Local Search and the Traveling Salesman Problem: A Feature-Based Characterization of Problem Hardness. - Olaf Mersmann; Bernd Bischl; Jakob Bossek; Heike Trautmann; Markus Wagner 007; Frank Neumann 001 (LION, 2012)

A novel feature-based approach to characterize algorithm performance for the traveling salesperson problem. - Olaf Mersmann; Bernd Bischl; Heike Trautmann; Markus Wagner 007; Jakob Bossek; Frank Neumann 001 (Ann. Math. Artif. Intell., 2013)

Learning Feature-Parameter Mappings for Parameter Tuning via the Profile Expected Improvement. - Jakob Bossek; Bernd Bischl; Tobias Wagner 001; Günter Rudolph (GECCO, 2015)

Evaluation of a Multi-Objective EA on Benchmark Instances for Dynamic Routing of a Vehicle. - Stephan Meisel; Christian Grimme; Jakob Bossek; Martin Wölck; Günter Rudolph; Heike Trautmann (GECCO, 2015)

Understanding Characteristics of Evolved Instances for State-of-the-Art Inexact TSP Solvers with Maximum Performance Difference. - Jakob Bossek; Heike Trautmann (AI*IA, 2016)

Evolving Instances for Maximizing Performance Differences of State-of-the-Art Inexact TSP Solvers. - Jakob Bossek; Heike Trautmann (LION, 2016)

ecr 2.0: a modular framework for evolutionary computation in R. - Jakob Bossek (GECCO (Companion), 2017)

A pareto-beneficial sub-tree mutation for the multi-criteria minimum spanning tree problem. - Jakob Bossek; Christian Grimme (SSCI, 2017)

An extended mutation-based priority-rule integration concept for multi-objective machine scheduling. - Jakob Bossek; Christian Grimme (SSCI, 2017)

OpenML: An R Package to Connect to the Networked Machine Learning Platform OpenML. - Giuseppe Casalicchio; Jakob Bossek; Michel Lang; Dominik Kirchhoff; Pascal Kerschke; Benjamin Hofner; Heidi Seibold; Joaquin Vanschoren; Bernd Bischl (CoRR, 2017)

mcMST: A Toolbox for the Multi-Criteria Minimum Spanning Tree Problem. - Jakob Bossek (J. Open Source Softw., 2017)

smoof: Single- and Multi-Objective Optimization Test Functions. - Jakob Bossek (R J., 2017)

Performance assessment of multi-objective evolutionary algorithms with the R package ecr. - Jakob Bossek (GECCO (Companion), 2018)

Local search effects in bi-objective orienteering. - Jakob Bossek; Christian Grimme; Stephan Meisel; Günter Rudolph; Heike Trautmann (GECCO, 2018)

Parameterization of state-of-the-art performance indicators: a robustness study based on inexact TSP solvers. - Pascal Kerschke; Jakob Bossek; Heike Trautmann (GECCO (Companion), 2018)

Solving Scalarized Subproblems within Evolutionary Algorithms for Multi-criteria Shortest Path Problems. - Jakob Bossek; Christian Grimme (LION, 2018)

Multi-objective Performance Measurement: Alternatives to PAR10 and Expected Running Time. - Jakob Bossek; Heike Trautmann (LION, 2018)

Leveraging TSP Solver Complementarity through Machine Learning. - Pascal Kerschke; Lars Kotthoff; Jakob Bossek; Holger H. Hoos; Heike Trautmann (Evol. Comput., 2018)

grapherator: A Modular Multi-Step Graph Generator. - Jakob Bossek (J. Open Source Softw., 2018)

Bi-objective Orienteering: Towards a Dynamic Multi-objective Evolutionary Algorithm. - Jakob Bossek; Christian Grimme; Stephan Meisel; Günter Rudolph; Heike Trautmann (EMO, 2019)

Evolving diverse TSP instances by means of novel and creative mutation operators. - Jakob Bossek; Pascal Kerschke; Aneta Neumann; Markus Wagner 007; Frank Neumann 001; Heike Trautmann (FOGA, 2019)

Time complexity analysis of RLS and (1 + 1) EA for the edge coloring problem. - Jakob Bossek; Dirk Sudholt (FOGA, 2019)

Runtime analysis of randomized search heuristics for dynamic graph coloring. - Jakob Bossek; Frank Neumann 001; Pan Peng 001; Dirk Sudholt (GECCO, 2019)

On the benefits of biased edge-exchange mutation for the multi-criteria spanning tree problem. - Jakob Bossek; Christian Grimme; Frank Neumann 001 (GECCO, 2019)

One-Shot Decision-Making with and without Surrogates. - Jakob Bossek; Pascal Kerschke; Aneta Neumann; Frank Neumann 001; Carola Doerr (CoRR, 2019)

OpenML: An R package to connect to the machine learning platform OpenML. - Giuseppe Casalicchio; Jakob Bossek; Michel Lang; Dominik Kirchhoff; Pascal Kerschke; Benjamin Hofner; Heidi Seibold; Joaquin Vanschoren; Bernd Bischl (Comput. Stat., 2019)

Towards Decision Support in Dynamic Bi-Objective Vehicle Routing. - Jakob Bossek; Christian Grimme; Günter Rudolph; Heike Trautmann (CEC, 2020)

Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection. - Jakob Bossek; Pascal Kerschke; Heike Trautmann (CEC, 2020)

More effective randomized search heuristics for graph coloring through dynamic optimization. - Jakob Bossek; Frank Neumann 001; Pan Peng 001; Dirk Sudholt (GECCO, 2020)

The node weight dependent traveling salesperson problem: approximation algorithms and randomized search heuristics. - Jakob Bossek; Katrin Casel; Pascal Kerschke; Frank Neumann 001 (GECCO, 2020)

Initial design strategies and their effects on sequential model-based optimization: an exploratory case study based on BBOB. - Jakob Bossek; Carola Doerr; Pascal Kerschke (GECCO, 2020)

Dynamic bi-objective routing of multiple vehicles. - Jakob Bossek; Christian Grimme; Heike Trautmann (GECCO, 2020)

Evolving diverse sets of tours for the travelling salesperson problem. - Anh Viet Do; Jakob Bossek; Aneta Neumann; Frank Neumann 001 (GECCO, 2020)

Runtime analysis of evolutionary algorithms with biased mutation for the multi-objective minimum spanning tree problem. - Vahid Roostapour; Jakob Bossek; Frank Neumann 001 (GECCO, 2020)

Evolving Sampling Strategies for One-Shot Optimization Tasks. - Jakob Bossek; Carola Doerr; Pascal Kerschke; Aneta Neumann; Frank Neumann 001 (PPSN (1), 2020)

Optimising Tours for the Weighted Traveling Salesperson Problem and the Traveling Thief Problem: A Structural Comparison of Solutions. - Jakob Bossek; Aneta Neumann; Frank Neumann 001 (PPSN (1), 2020)

Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem. - Moritz Seiler; Janina Pohl; Jakob Bossek; Pascal Kerschke; Heike Trautmann (PPSN (1), 2020)

A multi-objective perspective on performance assessment and automated selection of single-objective optimization algorithms. - Jakob Bossek; Pascal Kerschke; Heike Trautmann (Appl. Soft Comput., 2020)

Initial Design Strategies and their Effects on Sequential Model-Based Optimization. - Jakob Bossek; Carola Doerr; Pascal Kerschke (CoRR, 2020)

Benchmarking in Optimization: Best Practice and Open Issues. - Thomas Bartz-Beielstein; Carola Doerr; Jakob Bossek; Sowmya Chandrasekaran; Tome Eftimov; Andreas Fischbach; Pascal Kerschke; Manuel López-Ibáñez 001; Katherine M. Malan; Jason H. Moore; Boris Naujoks; Patryk Orzechowski; et al. (CoRR, 2020)

Computing Diverse Sets of Solutions for Monotone Submodular Optimisation Problems. - Aneta Neumann; Jakob Bossek; Frank Neumann 001 (CoRR, 2020)

Do additional optima speed up evolutionary algorithms? - Jakob Bossek; Dirk Sudholt (FOGA, 2021)

On the potential of normalized TSP features for automated algorithm selection. - Jonathan Heins; Jakob Bossek; Janina Pohl; Moritz Seiler; Heike Trautmann; Pascal Kerschke (FOGA, 2021)

Computing diverse sets of high quality TSP tours by EAX-based evolutionary diversity optimisation. - Adel Nikfarjam; Jakob Bossek; Aneta Neumann; Frank Neumann 001 (FOGA, 2021)

Evolutionary diversity optimization and the minimum spanning tree problem. - Jakob Bossek; Frank Neumann 001 (GECCO, 2021)

Generating instances with performance differences for more than just two algorithms. - Jakob Bossek; Markus Wagner 007 (GECCO Companion, 2021)

Breeding diverse packings for the knapsack problem by means of diversity-tailored evolutionary algorithms. - Jakob Bossek; Aneta Neumann; Frank Neumann 001 (GECCO, 2021)

Diversifying greedy sampling and evolutionary diversity optimisation for constrained monotone submodular functions. - Aneta Neumann; Jakob Bossek; Frank Neumann 001 (GECCO, 2021)

Entropy-based evolutionary diversity optimisation for the traveling salesperson problem. - Adel Nikfarjam; Jakob Bossek; Aneta Neumann; Frank Neumann 001 (GECCO, 2021)

Exact Counting and Sampling of Optima for the Knapsack Problem. - Jakob Bossek; Aneta Neumann; Frank Neumann 001 (LION, 2021)

Time Complexity Analysis of Randomized Search Heuristics for the Dynamic Graph Coloring Problem. - Jakob Bossek; Frank Neumann 001; Pan Peng 001; Dirk Sudholt (Algorithmica, 2021)

Exploring the feature space of TSP instances using quality diversity. - Jakob Bossek; Frank Neumann 001 (GECCO, 2022)

Evolutionary diversity optimization for combinatorial optimization: tutorial at GECCO'22, Boston, USA. - Jakob Bossek; Aneta Neumann; Frank Neumann 001 (GECCO Companion, 2022)

On the potential of automated algorithm configuration on multi-modal multi-objective optimization problems. - Jeroen Rook; Heike Trautmann; Jakob Bossek; Christian Grimme (GECCO Companion, 2022)

BBE: Basin-Based Evaluation of Multimodal Multi-objective Optimization Problems. - Jonathan Heins; Jeroen Rook; Lennart Schäpermeier; Pascal Kerschke; Jakob Bossek; Heike Trautmann (PPSN (1), 2022)

Co-evolutionary Diversity Optimisation for the Traveling Thief Problem. - Adel Nikfarjam; Aneta Neumann; Jakob Bossek; Frank Neumann 001 (PPSN (1), 2022)

On the Impact of Basic Mutation Operators and Populations within Evolutionary Algorithms for the Dynamic Weighted Traveling Salesperson Problem. - Jakob Bossek; Aneta Neumann; Frank Neumann 001 (GECCO, 2023)

Generating diverse and discriminatory knapsack instances by searching for novelty in variable dimensions of feature-space. - Alejandro Marrero; Eduardo Segredo; Emma Hart; Jakob Bossek; Aneta Neumann (GECCO, 2023)

Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP. - Moritz Vinzent Seiler; Jeroen Rook; Jonathan Heins; Oliver Ludger Preuß; Jakob Bossek; Heike Trautmann (SSCI, 2023)

On the Impact of Operators and Populations within Evolutionary Algorithms for the Dynamic Weighted Traveling Salesperson Problem. - Jakob Bossek; Aneta Neumann; Frank Neumann 001 (CoRR, 2023)

A study on the effects of normalized TSP features for automated algorithm selection. - Jonathan Heins; Jakob Bossek; Janina Pohl; Moritz Seiler; Heike Trautmann; Pascal Kerschke (Theor. Comput. Sci., 2023)

Do additional target points speed up evolutionary algorithms? - Jakob Bossek; Dirk Sudholt (Theor. Comput. Sci., 2023)

Generalised Kruskal Mutation for the Multi-Objective Minimum Spanning Tree Problem. - Jakob Bossek; Christian Grimme (GECCO, 2024)

Guiding Quality Diversity on Monotone Submodular Functions: Customising the Feature Space by Adding Boolean Conjunctions. - Marcus Schmidbauer; Andre Opris; Jakob Bossek; Frank Neumann 001; Dirk Sudholt (GECCO, 2024)

On Single-Objective Sub-Graph-Based Mutation for Solving the Bi-Objective Minimum Spanning Tree Problem. - Jakob Bossek; Christian Grimme (Evol. Comput., 2024)

Runtime Analysis of Quality Diversity Algorithms. - Jakob Bossek; Dirk Sudholt (Algorithmica, 2024)

KernelMatmul: Scaling Gaussian Processes to Large Time Series. - Tilman Hoffbauer; Holger H. Hoos; Jakob Bossek (AAAI, 2025)

Automated Algorithm Configuration and Systematic Benchmarking for Heterogeneous MNK-Landscapes. - Oliver Ludger Preuß; Carolin Mensendiek; Jeroen Rook; Jakob Bossek; Heike Trautmann (GECCO, 2025)

Cluster Prevention in Evolutionary Diversity Optimization for Parallel Machine Scheduling. - Dominic Wittner; Jakob Bossek (GECCO, 2025)