Evolutionary Computation for Single and Multi-Objective Optimization

By Prof. Deepak Sharma   |   IIT Guwahati
Learners enrolled: 466
Evolutionary computation (EC) is a sub-field of computational intelligence that use ideas and get inspiration from natural evolution. It is based on Darwin’s principle of evolution where the population of individuals iteratively performs search and optimization. EC techniques can be applied to optimization, learning, design and many more. This course will concentrate on the concepts, algorithms, hand-calculations, graphical examples, and applications of EC techniques. Topics will be covered include binary and real-coded genetic algorithms, differential evolution, particle swarm optimization, multi-objective optimization and evolutionary algorithms, and statistical assessment. Students will be taught how these approaches identify and exploit biological processes in nature, allowing a wide range of applications to be solved in industry and business. Students will have the opportunity to build and experiment with several different types of EC techniques through-out the course.

Final and Pre-final year UG students, PG Students and Candidates from Industries
PREREQUISITES : Elementary Mathematics and Programming
INDUSTRIES  SUPPORT     : All R&D industries that involve design and optimization of product and system
Course Status : Completed
Course Type : Elective
Duration : 8 weeks
Category :
  • Mechanical Engineering
  • Computational Engineering
  • Computational Mechanics
  • Computational Thermo Fluids
Credit Points : 2
Level : Postgraduate
Start Date : 18 Jan 2021
End Date : 12 Mar 2021
Enrollment Ends : 01 Feb 2021
Exam Date : 21 Mar 2021 IST

Note: This exam date is subjected to change based on seat availability. You can check final exam date on your hall ticket.

Page Visits

Course layout

Week 1:Introduction and Principles of Evolutionary Computation (EC):Introduction to Optimization, Generalized Formulation, Scope of Optimization via Applications, Characteristic of Optimization Functions;Principles of EC: Natural Evolutional and Genetics, Generalized Framework, Behavior and Typical run of EC, Advantages and Limitations
Week 2:Binary-Coded Genetic Algorithm (BGA): Introduction, Binary Representation and Decoding, Working Principle of binary coded GA (BGA), BGA on Generalized Framework,Operators, Hand Calculations, Graphical Examples;
Week 3: Real-Coded Genetic Algorithm (RGA): Concepts and Need of Real-Coded GA (RGA), Algorithm, RGA on Generalized Framework, Operators, Hand Calculations, Graphical Examples, Case studies;
Week 4:Other EC Techniques: Differential Evolution (DE): Introduction, Concepts, Operators, Algorithm, DE on Generalized Framework, Graphical Examples, Case studies; Particle Swarm Optimization (PSO): Introduction, Concepts, Operators, PSO on Generalized Framework, Graphical Examples, Case studies;
Week 5:Constraint Handling Techniques : Generalized Constraint Formulation, Karush Kuhn Tucker (KKT) conditions, Penalty Function Method, Parameter-Less Deb’s Method, Hand Calculations, Graphical Examples, Case studies
Week 6 Introduction to Multi-Objective Optimization : Introduction, Generalized Formulation, Concept of Dominance and Pareto-optimality, Graphical Examples, Terminologies, Difference with Single-objective optimization, Approaches to multi-objective optimization;
Week 7: Classical Multi-Objective Optimization Methods : Classical Multi-Objective Optimization Methods: Weighted- Sum Method, ε-Constraint Method, Weighted Metric Methods, Hand Calculations, Difficulties with Classical approaches, Ideal Multi- Objective Optimization Approach;
Week 8:Multi-Objective Evolutionary Algorithms (MOEAs): Introduction, MOEAs on generalized Framework, Algorithms: NSGA-II, SPEA2, Graphical Examples, Case Studies; Hypervolume Indicator (HV) for Performance Assessment

Books and references

• K. Deb, Multi-objective Optimization using Evolutionary Algorithms, Wiley, 2001.
• Carlos Coello Coello, Gary B. Lamont, David A. van Veldhuizen, Evolutionary Algorithms for Solving Multi-Objective Problems, Springer, 2007

Instructor bio

Prof. Deepak Sharma

IIT Guwahati
Deepak Sharma is an Associate Professor in the Department of Mechanical Engineering, Indian Institute of Technology (IIT) Guwahati, India. He obtained his Ph.D. and M.Tech. degrees from IIT Kanpur, India. Prior to joining IIT Guwahati, he has worked with many international research teams at Helsinki School of Economics, Finland; Université de Strasbourg, France; National University of Singapore, Singapore, Karlsruhe Institute of Technology, Germany, and Asian Institute of Technology, Bangkok, Thailand. He was awarded for NVIDIA Innovation Award in 21st IEEE International Conference of High Performance Computing in 2014, DAAD's Research Stays fellowship for summer 2013, best student paper awards in IEEE Congress on Evolutionary Computation (CEC) conferences in 2007 and 2008. He has been constantly involved in many sponsored and consultancy projects from SERB, Ministry of Heavy Industries and Public Enterprises. He has published more than 50 papers in the journals and conferences of high repute. His research interests include Optimization and Soft Computing Techniques for Design and Optimization, Evolutionary Multi-Objective Optimization, and GPU Computing.

Course certificate

The course is free to enroll and learn from. But if you want a certificate, you have to register and write the proctored exam conducted by us in person at any of the designated exam centres.
The exam is optional for a fee of Rs 1000/- (Rupees one thousand only).
Date and Time of Exams: 21 March 2021 Morning session 9am to 12 noon; Afternoon Session 2pm to 5pm.
Registration url: Announcements will be made when the registration form is open for registrations.
The online registration form has to be filled and the certification exam fee needs to be paid. More details will be made available when the exam registration form is published. If there are any changes, it will be mentioned then.
Please check the form for more details on the cities where the exams will be held, the conditions you agree to when you fill the form etc.


Average assignment score = 25% of average of best 6 assignments out of the total 8 assignments given in the course.
Exam score = 75% of the proctored certification exam score out of 100

Final score = Average assignment score + Exam score

YOU WILL BE ELIGIBLE FOR A CERTIFICATE ONLY IF AVERAGE ASSIGNMENT SCORE >=10/25 AND EXAM SCORE >= 30/75. If one of the 2 criteria is not met, you will not get the certificate even if the Final score >= 40/100.

Certificate will have your name, photograph and the score in the final exam with the breakup.It will have the logos of NPTEL and IIT Guwahati .It will be e-verifiable at nptel.ac.in/noc.

Only the e-certificate will be made available. Hard copies will not be dispatched.

Once again, thanks for your interest in our online courses and certification. Happy learning.

- NPTEL team

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