SSIE - 483/583: Evolutionary Systems and Biologically Inspired Computing

Spring 2024

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

What is Life?

What is so cool about life?
Life and Information
The Logical Mechanisms of Life

Imitation of Life

Computational Beauty of Nature
Self-similarity: fractals, L-systems, chaos

Life as (Self-)Organization

Artificial Life, Complex Systems and Complex Networks
Self-Organization and Emergent Complex Behavior
Automata Networks
Development and Morphogenesis

What is Computation?

What is so cool about computation?
Universal Computation and Life

Life as Evolution of Turing Machines

Von Neumann, Self-Replication and Open-ended evolution
Evolution and Adaptation
Biology through the lens of computer science
Genetic and Evolutionary Algorithms
Genetic Programming
Evolutionary Robotics and Synthetic Biology

Learning

Neurocomputing
Learning and evolution

Collective Behavior and Swarm Intelligence

Social Insects, Stigmergy and Swarm Intelligence
Communication and Multi-Agent simulation
Collective Computation and the Extended Mind

Immunocomputing

A distributed design for computational intelligence
Multi-level complexity
Engineering Applications

Discussion Topics

Evolutionary robots and embodied cognition
Viral evolution and epidemics
Bio-inspired Hardware
Bio-inspired design and problem-solving
Inferring Bio-Networks
Whole organism modeling
Biomolecular Self-Assembly
DNA Computation
Quantum Computation
Synthetic Biology

Course Evaluation

Participation: 15%.

Based upon attendance and participation.

Labs

ISE483: 35%. Students complete five (best four graded) lab assignments using algorithms introduced in class. There is also a ungraded lab 0, for a total of six sessions.
SSIE583: 25%. Groups develop, deliver, and assist grading two (out of six) ISE-484 lab assignments.

SSIE583 - Paper Presentation: 25%

SSIE583 students will present and discuss a paper in class. Presentation of research paper plus leading of discussion. Students are assigned to papers as lead discussants, but all students are supposed to read and participate in discussion of every paper. During class, a lead discussant prepares a short summary of the paper (15 minutes). The summary should: 1) Identify the key goals of the paper (not go in detail over every section); 2) What discussant liked and did not like; 3) What authors achieved and did not; 4) Any other relevant connections to other class readings and beyond. After summary, discussion is opened to all, and role of lead discussant is to lead the discussion to make sure we address the important paper contributions. Also, discussant should prepare 2-3 discussion questions.

Project or Paper: 50% (ISE483) or 35% (SSIE583)

Students are expected to continuously consult with the instructor regarding the scope and depth of the project paper. Reusing and expanding labs is highly encouraged.

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

Lab	Assignment
Lab 0	Python review (No Assignment)
Lab 1	Measuring (uncertainty-based) information
Lab 2	L-systems
Lab 3	Cellular Automata & Boolean Networks
Lab 4	Evolutionary Algorithms

Course Materials and Readings

Lecture Notes

Lecture Slides

Lecture	File & Description
Lecture 1	What is Life?
Lecture 2	Life and Information
Lecture 3	Uncertainty-based Information and the Logic and Organization of Life
Lecture 4	The Logical Mechanisms of Life
Lecture 5	Modeling Principles of Organization
Lecture 6	Self-Similarity and L-Systems
Lecture 7	Dynamical Systems and Attractor Behavior
Lecture 8	Chaos and the Logistic Map
Lecture 9	Boolean Networks and Self-Organization
Lecture 10	Genes and Self-Organization
Lecture 11	Automata and the Criticality Hypothesis
Lecture 12	Computation in Cellular Automata
Lecture 13	Evolution and Genes
Lecture 14	Genetic Turing Tape and the threshold of Complexity
Lecture 15	Genetic Algorithms
Lecture 16	Evolutionary Algorithms and Genetic Encodings
Lecture 17	Adding Genomic Complexity and Multi-Level Selection

Class 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

SSIE 583 Student Presentations

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

Class Book

Floreano, D. and C. Mattiussi [2008]. Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies. MIT Press. May be available in electronic format for SUNY students.

Recommended and Alternative Books

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 16, 2024

SSIE - 483/583: Evolutionary Systems and Biologically Inspired Computing

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Lecture Outline