Contents:
Course Instructor
Take advantage of learning and understanding the fast-growing field of evolutionary
computation from someone who has almost 18 years of research and
teaching experience in the field and has written two popular text
books and over 150 international journal and conference papers:
Prof. Kalyanmoy Deb
Department of Mechanical Engineering
Indian Institute of Technology Kanpur
Kanpur, PIN 208016, India
Email: deb@iitk.ac.in
http://www.iitk.ac.in/kangal
Department of Mechanical Engineering
Indian Institute of Technology Kanpur
Kanpur, PIN 208016, India
Email: deb@iitk.ac.in
http://www.iitk.ac.in/kangal
Prof. Deb is a fellow of Intl. Society of Genetic and Evolutionary
Computation (ISGEC) and Indian National Academy of Engineering
(INAE). He is also the associate editor of two leading journals in the
evolutionary computation field: IEEE Trans. on Evolutionary
Computation and Evolutionary Computation Journal from MIT Press and in
the editorial board of a few other journals including Genetic
Programming and Evolvable Machines and Engineering Optimization.
Course Dates
16-20 May, 2005
Course Topics
- Introduction to Optimization and Classical Methods: 90 min.
- Introduction to Evolutionary Algorithms (EAs): 90 min.
- Real-Parameter EAs: 120 min.
- Advanced Evolutionary Algorithms: 120 min.
- Evolutionary Multi-Objective Optimization (EMO): 120 min.
- Advanced Topics on EMO: 120 min.
- Some Theories of EAs: 90 min.
- Other EA Topics: 90 min.
- For further reading materials, refer to KanGAL publications page
1.1 A brief overview of different types of optimization problems
1.2 Classical optimization methods
1.2.1 Gradient-based methods
1.2.2 Direct search methods
1.3 Constraint handling
1.4 Mixed integer programming
1.5 Difficulties with classical methods
2.1 What are EAs?
2.2 An introduction to binary-coded genetic algorithms (GAs)
2.3 Theoretical underpinnings
2.4 Some simple example case studies
2.5 Constraint handling in GAs
3.1 Real-parameter GAs
3.1.1 Real-parameter recombination and mutation operators
3.2 Evolution strategies (ESs)
3.3 Differential evolution (DE)
3.4 Particle swarm optimization (PSO)
4.1 Knowledge-augmented EAs
4.2 Function Approximation in EAs
4.3 Multi-modal EAs
4.3.1 Niching and speciation
5.1 Introduction to multi-objective optimization
5.2 Classical methods
5.3 EMO methodologies (NSGA-II, SPEA2, etc.)
5.4 Constraint handling
5.5 Some case studies
6.1 Three main applications
6.1.1 Decision-making easier
6.1.2 Unveiling important information in a problem
6.1.3 EMO for other optimization problem-solving
6.2 Interactive EMO
6.3 Robust EMO and other topics
7.1 Engineering approach
7.1.1 Population sizing
7.1.2 Control maps
7.2 Dynamical systems approach
7.3 Statistical dynamics approach
7.4 Main informative results
8.1 Genetic programming (GP)
8.2 Self-adaptive EAs
8.3 Scheduling EAs
8.4 EAs with neural nets and fuzzy logic controllers
Further Reading Materials
This page is maintained by Dr. Kalyanmoy Deb. Please contact at deb@iitk.ac.in for any comments.
Page last updated on April 15, 2005 by Santosh Tiwari (tiwaris@iitk.ac.in)
Page last updated on April 15, 2005 by Santosh Tiwari (tiwaris@iitk.ac.in)