J. David Schaffer

According to our database, J. David Schaffer authored at least 45 papers between 1985 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

On the Preservation of Input/Output Directed Graph Informativeness under Crossover.

Andreas Duus Pape, J. David Schaffer, Hiroki Sayama, Christopher Zosh

CoRR, 2024

2023

A Bio-Inspired Computational Astrocyte Model for Spiking Neural Networks.

Jacob Kiggins, J. David Schaffer, Cory E. Merkel

Proceedings of the International Joint Conference on Neural Networks, 2023

2021

Towards an Automatic Speech-Based Diagnostic Test for Alzheimer's Disease.

Roozbeh Sadeghian, J. David Schaffer, Stephen A. Zahorian

Frontiers Comput. Sci., 2021

2020

Evolving Spiking Neural Networks for Robot Sensory-motor Decision Tasks of Varying Difficulty.

J. David Schaffer

Proceedings of the NICE '20: Neuro-inspired Computational Elements Workshop, 2020

2017

Speech Processing Approach for Diagnosing Dementia in an Early Stage.

Roozbeh Sadeghian, J. David Schaffer, Stephen A. Zahorian

Proceedings of the 18th Annual Conference of the International Speech Communication Association, 2017

2016

A novel approach to signal classification with an application to identifying the alcoholic brain.

Arnab Roy, J. David Schaffer, Craig B. Laramee

Appl. Soft Comput., 2016

2015

Predicting with Confidence: Extensions to the GRNN Oracle Enabling Quantification of Confidence in Predictions.

Walker H. Land Jr., J. David Schaffer

Proceedings of the Complex Adaptive Systems 2015 Conference, San Jose, 2015

Evolving spiking neural networks: A novel growth algorithm corrects the teacher.

J. David Schaffer

Proceedings of the 2015 IEEE Symposium on Computational Intelligence for Security and Defense Applications, 2015

2014

New crossover operators for multiple subset selection tasks.

Arnab Roy, J. David Schaffer, Craig B. Laramee

CoRR, 2014

2013

Evolving Spike Neural Network Sensors to Characterize the Alcoholic Brain Using Visually Evoked Response Potential.

Arnab Roy, J. David Schaffer, Craig B. Laramee

Proceedings of the Complex Adaptive Systems 2013 Conference, 2013

Investigating the GRNN Oracle as a Method for Combining Multiple Predictive Models of Colon Cancer Recurrence from Gene Microarrays.

Aaron S. Campbell, Walker H. Land Jr., Dan Margolis, Ravi Mathur, J. David Schaffer

Proceedings of the Complex Adaptive Systems 2013 Conference, 2013

2012

GRNN Ensemble Classifier for Lung Cancer Prognosis Using Only Demographic and TNM features.

J. David Schaffer, Jin-Woo Park, Erin Barnes, Qiyi Lu, Xingye Qiao, Youping Deng, Yan Li, Walker H. Land Jr.

Proceedings of the Complex Adaptive Systems 2012 Conference, 2012

Evolving data sets to highlight the performance differences between machine learning classifiers.

Thomas Raway, J. David Schaffer, Kenneth J. Kurtz, Hiroki Sayama

Proceedings of the Genetic and Evolutionary Computation Conference, 2012

2011

Evolutionary computation with noise perturbation and cluster analysis to discover biomarker sets.

Ravi Mathur, J. David Schaffer, Walker H. Land Jr., John J. Heine, Steven Eschrich, Timothy Yeatman

Proceedings of the Complex Adaptive Systems 2011 Conference, 2011

Evolving spiking neural networks for robot control.

R. Batllori, Craig B. Laramee, Walker H. Land Jr., J. David Schaffer

Proceedings of the Complex Adaptive Systems 2011 Conference, 2011

Perturbation and candidate analysis to combat overfitting of gene expression microarray data.

Ravi Mathur, J. David Schaffer, Walker H. Land Jr., John J. Heine, Jonathan M. Hernandez, Timothy Yeatman

Int. J. Comput. Biol. Drug Des., 2011

2010

Evolving Spiking Neural Networks for predicting transcription factor binding sites.

Heike Sichtig, J. David Schaffer, Alberto Riva

Proceedings of the International Joint Conference on Neural Networks, 2010

2009

A series of failed and partially successful fitness functions for evolving spiking neural networks.

J. David Schaffer, Heike Sichtig, Craig B. Laramee

Proceedings of the Genetic and Evolutionary Computation Conference, 2009

2008

SSNNS -: a suite of tools to explore spiking neural networks.

Heike Sichtig, J. David Schaffer, Craig B. Laramee

Proceedings of the Genetic and Evolutionary Computation Conference, 2008

2003

Envolvable Visual Commercial Detector.

Lalitha Agnihotri, Nevenka Dimitrova, Thomas McGee, Sylvie Jeannin, J. David Schaffer, Jan Nesvadba

Proceedings of the 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2003), 2003

2002

Improving Digital Video Commercial Detectors With Genetic Algorithms.

J. David Schaffer, Lalitha Agnihotri, Nevenka Dimitrova, Thomas McGee, Sylvie Jeannin

Proceedings of the GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference, 2002

2000

Code Compaction Using Genetic Algorithms.

Keith E. Mathias, Larry J. Eshelman, J. David Schaffer, Lex Augusteijn, Paul F. Hoogendijk, Rik van de Wiel

Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '00), 2000

Niches in NK-Landscapes.

Keith E. Mathias, Larry J. Eshelman, J. David Schaffer

Proceedings of the Sixth Workshop on Foundations of Genetic Algorithms, 2000

TV Content Recommender System.

Srinivas Gutta, Kaushal Kurapati, K. P. Lee, Jacquelyn Martino, John Milanski, J. David Schaffer, John Zimmerman

Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on on Innovative Applications of Artificial Intelligence, July 30, 2000

1998

The Effects of Control Parameters and Restarts on Search Stagnation in Evolutionary Programming.

Keith E. Mathias, J. David Schaffer, Larry J. Eshelman, Murali Mani

Proceedings of the Parallel Problem Solving from Nature, 1998

The Effect of Incest Prevention on Genetic Drift.

J. David Schaffer, Murali Mani, Larry J. Eshelman, Keith E. Mathias

Proceedings of the Fifth Workshop on Foundations of Genetic Algorithms, 1998

1997

Crossover Operator Biases: Exploiting the Population Distribution.

Larry J. Eshelman, Keith E. Mathias, J. David Schaffer

Proceedings of the 7th International Conference on Genetic Algorithms, 1997

1996

Convergence Controlled Variation.

Larry J. Eshelman, Keith E. Mathias, J. David Schaffer

Proceedings of the 4th Workshop on Foundations of Genetic Algorithms. San Diego, 1996

1994

Productive Recombination and Propagating and Preserving Schemata.

Larry J. Eshelman, J. David Schaffer

Proceedings of the Third Workshop on Foundations of Genetic Algorithms. Estes Park, Colorado, USA, July 31, 1994

1993

Designing Multiplierless Digital Filters Using Genetic Algorithms.

J. David Schaffer, Larry J. Eshelman

Proceedings of the 5th International Conference on Genetic Algorithms, 1993

Crossover's Niche.

Larry J. Eshelman, J. David Schaffer

Proceedings of the 5th International Conference on Genetic Algorithms, 1993

1992

Real-Coded Genetic Algorithms and Interval-Schemata.

Larry J. Eshelman, J. David Schaffer

Proceedings of the Second Workshop on Foundations of Genetic Algorithms. Vail, 1992

1991

On Crossover as an Evolutionarily Viable Strategy.

J. David Schaffer, Larry J. Eshelman

Proceedings of the 4th International Conference on Genetic Algorithms, 1991

Preventing Premature Convergence in Genetic Algorithms by Preventing Incest.

Larry J. Eshelman, J. David Schaffer

Proceedings of the 4th International Conference on Genetic Algorithms, 1991

1990

Spurious Correlations and Premature Convergence in Genetic Algorithms.

J. David Schaffer, Larry J. Eshelman, Daniel Offutt

Proceedings of the First Workshop on Foundations of Genetic Algorithms. Bloomington Campus, 1990

1989

Representation and Hidden Bias II: Eliminating Defining Length Bias in Genetic Search via Shuffle Crossover.

Rich Caruana, Larry J. Eshelman, J. David Schaffer

Proceedings of the 11th International Joint Conference on Artificial Intelligence. Detroit, 1989

Using Multiple Representations to Improve Inductive Bias: Gray and Binary Coding for Genetic Algorithms.

Rich Caruana, J. David Schaffer, Larry J. Eshelman

Proceedings of the Sixth International Workshop on Machine Learning (ML 1989), 1989

A Study of Control Parameters Affecting Online Performance of Genetic Algorithms for Function Optimization.

J. David Schaffer, Rich Caruana, Larry J. Eshelman, Rajarshi Das

Proceedings of the 3rd International Conference on Genetic Algorithms, 1989

Biases in the Crossover Landscape.

Larry J. Eshelman, Rich Caruana, J. David Schaffer

Proceedings of the 3rd International Conference on Genetic Algorithms, 1989

1988

Adaptive knowledge representation: A content sensitive recombination mechanism for genetic algorithms.

J. David Schaffer, Amy Morishima

Int. J. Intell. Syst., 1988

Representation and Hidden Bias: Gray vs. Binary Coding for Genetic Algorithms.

Rich Caruana, J. David Schaffer

Proceedings of the Machine Learning, 1988

1987

An Adaptive Crossover Distribution Mechanism for Genetic Algorithms.

J. David Schaffer, Amy Morishima

Proceedings of the 2nd International Conference on Genetic Algorithms, 1987

1985

Multi-Objective Learning via Genetic Algorithms.

J. David Schaffer, John J. Grefenstette

Proceedings of the 9th International Joint Conference on Artificial Intelligence. Los Angeles, 1985

Multiple Objective Optimization with Vector Evaluated Genetic Algorithms.

J. David Schaffer

Proceedings of the 1st International Conference on Genetic Algorithms, 1985

Learning Multiclass Pattern Discrimination.

J. David Schaffer

Proceedings of the 1st International Conference on Genetic Algorithms, 1985