Instructor: Luis M. Rocha, George J. Klir Professor of Systems Science. Systems Science and Industrial Engineering Department, Thomas J. Watson College of Engineering & Applied Science, Binghamton University (SUNY). Principal investigator of the Complex Adaptive Systems and Computational Intelligence (CASCI) lab and director of the Center for Social and Biomedical Complexity.

Teaching Assistant: Samer Abubaker

Class Location and Time: Mondays, 6:00-9:00PM, Engineering Building, Room G22 (Lecture), and Complex Adaptive Systems and Computational Intelligence Lab (Labs)


Course Description

Description: Biological organisms cope with the demands of their environments using solutions quite unlike the traditional human-engineered approaches to problem solving. Biological systems tend to be adaptive, reactive, and distributed. Bio-inspired computing is a field devoted to tackling complex problems using computational methods modeled after design principles encountered in nature. This course is strongly grounded on the foundations of complex systems and theoretical biology. It aims to provide an understanding of the distributed architectures of natural complex systems, and how those are used to produce computational tools with enhanced robustness, scalability, flexibility and which can interface more effectively with humans. It is a multi-disciplinary field strongly based on biology, complexity, computer science, informatics, cognitive science, robotics, and cybernetics.

Aims: Students are introduced to fundamental topics in evolutionary systems and bio-inspired computing, and build up their proficiency in the application of various algorithms in real-world problems.

Syllabus

Lecture Outline

Course Evaluation

Office Hours

Luis Rocha: Wednesdays 8:30 - 11am, Engineering Building C3 (Complex Adaptive Systems and Computational Intelligence Lab) or Online, or by appointment

Samer Abubaker: Tuesdays and Thursdays: 11:30am-2:00pm, Engineering Building, K1

Labs

Course Materials and Readings

Aleksander, I. [2002]. "Understanding Information Bit by Bit". In: It must be beautiful : great equations of modern science. G. Farmelo (Ed.), Grant.
Cobb, Matthew. [2013]. "1953: When Genes Became 'Information'." Cell 153 (3): 503-506.
Dennet, D.C. [2005]. "Show me the Science". New York Times, August 28, 2005
Gleick, J. [2011]. The Information: A History, a Theory, a Flood. Random House. Chapter 8.
Langton, C. [1989]. "Artificial Life". In Artificial Life. C. Langton (Ed.). Addison-Wesley. pp. 1-47.
James, R., and Crutchfield, J. (2017). Multivariate Dependence beyond Shannon Information. Entropy, 19(10), 531.
Nunes de Castro, Leandro [2006]. Fundamentals of Natural Computing: Basic Concepts, Algorithms, and Applications. Chapman & Hall. Chapter 1, pp. 1-23.
Pattee, H. [1989], "Simulations, Realizations, and Theories of Life". In Artificial Life. C. Langton (Ed.). Addison-Wesley. pp. 63-77.
Prokopenko, Mikhail, Fabio Boschetti, and Alex J. Ryan. "An information theoretic primer on complexity, self organization, and emergence." Complexity 15.1 (2009): 11-28.
Polt, R. [2012]. "Anything but Human". New York Times, August 5, 2012
Wigner, E.P. [1960], "The unreasonable effectiveness of mathematics in the natural sciences". Richard courant lecture in mathematical sciences delivered at New York University, May 11, 1959. Comm. Pure Appl. Math., 13: 1-14.

More to be added as class progresses

Adami, C. [2006]. "Digital Genetics: Unraveling the Genetic Basis of Evolution". Nature Reviews Genetics. 7:109-118.
Ben‐Jacob, E. (2009). Learning from bacteria about natural information processing. Annals of the New York Academy of Sciences, 1178(1), 78-90.
Conrad, M. [1990]. "The geometry of evolution." Biosystems 24: 61-81.
Crutchfield, J.P. and M. Mitchell [1995]."The evolution of emergent computation." PNAS, 92: 10742-10746.
Glickman, Matthew, Justin Balthrop, and Stephanie Forrest. 2005. "A Machine Learning Evaluation of an Artificial Immune System." Evolutionary Computation 13 (2): 179. 
Hinton, G.E. and S.J. Nowlan [1987]."How learning can guide evolution." Complex Systems. 1:495-502.
Kauffman, S.A. [1969]. "Metabolic stability and epigenesis in randomly constructed genetic nets". Journal of Theoretical Biology 22(3):437-467.
Langton, C. [1989]. "Artificial Life". In Artificial Life. C. Langton (Ed.). Addison-Wesley. pp. 1-47.
Leemput, Ingrid A van de, Marieke Wichers, Angélique O J Cramer, Denny Borsboom, Francis Tuerlinckx, Peter Kuppens, Egbert H van Nes, et al. "Critical Slowing down as Early Warning for the Onset and Termination of Depression." PNAS 111, no. 1 (January 2014): 87–92.
Lindgren, K. [1991]."Evolutionary Phenomena in Simple Dynamics." In: Artificial Life II. Langton et al (Eds). Addison-wesley, pp. 295-312.
H. Lipson and J. B. Pollack [2000], "Automatic design and Manufacture of Robotic Lifeforms", Nature 406: 974-97.
Maccallum, R. M., Mauch, M., Burt, A., and Leroi, A. M. (2012). "Evolution of music by public choice". PNAS,109 (30): 12081-12086.
Pattee, Howard H. [1969] "How does a molecule become a message?" Communication in development 3: 1-16.
Pattee, H. [1989], "Simulations, Realizations, and Theories of Life". In Artificial Life. C. Langton (Ed.). Addison-Wesley. pp. 63-77.
Ray, T. S. 1992. "Evolution, ecology and optimization of digital organisms". Santa Fe Institute working paper 92-08-042.
Schmidt, M. and H. Lipson [2009]. "Distilling Free-Form Natural Laws from Experimental Data". Science, 324: 81-85.
Sims,K. [1994]. "Evolving Virtual Creatures". Proceedings of the 21st annual conference on Computer graphics and interactive techniques, pp. 15 – 22.
Szolnoki, A., Wang, Z., & Perc, M. (2012). Wisdom of groups promotes cooperation in evolutionary social dilemmas. Scientific Reports, 2(1), 1-6.
Tero, A., Takagi, S., Saigusa, T., Ito, K., Bebber, D. P., Fricker, M. D., ... & Nakagaki, T. (2010). Rules for biologically inspired adaptive network design. Science, 327(5964), 439-442.
Varela, Francisco J.; Maturana, Humberto R.; & Uribe, R. [1974]. "Autopoiesis: the organization of living systems, its characterization and a model". Biosystems. 5 187–196.
Yang, Yushi, Francesco Turci, Erika Kague, Chrissy L. Hammond, John Russo, and C. Patrick Royall. "Dominating Lengthscales of Zebrafish Collective Behaviour." PLOS Computational Biology 18, no. 1 (January 13, 2022): e1009394.

More to be added as class progresses

Flake, G. W. [1998]. The Computational Beauty of Nature: Computer Explorations of Fractals, Complex Systems, and Adaptation. MIT Press. Via BU/SUNY library.
Forbes, N. [2004]. Imitation of Life: How Biology is Inspiring Computing. MIT Press. Available online via BU/SUNY library.
Gleick, J. [2011]. The Information: A History, a Theory, a Flood. Random House. Via BU/SUNY library.
De Jong, K. [2016] Evolutionary Computation: A Unified Approach. MIT Press.
Mitchell, M. [2019]. Artificial intelligence : a guide for thinking humans. Farrar, Straus and Giroux. Via BU/SUNY library
Mitchell, M. [2009]. Complexity: A Guided Tour. Oxford University Press. Available online via BU/SUNY library.
Mitchell, M. [1999]. An Introduction to Genetic Algorithms. MIT Press. Via BU/SUNY library
Nunes de Castro, Leandro [2006]. Fundamentals of Natural Computing: Basic Concepts, Algorithms, and Applications. Chapman & Hall. Via BU/SUNY Library. On Google Books.
Nunes de Castro, Leandro and Fernando J. Von Zuben [2005]. Recent Developments in Biologically Inspired Computing. MIT Press. Available online via BU/SUNY library.
Prusinkiewicz and Lindenmeyer [1996] The algorithmic beauty of plants.

Last Modified: May 9th, 2022