Book Editors / Book Chapters


  • B.-T. Zhang and Y.-Y. Choi, Communication: Humans, Animals, and Artificial Intelligence (Korean translation of "Menschen, Tiere und Max: Natuerliche Kommunikation und kuenstliche Intelligenz" by I. Wachsmuth, Springer, 2012), Seoul National University Press, 2014.
  • B.-T., Zhang, Communication as moving target tracking: Dynamic Bayesian inference with an action-perception-learning cycle, In  Wachsmuth, Ipke, Jan de Ruiter, Petra Jaecks and Stefan Kopp (eds.), Alignment in Communication. Towards a New Theory of Communication, Chapter 7, John Benjamins, 2013. [PDF]
  • PRICAI 2010: Trends in Artificial Intelligence, 11th Pacific Rim International Conference on Artificial Intelligence, Proceedings,B.-T. Zhang and M. A. Orgun (Eds.), Springer, 2010.

  • Intelligent and Evolutionary Systems, Gen, M., Green, D., Katai, O., McKay, B., Zhang, B.-T., Namatame, A., Sarker, R.A. (Eds.), Springer-Verlag, 2009.

  • Supervised learning methods for microRNA studies, Byoung-Tak Zhang and Jin-Wu Nam, Machine Learning in Bioinformatics, Chapter 16, John Wiley & Sons, 2008.

  • Evolutionary multiobjective optimization for DNA sequence design, S.-Y. Shin, I.-H. Lee, B.-T. Zhang, Multi-Objective Optimization in Computational Intelligence: Theory and Practice, Chapter 9, Information Science Reference, 2008.

  • Combining information-based supervised and unsupervised feature selection, Sang-Kyun Lee, Seung-Joon Yi, Byoung-Tak Zhang, Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing), Chapter 24, pp. 489-496, Springer-Verlag, 2006.

  • Biocomputer: constructing a computer using biotechnology, B.-T. Zhang, Stories of Biotechnology (미래를 들려주는 생물공학 이야기, in Korean), Chapter 7-2, Thinking Tree, 2006.

  • A unified Bayesian framework for evolutionary learning and optimization, Zhang, B.-T., Advances in Evolutionary Computing, Chapter 15, pp. 393-412, Springer-Verlag, 2003. [PDF]

  • Bayesian network classifiers for gene expression analysis, Zhang, B.-T. and Hwang, K.-B., A Practical Approach to Microarray Data Analysis, Chapter 8, pp. 150-165, Kluwer Academic Publishers, 2003.

  • Analysis of gene expression profiles and drug activity patterns by clustering and Bayesian network learning, Chang, J.-H., Hwang, K.-B., and Zhang, B.-T., Methods of Microarray Data Analysis II, Chapter 11, pp. 169-184, Kluwer Academic Publishers, 2002. [PDF]

  • Applying machine learning techniques to analysis of gene expression data: cancer diagnosis, Hwang, K.-B., Cho, D.-Y., Park, S.-W., Kim, S.-D., and Zhang, B.-T., Methods of Microarray Data Analysis, Chapter 12, pp. 167-182, Kluwer Academic Publishers, 2002. [PDF]

  • Co-evolutionary fitness switching: learning complex collective behaviors using genetic programming, Zhang, B.-T. and Cho, D.-Y., Advances in Genetic Programming, vol. 3, Chapter 18, pp. 425-445, MIT Press, 1999. [PDF]

  • Adaptive fitness functions for dynamic growing/pruning of program trees, Zhang, B.-T. and Mühlenbein., H., Advances in Genetic Programming, vol. 2, Chapter 12, pp. 241-256, MIT Press, 1996.


This page is maintained by Byoung-Hee Kim .
Last update: January 20, 2015.