BM4091 Genetic Algorithms and Fuzzy Logics Syllabus:

BM4091 Genetic Algorithms and Fuzzy Logics Syllabus – Anna University PG Syllabus Regulation 2021

COURSE OBJECTIVES:

 To provide basic knowledge about the fundamentals of genetic algorithm
 To familiarize with the ant colony and particle swam optimization techniques
 To learn the basics of fuzzy logic
 To enrich the students knowledge with fuzzy systems and its applications
 To lean the neuro fuzzy system and fuzzy logic controller

UNIT I GENETIC ALGORITHMS

Introduction, Building block hypothesis, 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, Applications of GA.

UNIT II OTHER OPTIMIZATION TECHNIQUES

Ant Colony Optimization: Introduction – From real to artificial ants- Theoretical considerations – Particle Swarm Optimization-:Introduction – Principles of bird flocking and fish schooling – Evolution of PSO – Operating principles – PSO Algorithm

UNIT III FUZZY LOGIC

Introduction to Fuzzy Logic, Classical and Fuzzy Sets, Membership Function, Operations on Fuzzy Sets, Fuzzy Arithmetic, Compliment, Intersections, Unions, Fuzzy Relation

UNIT IV FUZZY RULE BASED SYSTEM

Linguistic Hedges. Rule based system, Fuzzification and Defuzzification, Fuzzy inference systems – Mamdani and Sugeno model, Fuzzy clustering- fuzzy c- means algorithm- fuzzy control method fuzzy decision making.

UNIT V ADVANCES AND APPLICATIONS

Case studies: Fuzzy logic control of Blood pressure during Anaesthesia, Fuzzy logic application to Biosignals and medical Image processing , Adaptive fuzzy system. Introduction to Neuro-fuzzy logic

COURSE OUTCOMES:

CO1: Apprehend the principles of genetic algorithms as well as techniques used in its implementation.
CO2: Apply the optimization algorithms for real time applications
CO3: Apply fuzzy logic and reasoning to handle uncertainty and solve engineering problems.
CO4: Design a fuzzy rule based system for biomedical application
CO5: Apply the fuzzy controller for resulting the blood pressure

TOTAL:45 PERIODS

REFERENCES

1. Marco Dorigo and Thomas Stutzle, “Ant Colony optimization”, Prentice Hall of India, New Delhi, 2004.
2. David E. Goldberg, “Genetic Algorithms in search, Optimization & Machine Learning”, Pearson Education, 2006
3. Kenneth A DeJong,“Evolutionary Computation A Unified Approach”, Prentice Hall of India, New Delhi, 2006
4. H.-J. Zimmermann, “Fuzzy Set Theory and its Applications”, Springer Science+Business Media New York, 4th edition, 2001
5. Timothy Ross, “Fuzzy Logic with Engineering Applications”, Wiley, 2016