Abstract
This paper describes an introductory course on biologically based sub-symbolic machine learning paradigms. Specifically, this paper covers Artificial Neural Networks, Genetic Algorithms and Genetics-Based Machine Learning. It provides the structure, motivation, content, texts and tools for the course. This course is suitable for an upper division undergraduate level course or as an introductory graduate course. The paper includes a section on bibliographical references to aid the instructor in preparing for this course.
- 1 Abunawass, A. M., & Maki, W. S. (1989). Cumulative negative transfer during successive training: Analysis of a second sequential learning problem. Proceedings of the International Joint Conference on Neural Networks, 2, pp 623.]]Google Scholar
Cross Ref
- 2 Abunawass, A. M., Bukhres, O., Fisher, T. G., & Magel, K. (1990). Proceedings of the 21st SIGCSE technical symposium, pp 240-244.]] Google Scholar
Digital Library
- 3 Anderson, J., & Rosenfeld, E. (Eds). Neurocomputing: Foundations of Research. Cambridge: The MIT Press.]] Google Scholar
Digital Library
- 4 Booker, L. (1987). Improving search in genetic algorithms. In L. Davis (ed.) Genetic Algorithms and Simulated Annealing. London: Pitman Publishers.]]Google Scholar
- 5 Butler, R., Eggen, R., & Wallace, S. (1988). Introducing parallel processing at the undergraduate level. SIGCSE Bulletin, 20, 1, pp 63-67.]] Google Scholar
Digital Library
- 6 Caudill, M. (1987). Neural Networks Primer. A/ Expert. pp 46-52.]] Google Scholar
Digital Library
- 7 Davis, L. (ed.) (1991). Handbook of Genetic Algorithms. New York: Van Nostrand Reinhold.]]Google Scholar
- 8 Denning, P.(Chair) (1989). Computing as a discipline. Communications of the ACM, 1, pp 9-23.]] Google Scholar
Digital Library
- 9 Edelman, G. (1988). Neural Darwinism. New York: Basic Books.]]Google Scholar
- 10 Ficek, R. (1991). Genetics-Based Machine Learning: Classifier Systems in the Computer Science Curriculum. Proceedings of the 24th Annual Small College Computing Symposium. pp 207-212.]]Google Scholar
- 11 Gallant, S.I. (1988). Connectionist Expert Systems. Communications of ACM, 2, 152-169.]] Google Scholar
Digital Library
- 12 Goldberg, David E. (1989). Genetic Algorithms in Search, Optimization and Machine Learning. Reading, MA. Addison Wesley.]] Google Scholar
Digital Library
- 13 Harp, S., & Samad, T. (1991). Genetic synthesis of neural network architecture, in L. Davis (ed.), Handbook of Genetic Algorithms. New York: Van Nostrand Reinhold, pp. 202-221.]]Google Scholar
- 14 Harp, S., Samad, T. & Guha, A. (1990). Designing application-specific neural networks using the genetic algorithms. Advances in Neural Information Processing Systems, 2.]] Google Scholar
Digital Library
- 15 Harp, S., Samad, T. & Guha, A. (1989). Towards the genetic synthesis of neural networks. In J.D. Schaffer (ed.), Proceedings of the Third International Conference on Genetic Algorithms. Morgan Kaufmann.]] Google Scholar
Digital Library
- 16 Hebb, D. O. (1949). The Organization of Behavior, New York: Wiley.]]Google Scholar
- 17 Hinton, G. E., & Sejnowski, T. J. (1986). Learning and relearning in Boltzmann Machines. In D. E. Rumelhart, j. L. McClelland and the PDP Research Group (Eds.), Parallel distributed processing: Explorations in the microstructure of cognition. Volume 1: Foundations. (pp. 298-299). Cambridge, MA: The MIT Press.]] Google Scholar
Digital Library
- 18 Holland, John H. (1975). Adaptation in Natural and Artificial Systems. Ann Arbor: The University of Michigan Press.]] Google Scholar
Digital Library
- 19 Holland, John H. (1987). Genetic Algorithms and Classifier Systems: Foundations and Future Directions. Proceedings of the Second International Conference on Genetic Algorithms. pp 82-89.]] Google Scholar
Digital Library
- 20 Hopfield, J. (1984). Neurons With Graded Response Have Collective Computational Properties. Proceedings National Academy of Science; 81: pp 3088-3092]]Google Scholar
- 21 Kohonen, T. (1984). Self Organization and Associative Memory. Springer Verlag.]] Google Scholar
Digital Library
- 22 Maki, W. S., & Abunawass, A. M. (1991). A connectionist approach to conditional discrimination: Learning, short-term memory, and attention. In M. L. Commons, S. Grossberg, & J. E. R. Staddon (Eds.), Quantitative analysis of behavior: Neural network models of conditioning and action (pp 241-278). Hillsdale, NJ: Lawrence Erlbaum Associates.]]Google Scholar
- 23 McClelland, J., & Rumelhart, D. (1989). Explorations in parallel distributedprocessing: A Handbook of Models, Programs, and Exercises. MIT Press Cambridge, MA.]] Google Scholar
Digital Library
- 24 McClelland, J., Rumelhart, D., & the PDP Research Group (1986). Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Volume 2: Psychological and Biological Models. MIT Press Cambridge, MA.]] Google Scholar
Digital Library
- 25 McCloskey, M., & Cohen, N. J. (1989). Catastrophic interference in connectionist networks: The sequential learning problem. The Psychology of Learning and Motivation, 24, pp 109-165.]]Google Scholar
- 26 Minsky, M., & Papert, S. (1969). Perceptrons: An Introduction to Computational Geometry. Cambridge MA: MIT Press.]] Google Scholar
Digital Library
- 27 Montana, D. J., & Davis, L. (1989). Training feeAforward neural networks using genetic algorithms. Proceedings of the Eleventh International Joint Conference on Artificial Intelligence, pp. 762-767.]]Google Scholar
- 28 Nevison, C. (1988). An undergraduate parallel processing laboratory. SIGCSE Bulletin, 20, 1, pp 68-77.]] Google Scholar
Digital Library
- 29 Riolo, Rick L. (1987). Bucket brigade performance: I. Long sequences of classifiers. Proceedings of the Second International Conference on Genetic Algorithms. pp 184-195.]] Google Scholar
Digital Library
- 30 Riolo, Rick L. (1987). Bucket brigade performance: II. Default hierarchies. Proceedings of the Second International Conference on Genetic Algorithms. pp 196-201.]] Google Scholar
Digital Library
- 31 Rumelhart, D. E., McClelland, J. L. & the PDP Research Group. (1986). Parallel distributed processing: Explorations in the microstructure of cognition. Volume 1: Foundations. Cambridge, MA: The MIT Press.]] Google Scholar
Digital Library
- 32 Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by orror propagating. In D. E. Rumelhart, J. L. McClelland and the PDP Research Group (Eds.), Parallel distributed processing: Explorations in the microstructure of cognition. Volume 1: Foundations. (pp. 318- 362). Cambridge, MA: The MIT Press.]] Google Scholar
Digital Library
- 33 Rumelhart, D. E., Hinton, G. E., & Willtiams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323,533-536.]]Google Scholar
Cross Ref
- 34 Schneider, W. (1987). Session I presidential address: Connectionism: Is it a paradigm shift for psychology?. Behavior Research Methods, Instruments, & Computers, 2, pp 73-83.]]Google Scholar
- 35 Sejnowski, T., & Rosenberg, C., (1987) Parallel networks that learn to pronounce English text. Complex Systems, 1, 145-168.]]Google Scholar
- 36 Sondak, N. Neural Networks and artificial intelligence. Proceedings of the Twentieth SIGCSE technical symposium, April 1989.]] Google Scholar
Digital Library
- 37 Special Issue on Artificial Neural Systems. Computer, March 1988.]]Google Scholar
- 38 The Association of the Computing Machinery Curricula Recommendation for Computer Science, Vol I, New York NY, 1979.]]Google Scholar
- 39 Wasserman, P. D. (1989). Neural Computing: Theory and Practice. New York: Van Nostrand Reinhold.]] Google Scholar
Digital Library
- 40 Whitson, G. M. (1988). An introduction to the parallel distributed processing model of cognition and some examples of how it is changing the teaching of artificial intelligence. SIGCSE Bulletin, 20, 1, pp 59-62,.]] Google Scholar
Digital Library
- 41 Will, C. A. (1988). DARPA neural network study (review). Neural Networks Reviews, 2, pp 74-102.]]Google Scholar
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Biologically based machine learning paradigms: an introductory course
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