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MALEO Publications

In the following you can find a list of our most recent work published since 2023. For more information we refer to our official Research Information Systems (RIS) page at UPB and the personal webpages / Google scholar profils of our staff members.

Journal Articles

  1. Bossek, Jakob and Sudholt, Dirk, “Runtime Analysis of Quality Diversity Algorithms,” Algorithmica, 2024.
  2. Prager, Raphael Patrick and Trautmann, Heike, “Exploratory Landscape Analysis for Mixed-Variable Problems,” IEEE Transactions on Evolutionary Computation, pp. 1–1, 2024.
  3. Prager, Raphael Patrick and Trautmann, Heike, “Pflacco: Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems in Python,” Evolutionary Computation, pp. 1–25, 2023.
  4. Heins, Jonathan and Bossek, Jakob and Pohl, Janina and Seiler, Moritz and Trautmann, Heike and Kerschke, Pascal, “A Study on the Effects of Normalized TSP Features for Automated Algorithm Selection,” Theoretical Computer Science, vol. 940, pp. 123–145, 2023.
  5. Bossek, Jakob and Sudholt, Dirk, “Do Additional Target Points Speed Up Evolutionary Algorithms?,” Theoretical Computer Science, p. 113757, 2023.
  6. Bossek, Jakob and Grimme, Christian, “On Single-Objective Sub-Graph-Based Mutation for Solving the Bi-Objective Minimum Spanning Tree Problem,” Evolutionary Computation, pp. 1–35, 2023.
  7. Rodriguez-Fernandez, Angel E. and Schäpermeier, Lennart and Hernández, Carlos and Kerschke, Pascal and Trautmann, Heike and Schütze, Oliver, “Finding ϵ-Locally Optimal Solutions for Multi-Objective Multimodal Optimization,” IEEE Transactions on Evolutionary Computation, pp. 1–1, 2024.

Conference Articles

  1. Rook, Jeroen and Hoos, Holger H. and Trautmann, Heike, “Multi-objective Ranking using Bootstrap Resampling,” in Proceedings of the Genetic and Evolutionary Computation Conference Companion, New York, NY, USA, 2024, pp. 155–158.
  2. Bossek, Jakob and Grimme, Christian, “Generalised Kruskal Mutation for the Multi-Objective Minimum Spanning Tree Problem,” in Proceedings of the Genetic and Evolutionary Computation Conference, New York, NY, USA, 2024, pp. 133–141.
  3. Schmidbauer, Marcus and Opris, Andre and Bossek, Jakob and Neumann, Frank and Sudholt, Dirk, “Guiding Quality Diversity on Monotone Submodular Functions: Customising the Feature Space by Adding Boolean Conjunctions,” in Proceedings of the Genetic and Evolutionary Computation Conference, New York, NY, USA, 2024, pp. 1614–1622.
  4. Preuß, Oliver and Rook, Jeroen and Trautmann, Heike, “On the Potential of Multi-Objective Automated Algorithm Configuration on Multi-Modal Multi-Objective Optimisation Problems,” in Applications of Evolutionary Computation, Cham, 2024, pp. 305–321.
  5. Seiler, Moritz and Skvorc, Urban and Cenikj, Gjorgjina and Doerr, Carola and Trautmann, Heike, “Learned Features vs. Classical ELA on Affine BBOB Functions,” in Parallel Problem Solving from Nature — PPSN XVIII, Cham, 2024, pp. 1–14.
  6. Dietrich, Konstantin and Prager, Raphael and Doerr, Carola and Trautmann, Heike, “Hybridizing Target- and SHAP-encoded Features for Algorithm Selection in Mixed-variable Black-box Optimization,” in Parallel Problem Solving from Nature — PPSN XVIII, Cham, 2024, pp. 1–14.
  7. Bossek, Jakob and Neumann, Aneta and Neumann, Frank, “On the Impact of Basic Mutation Operators and Populations within Evolutionary Algorithms for the Dynamic Weighted Traveling Salesperson Problem,” in Proceedings of the Genetic and Evolutionary Computation Conference, 2023, pp. 248–256.
  8. Bossek, Jakob and Sudholt, Dirk, “Runtime Analysis of Quality Diversity Algorithms,” in Proceedings of the Genetic and Evolutionary Computation Conference, 2023, pp. 1546–1554.
  9. Prager, Raphael Patrick and Dietrich, Konstantin and Schneider, Lennart and Schäpermeier, Lennart and Bischl, Bernd and Kerschke, Pascal and Trautmann, Heike and Mersmann, Olaf, “Neural Networks as Black-Box Benchmark Functions Optimized for Exploratory Landscape Features,” in Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, 2023, pp. 129–139.
  10. Prager, Raphael Patrick and Trautmann, Heike, “Nullifying the Inherent Bias of Non-invariant Exploratory Landscape Analysis Features,” in Applications of Evolutionary Computation, 2023, pp. 411–425.
  11. Schäpermeier, Lennart and Kerschke, Pascal and Grimme, Christian and Trautmann, Heike, “Peak-A-Boo! Generating Multi-objective Multiple Peaks Benchmark Problems with Precise Pareto Sets,” in Evolutionary Multi-Criterion Optimization, 2023, pp. 291–304.
  12. Marrero, Alejandro and Segredo, Eduardo and Hart, Emma and Bossek, Jakob and Neumann, Aneta, “Generating Diverse and Discriminatory Knapsack Instances by Searching for Novelty in Variable Dimensions of Feature-Space,” in Proceedings of the Genetic and Evolutionary Computation Conference, 2023, pp. 312–320.
  13. Seiler, Moritz Vinzent and Rook, Jeroen and Heins, Jonathan and Preuß, Oliver and Bossek, Jakob and Trautmann, Heike, “Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP,” in 2023 IEEE Symposium Series on Computational Intelligence (SSCI), 2023, pp. 361–368.