J. David Schaffer

J. David Schaffer, Jan Nesvadba, Nevenka Dimitrova, 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

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

J. David Schaffer, J. D. Schaffer, 2015, 2015 IEEE Symposium on Computational Intelligence for Security and Defense Applications (CISDA).

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

Keith E. Mathias, Larry J. Eshelman, J. David Schaffer, 1998, PPSN.

The Generalized Regression Neural Network Oracle

J. David Schaffer, J. D. Schaffer, Walker H. LandJr., 2019, The Art and Science of Machine Intelligence.

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

J. David Schaffer, J. D. Schaffer, J. Schaffer, 2020, NICE.

Spurious Correlations and Premature Convergence in Genetic Algorithms

Larry J. Eshelman, J. David Schaffer, J. D. Schaffer, 1990, FOGA.

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

J. David Schaffer, Arnab Roy, Craig B. Laramee, 2016, Appl. Soft Comput..

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

J. David Schaffer, Ravi Mathur, John J. Heine, 2011, Complex Adaptive Systems.

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

J. David Schaffer, Heike Sichtig, Craig B. Laramee, 2009, GECCO '09.

Improving Digital Video Commercial Detectors With Genetic Algorithms

J. David Schaffer, Nevenka Dimitrova, Lalitha Agnihotri, 2002, GECCO.

Hybrid GA-SVM-Oracle Paradigm

J. David Schaffer, Walker H. LandJr., 2020 .

Convergence Controlled Variation

Keith E. Mathias, Larry J. Eshelman, J. David Schaffer, 1996, FOGA.

COGANN-92 : International Workshop on Combinations of Genetic Algorithms and Neural Networks, June 6, 1992 Baltimore, Maryland

L. Darrell Whitley, J. David Schaffer, J. D. Schaffer, 1992 .

Proceedings of the 3rd International Conference on Genetic Algorithms, George Mason University, Fairfax, Virginia, USA, June 1989

J. David Schaffer, 1989, ICGA.

An Adaptive Crossover Distribution Mechanism for Genetic Algorithms

J. David Schaffer, J. D. Schaffer, Amy Morishima, 1987, ICGA.

Preventing Premature Convergence in Genetic Algorithms by Preventing Incest

Larry J. Eshelman, J. David Schaffer, J. D. Schaffer, 1991, ICGA.

Classical Bayesian Theory and Networks

J. David Schaffer, Walker H. LandJr., J. Schaffer, 2019, The Art and Science of Machine Intelligence.

Biases in the Crossover Landscape

Larry J. Eshelman, J. David Schaffer, Rich Caruana, 1989, ICGA.

On Crossover as an Evolutionarily Viable Strategy

Larry J. Eshelman, J. David Schaffer, J. D. Schaffer, 1991, ICGA.

Simple Design Rules for Spike Neural Network Based General Purpose Networks

J. David Schaffer, Arnab Roy, Craig Laramee, 2012 .

TV Content Recommender System

John Zimmerman, J. David Schaffer, Srinivas Gutta, 2000, AAAI/IAAI.

Medical Information Technology

J. David Schaffer, Thomas Wendler, William P. Lord, 2006 .

Initial Experiments Evolving Spiking Neural Networks with Supervised Learning Capability

J. David Schaffer, J. D. Schaffer, J. Schaffer, 2017 .

Designing Multiplierless Digital Filters Using Genetic Algorithms

Larry J. Eshelman, J. David Schaffer, J. D. Schaffer, 1993, ICGA.

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

Yan Li, J. David Schaffer, Jin-Woo Park, 2012, Complex Adaptive Systems.

The Support Vector Machine

J. David Schaffer, Walker H. LandJr., 2020 .

Proceedings of the third international conference on Genetic algorithms

J. David Schaffer, R. A. Towle, Study, 1989 .

Evolutionary Computation in Practice: Case Studies

Donald H. Kraft, James P. Crutchfield, Hitoshi Iba, 1997 .

Bayesian Probabilistic Neural Network (BPNN)

J. David Schaffer, Walker H. Land, J. D. Schaffer, 2019, The Art and Science of Machine Intelligence.

Machine Intelligence Mixture of Experts and Bayesian Networks

J. David Schaffer, J. D. Schaffer, Walker H. LandJr., 2019, The Art and Science of Machine Intelligence.

Background on Genetic Algorithms

J. David Schaffer, J. D. Schaffer, Walker H. LandJr., 2019, The Art and Science of Machine Intelligence.

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

Larry J. Eshelman, J. David Schaffer, Rich Caruana, 1989, IJCAI.

A tale of three bio-inspired computational approaches

J. David Schaffer, J. D. Schaffer, J. David Schaffer, 2014, Sensing Technologies + Applications.

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

Rajarshi Das, Larry J. Eshelman, J. David Schaffer, 1989, ICGA.

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

Larry J. Eshelman, J. David Schaffer, Rich Caruana, 1989, ML.

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

J. David Schaffer, Ravi Mathur, John J. Heine, 2011, Int. J. Comput. Biol. Drug Des..

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

J. David Schaffer, Rich Caruana, J. D. Schaffer, 1988, ML.

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

J. David Schaffer, J. D. Schaffer, Amy Morishima, 1988, Int. J. Intell. Syst..

Foundations of Genetic Algorithms-2

Larry J. Eshelman, J. David Schaffer, J. D. Schaffer, 1993 .

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

J. David Schaffer, Ravi Mathur, Dan Margolis, 2013, Complex Adaptive Systems.

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

J. David Schaffer, Hiroki Sayama, Kenneth J. Kurtz, 2012, GECCO '12.

Crossover Operator Biases: Exploiting the Population Distribution

Keith E. Mathias, Larry J. Eshelman, J. David Schaffer, 1997, ICGA.

Speech Processing Approach for Diagnosing Dementia in an Early Stage

J. David Schaffer, Roozbeh Sadeghian, Stephen A. Zahorian, 2017, INTERSPEECH.

Productive Recombination and Propagating and Preserving Schemata

Larry J. Eshelman, J. David Schaffer, J. D. Schaffer, 1994, FOGA.

Proceedings of the third international conference on Genetic algorithms

J. David Schaffer, J. Schaffer, 1989 .

Using genetic search to exploit the emergent behavior of neural networks

Larry J. Eshelman, J. David Schaffer, J. D. Schaffer, 1990 .

Alzheimer’s Disease and Speech Background

J. David Schaffer, J. D. Schaffer, Walker H. LandJr., 2019, The Art and Science of Machine Intelligence.

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

J. David Schaffer, Heike Sichtig, Craig B. Laramee, 2008, GECCO '08.

Evolving spiking neural networks for robot control

J. David Schaffer, Walker H. Land, Craig B. Laramee, 2011, Complex Adaptive Systems.

Multi-Objective Learning via Genetic Algorithms

J. David Schaffer, John J. Grefenstette, J. D. Schaffer, 1985, IJCAI.

The Effect of Incest Prevention on Genetic Drift

Keith E. Mathias, Larry J. Eshelman, J. David Schaffer, 1998, FOGA.

Learning Multiclass Pattern Discrimination

J. David Schaffer, J. Schaffer, 1985, ICGA.

Niches in NK-Landscapes

Keith E. Mathias, Larry J. Eshelman, J. David Schaffer, 2000, FOGA.

Predicting with Confidence: Classifiers that Know What They Don’t Know

J. David Schaffer, Walker H. Land, 2017 .

Developing an Evolutionary Algorithm to Search for an Optimal Multi-Mother Wavelet Packets Combination

J. David Schaffer, Kenneth J. McLeod, J. D. Schaffer, 2015 .