Genetic algorithms: Multi-objective methods (tutorial lecture part II)

Nirupam Chakraborti 

Indian Institute of Technology, Kharagpur (IIT-KGP), kharagpur 721302, India


The proposed tutorial on Genetic Algorithms is designed to provide the basic exposure in Genetic Algorithms to graduate students and practicing engineers at large. The lectures will be decomposed into three sessions, and the organization of this session will be as follows:

Session 2 (The Multi-objective Approach)

The dominating and non-dominating solutions, Strong and weak optimality. Some recent Multi-objective algorithms. Distance Based Genetic Algorithms, Strength Pareto Evolutionary Algorithm, Pareto Converging Genetic Algorithms, Non-dominated Sorting Genetic Algorithm, Predator-Prey Algorithm, Generalized Differential Evolution.


Related papers
  1. Genetic algorithms: Explanation of basic ideas (tutorial lecture part I)
  2. Tailor-made material design: An evolutionary approach using multi-objective genetic algorithms
  3. The genetic algorithm-neural net combination (tutorial lecture part III)
  4. Genetic algorithm based search on the role of variables in the work hardening process of multiphase steels
  5. Modeling of recrystallization in cold rolled copper using inverse cellular automata and genetic algorithms
  6. Genetic algorithm based multi-objective optimization of an ironmaking rotary kiln

Presentation: Invited at E-MRS Fall Meeting 2007, Genetic algorithms for beginners, by Nirupam Chakraborti
See On-line Journal of E-MRS Fall Meeting 2007

Submitted: 2007-06-13 22:49
Revised:   2009-06-07 00:44