MCSE- 205 Soft Computing

Unit – I Introduction of soft computing, soft computing vs hard computing. Soft computing techniques.

Computational Intelligence and applications, problem space and searching: Graph searching, different

searching algorithms like breadth first search, depth first search techniques, heuristic searching

Techniques like Best first Search, A* algorithm, AO* Algorithms.

Game Playing: Minimax search procedure, adding alpha-beta cutoffs, additional refinements, Iterative

deepening, Statistical Reasoning: Probability and Bayes theorem, Certainty factors and Rules based

systems, Bayesian Networks, Dempster Shafer theorem

Unit II: Neural Network: Introduction, Biological neural network: Structure of a brain, Learning

methodologies. Artificial Neural Network(ANN): Evolution of, Basic neuron modeling , Difference between

ANN and human brain, characteristics, McCulloch-Pitts neuron models, Learning (Supervised &

Unsupervised) and activation function, Architecture, Models, Hebbian learning , Single layer Perceptron,

Perceptron learning, Windrow-Hoff/ Delta learning rule, winner take all , linear Separability, Multilayer

Perceptron, Adaline, Madaline, different activation functions Back propagation network, derivation of

EBPA, momentum, limitation, Applications of Neural network.

Unit III: Unsupervised learning in Neural Network: Counter propagation network, architecture,

functioning & characteristics of counter Propagation network, Associative memory, hope field network and

Bidirectional associative memory. Adaptive Resonance Theory: Architecture, classifications,

Implementation and training. Introduction to Support Vector machine, architecture and algorithms,

Introduction to Kohanan’s Self organization map, architecture and algorithms

Unit – IV Fuzzy systems: Introduction, Need, classical sets (crisp sets) and operations on classical sets

Interval Arithmetics ,Fuzzy set theory and operations, Fuzzy set versus crisp set, Crisp relation & fuzzy

relations, Membership functions, Fuzzy rule base system : fuzzy propositions, formation, decomposition &

aggregation of fuzzy rules, fuzzy reasoning, fuzzy inference systems, fuzzy decision making &

Applications of fuzzy logic, fuzzification and defuzzification. Fuzzy associative memory.

Fuzzy Logic Theory, Modeling & Control Systems

Unit – V Genetic algorithm : Introduction, working principle, Basic operators and Terminologies like

individual, gene, encoding, fitness function and reproduction, Genetic modeling: Significance of Genetic

operators, Inheritance operator, cross over, inversion & deletion, mutation operator, Bitwise operator, GA

optimization problems, including JSPP (Job shop scheduling problem), TSP (Travelling salesman

problem), Applications of GA, Differences & similarities between GA & other traditional methods.

Evolutionary Computing: Concepts & Applications. Swarm Intelligence.

References:-

1. S.N. Shivnandam, “Principle of soft computing”, Wiley India.

2. David Poole, Alan Mackworth “Computational Intelligence: A logical Approach” Oxford.

3. Russell & Yuhui, “Computational Intelligence: Concepts to Implementations”, Elsevier.

4. Eiben and Smith “Introduction to Evolutionary Computing” Springer

5. Janga Reddy Manne; "Swarm Intelligence and Evolutionary Computing"; Lap Lambert Academic

Publishing

6. E. Sanchez, T. Shibata, and L. A. Zadeh, Eds., "Genetic Algorithms and Fuzzy Logic Systems: Soft

Computing Perspectives, Advances in Fuzzy Systems - Applications and Theory", Vol. 7, River

Edge, World Scientific, 1997.

7. Ajith Abraham et.al, “Soft computing as transdisciplinary science and technology: proceedings of 4th

IEEE International Workshop WSTST’ 05” Springer.

8. D.E. Goldberg “Genetic algorithms, optimization and machine learning" Addison Wesley

9. De Jong, Kenneth "A Evolutionary Computation : A Unified Approach" Prentice-Hall Of India Private

Limited

10. Rich E and Knight K, Artificial Intelligence, TMH, New Delhi.