Hasso-Plattner-Institut
  
Hasso-Plattner-Institut
Prof. Dr. Tobias Friedrich
  
 

All publications in 2017

The following listing contains all publications of the current members of the Algorithm Engineering group in 2017. For prior publications, see the list of all publications or the individual lists of publications of each group member, linked from the staff list.

Conference Publications

  • Friedrich_et_al_Subclasses_of_Linear_Functions.pdf
    Friedrich, Tobias; Kötzing, Timo; Lagodzinski, J. A. Gregor; Neumann, Frank; Schirneck, Martin Analysis of the (1+1) EA on Subclasses of Linear Functions under Uniform and Linear Constraints. Foundations of Genetic Algorithms (FOGA) 2017
     
  • Paper.pdf
    Friedrich, Tobias; Kötzing, Timo; Quinzan, Francesco; Sutton, Andrew Michael Resampling vs Recombination: a Statistical Run Time Estimation. Foundations of Genetic Algorithms (FOGA) 2017
     
  • restarts-aaai2017cameraReady.pdf
    Friedrich, Tobias; Kötzing, Timo; Wagner, Markus A Simple Bet-and-run Strategy for Speeding Up Traveling Salesperson and Minimum Vertex Cover. Conference on Artificial Intelligence (AAAI) 2017
     
  • camera-ready-AAAI.pdf
    Friedrich, Tobias; Krohmer, Anton; Rothenberger, Ralf; Sutton, Andrew M. Phase Transitions for Scale-Free SAT Formulas. Conference on Artificial Intelligence (AAAI) 2017
     
  • 14809-64744-1-SM.pdf
    Friedrich, Tobias; Neumann, Frank What’s Hot in Evolutionary Computation. Conference on Artificial Intelligence (AAAI) 2017
     
  • umdaOneMax.pdf
    Krejca, Martin S.; Witt, Carsten Lower Bounds on the Run Time of the Univariate Marginal Distribution Algorithm on OneMax. Foundations of Genetic Algorithms (FOGA) 2017
     
  • paper13FOGA17.pdf
    Pourhassan, Mojgan; Friedrich, Tobias; Neumann, Frank On the Use of the Dual Formulation for Minimum Vertex Cover in Evolutionary Algorithms. Foundations of Genetic Algorithms (FOGA) 2017
     

Journal Publications

  • submission.pdf
    Friedrich, Tobias; Kötzing, Timo; Krejca, Martin S.; Sutton, Andrew M. The Compact Genetic Algorithm is Efficient under Extreme Gaussian Noise. IEEE Transactions on Evolutionary Computation 2017