17CS751 Soft and Evolutionary Computing syllabus for IS

Introduction to soft computing:

ANN, FS,GA, SI, ES, Comparing among intelligent systems

ANN:

introduction, biological inspiration, BNN&ANN, classification, first Generation NN, perceptron, illustrative problems

Text Book 1: Chapter1: 1.1-1.8, Chapter2: 2.1-2.6

Adaline, Medaline, ANN:

(2nd generation), introduction, BPN, KNN,HNN, BAM, RBF,SVM and illustrative problems

Text Book 1: Chapter2: 3.1,3.2,3.3,3.6,3.7,3.10,3.11

Fuzzy logic:

introduction, human learning ability, undecidability, probability theory, classical set and fuzzy set, fuzzy set operations, fuzzy relations, fuzzy compositions, natural language and fuzzy interpretations, structure of fuzzy inference system, illustrative problems

Text Book 1: Chapter 5

Introduction to GA, GA, procedures, working of GA, GA applications, applicability, evolutionary programming, working of EP, GA based Machine learning classifier system, illustrative problems Text Book 1: Chapter 7

Swarm Intelligent system:

Introduction, Background of SI, Ant colony system Working of ACO, Particle swarm Intelligence(PSO).

Text Book 1: 8.1-8.4, 8.7

Course outcomes:

The students should be able to:

• Understand soft computing techniques
• Apply the learned techniques to solve realistic problems
• Differentiate soft computing with hard computing techniques

Question paper pattern:

• The question paper will have ten questions.
• There will be 2 questions from each module.
• Each question will have questions covering all the topics under a module.
• The students will have to answer 5 full questions, selecting one full question from each module.

Text Books:

1. Soft computing : N. P Padhy and S P Simon , Oxford University Press 2015

Reference Books:

1. Principles of Soft Computing, Shivanandam, Deepa S. N Wiley India, 2011.