| Self-adaptation via Multi-objectivisation: An Empirical Study. | 0 | 0.34 | 2022 |
| Self-adaptation via Multi-objectivisation: A Theoretical Study | 0 | 0.34 | 2022 |
| Fast Non-elitist Evolutionary Algorithms with Power-law Ranking Selection | 0 | 0.34 | 2022 |
| Escaping Local Optima With Non-Elitist Evolutionary Algorithms | 0 | 0.34 | 2021 |
| Preface To The Special Issue On Theory Of Genetic And Evolutionary Computation | 0 | 0.34 | 2021 |
| More precise runtime analyses of non-elitist EAs in uncertain environments | 0 | 0.34 | 2021 |
| Non-elitist evolutionary algorithms excel in fitness landscapes with sparse deceptive regions and dense valleys | 0 | 0.34 | 2021 |
| Runtime analysis of population-based evolutionary algorithms | 0 | 0.34 | 2021 |
| Runtime Analyses Of The Population-Based Univariate Estimation Of Distribution Algorithms On Leadingones | 0 | 0.34 | 2021 |
| Parallel Black-Box Complexity With Tail Bounds | 1 | 0.35 | 2020 |
| Self-Adaptation in Nonelitist Evolutionary Algorithms on Discrete Problems With Unknown Structure | 0 | 0.34 | 2020 |
| Runtime analysis of the univariate marginal distribution algorithm under low selective pressure and prior noise | 0 | 0.34 | 2019 |
| On the limitations of the univariate marginal distribution algorithm to deception and where bivariate EDAs might help. | 1 | 0.36 | 2019 |
| Parallel Black-Box Complexity with Tail Bounds. | 0 | 0.34 | 2019 |
| Level-Based Analysis of the Univariate Marginal Distribution Algorithm. | 3 | 0.38 | 2019 |
| Runtime analysis of evolutionary algorithms - basic introduction - introductory tutorial at GECCO 2019. | 0 | 0.34 | 2019 |
| Escaping Local Optima Using Crossover With Emergent Diversity. | 11 | 0.61 | 2018 |
| Improved runtime bounds for the univariate marginal distribution algorithm via anti-concentration. | 6 | 0.46 | 2018 |
| Tutorials at PPSN 2018. | 0 | 0.34 | 2018 |
| Theoretical Analysis of Stochastic Search Algorithms. | 1 | 0.36 | 2017 |
| Populations can be essential in tracking dynamic optima. | 7 | 0.48 | 2017 |
| Runtime analysis of population-based evolutionary algorithms: introductory tutorial at GECCO 2017. | 1 | 0.36 | 2017 |
| Runtime analysis of non-elitist populations: from classical optimisation to partial information | 8 | 0.54 | 2016 |
| Escaping Local Optima using Crossover with Emergent or Reinforced Diversity. | 0 | 0.34 | 2016 |
| Limits to Learning in Reinforcement Learning Hyper-heuristics. | 2 | 0.38 | 2016 |
| Escaping Local Optima with Diversity Mechanisms and Crossover. | 10 | 0.52 | 2016 |
| Runtime Analysis of Population-based Evolutionary Algorithms. | 0 | 0.34 | 2016 |
| Evolution and Computing (Dagstuhl Seminar 16011). | 0 | 0.34 | 2016 |
| Simplified Runtime Analysis of Estimation of Distribution Algorithms | 12 | 0.62 | 2015 |
| Populations can be Essential in Dynamic Optimisation | 4 | 0.42 | 2015 |
| Level-Based Analysis of Genetic Algorithms for Combinatorial Optimization | 0 | 0.34 | 2015 |
| Evolution under partial information | 6 | 0.55 | 2014 |
| Refined upper bounds on the expected runtime of non-elitist populations from fitness-levels | 8 | 0.61 | 2014 |
| Concentrated Hitting Times of Randomized Search Heuristics with Variable Drift. | 23 | 0.85 | 2014 |
| Runtime analysis of the (1+1) EA on computing unique input output sequences | 14 | 0.61 | 2014 |
| Runtime analysis of selection hyper-heuristics with classical learning mechanisms | 8 | 0.49 | 2014 |
| A Parameterised Complexity Analysis of Bi-level Optimisation with Evolutionary Algorithms. | 3 | 0.42 | 2014 |
| Editorial for the Special Issue on Theoretical Foundations of Evolutionary Computation | 0 | 0.34 | 2014 |
| Level-Based Analysis of Genetic Algorithms and Other Search Processes | 13 | 0.68 | 2014 |
| Unbiased Black-Box Complexity Of Parallel Search | 25 | 0.73 | 2014 |
| A runtime analysis of simple hyper-heuristics: to mix or not to mix operators | 19 | 0.83 | 2013 |
| Runtime analysis of evolutionary algorithms: basic introduction | 1 | 0.52 | 2013 |
| The generalized minimum spanning tree problem: a parameterized complexity analysis of bi-level optimisation | 4 | 0.45 | 2013 |
| General Drift Analysis with Tail Bounds. | 19 | 1.32 | 2013 |
| Editorial to the special issue on “Theoretical Foundations of Evolutionary Computation” | 0 | 0.34 | 2012 |
| Black-box search by unbiased variation | 82 | 3.39 | 2012 |
| On the Impact of Mutation-Selection Balance on the Runtime of Evolutionary Algorithms | 37 | 1.41 | 2012 |
| Faster black-box algorithms through higher arity operators | 30 | 1.37 | 2011 |
| Finite First Hitting Time versus Stochastic Convergence in Particle Swarm Optimisation | 5 | 0.52 | 2011 |
| Non-uniform mutation rates for problems with unknown solution lengths | 12 | 0.70 | 2011 |
