This is an archived page of a former group member.

Martin Krejca can now be found at the Sorbonne University, LIP6, Paris.

# Research Interests

I like getting to the core of things. More specifically, I am interested in the complexity of discrete processes and the reasons behind their complexity. Currently, my main focus is on the analysis of randomized processes. However, I am also interested in complexity theory and game theory, where complexity seems to emerge from simple concepts. For all of these settings, I am not only interested in the complexity alone but also in the analysis and development of methods and tools used in order to derive good run time results.

# Publications

2021 [ nach oben ]

- A Simplified Run Time Analysis of the Univariate Marginal Distribution Algorithm on LeadingOnes. Doerr, Benjamin; Krejca, Martin S. in
*Theoretical Computer Science*(2021).**851**121–128. - Evolutionary Minimization of Traffic Congestion. Böther, Maximilian; Schiller, Leon; Fischbeck, Philipp; Molitor, Louise; Krejca, Martin S.; Friedrich, Tobias (2021). 937–945.Best Paper Award (RWA Track)
- A spectral independence view on hard spheres via block dynamics. Friedrich, Tobias; Göbel, Andreas; Krejca, Martin; Pappik, Marcus (2021). (Vol. 198) 66:1–66:15.

2020 [ nach oben ]

- Lower Bounds on the Run Time of the Univariate Marginal Distribution Algorithm on OneMax. Krejca, Martin S.; Witt, Carsten in
*Theoretical Computer Science*(2020).**832**143–165. - Significance-based Estimation-of-Distribution Algorithms. Doerr, Benjamin; Krejca, Martin S. in
*Transactions on Evolutionary Computation*(2020).**24**(6) 1025–1034. - Theory of Estimation-of-Distribution Algorithms. Krejca, Martin; Witt, Carsten in
*Theory of Evolutionary Computation: Recent Developments in Discrete Optimization*, B. Doerr, F. Neumann (eds.) (2020). 405–442. - The Univariate Marginal Distribution Algorithm Copes Well With Deception and Epistasis. Doerr, Benjamin; Krejca, Martin S. (2020). 51–66.Best-Paper Award
- Bivariate Estimation-of-Distribution Algorithms Can Find an Exponential Number of Optima. Doerr, Benjamin; Krejca, Martin S. (2020). 796–804.
- Memetic Genetic Algorithms for Time Series Compression by Piecewise Linear Approximation. Friedrich, Tobias; Krejca, Martin S.; Lagodzinski, J. A. Gregor; Rizzo, Manuel; Zahn, Arthur (2020). (Vol. 12534) 592–604.

2019 [ nach oben ]

- Routing for On-Street Parking Search using Probabilistic Data. Friedrich, Tobias; Krejca, Martin S.; Rothenberger, Ralf; Arndt, Tobias; Hafner, Danijar; Kellermeier, Thomas; Krogmann, Simon; Razmjou, Armin in
*AI Communications*(2019).**32**(2) 113–124. - Surfing on the seascape: Adaptation in a changing environment. Trubenova, Barbora; Kötzing, Timo; Krejca, Martin S.; Lehre, Per Kristian in
*Evolution: International Journal of Organic Evolution*(2019).**73**(7) 1356–1374. - Unbiasedness of Estimation-of-Distribution Algorithms. Friedrich, Tobias; Kötzing, Timo; Krejca, Martin S. in
*Theoretical Computer Science*(2019).**785**46–59. - First-hitting times under drift. Kötzing, Timo; Krejca, Martin S. in
*Theoretical Computer Science*(2019).**796**51–69. - Mixed Integer Programming versus Evolutionary Computation for Optimizing a Hard Real-World Staff Assignment Problem. Peters, Jannik; Stephan, Daniel; Amon, Isabel; Gawendowicz, Hans; Lischeid, Julius; Salabarria, Julius; Umland, Jonas; Werner, Felix; Krejca, Martin S.; Rothenberger, Ralf; Kötzing, Timo; Friedrich, Tobias (2019). 541–554.

2018 [ nach oben ]

- Escaping Local Optima Using Crossover with Emergent Diversity. Dang, Duc-Cuong; Friedrich, Tobias; Kötzing, Timo; Krejca, Martin S.; Lehre, Per Kristian; Oliveto, Pietro S.; Sudholt, Dirk; Sutton, Andrew M. in
*Transactions on Evolutionary Computation*, (K. C. Tan, ed.) (2018).**22**(3) 484–497. - Significance-based Estimation-of-Distribution Algorithms. Doerr, Benjamin; Krejca, Martin S. (2018). 1483–1490.
- First-Hitting Times for Finite State Spaces. Kötzing, Timo; Krejca, Martin S. (2018). 79–91.
- First-Hitting Times Under Additive Drift. Kötzing, Timo; Krejca, Martin S. (2018). 92–104.
- Memory-restricted Routing With Tiled Map Data. Bläsius, Thomas; Eube, Jan; Feldtkeller, Thomas; Friedrich, Tobias; Krejca, Martin S.; Lagodzinski, J. A. Gregor; Rothenberger, Ralf; Severin, Julius; Sommer, Fabian; Trautmann, Justin (2018). 3347–3354.

2017 [ nach oben ]

- The Compact Genetic Algorithm is Efficient under Extreme Gaussian Noise. Friedrich, Tobias; Kötzing, Timo; Krejca, Martin S.; Sutton, Andrew M. in
*Transactions on Evolutionary Computation*(2017).**21**(3) 477–490. - Lower Bounds on the Run Time of the Univariate Marginal Distribution Algorithm on OneMax. Krejca, Martin S.; Witt, Carsten (2017). 65–79.

2016 [ nach oben ]

- Robustness of Ant Colony Optimization to Noise. Friedrich, Tobias; Kötzing, Timo; Krejca, Martin S.; Sutton, Andrew M. in
*Evolutionary Computation*(2016).**24**(2) 237–254. - Probabilistic Routing for On-Street Parking Search. Arndt, Tobias; Hafner, Danijar; Kellermeier, Thomas; Krogmann, Simon; Razmjou, Armin; Krejca, Martin S.; Rothenberger, Ralf; Friedrich, Tobias (2016). 6:1–6:13.
- The Benefit of Recombination in Noisy Evolutionary Search. Friedrich, Tobias; Kötzing, Timo; Krejca, Martin S.; Sutton, Andrew M. (2016). 161–162.
- Escaping Local Optima with Diversity Mechanisms and Crossover. Dang, Duc-Cuong; Friedrich, Tobias; Krejca, Martin S.; Kötzing, Timo; Lehre, Per Kristian; Oliveto, Pietro S.; Sudholt, Dirk; Sutton, Andrew Michael (2016). 645–652.
- Fast Building Block Assembly by Majority Vote Crossover. Friedrich, Tobias; Kötzing, Timo; Krejca, Martin S.; Nallaperuma, Samadhi; Neumann, Frank; Schirneck, Martin (2016). 661–668.
- EDAs cannot be Balanced and Stable. Friedrich, Tobias; Kötzing, Timo; Krejca, Martin S. (2016). 1139–1146.
- Graceful Scaling on Uniform versus Steep-Tailed Noise. Friedrich, Tobias; Kötzing, Timo; Krejca, Martin S.; Sutton, Andrew M. (2016). 761–770.
- Emergence of Diversity and its Benefits for Crossover in Genetic Algorithms. Dang, Duc-Cuong; Lehre, Per Kristian; Friedrich, Tobias; Kötzing, Timo; Krejca, Martin S.; Oliveto, Pietro S.; Sudholt, Dirk; Sutton, Andrew M. (2016). 890–900.

2015 [ nach oben ]

- Robustness of Ant Colony Optimization to Noise. Friedrich, Tobias; Kötzing, Timo; Krejca, Martin S.; Sutton, Andrew M. (2015). 17–24.Best-Paper Award (ACO/SI Track)
- The Benefit of Recombination in Noisy Evolutionary Search. Friedrich, Tobias; Kötzing, Timo; Krejca, Martin S.; Sutton, Andrew M. K. Elbassioni, K. Makino (eds.) (2015). 140–150.