Evolutionary Multi-Objective Optimization

Organized and chaired by Prof. Dr. Günter Rudolph and Dr. Heike Trautmann.




Dr. Günter Rudolph, TU Dortmund University, Germany

Dr. Rudolph is Professor of Computer Science at Dortmund University of Technology, Germany. He studied computer science at the Un iversities of Karlsruhe and Dortmund. After receiving the masters degree (Diplom-Informatiker) in Computer Science in 1991 he has been with the Department of Computer Science at the University of Dortmund in the field of parallel computing. From 1994 to 1996 he was scientist at the Informatics Center Dortmund (ICD). After earning the doctoral degree in computer science at the University of Dortmund in 1996 he returned to the University of Dortmund in 1997 for postdoctoral research in theoretical projects of the Collaborative Research Center on Computational Intelligence (SFB 531). From 2001 to 2005 he served in various positions in product and software development at Parsytec AG, Aachen (Germany), before he was appointed Professor of Computer Science.

He is General Chair of the 10th International Conference on Parallel Problem Solving from Nature (PPSN 2008), associate editor of the IEEE Transactions on Evolutionary Computation and editorial board member of the Journal on Evolutionary Computation (MIT Press) and the International Journal of Computational Intelligence Research. His main research interests are computer-aided applied optimization.



Dr. Heike Trautmann, TU Dortmund University, Germany

Dr. Heike Trautmann is a postdoctoral researcher at the Statistics Department, TU Dortmund, Germany. She graduated in Statistics, and after working in a consulting company for two years, she joined the Graduate School of Production Engineering and Logistics at the TU Dortmund and received her PhD in 2004. Her current research activities are focussed on multiobjective (evolutionary) optimisation -- in particular preference incorporation, performance assessment and stopping criteria -- as well as benchmarking concepts and Exploratory Landscape Analysis (ELA). She published several journal and conference papers regarding these research field and is involved in organizing special sessions and workshops at international conferences, i.e. the special sessions "Designing Evolutionary Processes" at CEC 2010 or the "Workshop on Automated Selection and Tuning of Algorithms" at PPSN 2012.


In many real-world applications one is faced with the problem that several objective functions have to be optimized simultaneously leading to a multi-objective optimization problem (MOP). In the recent past, bio-inspired evolutionary methods specialized for generating trade-off solutions of MOPs -- Evolutionary Multiobjective Algorithms (EMOAs) -- 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, EMOA 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 
  •  Local search for EMOAs 
  •  Performance assessment 
  •  Preference incorporation into EMOAs 
  •  Stopping criteria for EMOAs 
  •  Test functions and problems for benchmarking EMO methods
  •  Parallel models and implementations of EMO approaches 
  •  Theoretical foundations of EMO 
  •  Applications to real-world problems