EVOLVE International Conference

EVOLVE 2014 July 1-4
Beijing, China

HomeTracks and sessionsEvolutionary Multiobjective Optimization

Evolutionary Multiobjective Optimization

Organizers: Heike Trautmann, Günter Rudolph, Oliver Schütze


In many real-world applications one is faced with the problem that several objective functions have to be optimized concurrently leading to a multi-objective optimization problem (MOP). In the recent past, bio-inspired evolutionary methods specialized for generating trade-off solutions of MOPs -- Multiobjective Evolutionary Algorithms (MOEAs) -- have caught the interest of many researchers and have become an important and very active research field. Reasons for this include that these randomized set oriented methods are applicable to a wide range of MOPs including black-box optimization tasks, while in particular no differentiability assumptions are required and problem characteristics such as nonlinearity, multimodality or stochasticity can be handled as well. Furthermore, MOEAs are capable of delivering a finite size approximation of the solution set (the so-called Pareto set) in one run of the algorithm. The special session on Evolutionary Multiobjective Optimization (EMO) of the EVOLVE is intended to bring together researchers working on this area to discuss different issues including (but not limited to):

- Treatment of real valued, combinatorial or mixed-integer problems using
evolutionary algorithms (or related heuristics)
- Constraint handling techniques for EMO
- Many-objective optimization using EMO
- Large scale optimization using EMO
- Hybrid approaches
- Memetic strategies
- Performance assessment
- Preference incorporation into MOEAs
- Stopping criteria for MOEAs
- Test functions and problems for benchmarking EMO methods
- Parallel models and implementations of EMO approaches
- Theoretical foundations of EMO
- Applications to real-world problems

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