Evolutionary Computation for Single and Multi-Objective Optimization

Evolutionary Computation for Single and Multi-Objective Optimization

@VTU COE

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What you will learn

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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.

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

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

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

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

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

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

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Lec 23 : Strength Pareto Evolutionary Algorithm: SPEA2: Introduction
Preview 50:17
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Lec 24 : Strength Pareto Evolutionary Algorithm: SPEA2: Simulations
Preview 50:46
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Lec 25 : Performance Assessment of Multi-Objective EC Techniques
Preview 27:24
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Lec 26 : Closure of EC for Single and Multi-Objective Optimization
Preview 20:05
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Meet Your Instructor
@VTU COE
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Instructor
3.2 Rating
5446 Students
800 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.

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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