Hasso-Plattner-Institut
Prof. Dr. Tobias Friedrich
  
 

Publications of Dr. Timo Kötzing

The following listing contains all publications of Dr. Timo Kötzing. Further publications of the research group can be found on the current list of publications and the complete list of publications. Individual listings are available externally on DBLP and Google Scholar or locally as PDF.

[ 2017 ] [ 2016 ] [ 2015 ] [ 2014 ] [ 2013 ] [ 2012 ] [ 2011 ] [ 2010 ] [ 2009 ] [ 2008 ] [ 2007 ]

2017 [ to top ]

  • paper.pdf
    Dang, Duc-Cuong; Friedrich, Tobias; Kötzing, Timo; Krejca, Martin S.; Lehre, Per Kristian; Oliveto, Pietro S.; Sudholt, Dirk; Sutton, Andrew M. Escaping Local Optima Using Crossover with Emergent Diversity. IEEE Transactions on Evolutionary Computation 2017
     
  • cGA_with_noise.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: 477-490
     
  • 0001K017.pdf
    Friedrich, Tobias; Kötzing, Timo; Wagner, Markus A Generic Bet-and-Run Strategy for Speeding Up Stochastic Local Search. Association for the Advancement of Artificial Intelligence (AAAI) 2017: 801-807
     
  • KoetzingSchirneckSeidel-2017-NormalFormsInSemanticLanguageLearning.pdf
    Kötzing, Timo; Schirneck, Martin; Seidel, Karen Normal Forms in Semantic Language Identification. Algorithmic Learning Theory (ALT) 2017
     
  • ScalingUpLocalSearchForMinimumVertexCoverInLargeGraphsByParallelKernelization.pdf
    Gao, Wanru; Friedrich, Tobias; Kötzing, Timo; Neumann, Frank Scaling up Local Search for Minimum Vertex Cover in Large Graphs by Parallel Kernelization. Australasian Conference on Artificial Intelligence (AUSAI) 2017: 131-143
     
  • p25-friedrich_foga17.pdf
    Friedrich, Tobias; Kötzing, Timo; Quinzan, Francesco; Sutton, Andrew Michael Resampling vs Recombination: a Statistical Run Time Estimation. Foundations of Genetic Algorithms (FOGA) 2017: 25-35
     
  • p45-friedrich_foga17.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: 45-54
     
  • p313-friedrich_poster.pdf
    Friedrich, Tobias; Kötzing, Timo; Melnichenko, Anna Analyzing Search Heuristics with Differential Equations. Genetic and Evolutionary Computation Conference (GECCO) 2017: 313-314
     
  • p921-doerr_proc.pdf
    Doerr, Benjamin; Kötzing, Timo; Lagodzinski, J. A. Gregor; Lengler, Johannes Bounding Bloat in Genetic Programming. Genetic and Evolutionary Computation Conference (GECCO) 2017: 921-928
     
  • p1359-doerr_proc.pdf
    Doerr, Benjamin; Fischbeck, Philipp; Frahnow, Clemens; Friedrich, Tobias; Kötzing, Timo; Schirneck, Martin Island Models Meet Rumor Spreading. Genetic and Evolutionary Computation Conference (GECCO) 2017: 1359-1366
     
  • p1367-doerr_proc.pdf
    Doerr, Benjamin; Doerr, Carola; Kötzing, Timo Unknown Solution Length Problems With No Asymptotically Optimal Run Time. Genetic and Evolutionary Computation Conference (GECCO) 2017: 1367-1374
     
  • p1407-shi_proc.pdf
    Shi, Feng; Schirneck, Martin; Friedrich, Tobias; Kötzing, Timo; Neumann, Frank Reoptimization Times of Evolutionary Algorithms on Linear Functions Under Dynamic Uniform Constraints. Genetic and Evolutionary Computation Conference (GECCO) 2017: 1407-1414
     

2016 [ to top ]

  • RobustnessOfPopulationsInStochasticEnvironmentsJournal.pdf
    Gießen, Christian; Kötzing, Timo Robustness of Populations in Stochastic Environments. Algorithmica 2016: 462-489
     
  • ConcentrationOfFirstHittingTimesUnderAdditiveDriftJournal.pdf
    Kötzing, Timo Concentration of First Hitting Times Under Additive Drift. Algorithmica 2016: 490-506
     
  • RobustnessOfAntColonyOptimizationToNoiseJournal.pdf
    Friedrich, Tobias; Kötzing, Timo; Krejca, Martin S.; Sutton, Andrew M. Robustness of Ant Colony Optimization to Noise. Evolutionary Computation 2016: 237-254
     
  • StronglyNonUShapedLanguageLearningResultsByGeneralTechniquesJournal.pdf
    Case, John; Kötzing, Timo Strongly non-U-shaped language learning results by general techniques. Information and Computation 2016: 1-15
     
  • OnTheRoleOfUpdateConstraintsAndTextTypesInIterativeLearningJournal.pdf
    Jain, Sanjay; Kötzing, Timo; Ma, Junqi; Stephan, Frank On the Role of Update Constraints and Text-Types in Iterative Learning. Information and Computation 2016: 152-168
     
  • EnlargingLearnableClassesJournal.pdf
    Jain, Sanjay; Kötzing, Timo; Stephan, Frank Enlarging learnable classes. Information and Computation 2016: 194-207
     
  • AMapOfUpdateConstraintsInInductiveInferenceJournal.pdf
    Kötzing, Timo; Palenta, Raphaela A map of update constraints in inductive inference. Theoretical Computer Science 2016: 4-24
     
  • TopologicalSeparationsInInductiveInferenceJournal.pdf
    Case, John; Kötzing, Timo Topological Separations in Inductive Inference. Theoretical Computer Science 2016: 33-45
     
  • AntColonyOptimizationBeatsResamplingOnNoisyFunctions.pdf
    Friedrich, Tobias; Kötzing, Timo; Quinzan, Francesco; Sutton, Andrew M. Ant Colony Optimization Beats Resampling on Noisy Functions. Genetic and Evolutionary Computation Conference (GECCO) 2016: 3-4
     
  • TheBenefitOfRecombinationInNoisyEvolutionarySearchGecco.pdf
    Friedrich, Tobias; Kötzing, Timo; Krejca, Martin S.; Sutton, Andrew M. The Benefit of Recombination in Noisy Evolutionary Search. Genetic and Evolutionary Computation Conference (GECCO) 2016: 161-162
     
  • EscapingLocalOptimaWithDiversityMechanismsAndCrossover.pdf
    Dang, Duc-Cuong; Friedrich, Tobias; Krejca, Martin S.; Kötzing, Timo; Lehre, Per Kristian; Oliveto, Pietro S.; Sudholt, Dirk; Sutton, Andrew Michael Escaping Local Optima with Diversity Mechanisms and Crossover. Genetic and Evolutionary Computation Conference (GECCO) 2016: 645-652
     
  • FastBuildingBlockAssemblyByMajorityVoteCrossover.pdf
    Friedrich, Tobias; Kötzing, Timo; Krejca, Martin S.; Nallaperuma, Samadhi; Neumann, Frank; Schirneck, Martin Fast Building Block Assembly by Majority Vote Crossover. Genetic and Evolutionary Computation Conference (GECCO) 2016: 661-668
     
  • TheRightMutationStrengthForMulti-ValuedDecisionVariables.pdf
    Doerr, Benjamin; Doerr, Carola; Kötzing, Timo The Right Mutation Strength for Multi-Valued Decision Variables. Genetic and Evolutionary Computation Conference (GECCO) 2016: 1115-1122
     
  • FriedrichKK16.pdf
    Friedrich, Tobias; Kötzing, Timo; Krejca, Martin S. EDAs cannot be Balanced and Stable. Genetic and Evolutionary Computation Conference (GECCO) 2016: 1139-1146
     
  • Graceful.pdf
    Friedrich, Tobias; Kötzing, Timo; Krejca, Martin S.; Sutton, Andrew M. Graceful Scaling on Uniform versus Steep-Tailed Noise. Parallel Problem Solving From Nature (PPSN) 2016: 761-770
     
  • OnTheRobustnessOfEvolvingPopulations.pdf
    Friedrich, Tobias; Kötzing, Timo; Sutton, Andrew M. On the Robustness of Evolving Populations. Parallel Problem Solving From Nature (PPSN) 2016: 771-781
     
  • ProvablyOptimalSelf-AdjustingStepSizesForMulti-ValuedDecisionVariables.pdf
    Doerr, Benjamin; Doerr, Carola; Kötzing, Timo Provably Optimal Self-Adjusting Step Sizes for Multi-Valued Decision Variables. Parallel Problem Solving From Nature (PPSN) 2016: 782-791
     
  • EmergenceOfDiversityAndItsBenefitsForCrossoverInGeneticAlgorithms.pdf
    Dang, Duc-Cuong; Lehre, Per Kristian; Friedrich, Tobias; Kötzing, Timo; Krejca, Martin S.; Oliveto, Pietro S.; Sudholt, Dirk; Sutton, Andrew M. Emergence of Diversity and its Benefits for Crossover in Genetic Algorithms. Parallel Problem Solving From Nature (PPSN) 2016: 890-900
     
  • Kötzing_Schirneck_Towards_an_Atlas_of_Computational_Learning.pdf
    Kötzing, Timo; Schirneck, Martin Towards an Atlas of Computational Learning Theory. Symposium on Theoretical Aspects of Computer Science (STACS) 2016: 47:1-47:13
     

2015 [ to top ]

  • UnbiasedBlackBoxComplexitiesOfJumpFunctionsJournal.pdf
    Doerr, Benjamin; Doerr, Carola; Kötzing, Timo Unbiased Black-Box Complexities of Jump Functions. Evolutionary Computation 2015: 641-670
     
  • FastLearningOfRestrictedRegularExpressionsAndDTDsJournal.pdf
    Freydenberger, Dominik D.; Kötzing, Timo Fast Learning of Restricted Regular Expressions and DTDs. Theory of Computing Systems 2015: 1114-1158
     
  • (1+1)EAOnGeneralizedDynamicOneMax.pdf
    Kötzing, Timo; Lissovoi, Andrei; Witt, Carsten (1+1) EA on Generalized Dynamic OneMax. Foundations of Genetic Algorithms (FOGA) 2015: 40-51
     
  • RobustnessOfAntColonyOptimizationToNoise.pdf
    Friedrich, Tobias; Kötzing, Timo; Krejca, Martin S.; Sutton, Andrew M. Robustness of Ant Colony Optimization to Noise. Genetic and Evolutionary Computation Conference (GECCO) 2015: 17-24
    Best Paper Award (ACO/SI Track)
     
  • SolvingProblemsWithUnknownSolutionLengthAt(Almost)NoExtraCost.pdf
    Doerr, Benjamin; Doerr, Carola; Kötzing, Timo Solving Problems with Unknown Solution Length at (Almost) No Extra Cost. Genetic and Evolutionary Computation Conference (GECCO) 2015: 831-838
     
  • TheBenefitOfRecombinationInNoisyEvolutionarySearch.pdf
    Friedrich, Tobias; Kötzing, Timo; Krejca, Martin S.; Sutton, Andrew M. The Benefit of Recombination in Noisy Evolutionary Search. International Symposium of Algorithms and Computation (ISAAC) 2015: 140-150
     

2014 [ to top ]

  • TheUnbiasedBlack-BoxComplexityOfPartitionIsPolynomial.pdf
    Doerr, Benjamin; Doerr, Carola; Kötzing, Timo The unbiased black-box complexity of partition is polynomial. Artificial Intelligence 2014: 275-286
     
  • TheMaxProblemRevisitedTheImportanceOfMutationInGeneticProgrammingJournal.pdf
    Kötzing, Timo; Sutton, Andrew M.; Neumann, Frank; O'Reilly, Una-May The Max problem revisited: The importance of mutation in genetic programming. Theoretical Computer Science 2014: 94-107
     
  • IterativeLearningFromPositiveDataAndCountersJournal.pdf
    Kötzing, Timo Iterative learning from positive data and counters. Theoretical Computer Science 2014: 155-169
     
  • AMapOfUpdateConstraintsInInductiveInference.pdf
    Kötzing, Timo; Palenta, Raphaela A Map of Update Constraints in Inductive Inference. Algorithmic Learning Theory (ALT) 2014: 40-54
     
  • OnTheRoleOfUpdateConstraintsAndText-TypesInIterativeLearning.pdf
    Jain, Sanjay; Kötzing, Timo; Ma, Junqi; Stephan, Frank On the Role of Update Constraints and Text-Types in Iterative Learning. Algorithmic Learning Theory (ALT) 2014: 55-69
     
  • UnbiasedBlack-BoxComplexitiesOfJumpFunctionsHowToCrossLargePlateaus.pdf
    Doerr, Benjamin; Doerr, Carola; Kötzing, Timo Unbiased black-box complexities of jump functions: how to cross large plateaus. Genetic and Evolutionary Computation Conference (GECCO) 2014: 769-776
     
  • RobustnessOfPopulationsInStochasticEnvironments1.pdf
    Gießen, Christian; Kötzing, Timo Robustness of populations in stochastic environments. Genetic and Evolutionary Computation Conference (GECCO) 2014: 1383-1390
     
  • ConcentrationOfFirstHittingTimesUnderAdditiveDrift.pdf
    Kötzing, Timo Concentration of first hitting times under additive drift. Genetic and Evolutionary Computation Conference (GECCO) 2014: 1391-1398
     
  • ASolutionToWiehagensThesis.pdf
    Kötzing, Timo A Solution to Wiehagen's Thesis. Symposium on Theoretical Aspects of Computer Science (STACS) 2014: 494-505
     

2013 [ to top ]

  • MoreEffectiveCrossoverOperatorsForTheAllPairsShortestPathProblemJournal.pdf
    Doerr, Benjamin; Johannsen, Daniel; Kötzing, Timo; Neumann, Frank; Theile, Madeleine More effective crossover operators for the all-pairs shortest path problem. Theoretical Computer Science 2013: 12-26
     
  • BlackBoxComplexitiesOfCombinatorialProblemsJournal.pdf
    Doerr, Benjamin; Kötzing, Timo; Lengler, Johannes; Winzen, Carola Black-box complexities of combinatorial problems. Theoretical Computer Science 2013: 84-106
     
  • Memory-LimitedNon-U-ShapedLearningWithSolvedOpenProblems.pdf
    Case, John; Kötzing, Timo Memory-limited non-U-shaped learning with solved open problems. Theoretical Computer Science 2013: 100-123
     
  • TopologicalSeparationsInInductiveInference.pdf
    Case, John; Kötzing, Timo Topological Separations in Inductive Inference. Algorithmic Learning Theory (ALT) 2013: 128-142
     
  • OptimizingExpectedPathLengthsWithAntColonyOptimizationUsingFitnessProportionalUpdate.pdf
    Feldmann, Matthias; Kötzing, Timo Optimizing expected path lengths with ant colony optimization using fitness proportional update. Foundations of Genetic Algorithms (FOGA) 2013: 65-74
     
  • AnEffectiveHeuristicForTheSmallestGrammarProblem.pdf
    Benz, Florian; Kötzing, Timo An effective heuristic for the smallest grammar problem. Genetic and Evolutionary Computation Conference (GECCO) 2013: 487-494
     
  • FastLearningOfRestrictedRegularExpressionsAndDtds.pdf
    Freydenberger, Dominik D.; Kötzing, Timo Fast learning of restricted regular expressions and DTDs. International Conference on Database Theory (ICDT) 2013: 45-56
     
  • ANormalFormForArgumentationFrameworks.pdf
    Croitoru, Cosmina; Kötzing, Timo A Normal Form for Argumentation Frameworks. Theorie and Applications of Formal Argumentation (TAFA) 2013: 32-45
     
  • MenuoptimizerInteractiveOptimizationOfMenuSystems.pdf
    Bailly, Gilles; Oulasvirta, Antti; Kötzing, Timo; Hoppe, Sabrina MenuOptimizer: interactive optimization of menu systems. Symposium on User Interface Software and Technology (UIST) 2013: 331-342
     

2012 [ to top ]

  • LearningSecretsInteractivelyDynamicModelingInInductiveInference.pdf
    Case, John; Kötzing, Timo Learning secrets interactively. Dynamic modeling in inductive inference. Information and Computation 2012: 60-73
     
  • EfficientKeyPathwayMiningCombiningNetworksAndOMICSDataJournal.pdf
    Alcaraz, Nicolas; Friedrich, Tobias; Kötzing, Timo; Krohmer, Anton; Müller, Joachim; Pauling, Josch; Baumbach, Jan Efficient Key Pathway Mining: Combining Networks and OMICS Data. Integrative Biology 2012: 756-764
     
  • Computability-TheoreticLearningComplexity.pdf
    Case, John; Kötzing, Timo Computability-Theoretic Learning Complexity. Philosophical Transactions of the Royal Society A 2012: 3570-3596
     
  • TheoreticalAnalysisOfTwoACOApproachesForTheTravelingSalesmanProblem.pdf
    Kötzing, Timo; Neumann, Frank; Röglin, Heiko; Witt, Carsten Theoretical analysis of two ACO approaches for the traveling salesman problem. Swarm Intelligence 2012: 1-21
     
  • LearningInTheLimitWithLattice-StructuredHypothesisSpaces.pdf
    Heinz, Jeffrey; Kasprzik, Anna; Kötzing, Timo Learning in the limit with lattice-structured hypothesis spaces. Theoretical Computer Science 2012: 111-127
     
  • EnlargingLearnableClasses.pdf
    Jain, Sanjay; Kötzing, Timo; Stephan, Frank Enlarging Learnable Classes. Algorithmic Learning Theory (ALT) 2012: 36-50
     
  • AntsEasilySolveStochasticShortestPathProblems.pdf
    Doerr, Benjamin; Hota, Ashish; Kötzing, Timo Ants easily solve stochastic shortest path problems. Genetic and Evolutionary Computation Conference (GECCO) 2012: 17-24
     
  • BaumbachFKKMP12.pdf
    Baumbach, Jan; Friedrich, Tobias; Kötzing, Timo; Krohmer, Anton; Müller, Joachim; Pauling, Josch Efficient Algorithms for Extracting Biological Key Pathways with Global Constraints. Genetic and Evolutionary Computation Conference (GECCO) 2012: 169-176
     
  • TheMaxProblemRevisitedTheImportanceOfMutationInGeneticProgramming.pdf
    Kötzing, Timo; Sutton, Andrew M.; Neumann, Frank; O'Reilly, Una-May The max problem revisited: the importance of mutation in genetic programming. Genetic and Evolutionary Computation Conference (GECCO) 2012: 1333-1340
     
  • AcoBeatsEaOnADynamicPseudo-BooleanFunction.pdf
    Kötzing, Timo; Molter, Hendrik ACO Beats EA on a Dynamic Pseudo-Boolean Function. Parallel Problem Solving from Nature (PPSN) 2012: 113-122
     
  • DeliberativeAcceptabilityOfArguments.pdf
    Croitoru, Cosmina; Kötzing, Timo Deliberative Acceptability of Arguments. Starting AI Researcher Symposium (STAIRS) 2012: 71-82
     

2011 [ to top ]

  • IterativeLearningFromPositiveDataAndCounters.pdf
    Kötzing, Timo Iterative Learning from Positive Data and Counters. Algorithmic Learning Theory (ALT) 2011: 40-54
     
  • FasterBlack-BoxAlgorithmsThroughHigherArityOperators.pdf
    Doerr, Benjamin; Johannsen, Daniel; Kötzing, Timo; Lehre, Per Kristian; Wagner, Markus; Winzen, Carola Faster black-box algorithms through higher arity operators. Foundations of Genetic Algorithms (FOGA) 2011: 163-172
     
  • SimpleMax-MinAntSystemsAndTheOptimizationOfLinearPseudo-BooleanFunctions.pdf
    Kötzing, Timo; Neumann, Frank; Sudholt, Dirk; Wagner, Markus Simple max-min ant systems and the optimization of linear pseudo-boolean functions. Foundations of Genetic Algorithms (FOGA) 2011: 209-218
     
  • Black-BoxComplexitiesOfCombinatorialProblems.pdf
    Doerr, Benjamin; Lengler, Johannes; Kötzing, Timo; Winzen, Carola Black-box complexities of combinatorial problems. Genetic and Evolutionary Computation Conference (GECCO) 2011: 981-988
     
  • HowCrossoverHelpsInPseudo-BooleanOptimization.pdf
    Kötzing, Timo; Sudholt, Dirk; Theile, Madeleine How crossover helps in pseudo-boolean optimization. Genetic and Evolutionary Computation Conference (GECCO) 2011: 989-996
     
  • TooFastUnbiasedBlack-BoxAlgorithms.pdf
    Doerr, Benjamin; Kötzing, Timo; Winzen, Carola Too fast unbiased black-box algorithms. Genetic and Evolutionary Computation Conference (GECCO) 2011: 2043-2050
     
  • PacLearningAndGeneticProgramming.pdf
    Kötzing, Timo; Neumann, Frank; Spöhel, Reto PAC learning and genetic programming. Genetic and Evolutionary Computation Conference (GECCO) 2011: 2091-2096
     
  • MeasuringLearningComplexityWithCriteriaEpitomizers.pdf
    Case, John; Kötzing, Timo Measuring Learning Complexity with Criteria Epitomizers. Symposium on Theoretical Aspects of Computer Science (STACS) 2011: 320-331
     

2010 [ to top ]

  • SolutionsToOpenQuestionsForNonUShapedLearningWithMemoryLimitations.pdf
    Case, John; Kötzing, Timo Solutions to Open Questions for Non-U-Shaped Learning with Memory Limitations. Algorithmic Learning Theory (ALT) 2010: 285-299
     
  • TheoreticalPropertiesOfTwoACOApproachesForTheTravelingSalesmanProblem.pdf
    Kötzing, Timo; Neumann, Frank; Röglin, Heiko; Witt, Carsten Theoretical Properties of Two ACO Approaches for the Traveling Salesman Problem. International Conference on Swarm Intelligence (ANTS) 2010: 324-335
     
  • StronglyNon-U-ShapedLearningResultsByGeneralTechniques.pdf
    Case, John; Kötzing, Timo Strongly Non-U-Shaped Learning Results by General Techniques. Conference On Learning Theory (COLT) 2010: 181-193
     
  • AntColonyOptimizationAndTheMinimumCutProblem.pdf
    Kötzing, Timo; Lehre, Per Kristian; Neumann, Frank; Oliveto, Pietro Simone Ant colony optimization and the minimum cut problem. Genetic and Evolutionary Computation Conference (GECCO) 2010: 1393-1400
     
  • StringExtensionLearningUsingLattices.pdf
    Kasprzik, Anna; Kötzing, Timo String Extension Learning Using Lattices. Language and Automata Theory and Applications (LATA) 2010: 380-391
     
  • MoreEffectiveCrossoverOperatorsForTheAll-PairsShortestPathProblem.pdf
    Doerr, Benjamin; Johannsen, Daniel; Kötzing, Timo; Neumann, Frank; Theile, Madeleine More Effective Crossover Operators for the All-Pairs Shortest Path Problem. Parallel Problem Solving from Nature (PPSN) 2010: 184-193
     

2009 [ to top ]

  • DifficultiesInForcingFairnessOfPolynomialTimeInductiveInference.pdf
    Case, John; Kötzing, Timo Difficulties in Forcing Fairness of Polynomial Time Inductive Inference. Algorithmic Learning Theory (ALT) 2009: 263-277
     

2008 [ to top ]

  • DynamicallyDelayedPostdictiveCompletenessAndConsistencyInLearning.pdf
    Case, John; Kötzing, Timo Dynamically Delayed Postdictive Completeness and Consistency in Learning. Algorithmic Learning Theory (ALT) 2008: 389-403
     
  • DynamicModelingInInductiveInference.pdf
    Case, John; Kötzing, Timo Dynamic Modeling in Inductive Inference. Algorithmic Learning Theory (ALT) 2008: 404-418
     

2007 [ to top ]

  • FeasibleIterationOfFeasibleLearningFunctionals.pdf
    Case, John; Kötzing, Timo; Paddock, Todd Feasible Iteration of Feasible Learning Functionals. Algorithmic Learning Theory (ALT) 2007: 34-48