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CE 515: Genetic Algorithms

Instructor: Prof. Rajib Kumar Bhattacharjya

Department of Civil Engineering

Indian Institute of Technology Guwahati

Room No. 105 (M Block)

Phone No. 2428

Email: rkbc@iitg.ernet.in


Lecture Note                       Quiz                        Code                       


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




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



  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

Method of Assessment








Mid-semester Exam


End-semester Exam







  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