CP7020 BIO-INSPIRED COMPUTING SYLLABUS 3RD SEM ME CSE SYLLABUS REG-2013

ANNA UNIVERSITY, CHENNAI

REGULATIONS - 2013
CP7020 BIO-INSPIRED COMPUTING SYLLABUS

ME 3RD SEM COMPUTER SCIENCE AND ENGINEERING SYLLABUS

CP7020 BIO-INSPIRED COMPUTING SYLLABUS

OBJECTIVES:

 Learn evolutionary theory and algorithms

 Understand Cellular Automata and artificial life

 Learn artificial neural systems and related learning algorithms

 Learn developmental and artificial immune systems

 Understand behavioral systems especially in the context of Robotics

 Understand collective systems such as ACO, PSO, and swarm robotics

UNIT I EVOLUTIONARY AND CELLULAR SYSTEMS

Foundations of evolutionary theory – Genotype – artificial evolution – genetic representations – initial population – fitness functions – selection and reproduction – genetic operators – evolutionary measures – evolutionary algorithms – evolutionary electronics – evolutionary algorithm case study Cellular systems – cellular automata – modeling with cellular systems – other cellular systems – computation with cellular systems – artificial life – analysis and synthesis of cellular systems

UNIT II NEURAL SYSTEMS

Biological nervous systems – artificial neural networks – neuron models – architecture – signal encoding – synaptic plasticity – unsupervised learning – supervised learning – reinforcement learning – evolution of neural networks – hybrid neural systems – case study

UNIT III DEVELOPMENTAL AND IMMUNE SYSTEMS

Rewriting systems – synthesis of developmental systems – evolutionary rewriting systems –evolutionary developmental programs Biological immune systems – lessons for artificial immune systems – algorithms and applications – shape space – negative selection algorithm – clonal selection algorithm - examples

UNIT IV BEHAVIORAL SYSTEMS

Behavior is cognitive science – behavior in AI – behavior based robotics – biological inspiration for robots – robots as biological models – robot learning – evolution of behavioral systems – learning in behavioral systems – co-evolution of body and control – towards self reproduction – simulation and reality

UNIT V COLLECTIVE SYSTEMS

Biological self-organization – Particle Swarm Optimization (PSO) – ant colony optimization (ACO) – swarm robotics – co-evolutionary dynamics – artificial evolution of competing systems – artificial evolution of cooperation – case study

TOTAL: 45 PERIODS

OUTCOMES:

Upon completion of the course, the students will be able to

1. Implement and apply evolutionary algorithms
2. Explain cellular automata and artificial life
3. Implement and apply neural systems
4. Explain developmental and artificial immune systems
5. Explain behavioral systems
6. Implement and apply collective intelligence systems

REFERENCES:

1. D. Floreano and C. Mattiussi, "Bio-Inspired Artificial Intelligence", MIT Press, 2008.

2. F. Neumann and C. Witt, “Bioinspired Computation in combinatorial optimization: Algorithms and their computational complexity”, Springer, 2010.

3. A. E. Elben and J. E. Smith, “Introduction to Evolutionary Computing”, Springer, 2010.

4. D. E. Goldberg, “Genetic algorithms in search, optimization, and machine learning”, Addison- Wesley, 1989.

5. Simon O. Haykin, “Neural Networks and Learning Machines”, Third Edition, Prentice Hall,

2008.

6. M. Dorigo and T. Stutzle, “Ant Colony Optimization”, A Bradford Book, 2004.

7. R. C. Ebelhart et al., “Swarm Intelligence”, Morgan Kaufmann, 2001.