I400/H400/I590: Biologically Inspired Computing

Fall 2007

Course 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. The goal is to produce informatics tools with enhanced robustness, scalability, flexibility and which can interface more effectively with humans. It is a multi-disciplinary field strongly based on Biology, Computer Science, Informatics, Cognitive Science, and robotics. In this course we study bio-inspired algorithms in security, information retrieval, computational intelligence, robotics, modeling and simulation, machine learning, and biology itself.

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

Syllabus

Lecture Outline

• What is Life? (3 lectures)
• What is so cool about life?
• Life and Information
• The Logical Mechanisms of Life
• What is Computation? (1 lecture)
• What is so cool about computation?
• Universal Computation and Computability
• Simulations and Realizations
• Imitation of Life (3 lectures)
• Computational Beauty of Nature (fractals, L-systems, chaos)
• Bio-inspired computing
• Natural computing
• Biology through the lens of computer science
• Artificial Life and Complex Systems (6 Lectures)
• Self-Organization and Emergent Complex Behavior
• Cellular Automata
• Development and Morphogenesis
• Open-ended evolution
• Evolutionary Algorithms (4 lectures)
• Evolution and Adaptation
• Genetic Algorithms
• Genetic Programming
• Learning (2 lectures)
• Artificial Neural Networks
• learning and evolution: Baldwin effect
• Collective Behavior (4 lectures)
• Social Insects, Stigmergy and Swarm Intelligence
• Competition and Cooperation
• Communication and Multi-Agent simulation
• Computer Immune Systems (3 lectures)
• A distributed design for computational intelligence
• Engineering Application
• Discussion Topics
• Evolutionary robots and embodied cognition
• Bio-inspired Hardware
• Bio-inspired design and problem-solving
• Inferring Bio-Networks
• Whole organism modeling
• Biomolecular Self-Assembly
• DNA Computation
• Quantum Computation

Course Evaluation

• Participation: 15%.
• Based upon attendance and participation.
• Presentation and Discussion: 35%
• Students will present and lead the discussion of an article related to the class materials. This includes presenting concepts necessary to understand the article.
• Project or Term Paper: 50%
• Depending on background (e.g. CS, informatics, Cog Sci, Psychology) and program (undergraduate, Masters or Phd), students will either tackle a real problem using bio-inspired algorithms, or write a term paper. In either case, students are expected to continuously consult with the instructor regarding the scope and depth of the project or paper.

Office Hours

• Luis Rocha
• Wednesdays: 10:00am – 12:00pm, Eigenmann Hall, Room #905

Course Materials

• Lecture notes
• 1. What is Life?
• 2. The Logical Mechanisms of Life
• 3. Reality is Stranger than Fiction (pdf document)
• 4. Formalizing and Modeling the World (pdf document)
• 5. Self-Organization and Emergent Complex Behavior
• 6. Von Neumann and Natural Selection (pdf document)
• 7. Modeling Evolution: Evolutionary Computation
• Lecture slides
• Lecture 1 - What is Life?
• Lecture 2 - Life and Information
• Lecture 3 - Life and Information Part II
• Lecture 4 - The Logical Mechanisms of Life
• Special Presentation - Christopher Schneider: Chris Langton's Artificial Life
• Lecture 5 - What is Computation?
• Lecture 6 - Fiction, Reality, and Information in Life
• Lecture 7 - Genetic Information at Work
• Lecture 8 - From Computation to Modeling Principles of Organization
• Lecture 9 - Self-Similarity and L-Systems
• Special Presentation - Ralf Frieser: Kristian Lindgren's "Evolutionary Phenomena in Simple Dynamics" Paper. Also available as a Flash presentation.
• Lecture 10 - From Recursion to Dynamical Systems
• Lecture 11 - Attractor Behavior
• Special Presentation - Kenrick Rawlings: Chris Adami's Digital genetics: unraveling the genetic basis for evolution
• Lecture 12 - Chaos and the Logistic Map
• Lecture 13 - Random Boolean Networks and High-dimensional State Spaces
• Special Presentation - Art Kolchinsky: Willadsen and Wiles Robustness and state-space structure of Boolean gene regulatory models
• Lecture 14 - Celular Automata and the Edge of Chaos
• Lecture 15 - Cellular Automata and Computation
• Special Presentation - Mike Conover: Dorigo et al "Ant Colony Optimization-- Artificial Ants as a Computational Intelligence Technique" Paper
• Lecture 16 - Self- Reproduction and Open-ended Evolution
• Lecture 17 - Evolutionary Algorithms
• Special Presentation - Didem Kadihasanoglu: Varela, Maturana and Uribe's Autopoiesis
• Lecture 18 - Genetic Algorithms
• Lecture 19 - Genetic Programming
• Lecture 20 - Collective Behavior
• Lecture 21 - Swarms, Stigmergy and Collective Intelligence
• Special Presentation - Joseph Renneisen: Helbing et al "Simulating dynamical features of escape panic" Paper
• Lecture 22 - Swarm Algorithms
• Special Presentation - Kate Lee: Karl Sims' Evolving Virtual Creatures
• Lecture 23 - Collective Intelligence and The Immune System
• Lecture 24 - The Adaptive Immune System
• Special Presentation - Al Abi-Haidar: Artificial Immune Systems
• Printed Resources in OnCourse
• I400 Folder
• H400 Folder
• I590 Folder
• Class Handouts
• Adami, C. [2006]. “Digital Genetics: Unraveling the Genetic Basis of Evolution”. Nature Reviews Genetics 7 (2006) 109-118
• Albert, R. [2004]. “Boolean modeling of genetic regulatory networks”. In: Complex Networks. E. Ben-Naim, H. Frauenfelder and Z. Toroczkai (Eds.), (Springer Verlag 2004)
• Aleksander, I. [2002]. “Understanding Information Bit by Bit”. In: It must be beautiful : great equations of modern science. G. Farmelo (Ed.), Granta, London.
• Chaves, M., E.D. Sontag and, R. Albert [2006]. “Methods of robustness analysis for Boolean models of gene control networks”. IEEE Proceedings in Systems Biology 153, 154-167 (2006)
• Dorigo, M., M. Birattari, T. Stützle, [2006]. "Ant Colony Optimization-- Artificial Ants as a Computational Intelligence Technique" (pdf), IEEE Computational Intelligence Magazine.
• Dussutour A, Fourcassie V, Helbing D, Deneubourg JL. [2004]. “Optimal traffic organization in ants under crowded conditions”. Nature. 428(6978):70-3
• Dennet, D.C. [2005]. "Show me the Science". New York Times, August 28, 2005
• Gershenson, C. (2004). “Introduction to Random Boolean Networks”. In Bedau, M., P. Husbands, T. Hutton, S. Kumar, and H. Suzuki (eds.) Workshop and Tutorial Proceedings, Ninth International Conference on the Simulation and Synthesis of Living Systems (ALife IX). pp. 160-173.
• Helbing, D., I. Farkas, T. Vicsek[2000]. “Simulating dynamical features of escape panic”. Nature 407, 487-490
• Helbing, D., A. Johansson, and H.Z. Al-Abideen [2007]. “The Dynamics of Crowd Disasters: An Empirical Study”. arXiv.org:physics/0701203
• Hofmeyer, Steven [2001]. “An interpretative introduction to the immune system” (pdf). In: Design Principles for the Immune system and Other Distributed Autonomous Systems.L. Segel and I. Cohen (Eds.) Santa Fe Institute Series in the Sciences of Complexity. Oxford University Press.
• Jablonka E. and M.J. Lamb [2005]. Precis of: Evolution in Four Dimensions. MIT Press. Chapter 1, Blueprint of life.
• Kanehisa, M. [2000]. Post-genome Informatics. Oxford University Press. Chapter 1, Blueprint of life, pp. 1-23.
• 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.
• Ray, T. S. [1992]. "Evolution, ecology and optimization of digital organisms". Santa Fe Institute working paper 92-08-042.
• Reil, T. 1999. “Dynamics of Gene Expression in an Artificial Genome - Implications for Biological and Artificial Ontogeny”. In Proceedings of the 5th European Conference on Advances in Artificial Life (September 13 - 17, 1999). D. Floreano, J. Nicoud, and F. Mondada, Eds. Lecture Notes In Computer Science, vol. 1674. Springer-Verlag, London, 457-466
• Ruiz-Mirazo, K., J. Umerez and A. Moreno [2007]. "Enabling conditions for ‘open-ended evolution’". Biol Philos. DOI 10.1007/s10539-007-9076-8
• Ruiz-Mirazo, K. and F. Mavelli [2007]. "Simulation Model for Functionalized Vesicles: Lipid-Peptide Integration in Minimal Protocells". Advances in Artificial Life. LCNS.. Vol. 4648, pp. 32-41. DOI - 10.1007/978-3-540-74913-4_4.
• Sims,K. [1994]. “Evolving Virtual Creatures”. Proceedings of the 21st annual conference on Computer graphics and interactive techniques, pp. 15 - 22.
• Varela, Francisco J.; Maturana, Humberto R.; & Uribe, R. [1974]. “Autopoiesis: the organization of living systems, its characterization and a model”. Biosystems 5 187–196.
• Willadsen, K. and Wiles, J. 2007 Robustness and state-space structure of Boolean gene regulatory models. Journal of Theoretical Biology, In press.
• Class Books
• Forbes, N. [2004]. Imitation of Life: How Biology is Inspiring Computing. MIT Press. Available in electronic format free of charge for IU students at 24x7 books Via the IU library.
• Flake, G. W. [1998]. The Computational Beauty of Nature: Computer Explorations of Fractals, Complex Systems, and Adaptation. MIT Press. Available in electronic format free of charge for IU students at MIT CogNet Via the IU library.
• Recommended Books
• Mitchell, M. [1999]. An Introduction to Genetic Algorithms. MIT Press. Available in electronic format free of charge for IU students at 24x7 books Via the IU library

For more information contact Luis Rocha at rocha@indiana.edu. Check the Web Design Credits, for due credit.
Last Modified: December 5, 2007