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Jakob Bossek
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- affiliation: RWTH Aachen University, Aachen, Germany
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2020 – today
- 2024
- [j12]Jakob Bossek, Dirk Sudholt:
Runtime Analysis of Quality Diversity Algorithms. Algorithmica 86(10): 3252-3283 (2024) - [j11]Jakob Bossek, Christian Grimme:
On Single-Objective Sub-Graph-Based Mutation for Solving the Bi-Objective Minimum Spanning Tree Problem. Evol. Comput. 32(2): 143-175 (2024) - [c49]Jakob Bossek
, Christian Grimme
Generalised Kruskal Mutation for the Multi-Objective Minimum Spanning Tree Problem. GECCO 2024 - [c48]Marcus Schmidbauer
Guiding Quality Diversity on Monotone Submodular Functions: Customising the Feature Space by Adding Boolean Conjunctions. GECCO 2024 - 2023
- [j10]Jonathan Heins
A study on the effects of normalized TSP features for automated algorithm selection. Theor. Comput. Sci. 940(Part): 123-145 (2023) - [j9]Jakob Bossek
Do additional target points speed up evolutionary algorithms? Theor. Comput. Sci. 950: 113757 (2023) - [c47]Jakob Bossek
On the Impact of Basic Mutation Operators and Populations within Evolutionary Algorithms for the Dynamic Weighted Traveling Salesperson Problem. GECCO 2023: 248-256 - [c46]Alejandro Marrero
Generating diverse and discriminatory knapsack instances by searching for novelty in variable dimensions of feature-space. GECCO 2023: 312-320 - [c45]Jakob Bossek
Runtime Analysis of Quality Diversity Algorithms. GECCO 2023: 1546-1554 - [c44]Moritz Vinzent Seiler, Jeroen Rook
Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP. SSCI 2023: 361-368 - [i26]Jakob Bossek, Aneta Neumann, Frank Neumann:
On the Impact of Operators and Populations within Evolutionary Algorithms for the Dynamic Weighted Traveling Salesperson Problem. CoRR abs/2305.18955 (2023) - [i25]Jakob Bossek, Dirk Sudholt:
Runtime Analysis of Quality Diversity Algorithms. CoRR abs/2305.18966 (2023) - [i24]Jakob Bossek, Christian Grimme:
On Single-Objective Sub-Graph-Based Mutation for Solving the Bi-Objective Minimum Spanning Tree Problem. CoRR abs/2306.00222 (2023) - 2022
- [c43]Jakob Bossek, Frank Neumann:
Exploring the feature space of TSP instances using quality diversity. GECCO 2022: 186-194 - [c42]Jeroen Rook
On the potential of automated algorithm configuration on multi-modal multi-objective optimization problems. GECCO Companion 2022: 356-359 - [c41]Jakob Bossek, Aneta Neumann, Frank Neumann:
Evolutionary diversity optimization for combinatorial optimization: tutorial at GECCO'22, Boston, USA. GECCO Companion 2022: 824-842 - [c40]Jonathan Heins
BBE: Basin-Based Evaluation of Multimodal Multi-objective Optimization Problems. PPSN (1) 2022: 192-206 - [c39]Adel Nikfarjam, Aneta Neumann, Jakob Bossek, Frank Neumann:
Co-evolutionary Diversity Optimisation for the Traveling Thief Problem. PPSN (1) 2022: 237-249 - [i23]Jakob Bossek, Frank Neumann:
Exploring the Feature Space of TSP Instances Using Quality Diversity. CoRR abs/2202.02077 (2022) - [i22]Adel Nikfarjam, Aneta Neumann, Jakob Bossek, Frank Neumann:
Co-Evolutionary Diversity Optimisation for the Traveling Thief Problem. CoRR abs/2207.14036 (2022) - 2021
- [j8]Jakob Bossek
Time Complexity Analysis of Randomized Search Heuristics for the Dynamic Graph Coloring Problem. Algorithmica 83(10): 3148-3179 (2021) - [c38]Jonathan Heins
On the potential of normalized TSP features for automated algorithm selection. FOGA 2021: 7:1-7:15 - [c37]Jakob Bossek
Do additional optima speed up evolutionary algorithms? FOGA 2021: 8:1-8:11 - [c36]Adel Nikfarjam, Jakob Bossek, Aneta Neumann
Computing diverse sets of high quality TSP tours by EAX-based evolutionary diversity optimisation. FOGA 2021: 9:1-9:11 - [c35]Jakob Bossek
Evolutionary diversity optimization and the minimum spanning tree problem. GECCO 2021: 198-206 - [c34]Aneta Neumann
Diversifying greedy sampling and evolutionary diversity optimisation for constrained monotone submodular functions. GECCO 2021: 261-269 - [c33]Jakob Bossek, Aneta Neumann
Breeding diverse packings for the knapsack problem by means of diversity-tailored evolutionary algorithms. GECCO 2021: 556-564 - [c32]Adel Nikfarjam, Jakob Bossek
Entropy-based evolutionary diversity optimisation for the traveling salesperson problem. GECCO 2021: 600-608 - [c31]Jakob Bossek
Generating instances with performance differences for more than just two algorithms. GECCO Companion 2021: 1423-1432 - [c30]Jakob Bossek
Exact Counting and Sampling of Optima for the Knapsack Problem. LION 2021: 40-54 - [i21]Jakob Bossek, Aneta Neumann, Frank Neumann:
Breeding Diverse Packings for the Knapsack Problem by Means of Diversity-Tailored Evolutionary Algorithms. CoRR abs/2104.13133 (2021) - [i20]Adel Nikfarjam, Jakob Bossek, Aneta Neumann, Frank Neumann:
Entropy-Based Evolutionary Diversity Optimisation for the Traveling Salesperson Problem. CoRR abs/2104.13538 (2021) - [i19]Jakob Bossek, Markus Wagner:
Generating Instances with Performance Differences for More Than Just Two Algorithms. CoRR abs/2104.14275 (2021) - [i18]Jakob Bossek, Frank Neumann, Pan Peng, Dirk Sudholt:
Time Complexity Analysis of Randomized Search Heuristics for the Dynamic Graph Coloring Problem. CoRR abs/2105.12525 (2021) - [i17]Jakob Bossek, Aneta Neumann, Frank Neumann:
Exact Counting and Sampling of Optima for the Knapsack Problem. CoRR abs/2106.07412 (2021) - [i16]Adel Nikfarjam, Jakob Bossek, Aneta Neumann, Frank Neumann:
Computing Diverse Sets of High Quality TSP Tours by EAX-Based Evolutionary Diversity Optimisation. CoRR abs/2108.05005 (2021) - 2020
- [j7]Jakob Bossek
A multi-objective perspective on performance assessment and automated selection of single-objective optimization algorithms. Appl. Soft Comput. 88: 105901 (2020) - [c29]Jakob Bossek
Towards Decision Support in Dynamic Bi-Objective Vehicle Routing. CEC 2020: 1-8 - [c28]Jakob Bossek
Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection. CEC 2020: 1-8 - [c27]Jakob Bossek
Dynamic bi-objective routing of multiple vehicles. GECCO 2020: 166-174 - [c26]Vahid Roostapour, Jakob Bossek, Frank Neumann:
Runtime analysis of evolutionary algorithms with biased mutation for the multi-objective minimum spanning tree problem. GECCO 2020: 551-559 - [c25]Anh Viet Do
Evolving diverse sets of tours for the travelling salesperson problem. GECCO 2020: 681-689 - [c24]Jakob Bossek, Carola Doerr
Initial design strategies and their effects on sequential model-based optimization: an exploratory case study based on BBOB. GECCO 2020: 778-786 - [c23]Jakob Bossek, Frank Neumann, Pan Peng, Dirk Sudholt:
More effective randomized search heuristics for graph coloring through dynamic optimization. GECCO 2020: 1277-1285 - [c22]Jakob Bossek, Katrin Casel, Pascal Kerschke
The node weight dependent traveling salesperson problem: approximation algorithms and randomized search heuristics. GECCO 2020: 1286-1294 - [c21]Moritz Seiler, Janina Pohl, Jakob Bossek, Pascal Kerschke
Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem. PPSN (1) 2020: 48-64 - [c20]Jakob Bossek, Carola Doerr
Evolving Sampling Strategies for One-Shot Optimization Tasks. PPSN (1) 2020: 111-124 - [c19]Jakob Bossek, Aneta Neumann
Optimising Tours for the Weighted Traveling Salesperson Problem and the Traveling Thief Problem: A Structural Comparison of Solutions. PPSN (1) 2020: 346-359 - [i15]Jakob Bossek, Katrin Casel, Pascal Kerschke, Frank Neumann:
The Node Weight Dependent Traveling Salesperson Problem: Approximation Algorithms and Randomized Search Heuristics. CoRR abs/2002.01070 (2020) - [i14]Jakob Bossek, Carola Doerr, Pascal Kerschke:
Initial Design Strategies and their Effects on Sequential Model-Based Optimization. CoRR abs/2003.13826 (2020) - [i13]Anh Viet Do, Jakob Bossek, Aneta Neumann, Frank Neumann:
Evolving Diverse Sets of Tours for the Travelling Salesperson Problem. CoRR abs/2004.09188 (2020) - [i12]Vahid Roostapour, Jakob Bossek, Frank Neumann:
Runtime Analysis of Evolutionary Algorithms with Biased Mutation for the Multi-Objective Minimum Spanning Tree Problem. CoRR abs/2004.10424 (2020) - [i11]Jakob Bossek, Pascal Kerschke, Heike Trautmann:
Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection. CoRR abs/2005.13289 (2020) - [i10]Jakob Bossek, Frank Neumann, Pan Peng, Dirk Sudholt:
More Effective Randomized Search Heuristics for Graph Coloring Through Dynamic Optimization. CoRR abs/2005.13825 (2020) - [i9]Jakob Bossek, Christian Grimme, Günter Rudolph, Heike Trautmann:
Towards Decision Support in Dynamic Bi-Objective Vehicle Routing. CoRR abs/2005.13865 (2020) - [i8]Jakob Bossek, Christian Grimme, Heike Trautmann:
Dynamic Bi-Objective Routing of Multiple Vehicles. CoRR abs/2005.13872 (2020) - [i7]Jakob Bossek, Aneta Neumann, Frank Neumann:
Optimising Tours for the Weighted Traveling Salesperson Problem and the Traveling Thief Problem: A Structural Comparison of Solutions. CoRR abs/2006.03260 (2020) - [i6]Moritz Seiler, Janina Pohl, Jakob Bossek, Pascal Kerschke, Heike Trautmann:
Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem. CoRR abs/2006.15968 (2020) - [i5]Thomas Bartz-Beielstein
Benchmarking in Optimization: Best Practice and Open Issues. CoRR abs/2007.03488 (2020) - [i4]Jakob Bossek, Frank Neumann:
Evolutionary Diversity Optimization and the Minimum Spanning Tree Problem. CoRR abs/2010.10913 (2020) - [i3]Aneta Neumann, Jakob Bossek, Frank Neumann:
Computing Diverse Sets of Solutions for Monotone Submodular Optimisation Problems. CoRR abs/2010.11486 (2020)
2010 – 2019
- 2019
- [j6]Giuseppe Casalicchio
OpenML: An R package to connect to the machine learning platform OpenML. Comput. Stat. 34(3): 977-991 (2019) - [c18]Jakob Bossek, Christian Grimme, Stephan Meisel, Günter Rudolph, Heike Trautmann:
Bi-objective Orienteering: Towards a Dynamic Multi-objective Evolutionary Algorithm. EMO 2019: 516-528 - [c17]Jakob Bossek, Pascal Kerschke
Evolving diverse TSP instances by means of novel and creative mutation operators. FOGA 2019: 58-71 - [c16]Jakob Bossek, Dirk Sudholt:
Time complexity analysis of RLS and (1 + 1) EA for the edge coloring problem. FOGA 2019: 102-115 - [c15]Jakob Bossek, Christian Grimme, Frank Neumann
On the benefits of biased edge-exchange mutation for the multi-criteria spanning tree problem. GECCO 2019: 516-523 - [c14]Jakob Bossek
Runtime analysis of randomized search heuristics for dynamic graph coloring. GECCO 2019: 1443-1451 - [i2]Jakob Bossek, Pascal Kerschke, Aneta Neumann, Frank Neumann, Carola Doerr:
One-Shot Decision-Making with and without Surrogates. CoRR abs/1912.08956 (2019) - 2018
- [j5]Pascal Kerschke
Leveraging TSP Solver Complementarity through Machine Learning. Evol. Comput. 26(4) (2018) - [j4]Jakob Bossek
grapherator: A Modular Multi-Step Graph Generator. J. Open Source Softw. 3(22): 528 (2018) - [c13]Jakob Bossek
Local search effects in bi-objective orienteering. GECCO 2018: 585-592 - [c12]Jakob Bossek
Performance assessment of multi-objective evolutionary algorithms with the R package ecr. GECCO (Companion) 2018: 1350-1356 - [c11]Pascal Kerschke
Parameterization of state-of-the-art performance indicators: a robustness study based on inexact TSP solvers. GECCO (Companion) 2018: 1737-1744 - [c10]Jakob Bossek
Solving Scalarized Subproblems within Evolutionary Algorithms for Multi-criteria Shortest Path Problems. LION 2018: 184-198 - [c9]Jakob Bossek
Multi-objective Performance Measurement: Alternatives to PAR10 and Expected Running Time. LION 2018: 215-219 - 2017
- [j3]Jakob Bossek
mcMST: A Toolbox for the Multi-Criteria Minimum Spanning Tree Problem. J. Open Source Softw. 2(17): 374 (2017) - [j2]Jakob Bossek
smoof: Single- and Multi-Objective Optimization Test Functions. R J. 9(1): 103 (2017) - [c8]Jakob Bossek
ecr 2.0: a modular framework for evolutionary computation in R. GECCO (Companion) 2017: 1187-1193 - [c7]Jakob Bossek
A pareto-beneficial sub-tree mutation for the multi-criteria minimum spanning tree problem. SSCI 2017: 1-8 - [c6]Jakob Bossek
An extended mutation-based priority-rule integration concept for multi-objective machine scheduling. SSCI 2017: 1-8 - [i1]Giuseppe Casalicchio, Jakob Bossek, Michel Lang
OpenML: An R Package to Connect to the Networked Machine Learning Platform OpenML. CoRR abs/1701.01293 (2017) - 2016
- [c5]Jakob Bossek
Understanding Characteristics of Evolved Instances for State-of-the-Art Inexact TSP Solvers with Maximum Performance Difference. AI*IA 2016: 3-12 - [c4]Jakob Bossek
Evolving Instances for Maximizing Performance Differences of State-of-the-Art Inexact TSP Solvers. LION 2016: 48-59 - 2015
- [c3]Stephan Meisel, Christian Grimme, Jakob Bossek
Evaluation of a Multi-Objective EA on Benchmark Instances for Dynamic Routing of a Vehicle. GECCO 2015: 425-432 - [c2]Jakob Bossek
Learning Feature-Parameter Mappings for Parameter Tuning via the Profile Expected Improvement. GECCO 2015: 1319-1326 - 2013
- [j1]Olaf Mersmann
A novel feature-based approach to characterize algorithm performance for the traveling salesperson problem. Ann. Math. Artif. Intell. 69(2): 151-182 (2013) - 2012
- [c1]Olaf Mersmann
Local Search and the Traveling Salesman Problem: A Feature-Based Characterization of Problem Hardness. LION 2012: 115-129
Coauthor Index
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