Class Timing

Wednesday      : 09.00 - 09.55 (Room No. 4208)

Thursday          : 09.00 - 09.55 (Room No. 4208)

Friday              : 09.00 - 09.55 (Room No. 4208)

Pre-requisites: Nil

Syllabus

Pre-Requisite: Nil Introduction to Evolutionary Computation: Biological and artificial evolution, evolutionary computation and AI, different historical branches of EC, a simple genetic algorithm. Search Operators: Crossover, mutation, crossover and mutation rates, Crossover for real-valued representations, mutation for real-valued representations, combinatorial GA, Selection Schemes: Fitness proportional selection and fitness scaling, ranking, tournament selection, selection pressure and its impact on evolutionary search. Theoretical Analysis of Evolutionary Algorithms: Schema theorems, convergence of the algorithms, computational time complexity of the algorithms, no free lunch theorem. Search Operators and Representations: Mixing different search operators, adaptive representations. Niching and Speciation: Fitness sharing, crowding and mating restriction. Constraint Handling: Common techniques, penalty methods, repair methods, Deb's penalty parameter method. Multiobjective evolutionary optimization: Pareto optimality, multi-objective evolutionary algorithms: MOGA, NSGA-II, etc. Applications of GA in engineering problems, job-shop scheduling and routing problems

References:

1. Goldberg D.E. Genetic Algorithms in Search, Optimization and Machine Learning. Pearson Education Asia 2002

2. K. Deb, Multi-Objective Optimization Using Evolutionary Algorithms, Wiley and Sons, 2009.

3. M. Mitchell, An introduction to genetic algorithms, MIT Press, 1996.

4. L. D. Davis, Evolutionary algorithms, Springer-Verlag, 1999.

5. K. Srinivasa Raju and D. Nagesh Kumar. Multicriterion Analysis in Engineering and Management. PHI Learning Pvt. Ltd., New Delhi, India 2010.

Students may look at the following links for GA

• www.iitk.ac.in/kangal

• www.math.princeton.edu

• www.genetic-programming.com

• www.garage.cse.msu.edu

Method of Assessment

1. L01: Introduction

2. L02: Classical optimization methods

3. L03: Region elimination methods

4. L04: Multivariable problems

5. L05: Convex fucntion

6. L06: Multi-variable problem with equality and non-equality constraints

7. L07: Penalty parameter approach

8. L08: Quadratic approximation methods

9. L09: Introduction to linear problem

10. L10: Introduction to linear problem (Cont.)

11. L11: Simplex method

12. L12: Tutorial #1

13. L13: Quiz # 1 (28/08/2015)

14. L14: Introduction to Genetic Algorithms

15. L15: Introduction to Genetic Algorithms (Cont.)

16. L16: Introduction to Genetic Algorithms (Cont.)

17. L17: Tutorial # 2

18. L18: Tutorial # 3

19. L19: Real coded GA

20. L20: Real coded GA (Cont.)

21. L21: Optimization Using Matlab

22. L22: Optimization Using Matlab

23. L23: Multi-modal function optimization

24. L24: Multi-objective GA

25. L25: Multi-objective GA (Cont.)

26. L26: Multi-objective GA (Cont.)

27. L27: Optimization Using Matlab

28. L28: Optimization Using Matlab

29. L29: Constraint Handling in GA

30. L30: ES

31. L31: ES (Cont.)

32. L32: Optimization Using Matlab

33. L33: Introduction to PSO

34. L34: Introduction to DE