Track on Evolutionary Multiobjective Optimization at GECCO'2006

DESCRIPTION:

Although most real-world problems have several (and normally conflicting) objectives that have to be satisfied at the same time, for the sake of simplicity, we tend to transform all but one of those objectives into constraints in order to simplify the optimization task.

Vilfredo Pareto stated in 1896 a concept (known today as "Pareto optimum") that constitutes the origin of research in multiobjective optimization. According to this concept, the solution to a multiobjective 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 multiobjective optimization dates back to the 1960s, with Rosenberg's doctoral dissertation. One of the reasons why evolutionary algorithms are so suitable for multiobjective 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.

The interest on Evolutionary Multiobjective Optimization (EMO) is reflected by the high volume of publications in this topic in the last few years (over 128 PhD theses, more than 545 journal papers, and more than 1236 conference papers). So, the aim of this track organized within the 2006 Genetic and Evolutionary Computation Conference (GECCO'2006) is to provide a forum to exchange ideas and discuss current research on all aspects of evolutionary multiobjective optimization. Both experts and newcomers working on EMO are welcome to submit their original papers on all aspects of evolutionary multiobjective optimization, which include (but are not limited to) the following topics:

• Real-world applications of EMO algorithms
• Test functions for EMO algorithms
• New EMO techniques
• Metrics for EMO algorithms
• Techniques to keep diversity in the population of an EMO algorithm
• Comparison of EMO techniques
• Theoretical aspects of EMO algorithms
• Uncertainty management in EMO algorithms
• Parallel issues of EMO algorithms
• Interactive EMO techniques
• Hybridization of EMO algorithms with mathematical programming techniques

Electronic Submissions

For submitting a paper, please follow the guidelines indicated at the conference's website:

http://www.sigevo.org/gecco-2006/submitting.html

Author's Schedule

Organizers: