NISL Publications

Journal Articles and Book Chapters

  1. Logan Perreault, Monica Thornton, John Sheppard, and Joseph DeBruycker, "Disjunctive Interaction in Continuous Time Bayesian Networks," International Journal of Approximate Reasoning, special FLAIRS 2016 issue on Uncertain Reasoning, Vol. 90, 2017, pp. 253-271.
  2. Kaveh Dehghanpour, M. Hashem Nehrir, John W. Sheppard, and Nathan C. Kelly, "Agent-Based Modeling of Retail Electrical Energy Markets with Demand Response," to appear in IEEE Transactions on Smart Grid, 2017.
  3. Liessman Sturlaugson, Logan Perreault, and John Sheppard, "Factored Performance Functions and Decision Making in Continuous Time Bayesian Networks," Journal of Applied Logic, special issue on Uncertain Reasoning, Vol. 22, July 2017, pp. 28-45.
  4. Shane Strasser, John Sheppard, Nathan Fortier, and Rollie Goodman, "Factored Evolutionary Algorithms," IEEE Transactions on Evolutionary Computation, Vol. 21, No. 2, April 2017, pp. 281-293.
  5. Kaveh Dehghanpour, M. Hashem Nehrir, John W. Sheppard, and Nathan Kelly, "Agent-Based Decision Making in Electrical Energy Markets Using Dynamic Bayesian Networks and Sparse Bayesian Learning," IEEE Transactions on Power Systems, Vol. 31, No. 6, November 2016, pp. 4744-4754.
  6. Liessman Sturlaugson and John W. Sheppard, "Uncertain Evidence in Continuous Time Bayesian Networks," International Journal of Approximate Reasoning, Vol. 70, March 2016, pp. 99-122.
  7. Caisheng Wang, Carol J Miller, M Hashem Nehrir, John W Sheppard, Shawn P McElmurry, "A Load Profile Management Integrated Power Dispatch Using a Newton-Like Particle Swarm Optimization Method," Sustainable Computing: Informatics and Systems, Special Issue on a Greener Water/Energy/Emissions, Vol. 8, December 2015, pp. 8-17.
  8. Houston King, Nathan Fortier, and John Sheppard, “An AI-ESTATE Conformant Interface for Net-Centric Diagnostic and Prognostic Reasoning,” IEEE Instrumentation and Measurement Magazine, Vol. 18, No. 4, August 2015, pp. 18-24.
  9. Liessman Sturlaugson and John Sheppard, “Sensitivity Analysis of Continuous Time Bayesian Network Reliability Models,” SIAM/ASA Journal of Uncertainty Quantification 3(1), 2015, pp. 346-369.
  10. Nathan Fortier, John Sheppard, and Shane Strasser, “Abductive Inference in Bayesian Networks using Distributed Overlapping Swarm Intelligence,” Soft Computing, 19(4):981-1001, April 2015.
  11. Patrick J. Donnelly and John W. Sheppard, “Classification of Monophonic Musical Instruments Using Bayesian Networks,” Computer Music Journal, 37(4):70-86, December 2013.
  12. Shane Strasser and John W. Sheppard, “Diagnostic Model Maturation,” IEEE Aerospace and Electronic Systems Magazine, Vol. 28, Issue 1., January 2013, pp. 34-43.
  13. Michael Schuh, John W. Sheppard, Shane Strasser, Rafal Angryk, and Clemente Izurieta, “A Visualization Tool for Knowledge Discovery in Maintenance Event Sequences,” IEEE Aerospace and Electronic Systems Magazine, Vol. 28, Issue 7, July 2013, pp. 30-39.
  14. Jesse Berwald, Tomas Gedeon, and John Sheppard, "Using Machine Learning to Predict Catastrophes in Dynamical Systems," Journal of Applied Computational Mathematics, Vol. 236, Issue 9, March 2012, pp. 2235-2245.
  15. Brian Haberman and John W. Sheppard, "Overlapping Particle Swarms for Energy-Efficient Routing in Sensor Networks," Wirelesss Networks, Online First, Springer, December 2011.
  16. Patrick Donnelly and John Sheppard, "Evolving Four-Part Harmony Using Genetic Algorithms," Applications of Evolutionary Computation Lecture Notes in Computer Science, LNCS 6625, Spring 2011, pp. 273-282.
  17. John W. Sheppard, Timothy J. Wilmering, and Mark A. Kaufman, "IEEE Standards for Prognostics and Health Management," IEEE Aerospace and Electronic Systems Magazine,, reprinted from IEEE AUTOTESTCON, Vol. 24, No. 9, September 2009, pp. 34-41.
  18. Kihoon Choi, Satnam Singh, Anuradha Kodali, Krishna Pattipati, John Sheppard, Setu Madhavi Namburu, Shunsuke Shigua, Danil Prokhorov, and Liu Qiao, "Novel Classifier Fusion Approaches for Fault Diagnosis in Automotive Systems," IEEE Transactions on Instrumentation and Measurement, Vol. 58, No. 3, March 2009, pp. 602-611.
  19. John W. Sheppard, "Guest Editorial: Special Section on the 2007 IEEE AUTOTESTCON,," IEEE Transactions on Instrumentation and Measurement, Vol. 58, No. 2, February 2009, pp. 238–239.
  20. Stephyn G. W. Butcher and John W. Sheppard, "Distributional Smoothing in Bayesian Fault Diagnosis," IEEE Transactions on Instrumentation and Measurement, Vol. 58, No. 2, February 2009, pp. 342–349.
  21. John W. Sheppard and Stephyn G. W. Butcher, “A Formal Analysis of Fault Diagnosis with D-Matrices,” Journal of Electronic Testing: Theory and Applications, 2007.

Refereed Conference and Workshop Papers

  1. Scott Wahl and John Sheppard, "Association Rule Mining in Fuzzy Political Donor Communities," to appear in Proceedings of the International Conference on Machine Learning and Data Mining (MLDM), July 2018.
  2. Stephyn Butcher and John Sheppard, "An Actor Model Implementation of Distributed Factored Evolutionary Algorithms," to appear in Proceedings of the GECCO Workshop on Parallel and Distributed Evolutionary Inspired Methods, July 2018.
  3. Stephyn Butcher, John Sheppard, and Brian Haberman, "Comparative Performance and Scaling of the Pareto Improving Particle Swarm Optimization Algorithm," to appear in Proceedings of the ACM Genetic and Evolutionary Computation Conference (GECCO), July 2018.
  4. Stephyn Butcher, John Sheppard, and Shane Strasser, "Information Sharing and Conflict Resolution in Distributed Factored Evolutionary Algorithms," to appear in Proceedings of the ACM Genetic and Evolutionary Computation Conference (GECCO), July 2018.
  5. Stephyn Butcher, John Sheppard, and Shane Strasser, "Pareto Improving Selection of the Global Best in Particle Swarm Optimization," to appear in Proceedings of the IEEE Congress on Evolutionary Computation (CEC), July 2018.
  6. Amy Peerlinck, John Sheppard, and Bruce Maxwell, "Using Deep Learning in Yield and Protein Prediction of Winter Wheat in Precision Agriculture," to appear in Proceedings of the International Conference on Precision Agriculture, May 2018.
  7. Richard McAllister and John Sheppard, "Evaluating Spatial Generalization of Deep Learning in Wind Vector Determination," to appear in Proceedings of the Florida Artificial Intelligence Symposium (FLAIRS), May 2018.
  8. Richard McAllister and John W. Sheppard, "Deep Learning for Wind Vector Determination," Proceedings of the IEEE Deep Learning Symposium, December 2017.
  9. Shane Strasser and John W. Sheppard, "Evaluating Factored Evolutionary Algorithm Performance on Binary Deceptive Functions," Proceedings of the IEEE Swarm Intelligence Symposium, December 2017.
  10. Shane Strasser, John Sheppard, and Stephyn Butcher, "A Formal Approach to Deriving Factored Evolutionary Algorithm Architectures," Proceedings of the IEEE Swarm Intelligence Symposium, December 2017.
  11. John W. Sheppard and Shane Strasser, "A Factored Evolutionary Optimization Approach to Bayesian Abductive Inference for Multiple Fault Diagnosis," IEEE AUTOTESTCON Conference Record, September 2017, pp. 53-62, winner of Walter E. Peterson Best New Technology Paper Award.
  12. Logan Perreault, Seth Berardinelli, Clemente Izurieta, and John Sheppard, "Using Classifiers for Software Defect Detection," Proceedings of the ISCA 26th International Conference on Software Engineering and Data Engineering, October 2017.
  13. Scott Wahl and John Sheppard, "Fuzzy Spectral Hierarchical Communities in Evolving Political Contribution Networks," Proceedings of the International Florida Artificial Intelligence Research Society (FLAIRS) Conference, May 2017, pp. 371-376.
  14. Shane Strasser and John Sheppard, "Convergence of Factored Evolutionary Algorithms," Proceedings of the Workshop on Foundations of Genetic Algorithms (FOGA), 2017, pp. 81-94.
  15. Rollie Goodman, Monica Thornton, Shane Strasser, and John Sheppard, "MICPSO: A Method for Incorporating Dependencies into Discrete Particle Swarm Optimization," Proceedings of the IEEE Swarm Intelligence Symposium, December 2016.
  16. Logan Perreault, Monica Thornton, and John W. Sheppard, "Deriving Prognostic Continuous Time Bayesian Networks from Fault Trees," Proceedings of the Annual Conference on Prognostics and Health Management, 2016, pp. 347-356.
  17. Logan Perreault, Monica Thornton, and John W. Sheppard, "Valuation and Optimization for Performance Based Logistics Using Continuous Time Bayesian Networks," IEEE AUTOTESTCON Conference Record, winner of Oscar W. Sepp Best Paper Award, September 2016.
  18. Stephyn G. W. Butcher, Shane Strasser, Jenna Hoole, Benjamin Demeo, and John W. Sheppard, "Relaxing Consensus in Distributed Factored Evolutionary Algorithms," Proceedings of the ACM Genetic and Evolutionary Computation Conference (GECCO), July 2016, pp. 5-12.
  19. Shane Strasser, Rollie Goodman, John W. Sheppard, and Stephyn G. W. Butcher, "A New Discrete Particle Swarm Optimization Algorithm," Proceedings of the ACM Genetic and Evolutionary Computation Conference (GECCO), July 2016, pp. 53-60.
  20. Shehzad Qureshi and John Sheppard, "Dynamic Sampling in Training Artificial Neural Networks with Overlapping Swarm Intelligence," Proceedings of the IEEE Congress on Evolutionary Computation (CEC), July 2016, pp. 440-446.
  21. Hasari Tosun, Ben Mitchell, and John Sheppard, "Assessing Diffusion of Spatial Features in Deep Belief Networks," Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN), July 2016, pp. 1625-1632.
  22. Hasari Tosun and John Sheppard, "Fast Classifier Learning under Bounded Computational Resources using Partitioned Restricted Boltzmann Machines," Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN), July 2016, pp. 2894-2900.
  23. Logan Perreault, Shane Strasser, Monica Thornton, and John Sheppard, "A Noisy-OR Model for Continuous Time Bayesian Networks," Proceedings of the International Florida Artificial Intelligence Research Society (FLAIRS) Conference, May 2016, pp. 668-673.
  24. Logan Perreault, Monica Thornton, Rollie Goodman, and John Sheppard, “A Swarm-Based Approach to Learning Phase-Type Distributions for Continuous Time Bayesian Networks,” to appear in Proceedings of the IEEE Swarm Intelligence Symposium (SIS), December 2015.
  25. Patrick J. Donnelly and John W. Sheppard, “Cross-Dataset Validation of Feature Sets in Musical Instrument Classification,” to appear in Proceedings of the ICDM Workshop on Big Media Data: Understanding, Search, and Mining, November 2015.
  26. Logan Perreault, Monica Thornton, Shane Strasser, and John Sheppard, “Deriving Prognostic Continuous Time Bayesian Networks from D-matrices,” to appear in IEEE AUTOTESTCON Conference Record, November 2015.
  27. Benjamin Mitchell, Hasari Tosun, and John Sheppard, “Deep Learning Using Partitioned Data Vectors,” Proceedings of the International Joint Conference on Neural Networks, July 2015.
  28. Nathan Fortier, John Sheppard, and Shane Strasser, “Parameter Estimation in Bayesian Networks Using Overlapping Swarm Intelligence,” Proceedings of Genetic and Evolutionary Computation Conference (GECCO), July 2015, pp. 9-16.
  29. Liessman Sturlaugson and John Sheppard, “The Long Run Behavior of Continuous Time Bayesian Networks,” Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI), July 2015, pp. 842-851.
  30. Scott Wahl and John Sheppard, “Hierarchical Fuzzy Spectral Clustering in Social Networks Using Spectral Characterization,” Proceedings of the Florida Artificial Intelligence Symposium (FLAIRS), May 2015, pp. 305-310.
  31. Logan Perreault, Mike Wittie, and John Sheppard, “Communication-Aware Distributed PSO for Dynamic Robotic Search,” Proceedings of IEEE Swarm Intelligence Symposium (SIS), December 2014, pp. 65-72.
  32. Nathan Fortier and John Sheppard, “Learning Bayesian Classifiers Using Overlapping Swarm Intelligence,” Proceedings of IEEE Swarm Intelligence Symposium (SIS), December 2014, pp. 205-212.
  33. Hasari Tosun and John Sheppard, “Training Restricted Boltzmann Machines with Overlapping Partitions,” Proceedings of the European Conference on Machine Learning-Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), September 2014, pp. 195-208.
  34. Houston King, Nathan Fortier, and John Sheppard, “An AI-ESTATE Conformant Interface for Net-Centric Diagnostic and Prognostic Reasoning,” IEEE AutoTest Conference Record, runner-up Best Student Paper Award, September 2014.
  35. Houston King, Logan Perreault, and John Sheppard, “Using Continuous-Time Bayesian Networks for Standards-Based Diagnostics and Prognostics,” IEEE AutoTest Conference Record, September 2014.
  36. Liessman Sturlaugson and John Sheppard, “Inference Complexity in Continuous Time Bayesian Networks,” Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI), July 2014, pp. 772-729.
  37. Patrick J. Donnelly and John W. Sheppard, “Clustering Spectral Filters for Extensible Feature Extraction in Musical Instrument Classification,” Proceedings of the Florida Artificial Intelligence Symposium (FLAIRS), May 2014, pp. 37-42.
  38. Liessman Sturlaugson and John W. Sheppard, “Factored Performance Functions with Structural Representation in Continuous Time Bayesian Networks,” Proceedings of the Florida Artificial Intelligence Symposium (FLAIRS), May 2014, pp. 512-517.
  39. Nicholas J. Ryhajlo, Liessman Sturlaugson, and John Sheppard, “Diagnostic Bayesian Networks with Fuzzy Evidence,” IEEE AUTOTESTCON Conference Record, Schaumburg, IL, September 2013, winner of Best Student Paper Award.
  40. Liessman Sturlaugson, Nathan Fortier, Patrick Donnelly, and John Sheppard, “Implementing AIESTATE with Prognostic Extensions in Java,” IEEE AUTOTESTCON Conference Record, Schaumburg, IL, September 2013, runner-up Best Student Paper Award.
  41. Nathan Fortier, John Sheppard, and Karthik Ganesan Pillai, “Bayesian Abductive Inference using Overlapping Swarm Intelligence,” Proceedings of the IEEE Swarm Intelligence Symposium (SIS), April 2013, pp. 263-270.
  42. Rachel Green and John Sheppard, “Comparing Frequency- and Style-Based Features for Twitter Author Identification,” Proceedings of the Florida Artificial Intelligence Symposium (FLAIRS), May 2013, pp. 64-69.
  43. Timothy Wylie, John Sheppard, Michael Schuh, and Rafal Angryk, “Cluster Analysis for Optimal Indexing,&rdquo Proceedings of the Florida Artificial Intelligence Symposium (FLAIRS), May 2013, pp. 166-171.
  44. Liessman Sturlaugson and John Sheppard, “Principal Component Analysis Preprocessing with Bayesian Networks for Battery Capacity Estimation,” Proceedings of the IEEE International Instrumentation and Measurement Conference (I2MTC), May 2013, pp. 98-101.
  45. Shane Strasser and John Sheppard, “An Empirical Evaluation of Bayesian Networks Derived from Fault Trees,” Proceedings of the IEEE Aerospace Conference, March 2013.
  46. Richard McAllister, John W. Sheppard, and Rafal Angryk, "Taxonomic Dimensionality Reduction in Bayesian Text Classification," Proceedings of the International Conference on Machine Learning Applications, December 2012, pp. 508-513.
  47. Benjamin Mitchell and John Sheppard, "Deep Structure Learning: Beyond Connectionist Approaches," Proceedings of the International Conference on Machine Learning Applications, December 2012, pp. 162-167.
  48. Nathan Fortier, John Sheppard, and Karthik Ganesan Pillai, "DOSI: A New Approach for Training Artificial Neural Networks Using Overlapping Swarm Intelligence," Proceedings of the International Conference on Soft Computing and Intelligence Systems and International Symposium on Advanced Intelligent Systems, November 2012, pp. 1420-1425.
  49. Karthik Ganesan Pillai and John Sheppard, "Abductive Inference in Bayesian Belief Networks Using Swarm Intelligence," Proceedings of the International Conference on Soft Computing and Intelligence Systems and International Symposium on Advanced Intelligent Systems, November 2012, pp. 375-380.
  50. Patrick J. Donnelly, Liessman E. Sturlaugson, and John W. Sheppard, “A Standards-Based Approach to Gray-Scale Health Assessment Using Fuzzy Fault Trees,”, IEEE AUTOTESTCON Conference Record, Anaheim, CA, September 2012, pp. 174-181
  51. Shane Strasser, Eben Howard, and John Sheppard, “An Integrated Toolset for Ontology-Guided Knowledge Discovery and Diagnostic Maturation Using Maintenance Data,” IEEE AUTOTESTCON Conference Record, Anaheim, CA, September 2012, pp. 280-290, runner-up Best Student Paper Award.
  52. Douglas Galarus, Rafal Angryk, and John Sheppard, “Automated Weather Sensor Quality Control,” Proceedings of the Florida Artificial Intelligence Symposium (FLAIRS), 2012.
  53. Michael Schuh, Rafal Angryk, and John Sheppard, “Evolving Kernel Functions with Particle Swarm Optimization and Genetic Programming,” Proceedings of the Florida Artificial Intelligence Symposium (FLAIRS), 2012.
  54. Shane Strasser and John W. Sheppard, "Diagnostic Alarm Sequence Maturation in Timed Failure Propagation Graphs," IEEE AUTOTESTCON Conference Record, Baltimore, MD, September, 2011, pp. 158-165, winner of Best Track Paper Award--Diagnostics and Health Assessment.
  55. Mike Schuh, John Sheppard, Shane Strasser, Rafal Angryk, and Clemente Izurieta, "Ontology-Guided Knowledge Discovery Through the Generation and Visualization of Event Sequences in Maintenance Data," IEEE AUTOTESTCON Conference Record, Baltimore MD, September, 2011, pp. 279-285, winner of Best Student Paper Award.
  56. Karthik Ganesan Pillai and John W. Sheppard, "Overlapping Swarm Intelligence for Training Artificial Neural Networks," Proceedings of the Swarm Intelligence Symposium (SIS), 2011 IEEE Symposium Series on Computational Intelligence, Paris, France, April 2011.
  57. Hasari Tosun and John Sheppard, "Incorporating Evidence Into Trust Propagation Models Using Markov Random Fields," Proceedings of the 3rd International Workshop on Security and Social Networking (SESOC), IEEE International Conference on Pervasive Computing, March 21, 2011, pp. 336-342.
  58. Shane Strasser, John Sheppard, Michael Schuh, Rafal Angryk, and Clemente Izurieta, "Graph-Based, Ontology-Guided Data Mining for D-Matrix Model Maturation," Proceedings of the IEEE Aerospace Conference, Big Sky, MT, March 2011.
  59. Scott Wahl and John Sheppard, "Extracting Decision Trees from Diagnostic Bayesian Networks to Guide Test Selection," Proceedings of the Annual Conference of the PHM Society, Prognostics and Health Management Society, June 2010.
  60. John W. Sheppard, Stephyn G. W. Butcher, and Patrick J. Donnelly, "Demonstrating Semantic Interoperability of Diagnostic Reasoners via AI-ESTATE," Proceedings of the IEEE Aerospace Conference, Big Sky, MT, March 2010.
  61. John W. Sheppard, Stephyn G. W. Butcher, and Patrick J. Donnelly, "Standard Diagnostic Services for the ATS Framework," IEEE AUTOTESTCON 2009 Conference Record, Anaheim, CA, September 2009, pp. 393-400.
  62. John W. Sheppard, Stephyn G. W. Butcher, Patrick J. Donnelly, and Benjamin R. Mitchell, “Demonstrating Semantic Interoperability of Diagnostic Models via AI-ESTATE,” Proceedings of the IEEE IEEE Aerospace Conference, Big Sky, MT, March 2009.
  63. John W. Sheppard, Timothy J. Wilmering, and Mark A. Kaufman, “IEEE Standards for Prognostics and Health Management,” IEEE AUTOTESTCON 2008 Conference Record, Salt Lake City, UT, September 2008, pp. 97-103.
  64. Edward Kao, Peter VanMaasdam, and John Sheppard, "Image-Based Tracking Utilizing Particle Swarms and Probabilistic Data Association," Proceedings of the IEEE Swarm Intelligence Symposium, St. Louis, MO, September 21-23, 2008.
  65. Stephyn G. W. Butcher and John W. Sheppard, "Asset-Specific Bayesian Diagnostics in Mixed Contexts," IEEE AUTOTESTCON 2007 Conference Record, Baltimore, MD, September 2007.
  66. Stephyn G. W. Butcher and John W. Sheppard, “Improving Diagnostic Accuracy by Blending Probabilities: Some Initial Experiments,” Proceedings of the 18th International Workshop on Principles of Diagnosis (DX-07), Nashville, TN, May 2007.
  67. Kihoon Choi, Satnam Singh, Krishna Pattipati, John W. Sheppard, Setu Madhavi Namburu, Shunsuke Chigusa, Danil V. Prokhorov, and Lui Qiao, "Novel Classifier Fusion Approaches for Fault Diagnosis in Automotive Systems," IEEE AUTOTESTCON 2007 Conference Record, Baltimore, MD, September 2007.
  68. Mark A. Kaufman, John W. Sheppard, and Timothy J. Wilmering, "Model-Based Standards for Diagnostic and Maintenance Information Integration," IEEE AUTOTESTCON 2007 Conference Record, Baltimore, MD, September 2007.
  69. Sean R. Martin, Steve E. Wright, and John W. Sheppard, "Offline and Online Evolutionary Bi-Directional RRT Algorithms for Efficient Re-Planning in Environments with Moving Obstacles," Proceedings of the 3rd annual IEEE Conference on Automation Science and Engineering, New York: IEEE Press, September 2007.
  70. Satnam Singh, Kihoon Choi, Anuradha Kodali, Krishna Pattipati, John Sheppard, Setu Madhavi Namburu, Shunsuke Chigusa, Danil V. Prokhorov, and Liu Qiao, “Dynamic Multiple Fault Diagnosis: Mathematical Formulations and Solution Techniques,” Proceedings of the 18th International Workshop on Principles of Diagnosis (DX-07), Nashville, TN, May 2007.
  71. Timothy J. Wilmering and John W. Sheppard, “Ontologies for Data Mining and Knowledge Discovery to Support Diagnostic Maturation,” Proceedings of the 18th International Workshop on Principles of Diagnosis (DX-07), Nashville, TN, May 2007.
  72. Stephen G. W. Butcher, John W. Sheppard, Mark A. Kaufman, Hanh Ha, and Craig MacDougall, “Experiments in Bayesian Diagnostics with IUID-Enabled Data,” IEEE AUTOTESTCON 2006 Conference Record, Anaheim, California, September 2006, pp. 605–614.
  73. John W. Sheppard, Stephyn G. W. Butcher, Mark A. Kaufman, and Craig MacDougall, “Not-So-Naive Bayesian Networks and Unique Identification in Developing Advanced Diagnostics,” Proceedings of the IEEE Aerospace Conference, Big Sky, Montana, March 2006.
  74. John W. Sheppard and Stephyn G. W. Butcher, “On the Linear Separability of Diagnostic Models,” IEEE AUTOTESTCON 2006 Conference Record, Anaheim, California, September 2006, pp. 626–635.
  75. John W. Sheppard and Timothy J. Wilmering, “Recent Advances in IEEE Standards for Diagnosis and Diagnostic Maturation,” Proceedings of the IEEE Aerospace Conference, Big Sky, Montana, March 2006.
  76. Brian Howard and John W. Sheppard, “The Royal Road Not Taken: A Re-Examination of the Reasons for GA Failure on R1,” Proceedings of the Genetic and Evolutionary Computation Conference, Seattle, Washington, June 2004.