Evolutionary Computation for Single and Multi-Objective Optimization

Evolutionary Computation for Single and Multi-Objective Optimization

@VTU COE

0.0
(0) 0 Students
Download Brochure

What you will learn

  • None

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.

play
Lec 3 : Binary-Coded Genetic Algorithm (BGA)
Preview 71:10
play
Lec 4 : Operators and Simulations of Binary-Coded Genetic Algorithm
Preview 68:59

play
Lec 5 : Real-Coded Genetic Algorithm (RGA)
Preview 70:35
play
Lec 6 : Operators and Simulations of Real-Coded Genetic Algorithm
Preview 71:53
play
Lec 7 : Algorithmic Implementation of BGA and RGA
Preview 52:57

play
Lec 8 : Particle Swarm Optimization (PSO)
Preview 60:50
play
Lec 9 : Simulations and Algorithmic Implementation of Particle Swarm Optimization
Preview 62:06
play
Lec 10 : Differential Evolution (DE)
Preview 60:23
play
Lec 11 : Simulations and Algorithmic Implementation of Differential Evolution
Preview 47:37

play
Lec 12 : Constrained Optimization: Introduction and Optimality
Preview 49:51
play
Lec 13 : Penalty Function Methods for Evolutionary Computing Techniques
Preview 64:32
play
Lec 14 : Evolutionary Computing Techniques: Separation of Objective Function and Constraints
Preview 49:20
play
Lec 15 : Simulations of Constraint Handling Techniques: Part 1
Preview 56:01
play
Lec 16 : Simulations of Constraint Handling Techniques: Part 2
Preview 32:11

play
Lec 17 : Introduction to Multi-Objective Optimization: Part 1
Preview 53:34
play
Lec 18 : Introduction to Multi-Objective Optimization: Part 2
Preview 19:35
play
Lec 19 : Multi-Objective Optimization: Ranking and Diversity
Preview 44:15

play
Lec 20 : Classical Multi-Objective Optimization Methods
Preview 64:01
play
Lec 21 : Non-Dominated Genetic Algorithm: NSGA-II: Introduction
Preview 64:26
play
Lec 22 : Non-Dominated Genetic Algorithm: NSGA-II: Simulations
Preview 64:21
play
Lec 22 : Non-Dominated Genetic Algorithm: NSGA-II: Simulations
Preview 64:21

play
Lec 23 : Strength Pareto Evolutionary Algorithm: SPEA2: Introduction
Preview 50:17
play
Lec 24 : Strength Pareto Evolutionary Algorithm: SPEA2: Simulations
Preview 50:46
play
Lec 25 : Performance Assessment of Multi-Objective EC Techniques
Preview 27:24
play
Lec 26 : Closure of EC for Single and Multi-Objective Optimization
Preview 20:05
img
No Discussion Found

0.0

0 Reviews

5
0
4
0
3
0
2
0
1
0
Meet Your Instructor
@VTU COE
img

Instructor
3.2 Rating
2542 Students
759 Courses
About Instructor

VTU is one of the largest Technological Universities in India with 24 years of Tradition of excellence in Engineering & Technical Education, Research and Innovations. It came into existence in the year 1998 to cater the needs of Indian industries for trained technical manpower with practical experience and sound theoretical knowledge.

video

Free

  • Course Duration
    24 h 22 m 6 s
  • Course Level
    Intermediate
  • Student Enrolled
    0
  • Language
    English
This Course Includes
  • 24 h 22 m 6 s Video Lectures
  • 0 Quizzes
  • 0 Assignments
  • 0 Downloadable Resources
  • Full Lifetime Access
  • Certificate of Completion