First International Conference on Evolutionary Multi-Criterion Optimization (EMO '01)
Venue: ETH Zurich,
|Event Date/Time: Mar 07, 2001||End Date/Time: Mar 09, 2001|
Multi-criterion optimization deals with multiple, often conflicting, objectives which naturally arise in a real-world scenario. The field of multi-criterion decision-making (MCDM) is well established and is practiced by many researchers and scientists. Unlike in single-objective optimization, a multi-criterion optimization problem gives rise to a number of optimal solutions, known as Pareto-optimal solutions, of which none can be said to be better than the others with respect to all objectives. Thus, one of the primary goals in multi-criterion optimization is to find or to approximate the set of Pareto-optimal solutions. Since evolutionary algorithms (EAs) work with a population of solutions, they have been used in multi-criterion optimization for over ten years. Till to date, there exists a number of EAs and application case studies, demonstrating the usefulness and efficiency of evolutionary multi-criterion optimization (EMO).
In order to boost more interests in the topic and to exchange recent research and application in the area, we have decided to organize this first ever international conference in one of the most beautiful places of the world. The conference is planned for two-and-half days starting on 7th March 2001 (Wednesday). A couple of tutorials, one on classical MCDM methods and the other on EMO methods, are planned particularly for new-comers to the field. The conference will be held at ETH Zurich, Switzerland. The city of Zurich has numerous hotels of wide ranges and the university is within walking distance from the city. Moreover, the university is well connected with the city by local transport facilities.
The EMO'01 conference is particularly interested in papers related to multi-criterion evolutionary optimization, including, but not limited to, the following topics:
convergence to Pareto-optimal front
diversity preservation among Pareto-optimal solutions
elitism in EMO algorithms
test problem development for EMO
complexity analysis of EMO algorithms
development of new EMO algorithms (using GAs, ES, EP, GP and others)
comparison of different EMO algorithms
comparison of EMO algorithms with classical and/or non-evolutionary MCDM methods
real-world applications of EMO
Interested persons are invited to submit original, full-length papers in English by October 16, 2000 (see submission guidelines). The conference proceedings will be published by Springer in the LNCS series.