Syllabus - Computational Intelligence (CS702 (A))

Computer Science and Engineering

Computational Intelligence (CS702 (A))

VII-Semester

Unit1

Introduction to Computational Intelligence; types of Computational Intelligence, components of Computational Intelligence. Concept of Learning/Training model. Parametric Models, Nonparametric Models. Multilayer Networks: Feed Forward network, Feedback network.

Unit2

Fuzzy Systems: Fuzzy set theory: Fuzzy sets and operations, Membership Functions, Concept of Fuzzy relations and their composition, Concept of Fuzzy Measures; Fuzzy Logic: Fuzzy Rules, Inferencing; Fuzzy Control - Selection of Membership Functions, Fuzzyfication, Rule Based Design & Inferencing, Defuzzyfication.

Unit3

Genetic Algorithms: Basic Genetics, Concepts, Working Principle, Creation of Offsprings, Encoding, Fitness Function, Selection Functions, Genetic Operators-Reproduction, Crossover, Mutation; Genetic Modeling, Benefits.

Unit4

Rough Set Theory - Introduction, Fundamental Concepts, Set approximation, Rough membership, Attributes, Optimization. Hidden Markov Models, Decision tree model.

Unit5

Introduction to Swarm Intelligence, Swarm Intelligence Techniques: Ant Colony Optimization, Particle Swarm Optimization, Bee Colony Optimization etc. Applications of Computational Intelligence.

Course Objective

After completing the course student should be able to: 1. Describe in-depth about theories, methods, and algorithms in computation Intelligence. 2. Compare and contrast traditional algorithms with nature inspired algorithms. 3. Examine the nature of a problem at hand and determine whether a computation intelligent technique/algorithm can solve it efficiently enough. 4. Design and implement Computation Intelligence algorithms and approaches for solving real-life problems.

Course Outcome

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Practicals

Reference Books

  • Russell C. Eberhart and Yuhui Shi, Computational Intelligence: Concepts to Implementations, Morgan Kaufmann Publishers.

  • Andries P. Engelbrecht, Computational Intelligence: An Introduction, Wiley Publishing.

  • Simon Haykin, Neural Networks: A Comprehensive Foundation, Prentice Hall.

  • David E. Goldberg, Genetic Algorithm in Search Optimization and Machine Learning, Pearson Education.

  • Jagdish Chand Bansal, Pramod Kumar Singh, Nikhil R. Pal, Evolutionary and Swarm Intelligence Algorithms, Springer Publishing, 2019.

  • S. Rajeskaran, G.A. VijaylakshmiPai, “Neural Networks, Fuzzy Logic, GeneticAlgorithms Synthesis and Applications”.

  • J.S. Roger Jang, C.T.Sun, E. Mizutani, “Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning & Machine Intelligence”, PHI, 2002.