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 J01.


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: Thursdays 9 - 11:30am, Engineering Building C3 (Complex Adaptive Systems and Computational Intelligence Lab) or Online, or by appointment

Samer Abubaker: Mondays and Wednesdays 10:30 - to 1:00 pm Engineering Building EB K1. Also available for onlinemeetings .

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

Conrad, M. [1990]. "The geometry of evolution." Biosystems 24: 61-81.
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.
Lindgren, K. [1991]."Evolutionary Phenomena in Simple Dynamics." In: Artificial Life II. Langton et al (Eds). Addison-wesley, pp. 295-312.
Pattee, H. [1989], "Simulations, Realizations, and Theories of Life". In Artificial Life. C. Langton (Ed.). Addison-Wesley. pp. 63-77.
Salahshour, Mohammad. [2022] "Interaction between Games Give Rise to the Evolution of Moral Norms of Cooperation." PLOS Computational Biology 18, no. 9 (September 29, 2022): e1010429.
Stanley, Kenneth O., Jeff Clune, Joel Lehman, and Risto Miikkulainen [2019]. "Designing Neural Networks through Neuroevolution." Nature Machine Intelligence 1, no. 1: 24–35.

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.
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: April 24, 2024