0-08-094832-4.50018-0
J. David Schaffer
Larry J. Eshelman
On Crossover as an Evolutionarily Viable Strategy.
61-68
1991
conf/icga/1991
ICGA
db/conf/icga/icga1991.html#SchafferE91
Larry J. Eshelman
J. David Schaffer
Preventing Premature Convergence in Genetic Algorithms by Preventing Incest.
115-122
1991
conf/icga/1991
ICGA
db/conf/icga/icga1991.html#EshelmanS91
J. David Schaffer
Larry J. Eshelman
Daniel Offutt
Spurious Correlations and Premature Convergence in Genetic Algorithms.
102-112
1990
conf/foga/1990
FOGA
db/conf/foga/foga1990.html#SchafferEO90
https://doi.org/10.1016/b978-0-08-050684-5.50010-0
Larry J. Eshelman
Rich Caruana
J. David Schaffer
Biases in the Crossover Landscape.
10-19
1989
conf/icga/1989
ICGA
db/conf/icga/icga1989.html#EshelmanCS89
J. David Schaffer
Rich Caruana
Larry J. Eshelman
Rajarshi Das
A Study of Control Parameters Affecting Online Performance of Genetic Algorithms for Function Optimization.
51-60
1989
conf/icga/1989
ICGA
db/conf/icga/icga1989.html#SchafferCED89
Rich Caruana
J. David Schaffer
Larry J. Eshelman
Using Multiple Representations to Improve Inductive Bias: Gray and Binary Coding for Genetic Algorithms.
375-378
1989
conf/icml/1989
ML
db/conf/icml/ml1989.html#CaruanaSE89
Rich Caruana
Larry J. Eshelman
J. David Schaffer
Representation and Hidden Bias II: Eliminating Defining Length Bias in Genetic Search via Shuffle Crossover.
750-755
1989
conf/ijcai/1989
IJCAI
db/conf/ijcai/ijcai89.html#CaruanaES89
http://ijcai.org/Proceedings/89-1/Papers/120.pdf
J. David Schaffer
Proceedings of the 3rd International Conference on Genetic Algorithms, George Mason University, Fairfax, Virginia, USA, June 1989
ICGA
Morgan Kaufmann
1989
1-55860-066-3
db/conf/icga/icga1989.html
J. David Schaffer
Amy Morishima
Adaptive knowledge representation: A content sensitive recombination mechanism for genetic algorithms.
229-246
1988
3
Int. J. Intell. Syst.
3
https://doi.org/10.1002/int.4550030304
db/journals/ijis/ijis3.html#SchafferM88
Rich Caruana
J. David Schaffer
Representation and Hidden Bias: Gray vs. Binary Coding for Genetic Algorithms.
153-161
1988
conf/icml/1988
ML
db/conf/icml/ml1988.html#CaruanaS88
J. David Schaffer
Amy Morishima
An Adaptive Crossover Distribution Mechanism for Genetic Algorithms.
36-40
1987
conf/icga/1987
ICGA
db/conf/icga/icga1987.html#SchafferM87
J. David Schaffer
Learning Multiclass Pattern Discrimination.
74-79
1985
conf/icga/1985
ICGA
db/conf/icga/icga1985.html#Schaffer85
J. David Schaffer
Multiple Objective Optimization with Vector Evaluated Genetic Algorithms.
93-100
1985
conf/icga/1985
ICGA
db/conf/icga/icga1985.html#Schaffer85a
J. David Schaffer
John J. Grefenstette
Multi-Objective Learning via Genetic Algorithms.
593-595
1985
conf/ijcai/1985
IJCAI
db/conf/ijcai/ijcai85.html#SchafferG85
http://ijcai.org/Proceedings/85-1/Papers/113.pdf
Lalitha Agnihotri
Lex Augusteijn
Erin Barnes
R. Batllori
Aaron S. Campbell
Rich Caruana
Rajarshi Das
Youping Deng
Nevenka Dimitrova
Steven Eschrich
Larry J. Eshelman
John J. Grefenstette
Srinivas Gutta
John J. Heine
Jonathan M. Hernandez
Paul F. Hoogendijk
Sylvie Jeannin
Jacob Kiggins
Kaushal Kurapati
Kenneth J. Kurtz
Walker H. Land Jr.
Craig B. Laramee
K. P. Lee
Yan Li
Qiyi Lu
Murali Mani
Dan Margolis
Jacquelyn Martino
Keith E. Mathias
Ravi Mathur
Thomas McGee
Cory E. Merkel
John Milanski
Amy Morishima
Jan Nesvadba
Daniel Offutt
Jin-Woo Park
Xingye Qiao
Thomas Raway
Alberto Riva
Arnab Roy 0004
Roozbeh Sadeghian
Hiroki Sayama
Heike Sichtig
Rik van de Wiel
Timothy Yeatman
Stephen A. Zahorian
John Zimmerman