Learning Multiclass Pattern Discrimination. - J. David Schaffer (ICGA, 1985)

Multiple Objective Optimization with Vector Evaluated Genetic Algorithms. - J. David Schaffer (ICGA, 1985)

Multi-Objective Learning via Genetic Algorithms. - J. David Schaffer; John J. Grefenstette (IJCAI, 1985)

An Adaptive Crossover Distribution Mechanism for Genetic Algorithms. - J. David Schaffer; Amy Morishima (ICGA, 1987)

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

Adaptive knowledge representation: A content sensitive recombination mechanism for genetic algorithms. - J. David Schaffer; Amy Morishima (Int. J. Intell. Syst., 1988)

Biases in the Crossover Landscape. - Larry J. Eshelman; Rich Caruana; J. David Schaffer (ICGA, 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 (ICGA, 1989)

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

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

Spurious Correlations and Premature Convergence in Genetic Algorithms. - J. David Schaffer; Larry J. Eshelman; Daniel Offutt (FOGA, 1990)

Preventing Premature Convergence in Genetic Algorithms by Preventing Incest. - Larry J. Eshelman; J. David Schaffer (ICGA, 1991)

On Crossover as an Evolutionarily Viable Strategy. - J. David Schaffer; Larry J. Eshelman (ICGA, 1991)

Real-Coded Genetic Algorithms and Interval-Schemata. - Larry J. Eshelman; J. David Schaffer (FOGA, 1992)

Crossover's Niche. - Larry J. Eshelman; J. David Schaffer (ICGA, 1993)

Designing Multiplierless Digital Filters Using Genetic Algorithms. - J. David Schaffer; Larry J. Eshelman (ICGA, 1993)

Productive Recombination and Propagating and Preserving Schemata. - Larry J. Eshelman; J. David Schaffer (FOGA, 1994)

Convergence Controlled Variation. - Larry J. Eshelman; Keith E. Mathias; J. David Schaffer (FOGA, 1996)

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

The Effect of Incest Prevention on Genetic Drift. - J. David Schaffer; Murali Mani; Larry J. Eshelman; Keith E. Mathias (FOGA, 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 (PPSN, 1998)

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

Niches in NK-Landscapes. - Keith E. Mathias; Larry J. Eshelman; J. David Schaffer (FOGA, 2000)

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

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

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

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

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

Evolving Spiking Neural Networks for predicting transcription factor binding sites. - Heike Sichtig; J. David Schaffer; Alberto Riva (IJCNN, 2010)

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)

Evolving spiking neural networks for robot control. - R. Batllori; Craig B. Laramee; Walker H. Land Jr.; J. David Schaffer (Complex Adaptive Systems, 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 (Complex Adaptive Systems, 2011)

Evolving data sets to highlight the performance differences between machine learning classifiers. - Thomas Raway; J. David Schaffer; Kenneth J. Kurtz; Hiroki Sayama (GECCO (Companion), 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. (Complex Adaptive Systems, 2012)

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 (Complex Adaptive Systems, 2013)

Evolving Spike Neural Network Sensors to Characterize the Alcoholic Brain Using Visually Evoked Response Potential. - Arnab Roy 004; J. David Schaffer; Craig B. Laramee (Complex Adaptive Systems, 2013)

New crossover operators for multiple subset selection tasks. - Arnab Roy 004; J. David Schaffer; Craig B. Laramee (CoRR, 2014)

Evolving spiking neural networks: A novel growth algorithm corrects the teacher. - J. David Schaffer (CISDA, 2015)

Predicting with Confidence: Extensions to the GRNN Oracle Enabling Quantification of Confidence in Predictions. - Walker H. Land Jr.; J. David Schaffer (Complex Adaptive Systems, 2015)

A novel approach to signal classification with an application to identifying the alcoholic brain. - Arnab Roy 004; J. David Schaffer; Craig B. Laramee (Appl. Soft Comput., 2016)

Speech Processing Approach for Diagnosing Dementia in an Early Stage. - Roozbeh Sadeghian; J. David Schaffer; Stephen A. Zahorian (INTERSPEECH, 2017)

Evolving Spiking Neural Networks for Robot Sensory-motor Decision Tasks of Varying Difficulty. - J. David Schaffer (NICE, 2020)

Towards an Automatic Speech-Based Diagnostic Test for Alzheimer's Disease. - Roozbeh Sadeghian; J. David Schaffer; Stephen A. Zahorian (Frontiers Comput. Sci., 2021)

A Bio-Inspired Computational Astrocyte Model for Spiking Neural Networks. - Jacob Kiggins; J. David Schaffer; Cory E. Merkel (IJCNN, 2023)

On the Preservation of Input/Output Directed Graph Informativeness under Crossover. - Andreas Duus Pape; J. David Schaffer; Hiroki Sayama; Christopher Zosh (CoRR, 2024)