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T. Zhang, Borrero, G., and Georgiopoulos, M., “A Winner-Take-All Methodology: Finding the Best Evolutionary Algorithm for the Global Optimization of Functions”, Genetic and Evolutionary Computation Conference. Philadelphia, July 7th-July 11th, pp. 1515-1516, 2012.

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A. J. Gonzalez, DeMara, R. F., and Georgiopoulos, M., “Vehicle model generation and optimization for embedded simulation”, Simulation Interoperability Workshop, pp. 206–213, 1998.

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G. Bebis, Georgiopoulos, M., and da Lobo, V. N., “Using Self-Organizing Maps to Learn Geometric Hash Functions for Model-Based Object Recognition”, IEEE Transactions on Neural Networks, vol. 9.3, pp. 560–570, 1998.

G. Bebis, Georgiopoulos, M., and da Lobo, V. N., “Using self-organizing maps to learn geometric hash functions for model-based object recognition”, IEEE Transactions on Neural Networks, vol. 9, pp. 560-570, 1998.

A. Gonzalez, Georgiopoulos, M., and DeMara, R., “Using context-based neural networks to maintain coherence in distributed simulations”, The Journal of Defense Modeling and Simulation, vol. 4, pp. 417-172, 2007.

A. Gonzalez, Georgiopoulos, M., and DeMara, R., “Using Context-Based Neural Networks to Maintain Coherence Among Entities’ States in a Distributed Simulation”, The Journal of Defense Modeling and Simulation, vol. 4, pp. 147-172, 2007.

G. Bebis, Georgiopoulos, M., Shah, M., and da Lobo, V. N., “Using Algebraic Functions of Views for Indexing-Based Object Recognition”, International Conference on Computer Vision, pp. 634–639, 1998.

D. Charalampidis, Kasparis, T., Jones, L., and Georgiopoulos, M., “Use of multifractals to detect anomalous propagation (AP) in weather radar”, Proceedings SPIE; Conference on Signals and Data Processing of Small Targets, pp. 13–22, 2008.

D. Charalampidis, Kasparis, T., Jones, W. L., and Georgiopoulos, M., “Use of multi-fractals to detect anomalous propagation (AP) in weather data”, in Proceedings SPIE; Conference on Signals and Data Processing of Small Targets, Orlando, FL, 2000.

S. Verzi, Heileman, G., Georgiopoulos, M., and Anagnostopoulos, G. C., “Universal approximation with Fuzzy ART and Fuzzy ARTMAP”, Neural Networks, pp. 1987–1992, 2003.

C. Li, Georgiopoulos, M., and Anagnostopoulos, G. C., “A Unifying Framework for Typical Multitask, Multiple Kernel Learning Problems”, IEEE Transactions on Neural Networks and Learning Systems, vol. 25, no. 7, pp. 1287-1297, 2014.

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R. C. Watkins, Reynolds, K. M., DeMara, R., Georgiopoulos, M., Gonzalez, A., and Eaglin, R., “TRACKING DIRTY PROCEEDS: EXPLORING DATA MINING TECHNOLOGIES AS TOOLS TO INVESTIGATE MONEY LAUNDERING”, Police Practice and Research, vol. 4.2, pp. 163–178, 2003.

D. Charalampidis, Kasparis, T., and Georgiopoulos, M., “Texture classification using ART-based neural networks and Fractals”, SPIE Conference on Signal Processing,Sensor Fusion,and Target Recognition VII, pp. 212–222, 1998.

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M. Georgiopoulos, DeMara, R. F., Gonzalez, A. J., Wu, A. S., Mollaghasemi, M., Gelenbe, E., Kysilka, M., Secretan, J., Sharma, C. A., and Alnsour, A. J., “A Sustainable Model for Integrating Current Topics in Machine Learning Research in the Undergraduate Curriculum”, IEEE Transactions on Education, vol. 52, pp. 503-512, 2009.

G. L. Heileman, Georgiopoulos, M., and Huang, J., “A survey of learning results for ART1 networks”, in IEEE World Congress on Computational Intelligence, Orlando, FL, 1994.

T. Zhang, Georgiopoulos, M., and Anagnostopoulos, G. C., “S-RACE: A Multi-objective Racing Algorithm”, GECCO 2013, Proceedings of the fifteenth annual conference on Genetic and evolutionary computation conference. Amsterdam, the Netherlands, July 6-10, pp. 1565-1572, 2013.

C. Christodoulou and Georgiopoulos, M., “Smart adaptive array antennas for wireless communcations”, Proceedings of SPIE; Conference on Digital Wireless Communication III, pp. 75–83, 2001.

M. Georgiopoulos and Spillers, R. M., “A simulation study of a limited sensing random access algorithm for a local area network with voice users”, in Proceedings Southeastcon, Knoxville, Tennessee, 1988.

C. Sentelle, Anagnostopoulos, G. C., and M. Georgiopoulos, R. DeMara, “A Simple Method for Solving the SVM Regularization Path for Semi-Definite Kernels”, IEEE Transactions on Neural Networks and Learning Systems, vol. 27, no. 4, pp. 709-722, 2016.

R. Ramirez-Padron, Foregger, D., Manuel, J., Georgiopoulos, M., and Mederos, B., “Similarity Kernels for Nearest Neighbor-based Outlier Detection”, Lecture Notes in Computer Science, vol. 6065/2010, pp. 159-170, 2010.

S. E. Skarman, Georgiopoulos, M., and Gonzalez, A. J., “Short-term electrical load forecasting using a fuzzy ARTMAP neural network”, Conference on Applications and Science of Computational Intelligence, pp. 181-191, 1998.

M. Georgiopoulos, Skarman, S., and Gonzalez, A. J., “Short-term electric load forecasting using a Fuzzy ARTMAP neural network”, in Conference on Applications and Science of Computational Intelligence, Orlando, FL, 1998.

T. Kasparis, Charalampidis, D., Georgiopoulos, M., and Rolland, J. P., “Segmentation of textured images based on fractals and image filtering”, Pattern Recognition, vol. 34.10, pp. 1963–1973, 2001.

T. Kasparis, Charalampidis, D., Georgiopoulos, M., and Rolland, J. P., “Segmentation of textured images based on fractals and image filtering”, Pattern Recognition, vol. 34.10, pp. 1963–1978, 2001.

W. J. Park, Jones, L., Charalampidis, D., Kasparis, T., and Georgiopoulos, M., “Sea-Ice extent classification using active/passive microwave measurements from QuickScat”, in AGU Spring meeting, Washington DC, 2001.

A. Koufakou, Ortiz, E., Georgiopoulos, M., Anagnostopoulos, G. C., and Reynolds, K., “A Scalable and Efficient Outlier Detection Strategy for Categorical Data”, International Conference on Tools with Artificial Intelligence (ICTAI), 2007.

A. Koufakou, Ortiz, E., Georgiopoulos, M., Anagnostopoulos, G. C., and Reynolds, M. K., “A scalable and efficient otlier detection strategy for categorical data”, in Annual IEE International Conference on Tools with Artificial Intelligence, 2007 (ICTAI 2007),, Patras, Greece, 2007.

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G. C. Anagnostopoulos, Georgiopoulos, M., Verzi, S., and Heileman, G., “Reducing generalization error and category proliferation in ellipsoid ARTMAP via tunable misclassification error tolerance: Boosted Ellipsoid ARTMAP”, Neural Networks, pp. 2650–2655, 2002.

Y. Huang, Li, C., Georgiopoulos, M., and Anagnostopoulos, G. C., “Reduced Rank Local Distance Metric Learning”, in European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML and PKDD 2013), Prague, Czech Republic, September 23-27, 2013.

E. Nold, Tucker, K., Long, R., and Georgiopoulos, M., “Real-time unsupervised neural networks for non-implementable in natural noise A refutable hypothesis based on experiment”, in Proceedings of the International Joint Conference on Neural Networks (IJCNN), Seattle, Washington, 1991.

M. Bassiouni, Georgiopoulos, M., and Thompson, J., “Real time simulation networking Network modeling and protocol alternatives”, in 11th Interservice / Industry Training Systems Conference, Fort Worth, Texas, 1989.

E. A. H. Zooghby, Christodoulou, C. G., and Georgiopoulos, M., “Radial basis function neural network algorithm for adaptivebeamforming in cellular communication systems”, IEEE-APS Conference on Antennas and Propagation for Wireless Communications, pp. 53–56, 1998.

S. J. Verzi, Heileman, G. L., Georgiopoulos, M., and Healy, M. J., “Rademacher penalization applied to Fuzzy ARTMAP and Boosted ARTMAP”, Proceedings of the International Joint Conference on Neural Networks, pp. 1191–1196, 2001.

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G. C. Anagnostopoulos and Georgiopoulos, M., “Putting the utility of match tracking in Fuzzy ARTMAP training to the test”, pp. 1–6, 2003.

M. Georgiopoulos, Dagher, I., Heileman, G. L., and Bebis, G., “Properties of Learning of a Fuzzy ART Variant”, Neural Networks, vol. 12.6, pp. 837–850, 1999.

J. Huang, Georgiopoulos, M., and Heileman, G. L., “Properties of learning in fuzzy ART”, in IEEE World Congress on Computational Intelligence, Orlando, Fl, 1994.

M. Georgiopoulos, Heileman, G. L., and Huang, J., “Properties of learning in ART1”, in Proceedings of the International Joint Conference on Neural Networks, Singapore, 1991.

M. Georgiopoulos, Heileman, G. L., and Huang, J., “Properties of learning in ART1”, Neural Networks, vol. 4, pp. 751-758, 1991.

G. C. Anagnostopoulos, Georgiopoulos, M., Ports, K., Richie, S., Cardinale, N., White, M., Kepuska, V., Chan, P. K., Wu, A., and Kysilka, M., “Project EMD-MLR: Educational Materials Development and Research in Machine Learning for Undergraduate students”, in 2006 ASEE, Chicago, Illinois, 2006.

G. C. Anagnostopoulos, Georgiopoulos, M., Ports, K., Richie, S., Cardinale, N., White, M., Kepuska, V., Chan, P. K., Wu, A., and Kysilka, M., “Project EMD-MLR: Educational Materials Development and Research in Machine Learning for Undergraduate students”, in the ASEE 2005 Annual Conference and Exposition, Portland, Oregon, 2005.

M. Zhong, Hecker, J., Maidhoff, I., Shibly, P., Georgiopoulos, M., Anagnostopoulos, G., and Mollaghasemi, M., “Probabilistic Neural Network: Comparisons of the Cross-Validation Approach and a Fast Heuristic to choose the Smoothing Parameters”, in 2005 Artificial Neural Networks in Engineering, St. Louis, MI, 2005.

J. Secretan, Georgiopoulos, M., and Castro, J., “A Privacy Preserving Probabilistic Neural Network for Horizontally Partitioned Databases”, 2007.

J. Castro, Secretan, J., Georgiopoulos, M., DeMara, R., Anagnostopoulos, G. C., and Gonzalez, A., “Pipelining of Fuzzy ARTMAP without Matchtracking: Correctness,Performance Bound and Beowulf Evaluation”, Neural Networks, vol. 20.1, pp. 109–128, 2007.

J. Castro, Secretan, J., Georgiopoulos, M., DeMara, R. F., Anagnostopoulos, G., and Gonzalez, A., “Pipelining of Fuzzy ARTMAP (FAM) without match-tracking”, in ANNIE 2004, St. Louis, MI, 2004.

J. Secretan, Castro, J., Chadha, A., Huber, B., Tapia, J., Georgiopoulos, M., Anagnostopoulos, G., and Richie, S., “Pipelining of ART architectures (FAM, EAM, GAM) without match-tracking (MT)”, in 2005 Artificial Neural Networks in Engineering, St. Louis, MI, 2005.

M. Bassiouni, Georgiopoulos, M., and Chiu, M., “Performance of standard and modified network protocols in a real-time application”, in 1997 International Performance, Computing and Communications Conference, Scottsdale, Arizona, 1997.

E. A. H. Zooghby, Christodoulou, C. G., and Georgiopoulos, M., “Performance of Radial-Basis Function Networks for Direction of Arrival Estimation with Antenna Arrays”, IEEE Transactions on Antennas and Propagation, vol. 45.11, pp. 1611–1617, 1997.

H. T. Owens, Georgiopoulos, M., and Belkerdid, M., “Performance of BCH and convolutional codes in direct sequence spread spectrum packet radio networks”, in MILCOM 89, Boston, Massachusetts, 1989.

M. Georgiopoulos, “Performance evaluation of frequency hopped receiver oriented spread spectrum packet radio networks”, in Proceedings of the International Conference on Communications, Boston, Massachusetts, 1989.

G. G. M. M. A. J. H. J.J. Vargas, R.F. DeMara, “PDU Bundling and Replication for Reduction of Distributed Simulation Communication Traffic”, Methodology,Technology,Journal of Defense Modeling and Simulation: Applications, vol. 1.3, pp. 171–183, 2004.

J. J. Vargas, DeMara, R. F., Gonzalez, A. J., Georgiopoulos, M., and Marshall, H., “PDU Bundling and Replication for Reduction of Distributed Simulation Communication Traffic”, Journal of Defense Modeling and Simulation, vol. 1.3, pp. 171–183, 2004.

J. Castro, Georgiopoulos, M., DeMara, R., and Gonzalez, A., “A Partitioned Fuzzy ARTMAP implementation for fast processing of large databases on sequential machines”, 2004.

C. Li, Georgiopoulos, M., and Anagnostopoulos, G. C., “Pareto-Path Multi-task Multiple Kernel Learning”, IEEE Transactions on Neural Networks and Learning Systems, vol. accepted for publication (acceptance was communicated in June 2014), 2014.

C. Li, M. Georgiopoulos, R. DeMara,, and Anagnostopoulos, G. C., “Pareto-Path Multi-Task Multiple Kernel Learning”, IEEE Transactions on Neural Networks and Learning Systems, vol. 26, no. 1, pp. 51-61, 2015.

T. Zhang, M. Georgiopoulos, R. DeMara,, and Anagnostopoulos, G. C., “Pareto Optimal Selection via SPRINT-Race”, IEEE Transactions on Cybernetics, vol. 48, no. 2, pp. 596-610, 2018.

J. Secretan, Georgiopoulos, M., Anagnostopoulos, G. C., Tapia, J., Chadha, A., and Huber, B., “Parallelizing the Fuzzy ARTMAP Algorithm on a Beowulf Clusters”, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN 2005), pp. 475–480, 2005.

J. Castro, Georgiopoulos, M., Secretan, J., DeMara, R., Anagnostopoulos, G. C., and Gonzalez, A., “Parallelization of Fuzzy ARTMAP to improve its convergence speed: The network partitioning approach and the data partitioning approach”, Nonlinear Analysis: Theory,Methods and Applications, vol. 63.5-7, pp. e877–e889, 2005.

G. M. Papadourakis, Heileman, G. L., and Georgiopoulos, M., “A parallel implementation of the Hopfield network on GAPP processors”, in Proceedings of the International Joint Conference on Neural Networks, Washington, DC, 1989.

M. Georgiopoulos, “Packet error probabilities in frequency hopped spread spectrum packet radio networks–Memoryless frequency hopping patterns considered”, IEEE Trans. on Comm., vol. 36, pp. 720-724, 1988.

M. Georgiopoulos, “Packet error probabilities in frequency hopped spread spectrum packet radio networks–Memoryless frequency hopping patterns considered”, in the 26th IEEE Conference on Decision and Control, Los Angeles, California, 1987.

M. Georgiopoulos, “Packet error probabilities in direct sequence spread spectrum packet radio networks”, IEEE Trans. on Comm., vol. 38, pp. 1599-1606, 1990.

M. Georgiopoulos, “Packet error probabilities in direct sequence spread spectrum packet radio networks with BCH codes”, in MILCOM 88, San Diego, California, 1988.

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M. Georgiopoulos, Koufakou, A., Anagnostopoulos, G. C., and Kasparis, T., “Over-training in Fuzzy ARTMAP: Myth or Reality?”, Proceedings of the International Joint Conference on Neural Networks, pp. 1186–1190, 2001.

H. G. L. I. Dagher, M. Georgiopoulos, “An Ordering Algorithm for Pattern Presentation in Fuzzy ARTMAP That Tends to Improve Generalization Performance”, IEEE Transactions on Neural Networks, vol. 10.4, pp. 768–778, 1999.

I. Dagher, Georgiopoulos, M., Heileman, G. L., and Bebis, G., “Ordered fuzzy ARTMAP: a fuzzy ARTMAP algorithm with a fixed order of pattern presentation”, IEEE International Joint Conference on Neural Networks, pp. 1717–1722, 1998.

M. Georgiopoulos, Fernlund, H., Bebis, G., and Heileman, G., “Order of Search in Fuzzy ART and Fuzzy ARTMAP: Effect of the Choice Parameter”, Neural Networks, vol. 9.9, pp. 1541-1559, 1996.

M. Georgiopoulos, Fernlund, H., Bebis, G., and Heileman, G. L., “Order of Search in Fuzzy ART and Fuzzy ARTMAP: A geometrical interpretation”, in International Conference on Neural Networks (ICNN 1996), Washington, DC, 1996.

G. Bebis and Georgiopoulos, M., “Optimal feed-forward neural network architectures”, IEEE Potentials, pp. 27-31, 1994.

T. Zhang, Georgiopoulos, M., and Anagnostopoulos, G. C., “Online Model Racing Based on Extreme Performance”, in GECCO 2014, Vancouver, Canada, July 12-16, 2014, accepted for publication, 2014.

A. Vartak, Georgiopoulos, M., and Anagnostopoulos, G. C., “On-line Gaussae Newton-based learning for fully recurrent neural networks”, Nonlinear Analysis, vol. 63, pp. e867–e876, 2005.

S. Verzi, Heileman, G. L., Georgiopoulos, M., and Anagnostopoulos, G. C., “Off-line structural risk minimization and BARTMAP-S”, in the 2002 International Joint Conference on Neural Networks, Honolulu, Hawaii, 2002.

S. Verzi, Heileman, G., Georgiopoulos, M., and Anagnostopoulos, G. C., “Off-line structural risk minimization and BARTMAP-S”, Neural Networks, pp. 2533–2538, 2002.

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A. H. Zooghby, Christodoulou, C. G., and Georgiopoulos, M., “A novel approach to adaptive nulling with neural networks”, Southeastcon, pp. 216–219, 1998.

T. Kasparis, Georgiopoulos, M., and Payne, E., “Non-linear filtering techniques for narrow-band interference rejection in direct sequence spread-spectrum systems”, in MILCOM 91, McLean, Virginia, 1991.

A. Koufakou, Secretan, J., and Georgiopoulos, M., “Non-derivable itemsets for fast outlier detection in large high dimensional categorical data”, Knowledge and Information Systems, pp. 1-29, 2010.

M. Georgiopoulos, Heileman, G. L., and Huang, J., “The N-N-N conjecture in ART1 Conference”, in International Joint Conference on Neural Networks (IJCNN), Baltimore, MD, 1992.

M. Georgiopoulos, Heileman, G. L., and J. Huang, “The N-N-N conjecture in ART1”, Neural Networks, vol. 5, pp. 745-753, 1992.

G. C. Anagnostopoulos and Georgiopoulos, M., “New geometrical perspective of fuzzy ART and fuzzy ARTMAP learning”, Proceedings of SPIE; Conference on Applications and Science of Computational Intelligence IV, pp. 22–32, 2001.

G. C. Anagnostopoulos and Georgiopoulos, M., “New Geometrical Concepts in Fuzzy-ART and Fuzzy-ARTMAP: Category Regions”, IEEE-INNS International Joint Conference on Neural Networks (IJCNN 2001), pp. 32–37, 2001.

E. A. H. Zooghby, Christodoulou, C., and Georgiopoulos, M., “A neural-network-based linearly constrained minimum variance beamformer”, Microwave and Optical Technology Letters, vol. 21.6, pp. 451–455, 1999.

A. H. Zooghby, Christodoulou, C. G., and Georgiopoulos, M., “A Neural Network-Based Smart Antenna for Multiple Source Tracking”, IEEE Transactions on Antennas and Propagation, vol. 48.5, pp. 768–776, 2000.

E. A. H. Zooghby, Christodoulou, C., and Georgiopoulos, M., “Neural Network-Based Adaptive Beamforming for One- and Two-Dimensional Antenna Arrays”, IEEE Transactions on Antennas and Propagation, vol. 46.1, pp. 1891–1893, 1998.

A. H. E. Zooghby, Christodoulou, C. G., and Georgiopoulos, M., “Neural network-based adaptive beamforming for one- and two-dimensional antenna arrays”, IEEE Transactions on Antennas and Propagation, vol. 46, pp. 1891-1893, 1998.

C. G. Christodoulou, Zooghby, E. A. H., and Georgiopoulos, M., “Neural network processing for adaptive array antennas”, IEEE Antennas and Propagation Society International Symposium, pp. 2584–2587, 1999.

E. A. Zooghby, Christodoulou, C. G., and Georgiopoulos, M., “Neural Network based beamforming for interference cancellation”, Conference on Applications and Science of Computational Intelligence, pp. 420–429, 1998.

G. L. Heileman, Papadourakis, G. M., and Georgiopoulos, M., “A neural network associative memory for real-time applications”, Neural Computation, vol. 2, pp. 107-115, 1990.

A. E. Zooghby, Christodoulou, C. G., and Georgiopoulos, M., “Neural network approach for direction of arrival estimation”, in Proceedings SPIE; Conference of Applications and Science of, Orlando, FL, 1997.

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C. Li, Georgiopoulos, M., and Anagnostopoulos, G. C., “Multi-Task Classification Hypothesis Space with Improved Generalization Bounds ”, IEEE Transactions on Neural Networks and Learning Systems, vol. accepted for publication (acceptance was communicated in July 2014), 2014.

C. Li, M. Georgiopoulos, R. DeMara,, and Anagnostopoulos, G. C., “Multi-Task Classification Hypothesis Space with Improved Generalization Bounds”, IEEE Transactions on Neural Networks and Learning Systems, vol. 26, no. 7, pp. 1468-1479, 2015.

E. A. H. Zooghby, Christodoulou, C. G., and Georgiopoulos, M., “Multiple sources neural network direction finding with arbitrary separations”, IEEE-APS Conference on Antennas and Propagation for Wireless Communications, pp. 57–60, 1998.

C. Christodoulou, Georgiopoulos, M., and Zooghby, E. A., “Multiple Source Angle of Arrival Estimation using Neural-Network-based Smart Antennas”, Proceedings SPIE; Conference on Digital Wireless Communications II, pp. 94–99, 2000.

E. A. H. Zooghby, Christodoulou, C. G., and Georgiopoulos, M., “Multiple mobile user tracking with neural network-based adaptive array antennas”, SPIE Conference on Digital Wireless Communications, pp. 88–97, 1999.

T. Zhang, M. Georgiopoulos, R. DeMara,, and Anagnostopoulos, G. C., “Multi-Objective Model Selection via Racing”, IEEE Transactions on Cybernetics, vol. 46, no. 8, pp. 1863-1876, 2015.

T.Rubio, Zhang, T., Georgiopoulos, M., and Kaylani, A., “Multi-Objective Evolutionary Optimization of Exemplar-Based Classifiers: A PNN Test Case”, in International Joint Conference on Neural Networks (IJCNN), San Jose, CA, 2011.

X. Rui, Anagnostopoulos, G. C., and II, W. D., “Multiclass Cancer Classification Using Semisupervised Ellipsoid ARTMAP and Particle Swarm Optimization with Gene Expression Data”, IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 4., pp. 65–77, 2007.

A. Kaylani, Georgiopoulos, M., Mollaghasemi, M., and Anagnostopoulos, G., “MO-GART: Multi-Objective Optimization of ART Architectures”, in 2008 IEEE Congress on Evolutionary Computation (IEEE CEC 2008), Hong Kong, 2008.

A. Kaylani, Georgiopoulos, M., Mollaghasemi, M., and Anagnostopoulos, G., “MO-GART: An adaptive multi-objective approach to evolving ART architectures”, IEEE Transactions on Neural Networks, vol. 21, pp. 529-550, 2010.

A. Kaylani, Georgiopoulos, M., Mollaghasemi, M., and Anagnostopoulos, G., “MO-GART: An adaptive multi-objective approach to evolving ART architectures”, IEEE Transactions on Neural Networks, vol. 21, pp. 529-550, 2010.

A. Henninger, Gonzalez, A., Georgiopoulos, M., and DeMara, R., “Modeling Semi-Automated Forces with Neural Networks: Performance Improvement through a Modular Approach”, 2000.

A. Kaylani, Georgiopoulos, M., Mollaghasemi, M., and Anagnostopoulos, G. C., “M-GFAM:An Elegant Approach to Genetically Optimize Fuzzy ARTMAP Neural Network Architectures”, Proceedings of the 8th International Conference on Natural Computing (ICNC 2007),part of the 10th Joint Conference on Information Sciences (JCIS 2007), pp. 1617-1623, 2007.

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