Publications

• Monson, Christopher, Introduction to Programming for the Independent Student: A Self-Starter's Course on the Principles and Practice of Bending Computers to Your Will, Amazon.com, ISBN 979-8670383400 [Kindle PDF] [Print PDF]
• Seth, J. N., Monson, C. K., Practical Cryptography in Python: Learning Correct Cryptography by Example, Apress, 2019 [Apress Site]
• Monson, C. K., Seppi, K. D., Confident but Weakly-Informed: Tackling PSO's Momentum Conundrum, In Proceedings of the IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2014), Orlando, Florida, pp. 57--64, 2014 [pdf] [BibTeX Entry]
• Monson, C. K., Seppi, K. D., Under-Informed Momentum in PSO, In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2014), Vancouver, British Columbia, Canada, 2014 [poster] [paper]
• Monson, C. K., Simple Adaptive Cognition for PSO, In Proceedings of the Congress on Evolutionary Computation (CEC 2011), New Orleans, Louisiana, 2011 [pdf] [BibTeX Entry]
• Monson, C. K. and Seppi, K. D., A Graphical Model for Evolutionary Optimization, Evolutionary Computation Journal, 16(3):289-313 [pdf] [ps] [BibTeX Entry]
• Carroll, J., Monson, C. K., and Seppi, K. D., A Bayesian CMAC for High-Assurance Supervised Learning, In Proceedings of the International Joint Conference on Neural Networks Workshop on Applications of Neural Networks in High-Assurance Systems 2007, Orlando, FL, 2007 [pdf]
• McNabb, A. W., Monson, C. K., and Seppi, K. D., Parallel PSO Using MapReduce, In Proceedings of the Congress on Evolutionary Computation (CEC 2007), Singapore, 2007 [pdf] [BibTeX Entry]
• Monson, C. K., Seppi, K. D., and Carroll, J., A Utile Function Optimizer, In Proceedings of the Congress on Evolutionary Computation (CEC 2007), Singapore, 2007 [pdf] [ps] [BibTeX Entry]
• McNabb, A. W., Monson, C. K., and Seppi, K. D., MRPSO: MapReduce Particle Swarm Optimization, In Proceedings of the Genetic and Evolutionary Computation Conference Poster Session (GECCO 2007), London, England
• Monson, C. K. (2006), No Free Lunch, Bayesian Inference, and Utility: A Decision-Theoretic Approach to Optimization.. Ph.D. Dissertation, Brigham Young University [pdf] [ps] [BibTeX Entry]
• Monson, C. K., Seppi, K. D. Adaptive Diversity in PSO, In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2006), Seattle, Washington, (page numbers coming) 2006 [pdf] [ps]
• Mullen, P. B., Monson, C. K., Seppi, K. D. Particle Swarm Optimization in Dynamic Pricing, In Proceedings of the Congress on Evolutionary Computation (CEC 2006), Vancouver, B.C., pp. 4375--4382, 2006 [pdf] [ps] [BibTeX Entry]
• Monson, C. K., Seppi, K. D. Linear Equality Constraints and Homomorphous Mappings in PSO, In Proceedings of the Congress on Evolutionary Computation (CEC 2005), Edinburgh, U.K, pp. 73-80, 2005 [pdf] [ps] [BibTeX Entry]
• Monson, C. K., Seppi, K. D. Exposing Origin-Seeking Bias in PSO, In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2005), Washington, D. C., pp. 241-248, 2005 [pdf] [ps] [BibTeX Entry]
• Monson, C. K, Seppi, K. D. Bayesian Optimization Models for Particle Swarms, In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2005), Washington, D. C., pp. 193-200, 2005 [pdf] [ps] [BibTeX Entry]
• Monson, C. K, Wingate, D., Seppi, K. D., and Peterson, T. S., Variable Resolution Discretization in the Joint Space, In Proceedings of the International Conference on Machine Learning and Applications (ICMLA 2004), Louisville, Kentucky, 2004 [pdf] [ps] [BibTeX Entry]
• Monson, C. K and Seppi, K. D., Improving on the Kalman Swarm: Extracting Its Essential Characteristics, In Late Breaking Papers of the Genetic and Evolutionary Computation Conference (GECCO 2004), Seattle, Washington, 2004 [pdf] [ps] [BibTeX Entry]
• Monson, C. K and Seppi, K. D., The Kalman Swarm: A New Approach to Particle Motion in Swarm Optimization, In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2004), Seattle, Washington, pp. 140-150, 2004 [pdf] [ps] [BibTeX Entry]
• Monson, C. K., Reinforcement Learning in the Joint Space: Value Iteration in Worlds with Continuous States and Actions, Master's Thesis, Brigham Young University Department of Computer Science, 2003 [pdf] [ps] [BibTeX Entry]