Course Name: Evolutionary Computation for Single and Multi-Objective Optimization

Course abstract

Evolutionary computation (EC) is a sub-field of computational intelligence that use ideas and get inspiration from natural evolution. It is based on Darwins 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.


Course Instructor

Media Object

Prof. Deepak Sharma

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; Univesite? de Strasbourg, France; National University of Singapore, Singapore, Karlsruhe Institute of Technology, Germany, and Asian Institute of Technology, Bangkok, Thailand. His research interests include Optimization and Soft Computing Techniques for Design and Optimization, Evolutionary Multi-Objective Optimization, and GPU Computing.
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Teaching Assistant(s)

No teaching assistant data available for this course yet
 Course Duration : Jan-Mar 2021
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 Syllabus
 Enrollment : 18-Nov-2020 to 25-Jan-2021
 Exam registration : 15-Jan-2021 to 12-Feb-2021
 Exam Date : 21-Mar-2021

Enrolled

466

Registered

32

Certificate Eligible

21

Certified Category Count

Gold

1

Silver

5

Elite

12

Successfully completed

3

Participation

2

Success

Elite

Silver

Gold




Legend

AVERAGE ASSIGNMENT SCORE >=10/25 AND EXAM SCORE >= 30/75 AND FINAL SCORE >=40
BASED ON THE FINAL SCORE, Certificate criteria will be as below:
>=90 - Elite + Gold
75-89 -Elite + Silver
>=60 - Elite
40-59 - Successfully Completed

Final Score Calculation Logic

  • Assignment Score = Average of best 6 out of 8 assignments.
  • Final Score(Score on Certificate)= 75% of Exam Score + 25% of Assignment Score
Evolutionary Computation for Single and Multi-Objective Optimization - Toppers list

BALASUBRAMANIAN SHANMUGAM 92%

TCS [GM ACCOUNT]

DANI REAGAN VIVEK J 88%

MEPCO SCHLENK ENGINEERING COLLEGE

Enrollment Statistics

Total Enrollment: 466

Registration Statistics

Total Registration : 33

Assignment Statistics



Assignment

Exam score

Final score

Score Distribution Graph - Legend

Assignment Score: Distribution of average scores garnered by students per assignment.
Exam Score : Distribution of the final exam score of students.
Final Score : Distribution of the combined score of assignments and final exam, based on the score logic.