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Publications of Author

1. Oliver Schütze, Alessandro Dell'Aere, Michael Dellnitz
   On Continuation Methods for the Numerical Treatment of Multi-Objective Optimization Problems. [Citation Graph (0, 0)][DBLP]
   Practical Approaches to Multi-Objective Optimization, 2005, pp:- [Conf]
   
2. Oliver Schütze
   A New Data Structure for the Nondominance Problem in Multi-objective Optimization. [Citation Graph (0, 0)][DBLP]
   EMO, 2003, pp:509-518 [Conf]
   
3. Oliver Schütze, Sanaz Mostaghim, Michael Dellnitz, Jürgen Teich
   Covering Pareto Sets by Multilevel Evolutionary Subdivision Techniques. [Citation Graph (0, 0)][DBLP]
   EMO, 2003, pp:118-132 [Conf]
   
4. Oliver Schütze, Laetitia Jourdan, Thomas Legrand, El-Ghazali Talbi, Jean Luc Wojkiewicz
   A Multi-objective Approach to the Design of Conducting Polymer Composites for Electromagnetic Shielding. [Citation Graph (0, 0)][DBLP]
   EMO, 2006, pp:590-603 [Conf]
   
5. Oliver Schütze, Marco Laumanns, Emilia Tantar, Carlos A. Coello Coello, El-Ghazali Talbi
   Convergence of stochastic search algorithms to gap-free pareto front approximations. [Citation Graph (0, 0)][DBLP]
   GECCO, 2007, pp:892-901 [Conf]
   
6. Oliver Schütze, Carlos A. Coello Coello, El-Ghazali Talbi
   Approximating the epsilon -Efficient Set of an MOP with Stochastic Search Algorithms. [Citation Graph (0, 0)][DBLP]
   MICAI, 2007, pp:128-138 [Conf]
   
7. 
   A new memetic strategy for the numerical treatment of multi-objective optimization problems. [Citation Graph (, )][DBLP]
   
   
8. 
   Computing finite size representations of the set of approximate solutions of an MOP with stochastic search algorithms. [Citation Graph (, )][DBLP]
   
   
9. 
   Evolutionary continuation methods for optimization problems. [Citation Graph (, )][DBLP]
   
   
10. 
    Using gradient information for multi-objective problems in the evolutionary context. [Citation Graph (, )][DBLP]
    
    
11. 
    New challenges for memetic algorithms on continuous multi-objective problems. [Citation Graph (, )][DBLP]
    
    
12. 
    Some comments on GD and IGD and relations to the Hausdorff distance. [Citation Graph (, )][DBLP]
    
    
13. 
    Approximating the Knee of an MOP with Stochastic Search Algorithms. [Citation Graph (, )][DBLP]
    
    
14. 
    Approximate Solutions in Space Mission Design. [Citation Graph (, )][DBLP]
    
    
15. 
    Using gradient-based information to deal with scalability in multi-objective evolutionary algorithms. [Citation Graph (, )][DBLP]
    
    
16. 
    Computing a Finite Size Representation of the Set of Approximate Solutions of an MOP [Citation Graph (, )][DBLP]
    
    
17. 
    Computing Gap Free Pareto Front Approximations with Stochastic Search Algorithms. [Citation Graph (, )][DBLP]