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Michael T. M. Emmerich: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. Michael T. M. Emmerich, André H. Deutz
    Test Problems Based on Lamé Superspheres. [Citation Graph (0, 0)][DBLP]
    EMO, 2006, pp:922-936 [Conf]
  2. Rui Li, Michael T. M. Emmerich, Jeroen Eggermont, Ernst G. P. Bovenkamp, Thomas Bäck, Jouke Dijkstra, Johan H. C. Reiber
    Mixed-Integer NK Landscapes. [Citation Graph (0, 0)][DBLP]
    PPSN, 2006, pp:42-51 [Conf]
  3. Michael T. M. Emmerich, Kyriakos Giannakoglou, Boris Naujoks
    Single- and multiobjective evolutionary optimization assisted by Gaussian random field metamodels. [Citation Graph (0, 0)][DBLP]
    IEEE Trans. Evolutionary Computation, 2006, v:10, n:4, pp:421-439 [Journal]
  4. Rui Li, Jeroen Eggermont, Michael T. M. Emmerich, Ernst G. P. Bovenkamp, Thomas Bäck, Jouke Dijkstra, Johan H. C. Reiber
    Towards Dynamic Fitness Based Partitioning for IntraVascular UltraSound Image Analysis. [Citation Graph (0, 0)][DBLP]
    EvoWorkshops, 2007, pp:391-398 [Conf]
  5. Michael T. M. Emmerich, André H. Deutz, Nicola Beume
    Gradient-Based/Evolutionary Relay Hybrid for Computing Pareto Front Approximations Maximizing the S-Metric. [Citation Graph (0, 0)][DBLP]
    Hybrid Metaheuristics, 2007, pp:140-156 [Conf]
  6. Boris Naujoks, Nicola Beume, Michael T. M. Emmerich
    Multi-objective optimisation using S-metric selection: application to three-dimensional solution spaces. [Citation Graph (0, 0)][DBLP]
    Congress on Evolutionary Computation, 2005, pp:1282-1289 [Conf]

  7. Enhancing Decision Space Diversity in Evolutionary Multiobjective Algorithms. [Citation Graph (, )][DBLP]

  8. Combining Aggregation with Pareto Optimization: A Case Study in Evolutionary Molecular Design. [Citation Graph (, )][DBLP]

  9. Optimizing Computed Tomographic Angiography Image Segmentation Using Fitness Based Partitioning. [Citation Graph (, )][DBLP]

  10. Enhancing search space diversity in multi-objective evolutionary drug molecule design using niching. [Citation Graph (, )][DBLP]

  11. On the limitations of adaptive resampling in using the student's t-test evolution strategies. [Citation Graph (, )][DBLP]

  12. How to Do Recombination in Evolution Strategies: An Empirical Study. [Citation Graph (, )][DBLP]

  13. A novel multi-objective optimization scheme for grid resource allocation. [Citation Graph (, )][DBLP]

  14. Mixed-Integer Evolution Strategies with Dynamic Niching. [Citation Graph (, )][DBLP]

  15. Metamodel-assisted mixed integer evolution strategies and their application to intravascular ultrasound image analysis. [Citation Graph (, )][DBLP]

  16. A novel chemogenomics analysis of G protein-coupled receptors (GPCRs) and their ligands: a potential strategy for receptor de-orphanization. [Citation Graph (, )][DBLP]

  17. A robust multi-objective resource allocation scheme incorporating uncertainty and service differentiation. [Citation Graph (, )][DBLP]

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