Special Session on Evolutionary Multiobjective Optimization

2012 IEEE Congress on Evolutionary Computation (CEC'2012)

June 10-15, 2012, Brisbane, Australia

Organizer: Prof. Carlos A. Coello Coello

CINVESTAV-IPN

Av. IPN No. 2508
Col. San Pedro Zacatenco
México, D.F. 07360, MEXICO
Phone: +52 55 5747 3800 x 6564
fax : +52 55 5747 3757
email: ccoello@cs.cinvestav.mx
URL : http://delta.cs.cinvestav.mx/~ccoello

Scope:

Most real-world problems have several (and normally conflicting) objectives that have to be satisfied at the same time. Such problems are called multi- objective and have become very popular in the last few years.

Vilfredo Pareto stated in 1896 a concept (known today as "Pareto optimum") that constitutes the origin of research in multi-objective optimization. According to this concept, the solution to a multi-objective optimization problem is normally not a single value, but instead a set of values (also called the Pareto set).

The interest of applying evolutionary computation techniques to multi-objective optimization dates back to the 1960s, with Rosenberg's doctoral dissertation. One of the reasons why evolutionary algorithms are so suitable for multi-objective optimization is because they can generate a whole set of solutions (the Pareto set) in a single run rather than requiring an iterative process like traditional mathematical programming techniques.

As reflected by the EMOO repository, the interest on Evolutionary Multiobjective Optimization (EMOO) is made evident by the high volume of publications on this topic that have been produced since its inception, in 1984, (the EMOO repository currently has over 245 PhD theses, more than 2450 journal papers, and more than 2500 conference papers). The main aim of this special session organized within the 2012 IEEE Congress on Evolutionary Computation (CEC'2012) (which will take place within the 2012 IEEE World Congress on Computational Intelligence) is to bring together both experts and newcomers working on EMOO to discuss different issues including (among others) the following:

1. Real-world applications of EMOO algorithms
2. Test problems for EMOO algorithms
3. New EMOO techniques including those using metaheuristics such as artificial immune systems, particle swarm optimization, differential evolution, cultural algorithms, etc.
4. Performance measures for EMOO algorithms
5. Techniques to keep diversity in the population
6. Comparative studies of EMOO algorithms
7. Theoretical aspects of EMOO algorithms
8. Memetic EMOO algorithms
9. Hybrid approaches combining, for example, EMOO algorithms with mathematical programming techniques
10. Parallel EMOO approaches

Author's Schedule

Program Committee

Last Update: September 20th, 2011.