J. Schaffer

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Multiple Objective Optimization with Vector Evaluated Genetic Algorithms

J. D. Schaffer, J. Schaffer, 1985, ICGA.

BIOINFORMATICS and Research Opportunities

Michael Q. Zhang, J. D. Schaffer, N. Dimitrova, 2006 .

Evolving spiking neural networks for robot control

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

Combinations of genetic algorithms and neural networks: a survey of the state of the art

J. D. Schaffer, Darrell Whitley, L. J. Eshelman, 1992, [Proceedings] COGANN-92: International Workshop on Combinations of Genetic Algorithms and Neural Networks.

Real-Coded Genetic Algorithms and Interval-Schemata

J. D. Schaffer, L. Eshelman, J. Schaffer, 1992, FOGA.

An evolutionary approach to law and economics

W. Samuels, A. Schmid, J. Schaffer, 2007 .

Some experiments in machine learning using vector evaluated genetic algorithms (artificial intelligence, optimization, adaptation, pattern recognition)

J. D. Schaffer, J. Schaffer, 1984 .

An Adaptive Crossover Distribution Mechanism for Genetic Algorithms

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

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

J. D. Schaffer, S. Zahorian, Roozbeh Sadeghian, 2021, Frontiers in Computer Science.

Evolutionary Computation in Practice: Case Studies

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

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.

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

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

Evolving Spiking Neural Networks for predicting transcription factor binding sites

J. David Schaffer, Alberto Riva, Heike Sichtig, 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

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.

Development of a coupled expert system for the spacecraft attitude control problem

B. J. Hsieh, K. Kawamura, Guy O. Beale, 1987 .

A Machine Intelligence Designed Bayesian Network Applied to Alzheimer's Detection Using Demographics and Speech Data

J. David Schaffer, Walker H. Land, J. D. Schaffer, 2016 .

Multi-Objective Learning via Genetic Algorithms

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

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

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

Preventing Premature Convergence in Genetic Algorithms by Preventing Incest

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

Biases in the Crossover Landscape

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

Convergence Controlled Variation

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

Code Compaction Using Genetic Algorithms

Paul F. Hoogendijk, Keith E. Mathias, Larry J. Eshelman, 2000, GECCO.

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

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

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

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

New crossover operators for multiple subset selection tasks pdf

J. David Schaffer, Arnab Roy, Craig B. Laramee, 2014, ArXiv.

Background on Genetic Algorithms

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

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

J. David Schaffer, Arnab Roy, Craig B. Laramee, 2013, Complex Adaptive Systems.

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

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

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

J. Schaffer, 2015, CISDA.

Taste-specific cell assemblies in a biologically informed model of the nucleus of the solitary tract.

Heike Sichtig, J. D. Schaffer, J David Schaffer, 2010, Journal of neurophysiology.

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.

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

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

Improving Digital Video Commercial Detectors With Genetic Algorithms

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

Initial Experiments Evolving Spiking Neural Networks with Supervised Learning Capability

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

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..

Niches in NK-Landscapes

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

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

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

Crossover's Niche

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

Classical Bayesian Theory and Networks

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

Proceedings of the third international conference on Genetic algorithms

J. David Schaffer, J. Schaffer, 1989 .

On Crossover as an Evolutionarily Viable Strategy

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

Alzheimer’s Disease and Speech Background

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

A Spike Neural Network Design Approach to Reduce Parameters for Evolving Signal Classifiers

J. David Schaffer, Arnab Roy, Craig Laramee, 2014 .

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.