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)
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.
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
| Lab | Assignment |
|---|---|
| Lab 0 |
Python review (No Assignment) |
| Lab 1 | |
| Lab 2 | |
| Lab 3 | |
| Lab 4 | |
| Lab 5 |
| Lecture | File & Description |
|---|---|
| Lectures 1 & 2 | What is Life? and Uncertainty-based Information |
| Lecture 3 | The Logical Mechanisms of Life |
| Lecture 4 | Modeling Principles of Organization |
| Lecture 5 | Self-Similarity and L-Systems |
| Lecture 6 | Dynamical Systems and Attractor Behavior |
| Lecture 7 | Chaos and the Logistic Map |
| Lecture 8 | Boolean Networks and Self-Organization |
| Lecture 9 | Cellular Automata and the Edge of Chaos |
| Lecture 10 | Computation in Cellular Automata |
| Lecture 11 | Emergence vs. Reductionism |
| Lecture 12 | Design Principles of Computation |
| Lecture 13 | The Information turn or genes as Turing tape |
| Lecture 14 | Genetic Algorithms |
| Lecture 15 | Evolutionary Algorithms and Genetic Encodings |
| Lecture 16 | Multi-level selection and Evolutionary dynamics |
| Lecture 17 | Collective Behavior |
| Lecture 18 | Swarms, Stigmergy, and Collective Intelligence |
| Lecture 19 | The Adaptive Immune System and Artificial Immune Systems |
| 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 |
| Floreano, D. and C. Mattiussi [2008]. Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies. MIT Press. Available in electronic format for SUNY students. |
Last Modified: May 9th, 2022