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Frank Neumann 0001
Person information
- affiliation: University of Adelaide, Australia
- affiliation: Max Planck Institute for Informatics, Saarbrücken, Germany
Other persons with the same name
- Frank Neumann 0002 — Technical University of Berlin, Germany
- Frank Neumann 0003 — Universität Hannover, Theoretische Chemie, Germany
- Frank Neumann 0004 — HTW Berlin - University of Applied Sciences, Germany
- Frank Neumann 0005 — University of Leicester, School of Mathematics and Actuarial Science, UK
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2020 – today
- 2024
- [j68]Adel Nikfarjam
, Aneta Neumann
On the Use of Quality Diversity Algorithms for the Travelling Thief Problem. ACM Trans. Evol. Learn. Optim. 4(2): 12 (2024) - [c249]Mingyu Guo, Jialiang Li, Aneta Neumann, Frank Neumann, Hung X. Nguyen:
Limited Query Graph Connectivity Test. AAAI 2024: 20718-20725 - [c248]Zahra Ghasemi, Mehdi Neshat, Chris Aldrich, John Karageorgos, Max Zanin, Frank Neumann, Lei Chen:
Enhanced Genetic Programming Models with Multiple Equations for Accurate Semi-Autogenous Grinding Mill Throughput Prediction. CEC 2024: 1-9 - [c247]Frank Neumann
Evolutionary computation for stochastic problems. GECCO Companion 2024: 1352-1368 - [c246]Saba Sadeghi Ahouei
Evolving Reliable Differentiating Constraints for the Chance-constrained Maximum Coverage Problem. GECCO 2024 - [c245]Denis Antipov
A Detailed Experimental Analysis of Evolutionary Diversity Optimization for OneMinMax. GECCO 2024 - [c244]Benjamin Doerr
A Block-Coordinate Descent EMO Algorithm: Theoretical and Empirical Analysis. GECCO 2024 - [c243]Thilina Pathirage Don
The Chance Constrained Travelling Thief Problem: Problem Formulations and Algorithms. GECCO 2024 - [c242]Sharlotte Gounder
Evolutionary Diversity Optimisation for Sparse Directed Communication Networks. GECCO 2024 - [c241]Adel Nikfarjam
Quality Diversity Approaches for Time-Use Optimisation to Improve Health Outcomes. GECCO 2024 - [c240]Andre Opris
Runtime Analyses of NSGA-III on Many-Objective Problems. GECCO 2024 - [c239]Ishara Hewa Pathiranage
Effective 2- and 3-Objective MOEA/D Approaches for the Chance Constrained Knapsack Problem. GECCO 2024 - [c238]Ishara Hewa Pathiranage
Using 3-Objective Evolutionary Algorithms for the Dynamic Chance Constrained Knapsack Problem. GECCO 2024 - [c237]Marcus Schmidbauer
Guiding Quality Diversity on Monotone Submodular Functions: Customising the Feature Space by Adding Boolean Conjunctions. GECCO 2024 - [c236]Xiankun Yan
Sampling-based Pareto Optimization for Chance-constrained Monotone Submodular Problems. GECCO 2024 - [c235]Furong Ye
What Performance Indicators to Use for Self-Adaptation in Multi-Objective Evolutionary Algorithms. GECCO 2024 - [c234]Denis Antipov
Runtime Analysis of Evolutionary Diversity Optimization on a Tri-Objective Version of the (LeadingOnes, TrailingZeros) Problem. PPSN (3) 2024: 19-35 - [c233]Xiankun Yan
Sliding Window Bi-objective Evolutionary Algorithms for Optimizing Chance-Constrained Monotone Submodular Functions. PPSN (1) 2024: 20-35 - [c232]Frank Neumann
Sliding Window 3-Objective Pareto Optimization for Problems with Chance Constraints. PPSN (3) 2024: 36-52 - [c231]Kokila Kasuni Perera
Multi-objective Evolutionary Approaches for the Knapsack Problem with Stochastic Profits. PPSN (1) 2024: 116-132 - [c230]Anh Viet Do
Evolutionary Multi-objective Diversity Optimization. PPSN (4) 2024: 117-134 - [c229]Jonathan Gadea Harder
Analysis of Evolutionary Diversity Optimisation for the Maximum Matching Problem. PPSN (3) 2024: 149-165 - [c228]Frank Neumann, Günter Rudolph:
Archive-Based Single-Objective Evolutionary Algorithms for Submodular Optimization. PPSN (3) 2024: 166-180 - [c227]Denis Antipov
Local Optima in Diversity Optimization: Non-trivial Offspring Population is Essential. PPSN (3) 2024: 181-196 - [i144]Zahra Ghasemi, Mehdi Neshat, Chris Aldrich, John Karageorgos, Max Zanin, Frank Neumann, Lei Chen:
Enhanced Genetic Programming Models with Multiple Equations for Accurate Semi-Autogenous Grinding Mill Throughput Prediction. CoRR abs/2401.05382 (2024) - [i143]Anh Viet Do, Mingyu Guo, Aneta Neumann, Frank Neumann:
Evolutionary Multi-Objective Diversity Optimization. CoRR abs/2401.07454 (2024) - [i142]Benjamin Doerr, Joshua D. Knowles, Aneta Neumann, Frank Neumann:
A Block-Coordinate Descent EMO Algorithm: Theoretical and Empirical Analysis. CoRR abs/2404.03838 (2024) - [i141]Ishara Hewa Pathiranage, Frank Neumann, Denis Antipov, Aneta Neumann:
Using 3-Objective Evolutionary Algorithms for the Dynamic Chance Constrained Knapsack Problem. CoRR abs/2404.06014 (2024) - [i140]Andre Opris, Duc-Cuong Dang, Frank Neumann, Dirk Sudholt:
Runtime Analyses of NSGA-III on Many-Objective Problems. CoRR abs/2404.11433 (2024) - [i139]Denis Antipov, Aneta Neumann, Frank Neumann, Andrew M. Sutton:
Runtime Analysis of Evolutionary Diversity Optimization on the Multi-objective (LeadingOnes, TrailingZeros) Problem. CoRR abs/2404.11496 (2024) - [i138]Jonathan Gadea Harder, Aneta Neumann, Frank Neumann:
Analysis of Evolutionary Diversity Optimisation for the Maximum Matching Problem. CoRR abs/2404.11784 (2024) - [i137]Xiankun Yan, Aneta Neumann, Frank Neumann:
Sampling-based Pareto Optimization for Chance-constrained Monotone Submodular Problems. CoRR abs/2404.11907 (2024) - [i136]Saba Sadeghi Ahouei, Jacob de Nobel, Aneta Neumann, Thomas Bäck, Frank Neumann:
Evolving Reliable Differentiating Constraints for the Chance-constrained Maximum Coverage Problem. CoRR abs/2405.18772 (2024) - [i135]Frank Neumann, Carsten Witt:
Sliding Window 3-Objective Pareto Optimization for Problems with Chance Constraints. CoRR abs/2406.04899 (2024) - [i134]Frank Neumann, Günter Rudolph:
Archive-based Single-Objective Evolutionary Algorithms for Submodular Optimization. CoRR abs/2406.13414 (2024) - [i133]Denis Antipov, Aneta Neumann, Frank Neumann:
Local Optima in Diversity Optimization: Non-trivial Offspring Population is Essential. CoRR abs/2407.08963 (2024) - [i132]Xiankun Yan, Aneta Neumann, Frank Neumann:
Sliding Window Bi-Objective Evolutionary Algorithms for Optimizing Chance-Constrained Monotone Submodular Functions. CoRR abs/2407.09731 (2024) - [i131]Maria Laura Santoni, Elena Raponi, Aneta Neumann, Frank Neumann, Mike Preuss, Carola Doerr:
Illuminating the Diversity-Fitness Trade-Off in Black-Box Optimization. CoRR abs/2408.16393 (2024) - [i130]Jack Kearney, Frank Neumann, Andrew M. Sutton:
Fixed-Parameter Tractability of the (1+1) Evolutionary Algorithm on Random Planted Vertex Covers. CoRR abs/2409.10144 (2024) - 2023
- [c226]Mingyu Guo, Max Ward
Scalable Edge Blocking Algorithms for Defending Active Directory Style Attack Graphs. AAAI 2023: 5649-5656 - [c225]Gabor Zoltai
A Study of Fitness Gains in Evolving Finite State Machines. AI (2) 2023: 479-490 - [c224]Frank Neumann, Aneta Neumann, Chao Qian, Anh Viet Do, Jacob de Nobel, Diederick Vermetten
Benchmarking Algorithms for Submodular Optimization Problems Using IOHProfiler. CEC 2023: 1-9 - [c223]Michael Stimson, William Reid, Aneta Neumann, Simon Ratcliffe, Frank Neumann:
Improving Confidence in Evolutionary Mine Scheduling via Uncertainty Discounting. CEC 2023: 1-10 - [c222]Frank Neumann
Fast Pareto Optimization Using Sliding Window Selection. ECAI 2023: 1771-1778 - [c221]Xiankun Yan
Optimizing Chance-Constrained Submodular Problems with Variable Uncertainties. ECAI 2023: 2826-2833 - [c220]Denis Antipov
Rigorous Runtime Analysis of Diversity Optimization with GSEMO on OneMinMax. FOGA 2023: 3-14 - [c219]Jack Kearney
Fixed-Parameter Tractability of the (1 + 1) Evolutionary Algorithm on Random Planted Vertex Covers. FOGA 2023: 96-104 - [c218]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 - [c217]Frank Neumann
3-Objective Pareto Optimization for Problems with Chance Constraints. GECCO 2023: 731-739 - [c216]Adel Nikfarjam
Evolutionary Diversity Optimisation in Constructing Satisfying Assignments. GECCO 2023: 938-945 - [c215]Diksha Goel
Evolving Reinforcement Learning Environment to Minimize Learner's Achievable Reward: An Application on Hardening Active Directory Systems. GECCO 2023: 1348-1356 - [c214]Aneta Neumann
Diversity Optimization for the Detection and Concealment of Spatially Defined Communication Networks. GECCO 2023: 1436-1444 - [c213]Frank Neumann
Evolutionary computation for stochastic problems. GECCO Companion 2023: 1463-1476 - [c212]Samuel Baguley
Fixed Parameter Multi-Objective Evolutionary Algorithms for the W-Separator Problem. GECCO 2023: 1537-1545 - [c211]Tobias Friedrich
Analysis of (1+1) EA on LeadingOnes with Constraints. GECCO 2023: 1584-1592 - [c210]Anh Viet Do, Mingyu Guo, Aneta Neumann, Frank Neumann:
Diverse Approximations for Monotone Submodular Maximization Problems with a Matroid Constraint. IJCAI 2023: 5558-5566 - [c209]Anh Viet Do, Aneta Neumann, Frank Neumann, Andrew M. Sutton:
Rigorous Runtime Analysis of MOEA/D for Solving Multi-Objective Minimum Weight Base Problems. NeurIPS 2023 - [i129]Frank Neumann, Aneta Neumann, Chao Qian, Anh Viet Do, Jacob de Nobel, Diederick Vermetten, Saba Sadeghi Ahouei, Furong Ye, Hao Wang, Thomas Bäck:
Benchmarking Algorithms for Submodular Optimization Problems Using IOHProfiler. CoRR abs/2302.01464 (2023) - [i128]Mingyu Guo, Jialiang Li, Aneta Neumann, Frank Neumann, Hung Nguyen:
Limited Query Graph Connectivity Test. CoRR abs/2302.13036 (2023) - [i127]Kokila Perera, Aneta Neumann, Frank Neumann:
Evolutionary Multi-Objective Algorithms for the Knapsack Problems with Stochastic Profits. CoRR abs/2303.01695 (2023) - [i126]Furong Ye, Frank Neumann, Jacob de Nobel, Aneta Neumann, Thomas Bäck:
What Performance Indicators to Use for Self-Adaptation in Multi-Objective Evolutionary Algorithms. CoRR abs/2303.04611 (2023) - [i125]Diksha Goel, Aneta Neumann, Frank Neumann, Hung Nguyen, Mingyu Guo:
Evolving Reinforcement Learning Environment to Minimize Learner's Achievable Reward: An Application on Hardening Active Directory Systems. CoRR abs/2304.03998 (2023) - [i124]Frank Neumann, Carsten Witt:
3-Objective Pareto Optimization for Problems with Chance Constraints. CoRR abs/2304.08774 (2023) - [i123]Frank Neumann, Carsten Witt:
Fast Pareto Optimization Using Sliding Window Selection. CoRR abs/2305.07178 (2023) - [i122]Adel Nikfarjam, Ralf Rothenberger, Frank Neumann, Tobias Friedrich:
Evolutionary Diversity Optimisation in Constructing Satisfying Assignments. CoRR abs/2305.11457 (2023) - [i121]Michael Stimson, William Reid, Aneta Neumann, Simon Ratcliffe, Frank Neumann:
Improving Confidence in Evolutionary Mine Scheduling via Uncertainty Discounting. CoRR abs/2305.17957 (2023) - [i120]Tobias Friedrich, Timo Kötzing, Aneta Neumann, Frank Neumann, Aishwarya Radhakrishnan:
Analysis of the (1+1) EA on LeadingOnes with Constraints. CoRR abs/2305.18267 (2023) - [i119]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) - [i118]Anh Viet Do, Aneta Neumann, Frank Neumann, Andrew M. Sutton:
Rigorous Runtime Analysis of MOEA/D for Solving Multi-Objective Minimum Weight Base Problems. CoRR abs/2306.03409 (2023) - [i117]Denis Antipov, Aneta Neumann, Frank Neumann:
Rigorous Runtime Analysis of Diversity Optimization with GSEMO on OneMinMax. CoRR abs/2307.07248 (2023) - [i116]Anh Viet Do, Mingyu Guo, Aneta Neumann, Frank Neumann:
Diverse Approximations for Monotone Submodular Maximization Problems with a Matroid Constraint. CoRR abs/2307.07567 (2023) - [i115]Xiankun Yan, Anh Viet Do, Feng Shi, Xiaoyu Qin, Frank Neumann:
Optimizing Chance-Constrained Submodular Problems with Variable Uncertainties. CoRR abs/2309.14359 (2023) - [i114]Gabor Zoltai, Yue Xie, Frank Neumann:
A Study of Fitness Gains in Evolving Finite State Machines. CoRR abs/2310.13203 (2023) - [i113]Zahra Ghasemi, Mehdi Neshat, Chris Aldrich, John Karageorgos, Max Zanin, Frank Neumann, Lei Chen:
A Hybrid Intelligent Framework for Maximising SAG Mill Throughput: An Integration of Expert Knowledge, Machine Learning and Evolutionary Algorithms for Parameter Optimisation. CoRR abs/2312.10992 (2023) - 2022
- [j67]Vahid Roostapour
Pareto optimization for subset selection with dynamic cost constraints. Artif. Intell. 302: 103597 (2022) - [j66]Johannes Lengler
Editorial. Algorithmica 84(6): 1571-1572 (2022) - [j65]Vahid Roostapour, Aneta Neumann
Single- and multi-objective evolutionary algorithms for the knapsack problem with dynamically changing constraints. Theor. Comput. Sci. 924: 129-147 (2022) - [j64]Anh Viet Do
Analysis of Evolutionary Diversity Optimization for Permutation Problems. ACM Trans. Evol. Learn. Optim. 2(3): 11:1-11:27 (2022) - [c208]Mingyu Guo, Jialiang Li
Practical Fixed-Parameter Algorithms for Defending Active Directory Style Attack Graphs. AAAI 2022: 9360-9367 - [c207]Hirad Assimi
Novelty-Driven Binary Particle Swarm Optimisation for Truss Optimisation Problems. EvoCOP 2022: 111-126 - [c206]Jakob Bossek, Frank Neumann:
Exploring the feature space of TSP instances using quality diversity. GECCO 2022: 186-194 - [c205]Adel Nikfarjam, Aneta Neumann, Frank Neumann:
On the use of quality diversity algorithms for the traveling thief problem. GECCO 2022: 260-268 - [c204]Anh Viet Do, Mingyu Guo, Aneta Neumann, Frank Neumann:
Niching-based evolutionary diversity optimization for the traveling salesperson problem. GECCO 2022: 684-693 - [c203]Adel Nikfarjam, Aneta Neumann, Frank Neumann:
Evolutionary diversity optimisation for the traveling thief problem. GECCO 2022: 749-756 - [c202]Yue Xie, Aneta Neumann, Frank Neumann:
An optimization strategy for the complex large-scale stockpile blending problem. GECCO Companion 2022: 770-773 - [c201]Jakob Bossek, Aneta Neumann, Frank Neumann:
Evolutionary diversity optimization for combinatorial optimization: tutorial at GECCO'22, Boston, USA. GECCO Companion 2022: 824-842 - [c200]Aneta Neumann, Denis Antipov, Frank Neumann:
Coevolutionary Pareto diversity optimization. GECCO 2022: 832-839 - [c199]Diksha Goel
Defending active directory by combining neural network based dynamic program and evolutionary diversity optimisation. GECCO 2022: 1191-1199 - [c198]Frank Neumann, Dirk Sudholt, Carsten Witt
The compact genetic algorithm struggles on Cliff functions. GECCO 2022: 1426-1433 - [c197]Aneta Neumann, Frank Neumann, Chao Qian:
Evolutionary submodular optimisation: tutorial. GECCO Companion 2022: 1427-1449 - [c196]Frank Neumann, Carsten Witt:
Runtime Analysis of Single- and Multi-Objective Evolutionary Algorithms for Chance Constrained Optimization Problems with Normally Distributed Random Variables. IJCAI 2022: 4800-4806 - [c195]Adel Nikfarjam, Aneta Neumann, Jakob Bossek, Frank Neumann:
Co-evolutionary Diversity Optimisation for the Traveling Thief Problem. PPSN (1) 2022: 237-249 - [c194]Adel Nikfarjam, Amirhossein Moosavi, Aneta Neumann, Frank Neumann:
Computing High-Quality Solutions for the Patient Admission Scheduling Problem Using Evolutionary Diversity Optimisation. PPSN (1) 2022: 250-264 - [c193]Aneta Neumann, Yue Xie, Frank Neumann:
Evolutionary Algorithms for Limiting the Effect of Uncertainty for the Knapsack Problem with Stochastic Profits. PPSN (1) 2022: 294-307 - [c192]Yue Xie, Aneta Neumann, Ty Stanford, Charlotte Lund Rasmussen
Evolutionary Time-Use Optimization for Improving Children's Health Outcomes. PPSN (2) 2022: 323-337 - [c191]Adel Nikfarjam, Anh Viet Do, Frank Neumann:
Analysis of Quality Diversity Algorithms for the Knapsack Problem. PPSN (2) 2022: 413-427 - [c190]Feng Shi
Runtime Analysis of Simple Evolutionary Algorithms for the Chance-Constrained Makespan Scheduling Problem. PPSN (2) 2022: 526-541 - [c189]Frank Neumann, Carsten Witt
Runtime Analysis of the (1+1) EA on Weighted Sums of Transformed Linear Functions. PPSN (2) 2022: 542-554 - [c188]Tobias Friedrich, Timo Kötzing, Frank Neumann, Aishwarya Radhakrishnan:
Theoretical Study of Optimizing Rugged Landscapes with the cGA. PPSN (2) 2022: 586-599 - [c187]Hirad Assimi
Run-of-mine stockyard recovery scheduling and optimisation for multiple reclaimers. SAC 2022: 1074-1083 - [i112]Anh Viet Do, Mingyu Guo, Aneta Neumann, Frank Neumann:
Niching-based Evolutionary Diversity Optimization for the Traveling Salesperson Problem. CoRR abs/2201.10316 (2022) - [i111]Jakob Bossek, Frank Neumann:
Exploring the Feature Space of TSP Instances Using Quality Diversity. CoRR abs/2202.02077 (2022) - [i110]Adel Nikfarjam, Aneta Neumann, Frank Neumann:
Evolutionary Diversity Optimisation for The Traveling Thief Problem. CoRR abs/2204.02709 (2022) - [i109]Diksha Goel, Max Ward
Defending Active Directory by Combining Neural Network based Dynamic Program and Evolutionary Diversity Optimisation. CoRR abs/2204.03397 (2022) - [i108]Frank Neumann, Dirk Sudholt, Carsten Witt:
The Compact Genetic Algorithm Struggles on Cliff Functions. CoRR abs/2204.04904 (2022) - [i107]Aneta Neumann, Denis Antipov, Frank Neumann:
Coevolutionary Pareto Diversity Optimization. CoRR abs/2204.05457 (2022) - [i106]Aneta Neumann, Yue Xie, Frank Neumann:
Evolutionary Algorithms for Limiting the Effect of Uncertainty for the Knapsack Problem with Stochastic Profits. CoRR abs/2204.05597 (2022) - [i105]Yue Xie, Aneta Neumann, Ty Stanford, Charlotte Lund Rasmussen
Evolutionary Time-Use Optimization for Improving Children's Health Outcomes. CoRR abs/2206.11505 (2022) - [i104]Adel Nikfarjam, Aneta Neumann, Jakob Bossek, Frank Neumann:
Co-Evolutionary Diversity Optimisation for the Traveling Thief Problem. CoRR abs/2207.14036 (2022) - [i103]Adel Nikfarjam, Anh Viet Do, Frank Neumann:
Analysis of Quality Diversity Algorithms for the Knapsack Problem. CoRR abs/2207.14037 (2022) - [i102]Adel Nikfarjam, Amirhossein Moosavi, Aneta Neumann, Frank Neumann:
Computing High-Quality Solutions for the Patient Admission Scheduling Problem using Evolutionary Diversity Optimisation. CoRR abs/2207.14112 (2022) - [i101]Frank Neumann, Carsten Witt:
Runtime Analysis of the (1+1) EA on Weighted Sums of Transformed Linear Functions. CoRR abs/2208.05670 (2022) - [i100]Tobias Friedrich, Timo Kötzing, Frank Neumann, Aishwarya Radhakrishnan:
Theoretical Study of Optimizing Rugged Landscapes with the cGA. CoRR abs/2211.13801 (2022) - [i99]Mingyu Guo, Max Ward, Aneta Neumann, Frank Neumann, Hung Nguyen:
Scalable Edge Blocking Algorithms for Defending Active Directory Style Attack Graphs. CoRR abs/2212.04326 (2022) - [i98]Feng Shi, Xiankun Yan, Frank Neumann:
Runtime Performance of Evolutionary Algorithms for the Chance-constrained Makespan Scheduling Problem. CoRR abs/2212.11478 (2022) - 2021
- [j63]Feng Shi
Runtime Performances of Randomized Search Heuristics for the Dynamic Weighted Vertex Cover Problem. Algorithmica 83(4): 906-939 (2021) - [j62]Jakob Bossek
Time Complexity Analysis of Randomized Search Heuristics for the Dynamic Graph Coloring Problem. Algorithmica 83(10): 3148-3179 (2021) - [j61]Frank Neumann, Mojgan Pourhassan, Carsten Witt
Improved Runtime Results for Simple Randomised Search Heuristics on Linear Functions with a Uniform Constraint. Algorithmica 83(10): 3209-3237 (2021) - [j60]Wanru Gao, Samadhi Nallaperuma, Frank Neumann:
Feature-Based Diversity Optimization for Problem Instance Classification. Evol. Comput. 29(1): 107-128 (2021) - [j59]Derek Weber
Amplifying influence through coordinated behaviour in social networks. Soc. Netw. Anal. Min. 11(1): 111 (2021) - [j58]Feng Shi, Frank Neumann, Jianxin Wang:
Time complexity analysis of evolutionary algorithms for 2-hop (1, 2)-minimum spanning tree problem. Theor. Comput. Sci. 893: 159-175 (2021) - [j57]Benjamin Doerr, Frank Neumann:
A Survey on Recent Progress in the Theory of Evolutionary Algorithms for Discrete Optimization. ACM Trans. Evol. Learn. Optim. 1(4): 16:1-16:43 (2021) - [c186]Anh Viet Do, Frank Neumann:
Pareto Optimization for Subset Selection with Dynamic Partition Matroid Constraints. AAAI 2021: 12284-12292 - [c185]Yue Xie, Aneta Neumann
Heuristic Strategies for Solving Complex Interacting Large-Scale Stockpile Blending Problems. CEC 2021: 1288-1295 - [c184]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 - [c183]Hirad Assimi
Novelty particle swarm optimisation for truss optimisation problems. GECCO Companion 2021: 67-68 - [c182]Jakob Bossek
Evolutionary diversity optimization and the minimum spanning tree problem. GECCO 2021: 198-206 - [c181]Aneta Neumann
Diversifying greedy sampling and evolutionary diversity optimisation for constrained monotone submodular functions. GECCO 2021: 261-269 - [c180]Jakob Bossek, Aneta Neumann
Breeding diverse packings for the knapsack problem by means of diversity-tailored evolutionary algorithms. GECCO 2021: 556-564 - [c179]Anh Viet Do, Mingyu Guo, Aneta Neumann
Analysis of evolutionary diversity optimisation for permutation problems. GECCO 2021: 574-582 - [c178]Adel Nikfarjam, Jakob Bossek
Entropy-based evolutionary diversity optimisation for the traveling salesperson problem. GECCO 2021: 600-608 - [c177]Aneta Neumann
Evolutionary submodular optimisation. GECCO Companion 2021: 918-940 - [c176]Yue Xie, Aneta Neumann
Heuristic strategies for solving complex interacting stockpile blending problem with chance constraints. GECCO 2021: 1079-1087 - [c175]Yue Xie, Aneta Neumann
Runtime analysis of RLS and the (1+1) EA for the chance-constrained knapsack problem with correlated uniform weights. GECCO 2021: 1187-1194 - [c174]William Reid, Aneta Neumann
Advanced mine optimisation under uncertainty using evolution. GECCO Companion 2021: 1605-1613 - [c173]Chao Bian, Chao Qian, Frank Neumann, Yang Yu:
Fast Pareto Optimization for Subset Selection with Dynamic Cost Constraints. IJCAI 2021: 2191-2197 - [c172]Jakob Bossek
Exact Counting and Sampling of Optima for the Knapsack Problem. LION 2021: 40-54 - [c171]Hirad Assimi
Modelling and optimization of run-of-mine stockpile recovery. SAC 2021: 450-458 - [c170]Tobias Friedrich
Solving Non-uniform Planted and Filtered Random SAT Formulas Greedily. SAT 2021: 188-206 - [i97]William Reid, Aneta Neumann, Simon Ratcliffe, Frank Neumann:
Advanced Ore Mine Optimisation under Uncertainty Using Evolution. CoRR abs/2102.05235 (2021) - [i96]Yue Xie, Aneta Neumann, Frank Neumann:
Heuristic Strategies for Solving Complex Interacting Stockpile Blending Problem with Chance Constraints. CoRR abs/2102.05303 (2021) - [i95]Yue Xie, Aneta Neumann, Frank Neumann, Andrew M. Sutton:
Runtime Analysis of RLS and the (1+1) EA for the Chance-constrained Knapsack Problem with Correlated Uniform Weights. CoRR abs/2102.05778 (2021) - [i94]Anh Viet Do, Mingyu Guo, Aneta Neumann, Frank Neumann:
Analysis of Evolutionary Diversity Optimisation for Permutation Problems. CoRR abs/2102.11469 (2021) - [i93]Derek Weber, Frank Neumann:
A General Method to Find Highly Coordinating Communities in Social Media through Inferred Interaction Links. CoRR abs/2103.03409 (2021) - [i92]Yue Xie, Aneta Neumann, Frank Neumann:
Heuristic Strategies for Solving Complex Interacting Large-Scale Stockpile Blending Problems. CoRR abs/2104.03440 (2021) - [i91]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) - [i90]Adel Nikfarjam, Jakob Bossek, Aneta Neumann, Frank Neumann:
Entropy-Based Evolutionary Diversity Optimisation for the Traveling Salesperson Problem. CoRR abs/2104.13538 (2021) - [i89]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) - [i88]Jakob Bossek, Aneta Neumann, Frank Neumann:
Exact Counting and Sampling of Optima for the Knapsack Problem. CoRR abs/2106.07412 (2021) - [i87]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) - [i86]Frank Neumann, Carsten Witt:
Runtime Analysis of Single- and Multi-Objective Evolutionary Algorithms for Chance Constrained Optimization Problems with Normally Distributed Random Variables. CoRR abs/2109.05799 (2021) - [i85]Feng Shi, Frank Neumann, Jianxin Wang:
Time Complexity Analysis of Evolutionary Algorithms for 2-Hop (1, 2)-Minimum Spanning Tree Problem. CoRR abs/2110.04701 (2021) - [i84]Hirad Assimi, Frank Neumann, Markus Wagner, Xiaodong Li:
Novelty-Driven Binary Particle Swarm Optimisation for Truss Optimisation Problems. CoRR abs/2112.07875 (2021) - [i83]Adel Nikfarjam, Aneta Neumann, Frank Neumann:
On the Use of Quality Diversity Algorithms for The Traveling Thief Problem. CoRR abs/2112.08627 (2021) - [i82]Hirad Assimi, Ben Koch, Chris Garcia, Markus Wagner, Frank Neumann:
Run-of-Mine Stockyard Recovery Scheduling and Optimisation for Multiple Reclaimers. CoRR abs/2112.12294 (2021) - [i81]Mingyu Guo, Jialiang Li, Aneta Neumann, Frank Neumann, Hung Nguyen:
Practical Fixed-Parameter Algorithms for Defending Active Directory Style Attack Graphs. CoRR abs/2112.13175 (2021) - 2020
- [j56]Feng Shi
Correction to: Reoptimization Time Analysis of Evolutionary Algorithms on Linear Functions Under Dynamic Uniform Constraints. Algorithmica 82(10): 3117-3123 (2020) - [j55]Behrooz Ghasemishabankareh
Probabilistic tree-based representation for solving minimum cost integer flow problems with nonlinear non-convex cost functions. Appl. Soft Comput. 86 (2020) - [j54]Aneta Neumann
Evolutionary Image Transition and Painting Using Random Walks. Evol. Comput. 28(4): 643-675 (2020) - [j53]Tat-Jun Chin
Robust Fitting in Computer Vision: Easy or Hard? Int. J. Comput. Vis. 128(3): 575-587 (2020) - [j52]Tobias Friedrich
Analysis of the (1 + 1) EA on subclasses of linear functions under uniform and linear constraints. Theor. Comput. Sci. 832: 3-19 (2020) - [j51]Mojgan Pourhassan, Vahid Roostapour, Frank Neumann
Runtime analysis of RLS and (1 + 1) EA for the dynamic weighted vertex cover problem. Theor. Comput. Sci. 832: 20-41 (2020) - [j50]Edgar Covantes Osuna
Design and analysis of diversity-based parent selection schemes for speeding up evolutionary multi-objective optimisation. Theor. Comput. Sci. 832: 123-142 (2020) - [c169]Benjamin Doerr, Carola Doerr, Aneta Neumann, Frank Neumann, Andrew M. Sutton:
Optimization of Chance-Constrained Submodular Functions. AAAI 2020: 1460-1467 - [c168]Derek Weber
Who's in the Gang? Revealing Coordinating Communities in Social Media. ASONAM 2020: 89-93 - [c167]Maryam Hasani-Shoreh, Renato Hermoza Aragonés, Frank Neumann:
Neural Networks in Evolutionary Dynamic Constrained Optimization: Computational Cost and Benefits. ECAI 2020: 275-282 - [c166]Hirad Assimi
Evolutionary Bi-Objective Optimization for the Dynamic Chance-Constrained Knapsack Problem Based on Tail Bound Objectives. ECAI 2020: 307-314 - [c165]Vanja Doskoc, Tobias Friedrich, Andreas Göbel, Aneta Neumann
Non-Monotone Submodular Maximization with Multiple Knapsacks in Static and Dynamic Settings. ECAI 2020: 435-442 - [c164]Yue Xie, Aneta Neumann
Specific single- and multi-objective evolutionary algorithms for the chance-constrained knapsack problem. GECCO 2020: 271-279 - [c163]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 - [c162]Anh Viet Do
Evolving diverse sets of tours for the travelling salesperson problem. GECCO 2020: 681-689 - [c161]Aneta Neumann
Evolutionary computation for digital art. GECCO Companion 2020: 901-926 - [c160]Jakob Bossek, Frank Neumann, Pan Peng, Dirk Sudholt:
More effective randomized search heuristics for graph coloring through dynamic optimization. GECCO 2020: 1277-1285 - [c159]Jakob Bossek, Katrin Casel, Pascal Kerschke
The node weight dependent traveling salesperson problem: approximation algorithms and randomized search heuristics. GECCO 2020: 1286-1294 - [c158]Ragav Sachdeva, Frank Neumann, Markus Wagner:
The Dynamic Travelling Thief Problem: Benchmarks and Performance of Evolutionary Algorithms. ICONIP (5) 2020: 220-228 - [c157]Jakob Bossek, Carola Doerr
Evolving Sampling Strategies for One-Shot Optimization Tasks. PPSN (1) 2020: 111-124 - [c156]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 - [c155]Aneta Neumann
Optimising Monotone Chance-Constrained Submodular Functions Using Evolutionary Multi-objective Algorithms. PPSN (1) 2020: 404-417 - [c154]Anh Viet Do
Maximizing Submodular or Monotone Functions Under Partition Matroid Constraints by Multi-objective Evolutionary Algorithms. PPSN (2) 2020: 588-603 - [c153]Maryam Hasani-Shoreh, Renato Hermoza Aragonés, Frank Neumann:
Using Neural Networks and Diversifying Differential Evolution for Dynamic Optimisation. SSCI 2020: 289-296 - [c152]Aneta Neumann
Human Interactive EEG-Based Evolutionary Image Animation. SSCI 2020: 678-685 - [p6]Frank Neumann, Andrew M. Sutton
Parameterized Complexity Analysis of Randomized Search Heuristics. Theory of Evolutionary Computation 2020: 213-248 - [p5]Frank Neumann, Mojgan Pourhassan, Vahid Roostapour:
Analysis of Evolutionary Algorithms in Dynamic and Stochastic Environments. Theory of Evolutionary Computation 2020: 323-357 - [e4]Benjamin Doerr, Frank Neumann:
Theory of Evolutionary Computation - Recent Developments in Discrete Optimization. Natural Computing Series, Springer 2020, ISBN 978-3-030-29413-7 [contents] - [i80]Frank Neumann, Andrew M. Sutton:
Parameterized Complexity Analysis of Randomized Search Heuristics. CoRR abs/2001.05120 (2020) - [i79]Feng Shi, Frank Neumann, Jianxin Wang:
Runtime Performances of Randomized Search Heuristics for the Dynamic Weighted Vertex Cover Problem. CoRR abs/2001.08903 (2020) - [i78]Maryam Hasani-Shoreh, Renato Hermoza Aragonés, Frank Neumann:
Neural Networks in Evolutionary Dynamic Constrained Optimization: Computational Cost and Benefits. CoRR abs/2001.11588 (2020) - [i77]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) - [i76]Hirad Assimi, Oscar Harper, Yue Xie, Aneta Neumann, Frank Neumann:
Evolutionary Bi-objective Optimization for the Dynamic Chance-Constrained Knapsack Problem Based on Tail Bound Objectives. CoRR abs/2002.06766 (2020) - [i75]Aneta Neumann, Bradley Alexander, Frank Neumann:
Evolutionary Image Transition and Painting Using Random Walks. CoRR abs/2003.01517 (2020) - [i74]Yue Xie, Aneta Neumann, Frank Neumann:
Specific Single- and Multi-Objective Evolutionary Algorithms for the Chance-Constrained Knapsack Problem. CoRR abs/2004.03205 (2020) - [i73]Anh Viet Do, Jakob Bossek, Aneta Neumann, Frank Neumann:
Evolving Diverse Sets of Tours for the Travelling Salesperson Problem. CoRR abs/2004.09188 (2020) - [i72]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) - [i71]Ragav Sachdeva, Frank Neumann, Markus Wagner:
The Dynamic Travelling Thief Problem: Benchmarks and Performance of Evolutionary Algorithms. CoRR abs/2004.12045 (2020) - [i70]Vahid Roostapour, Aneta Neumann, Frank Neumann:
Evolutionary Multi-Objective Optimization for the Dynamic Knapsack Problem. CoRR abs/2004.12574 (2020) - [i69]Jakob Bossek, Frank Neumann, Pan Peng, Dirk Sudholt:
More Effective Randomized Search Heuristics for Graph Coloring Through Dynamic Optimization. CoRR abs/2005.13825 (2020) - [i68]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) - [i67]Aneta Neumann, Frank Neumann:
Optimising Monotone Chance-Constrained Submodular Functions Using Evolutionary Multi-Objective Algorithms. CoRR abs/2006.11444 (2020) - [i66]Anh Viet Do, Frank Neumann:
Maximizing Submodular or Monotone Functions under Partition Matroid Constraints by Multi-objective Evolutionary Algorithms. CoRR abs/2006.12773 (2020) - [i65]Benjamin Doerr, Frank Neumann:
A Survey on Recent Progress in the Theory of Evolutionary Algorithms for Discrete Optimization. CoRR abs/2006.16709 (2020) - [i64]Maryam Hasani-Shoreh, Renato Hermoza Aragonés, Frank Neumann:
Using Neural Networks and Diversifying Differential Evolution for Dynamic Optimisation. CoRR abs/2008.04002 (2020) - [i63]Derek Weber, Frank Neumann:
Who's in the Gang? Revealing Coordinating Communities in Social Media. CoRR abs/2010.08180 (2020) - [i62]Frank Neumann, Mojgan Pourhassan, Carsten Witt:
Improved Runtime Results for Simple Randomised Search Heuristics on Linear Functions with a Uniform Constraint. CoRR abs/2010.10885 (2020) - [i61]Jakob Bossek, Frank Neumann:
Evolutionary Diversity Optimization and the Minimum Spanning Tree Problem. CoRR abs/2010.10913 (2020) - [i60]Aneta Neumann, Jakob Bossek, Frank Neumann:
Computing Diverse Sets of Solutions for Monotone Submodular Optimisation Problems. CoRR abs/2010.11486 (2020) - [i59]Anh Viet Do, Frank Neumann:
Pareto Optimization for Subset Selection with Dynamic Partition Matroid Constraints. CoRR abs/2012.08738 (2020)
2010 – 2019
- 2019
- [j49]Aneta Neumann, Frank Neumann, Tobias Friedrich:
Quasi-random Image Transition and Animation. Aust. J. Intell. Inf. Process. Syst. 16(1): 10-18 (2019) - [j48]Feng Shi
Reoptimization Time Analysis of Evolutionary Algorithms on Linear Functions Under Dynamic Uniform Constraints. Algorithmica 81(2): 828-857 (2019) - [j47]Holger H. Hoos, Frank Neumann, Heike Trautmann:
Foreword. Evol. Comput. 27(1): 1-2 (2019) - [j46]Pascal Kerschke
Automated Algorithm Selection: Survey and Perspectives. Evol. Comput. 27(1): 3-45 (2019) - [j45]Mojgan Pourhassan, Frank Neumann
Theoretical Analysis of Local Search and Simple Evolutionary Algorithms for the Generalized Travelling Salesperson Problem. Evol. Comput. 27(3): 525-558 (2019) - [j44]Mojgan Pourhassan, Feng Shi, Frank Neumann:
Parameterized Analysis of Multiobjective Evolutionary Algorithms and the Weighted Vertex Cover Problem. Evol. Comput. 27(4): 559-575 (2019) - [c151]Tobias Friedrich, Andreas Göbel, Frank Neumann, Francesco Quinzan, Ralf Rothenberger
Greedy Maximization of Functions with Bounded Curvature under Partition Matroid Constraints. AAAI 2019: 2272-2279 - [c150]Frank Neumann, Andrew M. Sutton:
Evolving Solutions to Community-Structured Satisfiability Formulas. AAAI 2019: 2346-2353 - [c149]Vahid Roostapour, Aneta Neumann
Pareto Optimization for Subset Selection with Dynamic Cost Constraints. AAAI 2019: 2354-2361 - [c148]Wanru Gao, Mojgan Pourhassan, Vahid Roostapour, Frank Neumann
Runtime Analysis of Evolutionary Multi-objective Algorithms Optimising the Degree and Diameter of Spanning Trees. EMO 2019: 504-515 - [c147]Jakob Bossek, Pascal Kerschke
Evolving diverse TSP instances by means of novel and creative mutation operators. FOGA 2019: 58-71 - [c146]Vahid Roostapour, Mojgan Pourhassan, Frank Neumann:
Analysis of baseline evolutionary algorithms for the packing while travelling problem. FOGA 2019: 124-132 - [c145]Feng Shi, Frank Neumann, Jianxin Wang:
Runtime analysis of evolutionary algorithms for the depth restricted (1, 2)-minimum spanning tree problem. FOGA 2019: 133-146 - [c144]Frank Neumann, Andrew M. Sutton
Runtime analysis of the (1 + 1) evolutionary algorithm for the chance-constrained knapsack problem. FOGA 2019: 147-153 - [c143]Benjamin Doerr, Carola Doerr
Fast re-optimization via structural diversity. GECCO 2019: 233-241 - [c142]Yue Xie, Oscar Harper, Hirad Assimi
Evolutionary algorithms for the chance-constrained knapsack problem. GECCO 2019: 338-346 - [c141]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 - [c140]Aneta Neumann
Evolutionary diversity optimization using multi-objective indicators. GECCO 2019: 837-845 - [c139]Aneta Neumann
Evolutionary computation for digital art. GECCO (Companion) 2019: 1056-1076 - [c138]Jakob Bossek
Runtime analysis of randomized search heuristics for dynamic graph coloring. GECCO 2019: 1443-1451 - [c137]Frank Neumann
Improved runtime results for simple randomised search heuristics on linear functions with a uniform constraint. GECCO 2019: 1506-1514 - [c136]Maryam Hasani-Shoreh, Frank Neumann:
On the Use of Diversity Mechanisms in Dynamic Constrained Continuous Optimization. ICONIP (1) 2019: 644-657 - [p4]Mohammad Reza Bonyadi, Zbigniew Michalewicz, Markus Wagner, Frank Neumann:
Evolutionary Computation for Multicomponent Problems: Opportunities and Future Directions. Optimization in Industry 2019: 13-30 - [i58]Benjamin Doerr, Carola Doerr, Frank Neumann:
Fast Re-Optimization via Structural Diversity. CoRR abs/1902.00304 (2019) - [i57]Álvaro Parra Bustos, Tat-Jun Chin, Frank Neumann, Tobias Friedrich, Maximilian Katzmann:
A Practical Maximum Clique Algorithm for Matching with Pairwise Constraints. CoRR abs/1902.01534 (2019) - [i56]Vahid Roostapour, Mojgan Pourhassan, Frank Neumann:
Analysis of Baseline Evolutionary Algorithms for the Packing While Travelling Problem. CoRR abs/1902.04692 (2019) - [i55]Yue Xie, Oscar Harper, Hirad Assimi, Aneta Neumann, Frank Neumann:
Evolutionary Algorithms for the Chance-Constrained Knapsack Problem. CoRR abs/1902.04767 (2019) - [i54]Mojgan Pourhassan, Vahid Roostapour, Frank Neumann:
Runtime Analysis of RLS and (1+1) EA for the Dynamic Weighted Vertex Cover Problem. CoRR abs/1903.02195 (2019) - [i53]Maryam Hasani-Shoreh, Maria Yaneli Ameca-Alducin, Wilson Blaikie, Frank Neumann, Marc Schoenauer:
On the Behaviour of Differential Evolution for Problems with Dynamic Linear Constraints. CoRR abs/1905.04099 (2019) - [i52]Maryam Hasani-Shoreh, Frank Neumann:
On the Use of Diversity Mechanisms in Dynamic Constrained Continuous Optimization. CoRR abs/1910.06062 (2019) - [i51]Vanja Doskoc, Tobias Friedrich, Andreas Göbel, Frank Neumann, Aneta Neumann, Francesco Quinzan:
Non-Monotone Submodular Maximization with Multiple Knapsacks in Static and Dynamic Settings. CoRR abs/1911.06791 (2019) - [i50]Benjamin Doerr, Carola Doerr, Aneta Neumann, Frank Neumann, Andrew M. Sutton:
Optimization of Chance-Constrained Submodular Functions. CoRR abs/1911.11451 (2019) - [i49]Jakob Bossek, Pascal Kerschke, Aneta Neumann, Frank Neumann, Carola Doerr:
One-Shot Decision-Making with and without Surrogates. CoRR abs/1912.08956 (2019) - 2018
- [c135]Frank Neumann
A Fully Polynomial Time Approximation Scheme for Packing While Traveling. ALGOCLOUD 2018: 59-72 - [c134]Aneta Neumann
On the Use of Colour-Based Segmentation in Evolutionary Image Composition. CEC 2018: 1-8 - [c133]Maria Yaneli Ameca-Alducin, Maryam Hasani-Shoreh, Wilson Blaikie, Frank Neumann
A Comparison of Constraint Handling Techniques for Dynamic Constrained Optimization Problems. CEC 2018: 1-8 - [c132]Tat-Jun Chin, Zhipeng Cai, Frank Neumann
Robust Fitting in Computer Vision: Easy or Hard? ECCV (12) 2018: 715-730 - [c131]Maria Yaneli Ameca-Alducin, Maryam Hasani-Shoreh, Frank Neumann
On the Use of Repair Methods in Differential Evolution for Dynamic Constrained Optimization. EvoApplications 2018: 832-847 - [c130]Wanru Gao, Tobias Friedrich, Frank Neumann
Randomized greedy algorithms for covering problems. GECCO 2018: 309-315 - [c129]Junhua Wu, Sergey Polyakovskiy
Evolutionary computation plus dynamic programming for the bi-objective travelling thief problem. GECCO 2018: 777-784 - [c128]Aneta Neumann
Evolutionary computation for digital art. GECCO (Companion) 2018: 937-955 - [c127]Aneta Neumann
Discrepancy-based evolutionary diversity optimization. GECCO 2018: 991-998 - [c126]Feng Shi, Frank Neumann
Runtime analysis of randomized search heuristics for the dynamic weighted vertex cover problem. GECCO 2018: 1515-1522 - [c125]Behrooz Ghasemishabankareh, Melih Ozlen
A Probabilistic Tree-Based Representation for Non-convex Minimum Cost Flow Problems. PPSN (1) 2018: 69-81 - [c124]Frank Neumann
Runtime Analysis of Evolutionary Algorithms for the Knapsack Problem with Favorably Correlated Weights. PPSN (2) 2018: 141-152 - [c123]Vahid Roostapour, Aneta Neumann
On the Performance of Baseline Evolutionary Algorithms on the Dynamic Knapsack Problem. PPSN (1) 2018: 158-169 - [i48]Junhua Wu, Sergey Polyakovskiy, Markus Wagner, Frank Neumann:
Evolutionary Computation plus Dynamic Programming for the Bi-Objective Travelling Thief Problem. CoRR abs/1802.02434 (2018) - [i47]Aneta Neumann, Wanru Gao, Carola Doerr, Frank Neumann, Markus Wagner:
Discrepancy-based Evolutionary Diversity Optimization. CoRR abs/1802.05448 (2018) - [i46]Maria Yaneli Ameca-Alducin, Maryam Hasani-Shoreh, Wilson Blaikie, Frank Neumann, Efrén Mezura-Montes:
A Comparison of Constraint Handling Techniques for Dynamic Constrained Optimization Problems. CoRR abs/1802.05825 (2018) - [i45]Tat-Jun Chin, Zhipeng Cai, Frank Neumann:
Robust Fitting in Computer Vision: Easy or Hard? CoRR abs/1802.06464 (2018) - [i44]Edgar Covantes Osuna, Wanru Gao, Frank Neumann, Dirk Sudholt:
Design and Analysis of Diversity-Based Parent Selection Schemes for Speeding Up Evolutionary Multi-objective Optimisation. CoRR abs/1805.01221 (2018) - [i43]Vahid Roostapour, Mojgan Pourhassan, Frank Neumann:
Analysis of Evolutionary Algorithms in Dynamic and Stochastic Environments. CoRR abs/1806.08547 (2018) - [i42]Tobias Friedrich, Andreas Göbel, Frank Neumann, Francesco Quinzan, Ralf Rothenberger
Greedy Maximization of Functions with Bounded Curvature under Partition Matroid Constraints. CoRR abs/1811.05351 (2018) - [i41]Aneta Neumann, Wanru Gao, Markus Wagner, Frank Neumann:
Evolutionary Diversity Optimization Using Multi-Objective Indicators. CoRR abs/1811.06804 (2018) - [i40]Vahid Roostapour, Aneta Neumann, Frank Neumann, Tobias Friedrich:
Pareto Optimization for Subset Selection with Dynamic Cost Constraints. CoRR abs/1811.07806 (2018) - [i39]Pascal Kerschke, Holger H. Hoos, Frank Neumann, Heike Trautmann:
Automated Algorithm Selection: Survey and Perspectives. CoRR abs/1811.11597 (2018) - 2017
- [j43]Benjamin Doerr, Frank Neumann
Time Complexity Analysis of Evolutionary Algorithms on Random Satisfiable k-CNF Formulas. Algorithmica 78(2): 561-586 (2017) - [j42]Samadhi Nallaperuma, Frank Neumann
Expected Fitness Gains of Randomized Search Heuristics for the Traveling Salesperson Problem. Evol. Comput. 25(4) (2017) - [j41]Sergey Polyakovskiy
The Packing While Traveling Problem. Eur. J. Oper. Res. 258(2): 424-439 (2017) - [j40]Mohammad Reza Bonyadi
Ahura: A Heuristic-Based Racer for the Open Racing Car Simulator. IEEE Trans. Comput. Intell. AI Games 9(3): 290-304 (2017) - [c122]Tobias Friedrich, Frank Neumann:
What's Hot in Evolutionary Computation. AAAI 2017: 5064-5066 - [c121]Wanru Gao, Tobias Friedrich, Timo Kötzing, Frank Neumann
Scaling up Local Search for Minimum Vertex Cover in Large Graphs by Parallel Kernelization. Australasian Conference on Artificial Intelligence 2017: 131-143 - [c120]Aneta Neumann
Evolutionary Image Transition Using Random Walks. EvoMUSART 2017: 230-245 - [c119]Mojgan Pourhassan, Tobias Friedrich, Frank Neumann
On the Use of the Dual Formulation for Minimum Weighted Vertex Cover in Evolutionary Algorithms. FOGA 2017: 37-44 - [c118]Tobias Friedrich, Timo Kötzing, Gregor Lagodzinski
Analysis of the (1+1) EA on Subclasses of Linear Functions under Uniform and Linear Constraints. FOGA 2017: 45-54 - [c117]Edgar Covantes Osuna
Speeding up evolutionary multi-objective optimisation through diversity-based parent selection. GECCO 2017: 553-560 - [c116]Aneta Neumann
Evolutionary image composition using feature covariance matrices. GECCO 2017: 817-824 - [c115]Feng Shi, Martin Schirneck
Reoptimization times of evolutionary algorithms on linear functions under dynamic uniform constraints. GECCO 2017: 1407-1414 - [c114]Junhua Wu, Markus Wagner, Sergey Polyakovskiy
Exact Approaches for the Travelling Thief Problem. SEAL 2017: 110-121 - [c113]Frank Neumann
Parameterized analysis of bio-inspired computing. SSCI 2017: 1-3 - [c112]Mojgan Pourhassan, Vahid Roostapour, Frank Neumann
Improved runtime analysis of RLS and (1+1) EA for the dynamic vertex cover problem. SSCI 2017: 1-6 - [i38]Frank Neumann, Sergey Polyakovskiy, Martin Skutella, Leen Stougie, Junhua Wu:
A Fully Polynomial Time Approximation Scheme for Packing While Traveling. CoRR abs/1702.05217 (2017) - [i37]Aneta Neumann, Zygmunt L. Szpak, Wojciech Chojnacki, Frank Neumann:
Evolutionary Image Composition Using Feature Covariance Matrices. CoRR abs/1703.03773 (2017) - [i36]Junhua Wu, Markus Wagner, Sergey Polyakovskiy, Frank Neumann:
Exact Approaches for the Travelling Thief Problem. CoRR abs/1708.00331 (2017) - [i35]Aneta Neumann, Frank Neumann, Tobias Friedrich:
Quasi-random Agents for Image Transition and Animation. CoRR abs/1710.07421 (2017) - 2016
- [j39]Dogan Corus
A Parameterised Complexity Analysis of Bi-level Optimisation with Evolutionary Algorithms. Evol. Comput. 24(1): 183-203 (2016) - [j38]Sergey Polyakovskiy
Solving hard control problems in voting systems via integer programming. Eur. J. Oper. Res. 250(1): 204-213 (2016) - [c111]Frank Neumann
Feature-based algorithm selection for constrained continuous optimisation. CEC 2016: 1461-1468 - [c110]Tat-Jun Chin, Yang Heng Kee, Anders P. Eriksson, Frank Neumann
Guaranteed Outlier Removal with Mixed Integer Linear Programs. CVPR 2016: 5858-5866 - [c109]Junhua Wu
On the Impact of the Renting Rate for the Unconstrained Nonlinear Knapsack Problem. GECCO 2016: 413-419 - [c108]Benjamin Doerr, Wanru Gao, Frank Neumann
Runtime Analysis of Evolutionary Diversity Maximization for OneMinMax. GECCO 2016: 557-564 - [c107]Tobias Friedrich, Timo Kötzing, Martin S. Krejca, Samadhi Nallaperuma, Frank Neumann
Fast Building Block Assembly by Majority Vote Crossover. GECCO 2016: 661-668 - [c106]Junhua Wu
Fast and Effective Optimisation of Arrays of Submerged Wave Energy Converters. GECCO 2016: 1045-1052 - [c105]Aneta Neumann
The Evolutionary Process of Image Transition in Conjunction with Box and Strip Mutation. ICONIP (3) 2016: 261-268 - [c104]Mojgan Pourhassan, Feng Shi, Frank Neumann
Parameterized Analysis of Multi-objective Evolutionary Algorithms and the Weighted Vertex Cover Problem. PPSN 2016: 729-739 - [c103]Wanru Gao, Tobias Friedrich, Frank Neumann
Fixed-Parameter Single Objective Search Heuristics for Minimum Vertex Cover. PPSN 2016: 740-750 - [c102]Wanru Gao, Samadhi Nallaperuma, Frank Neumann
Feature-Based Diversity Optimization for Problem Instance Classification. PPSN 2016: 869-879 - [e3]Tobias Friedrich, Frank Neumann, Andrew M. Sutton:
Proceedings of the 2016 on Genetic and Evolutionary Computation Conference, Denver, CO, USA, July 20 - 24, 2016. ACM 2016, ISBN 978-1-4503-4206-3 [contents] - [e2]Tobias Friedrich, Frank Neumann, Andrew M. Sutton:
Genetic and Evolutionary Computation Conference, GECCO 2016, Denver, CO, USA, July 20-24, 2016, Companion Material Proceedings. ACM 2016, ISBN 978-1-4503-4323-7 [contents] - [i34]Shayan Poursoltan, Frank Neumann:
A Feature-Based Prediction Model of Algorithm Selection for Constrained Continuous Optimisation. CoRR abs/1602.02862 (2016) - [i33]Mojgan Pourhassan, Feng Shi, Frank Neumann:
Parameterized Analysis of Multi-objective Evolutionary Algorithms and the Weighted Vertex Cover Problem. CoRR abs/1604.01495 (2016) - [i32]Aneta Neumann, Bradley Alexander, Frank Neumann:
Evolutionary Image Transition Based on Theoretical Insights of Random Processes. CoRR abs/1604.06187 (2016) - [i31]Mohammad Reza Bonyadi, Zbigniew Michalewicz, Frank Neumann, Markus Wagner:
Evolutionary computation for multicomponent problems: opportunities and future directions. CoRR abs/1606.06818 (2016) - [i30]Aneta Neumann, Bradley Alexander, Frank Neumann:
The Evolutionary Process of Image Transition in Conjunction with Box and Strip Mutation. CoRR abs/1608.01783 (2016) - [i29]Holger H. Hoos, Frank Neumann, Heike Trautmann:
Automated Algorithm Selection and Configuration (Dagstuhl Seminar 16412). Dagstuhl Reports 6(10): 33-74 (2016) - 2015
- [j37]Tobias Friedrich, Frank Neumann
Multiplicative Approximations, Optimal Hypervolume Distributions, and the Choice of the Reference Point. Evol. Comput. 23(1): 131-159 (2015) - [j36]Tobias Friedrich, Frank Neumann
Maximizing Submodular Functions under Matroid Constraints by Evolutionary Algorithms. Evol. Comput. 23(4): 543-558 (2015) - [j35]Markus Wagner, Frank Neumann
On the Performance of Different Genetic Programming Approaches for the SORTING Problem. Evol. Comput. 23(4): 583-609 (2015) - [j34]Markus Wagner
Efficient optimization of many objectives by approximation-guided evolution. Eur. J. Oper. Res. 243(2): 465-479 (2015) - [j33]Samadhi Nallaperuma, Markus Wagner
Analyzing the Effects of Instance Features and Algorithm Parameters for Max-Min Ant System and the Traveling Salesperson Problem. Frontiers Robotics AI 2: 18 (2015) - [j32]Anh Quang Nguyen, Andrew M. Sutton
Population size matters: Rigorous runtime results for maximizing the hypervolume indicator. Theor. Comput. Sci. 561: 24-36 (2015) - [c101]Sergey Polyakovskiy
Packing While Traveling: Mixed Integer Programming for a Class of Nonlinear Knapsack Problems. CPAIOR 2015: 332-346 - [c100]Frank Neumann
Parameterized Complexity Analysis of Evolutionary Algorithms. GECCO (Companion) 2015: 435-450 - [c99]Mojgan Pourhassan, Frank Neumann
On the Impact of Local Search Operators and Variable Neighbourhood Search for the Generalized Travelling Salesperson Problem. GECCO 2015: 465-472 - [c98]Mojgan Pourhassan, Wanru Gao, Frank Neumann
Maintaining 2-Approximations for the Dynamic Vertex Cover Problem Using Evolutionary Algorithms. GECCO 2015: 903-910 - [c97]Wanru Gao, Mojgan Pourhassan, Frank Neumann
Runtime Analysis of Evolutionary Diversity Optimization and the Vertex Cover Problem. GECCO (Companion) 2015: 1395-1396 - [c96]Benjamin Doerr, Frank Neumann
Improved Runtime Bounds for the (1+1) EA on Random 3-CNF Formulas Based on Fitness-Distance Correlation. GECCO 2015: 1415-1422 - [c95]Shayan Poursoltan, Frank Neumann
A Feature-Based Comparison of Evolutionary Computing Techniques for Constrained Continuous Optimisation. ICONIP (3) 2015: 332-343 - [c94]Shayan Poursoltan, Frank Neumann
A Feature-Based Analysis on the Impact of Set of Constraints for \varepsilon -Constrained Differential Evolution. ICONIP (3) 2015: 344-355 - [c93]Frank Neumann, Carsten Witt:
On the Runtime of Randomized Local Search and Simple Evolutionary Algorithms for Dynamic Makespan Scheduling. IJCAI 2015: 3742-3748 - [i28]Frank Neumann, Carsten Witt:
On the Runtime of Randomized Local Search and Simple Evolutionary Algorithms for Dynamic Makespan Scheduling. CoRR abs/1504.06363 (2015) - [i27]Laurent Hoeltgen, Markus Mainberger, Sebastian Hoffmann, Joachim Weickert, Ching Hoo Tang, Simon Setzer, Daniel Johannsen, Frank Neumann, Benjamin Doerr:
Optimising Spatial and Tonal Data for PDE-based Inpainting. CoRR abs/1506.04566 (2015) - [i26]Shayan Poursoltan, Frank Neumann:
A Feature-Based Analysis on the Impact of Set of Constraints for e-Constrained Differential Evolution. CoRR abs/1506.06848 (2015) - [i25]Shayan Poursoltan, Frank Neumann:
A Feature-Based Comparison of Evolutionary Computing Techniques for Constrained Continuous Optimisation. CoRR abs/1509.06842 (2015) - [i24]Wanru Gao
Feature-Based Diversity Optimization for Problem Instance Classification. CoRR abs/1510.08568 (2015) - [i23]Sergey Polyakovskiy, Frank Neumann:
The Packing While Traveling Problem. CoRR abs/1512.08831 (2015) - 2014
- [j31]Andrew M. Sutton
Parameterized Runtime Analyses of Evolutionary Algorithms for the Planar Euclidean Traveling Salesperson Problem. Evol. Comput. 22(4): 595-628 (2014) - [j30]Timo Kötzing, Andrew M. Sutton
The Max problem revisited: The importance of mutation in genetic programming. Theor. Comput. Sci. 545: 94-107 (2014) - [j29]Frank Neumann
Editorial for the Special Issue on Theoretical Foundations of Evolutionary Computation. IEEE Trans. Evol. Comput. 18(5): 625-627 (2014) - [c92]Markus Wagner
Single- and multi-objective genetic programming: New runtime results for sorting. IEEE Congress on Evolutionary Computation 2014: 125-132 - [c91]Shayan Poursoltan, Frank Neumann
A Feature-based analysis on the impact of linear constraints for ε-constrained differential evolution. IEEE Congress on Evolutionary Computation 2014: 3088-3095 - [c90]Samadhi Nallaperuma, Frank Neumann
EVOR: an online evolutionary algorithm for car racing games. GECCO 2014: 317-324 - [c89]Sergey Polyakovskiy
A comprehensive benchmark set and heuristics for the traveling thief problem. GECCO 2014: 477-484 - [c88]Frank Neumann
Parameterized complexity analysis of evolutionary algorithms. GECCO (Companion) 2014: 607-622 - [c87]Wanru Gao, Frank Neumann
Runtime analysis for maximizing population diversity in single-objective optimization. GECCO 2014: 777-784 - [c86]Samadhi Nallaperuma, Frank Neumann
A fixed budget analysis of randomized search heuristics for the traveling salesperson problem. GECCO 2014: 807-814 - [c85]Samadhi Nallaperuma, Markus Wagner, Frank Neumann:
Parameter Prediction Based on Features of Evolved Instances for Ant Colony Optimization and the Traveling Salesperson Problem. PPSN 2014: 100-109 - [c84]Tobias Friedrich, Frank Neumann:
Maximizing Submodular Functions under Matroid Constraints by Multi-objective Evolutionary Algorithms. PPSN 2014: 922-931 - [c83]Andrew M. Sutton, Frank Neumann:
Runtime Analysis of Evolutionary Algorithms on Randomly Constructed High-Density Satisfiable 3-CNF Formulas. PPSN 2014: 942-951 - [c82]Anh Quang Nguyen, Markus Wagner, Frank Neumann:
User Preferences for Approximation-Guided Multi-objective Evolution. SEAL 2014: 251-262 - [c81]Frank Neumann, Anh Quang Nguyen:
On the Impact of Utility Functions in Interactive Evolutionary Multi-objective Optimization. SEAL 2014: 419-430 - [i22]Dogan Corus, Per Kristian Lehre, Frank Neumann, Mojgan Pourhassan:
A Parameterized Complexity Analysis of Bi-level Optimisation with Evolutionary Algorithms. CoRR abs/1401.1905 (2014) - [i21]Sergey Polyakovskiy, Rudolf Berghammer, Frank Neumann:
Solving Hard Control Problems in Voting Systems via Integer Programming. CoRR abs/1408.5987 (2014) - [i20]Sergey Polyakovskiy, Frank Neumann:
Packing While Traveling: Mixed Integer Programming for a Class of Nonlinear Knapsack Problems. CoRR abs/1411.5768 (2014) - [i19]Hong Mei, Frank Neumann, Xin Yao, Leandro L. Minku:
Computational Intelligence for Software Engineering (NII Shonan Meeting 2014-13). NII Shonan Meet. Rep. 2014 (2014) - 2013
- [j28]Stefan Kratsch, Frank Neumann
Fixed-Parameter Evolutionary Algorithms and the Vertex Cover Problem. Algorithmica 65(4): 754-771 (2013) - [j27]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) - [j26]Tobias Friedrich, Trent Kroeger, Frank Neumann
Weighted preferences in evolutionary multi-objective optimization. Int. J. Mach. Learn. Cybern. 4(2): 139-148 (2013) - [j25]Benjamin Doerr, Daniel Johannsen, Timo Kötzing, Frank Neumann
More effective crossover operators for the all-pairs shortest path problem. Theor. Comput. Sci. 471: 12-26 (2013) - [c80]Samadhi Nallaperuma, Andrew M. Sutton
Fixed-parameter evolutionary algorithms for the Euclidean Traveling Salesperson problem. IEEE Congress on Evolutionary Computation 2013: 2037-2044 - [c79]Samadhi Nallaperuma, Andrew M. Sutton
Parameterized complexity analysis and more effective construction methods for ACO algorithms and the euclidean traveling salesperson problem. IEEE Congress on Evolutionary Computation 2013: 2045-2052 - [c78]Samadhi Nallaperuma, Markus Wagner
A feature-based comparison of local search and the christofides algorithm for the travelling salesperson problem. FOGA 2013: 147-160 - [c77]Samadhi Nallaperuma, Markus Wagner
Ant colony optimisation and the traveling salesperson problem: hardness, features and parameter settings. GECCO (Companion) 2013: 13-14 - [c76]Dogan Corus
The generalized minimum spanning tree problem: a parameterized complexity analysis of bi-level optimisation. GECCO 2013: 519-526 - [c75]Frank Neumann
Bioinspired computation in combinatorial optimization: algorithms and their computational complexity. GECCO (Companion) 2013: 567-590 - [c74]Markus Wagner
A fast approximation-guided evolutionary multi-objective algorithm. GECCO 2013: 687-694 - [c73]Raymond Tran, Junhua Wu
Fast and effective multi-objective optimisation of wind turbine placement. GECCO 2013: 1381-1388 - [c72]Anh Quang Nguyen, Andrew M. Sutton
Population size matters: rigorous runtime results for maximizing the hypervolume indicator. GECCO 2013: 1613-1620 - [e1]Frank Neumann, Kenneth A. De Jong:
Foundations of Genetic Algorithms XII, FOGA '13, Adelaide, SA, Australia, January 16-20, 2013. ACM 2013, ISBN 978-1-4503-1990-4 [contents] - [i18]Benjamin Doerr, Anton V. Eremeev, Frank Neumann, Madeleine Theile, Christian Thyssen:
Evolutionary Algorithms and Dynamic Programming. CoRR abs/1301.4096 (2013) - [i17]Tobias Friedrich, Frank Neumann, Christian Thyssen:
Multiplicative Approximations, Optimal Hypervolume Distributions, and the Choice of the Reference Point. CoRR abs/1309.3816 (2013) - 2012
- [j24]Timo Kötzing, Frank Neumann
Theoretical analysis of two ACO approaches for the traveling salesman problem. Swarm Intell. 6(1): 1-21 (2012) - [j23]Per Kristian Lehre
Editorial to the special issue on "Theoretical Foundations of Evolutionary Computation". Theor. Comput. Sci. 425: 2-3 (2012) - [j22]Rudolf Berghammer, Tobias Friedrich, Frank Neumann
Convergence of set-based multi-objective optimization, indicators and deteriorative cycles. Theor. Comput. Sci. 456: 2-17 (2012) - [c71]Andrew M. Sutton, Frank Neumann:
A Parameterized Runtime Analysis of Evolutionary Algorithms for the Euclidean Traveling Salesperson Problem. AAAI 2012: 1105-1111 - [c70]Kalyan Veeramachaneni, Markus Wagner
Optimizing energy output and layout costs for large wind farms using particle swarm optimization. IEEE Congress on Evolutionary Computation 2012: 1-7 - [c69]Joseph Yuen, Sophia Gao, Markus Wagner
An adaptive data structure for evolutionary multi-objective algorithms with unbounded archives. IEEE Congress on Evolutionary Computation 2012: 1-8 - [c68]Andrew M. Sutton
A parameterized runtime analysis of evolutionary algorithms for MAX-2-SAT. GECCO 2012: 433-440 - [c67]Frank Neumann
Computational complexity analysis of multi-objective genetic programming. GECCO 2012: 799-806 - [c66]Frank Neumann
Bioinspired computation in combinatorial optimization: algorithms and their computational complexity. GECCO (Companion) 2012: 1035-1058 - [c65]Timo Kötzing, Andrew M. Sutton
The max problem revisited: the importance of mutation in genetic programming. GECCO 2012: 1333-1340 - [c64]Olaf Mersmann
Local Search and the Traveling Salesman Problem: A Feature-Based Characterization of Problem Hardness. LION 2012: 115-129 - [c63]Andrew M. Sutton
A Parameterized Runtime Analysis of Simple Evolutionary Algorithms for Makespan Scheduling. PPSN (1) 2012: 52-61 - [c62]Tommaso Urli, Markus Wagner, Frank Neumann
Experimental Supplements to the Computational Complexity Analysis of Genetic Programming for Problems Modelling Isolated Program Semantics. PPSN (1) 2012: 102-112 - [c61]Markus Wagner, Frank Neumann
Parsimony Pressure versus Multi-objective Optimization for Variable Length Representations. PPSN (1) 2012: 133-142 - [i16]Frank Neumann:
Computational Complexity Analysis of Multi-Objective Genetic Programming. CoRR abs/1203.4881 (2012) - [i15]Markus Wagner, Jareth Day, Frank Neumann:
A Fast and Effective Local Search Algorithm for Optimizing the Placement of Wind Turbines. CoRR abs/1204.4560 (2012) - [i14]Benjamin Doerr, Daniel Johannsen, Timo Kötzing, Frank Neumann, Madeleine Theile:
More Effective Crossover Operators for the All-Pairs Shortest Path Problem. CoRR abs/1207.0369 (2012) - [i13]Andrew M. Sutton, Frank Neumann:
A Parameterized Runtime Analysis of Evolutionary Algorithms for the Euclidean Traveling Salesperson Problem. CoRR abs/1207.0578 (2012) - [i12]Olaf Mersmann, Bernd Bischl, Heike Trautmann, Markus Wagner, Frank Neumann:
A Novel Feature-Based Approach to Characterize Algorithm Performance for the Traveling Salesman Problem. CoRR abs/1208.2318 (2012) - 2011
- [j21]Frank Neumann
Computing Minimum Cuts by Randomized Search Heuristics. Algorithmica 59(3): 323-342 (2011) - [j20]Tobias Friedrich, Christian Horoba, Frank Neumann
Illustration of fairness in evolutionary multi-objective optimization. Theor. Comput. Sci. 412(17): 1546-1556 (2011) - [j19]Benjamin Doerr, Frank Neumann
Runtime analysis of the 1-ANT ant colony optimizer. Theor. Comput. Sci. 412(17): 1629-1644 (2011) - [j18]Benjamin Doerr, Anton V. Eremeev
Evolutionary algorithms and dynamic programming. Theor. Comput. Sci. 412(43): 6020-6035 (2011) - [c60]Tobias Friedrich, Trent Kroeger, Frank Neumann
Weighted Preferences in Evolutionary Multi-objective Optimization. Australasian Conference on Artificial Intelligence 2011: 291-300 - [c59]Greg Durrett, Frank Neumann
Computational complexity analysis of simple genetic programming on two problems modeling isolated program semantics. FOGA 2011: 69-80 - [c58]Timo Kötzing, Frank Neumann
Simple max-min ant systems and the optimization of linear pseudo-boolean functions. FOGA 2011: 209-218 - [c57]Thomas Jansen
Computational complexity and evolutionary computation. GECCO (Companion) 2011: 1053-1080 - [c56]Tobias Friedrich, Frank Neumann
Foundations of evolutionary multi-objective optimization. GECCO (Companion) 2011: 1213-1232 - [c55]Frank Neumann
On the effectiveness of crossover for migration in parallel evolutionary algorithms. GECCO 2011: 1587-1594 - [c54]Timo Kötzing, Frank Neumann
PAC learning and genetic programming. GECCO 2011: 2091-2096 - [c53]Karl Bringmann, Tobias Friedrich, Frank Neumann
Approximation-Guided Evolutionary Multi-Objective Optimization. IJCAI 2011: 1198-1203 - [c52]Markus Mainberger, Sebastian Hoffmann, Joachim Weickert, Ching Hoo Tang, Daniel Johannsen, Frank Neumann
Optimising Spatial and Tonal Data for Homogeneous Diffusion Inpainting. SSVM 2011: 26-37 - [i11]Markus Wagner, Frank Neumann:
Computational Complexity Results for Genetic Programming and the Sorting Problem. CoRR abs/1103.5797 (2011) - [i10]Markus Wagner, Jareth Day, Claire Diora Jordan, Trent Kroeger, Frank Neumann:
Evolving Pacing Strategies for Team Pursuit Track Cycling. CoRR abs/1104.0775 (2011) - [i9]Katya Vladislavleva, Tobias Friedrich, Frank Neumann, Markus Wagner:
Predicting the Energy Output of Wind Farms Based on Weather Data: Important Variables and their Correlation. CoRR abs/1109.1922 (2011) - 2010
- [b2]Frank Neumann, Carsten Witt:
Bioinspired Computation in Combinatorial Optimization. Natural Computing Series, Springer 2010, ISBN 978-3-642-16543-6, pp. 1-203 - [j17]Benjamin Doerr, Frank Neumann
Editorial. Algorithmica 57(1): 119-120 (2010) - [j16]Benjamin Doerr, Frank Neumann
In Memoriam: Ingo Wegener. Algorithmica 58(3): 541-542 (2010) - [j15]Thomas Jansen
Editorial for the Special Issue on Theoretical Aspects of Evolutionary Multi-Objective Optimization. Evol. Comput. 18(3): 333-334 (2010) - [j14]Tobias Friedrich, Jun He
Approximating Covering Problems by Randomized Search Heuristics Using Multi-Objective Models. Evol. Comput. 18(4): 617-633 (2010) - [j13]Tobias Friedrich
When to use bit-wise neutrality. Nat. Comput. 9(1): 283-294 (2010) - [j12]Tobias Friedrich, Nils Hebbinghaus, Frank Neumann
Plateaus can be harder in multi-objective optimization. Theor. Comput. Sci. 411(6): 854-864 (2010) - [j11]Frank Neumann
Ant Colony Optimization and the minimum spanning tree problem. Theor. Comput. Sci. 411(25): 2406-2413 (2010) - [c51]Timo Kötzing, Frank Neumann
Theoretical Properties of Two ACO Approaches for the Traveling Salesman Problem. ANTS Conference 2010: 324-335 - [c50]Frank Neumann
A few ants are enough: ACO with iteration-best update. GECCO 2010: 63-70 - [c49]Rudolf Berghammer, Tobias Friedrich, Frank Neumann
Set-based multi-objective optimization, indicators, and deteriorative cycles. GECCO 2010: 495-502 - [c48]Timo Kötzing, Per Kristian Lehre
Ant colony optimization and the minimum cut problem. GECCO 2010: 1393-1400 - [c47]Tobias Friedrich, Frank Neumann
Foundations of evolutionary multi-objective optimization. GECCO (Companion) 2010: 2557-2576 - [c46]Thomas Jansen
Computational complexity and evolutionary computation. GECCO (Companion) 2010: 2683-2710 - [c45]Süntje Böttcher, Benjamin Doerr, Frank Neumann
Optimal Fixed and Adaptive Mutation Rates for the LeadingOnes Problem. PPSN (1) 2010: 1-10 - [c44]Benjamin Doerr, Daniel Johannsen, Timo Kötzing, Frank Neumann
More Effective Crossover Operators for the All-Pairs Shortest Path Problem. PPSN (1) 2010: 184-193 - [c43]Stefan Kratsch, Per Kristian Lehre
Fixed Parameter Evolutionary Algorithms and Maximum Leaf Spanning Trees: A Matter of Mutation. PPSN (1) 2010: 204-213 - [c42]Frank Neumann
How Crossover Speeds Up Evolutionary Algorithms for the Multi-criteria All-Pairs-Shortest-Path Problem. PPSN (1) 2010: 667-676 - [c41]Adam Ghandar
Evolving Fuzzy Rules: Evaluation of a New Approach. SEAL 2010: 250-259 - [p3]Christian Horoba, Frank Neumann
Approximating Pareto-Optimal Sets Using Diversity Strategies in Evolutionary Multi-Objective Optimization. Advances in Multi-Objective Nature Inspired Computing 2010: 23-44 - [i8]Greg Durrett, Frank Neumann, Una-May O'Reilly:
Computational Complexity Analysis of Simple Genetic Programming On Two Problems Modeling Isolated Program Semantics. CoRR abs/1007.4636 (2010) - [i7]Timo Kötzing, Frank Neumann, Dirk Sudholt, Markus Wagner:
Simple Max-Min Ant Systems and the Optimization of Linear Pseudo-Boolean Functions. CoRR abs/1007.4707 (2010)
2000 – 2009
- 2009
- [j10]Frank Neumann
Runtime Analysis of a Simple Ant Colony Optimization Algorithm. Algorithmica 54(2): 243-255 (2009) - [j9]Tobias Friedrich, Jun He
Analyses of Simple Hybrid Algorithms for the Vertex Cover Problem. Evol. Comput. 17(1): 3-19 (2009) - [j8]Frank Neumann
Analysis of different MMAS ACO algorithms on unimodal functions and plateaus. Swarm Intell. 3(1): 35-68 (2009) - [j7]Tobias Friedrich, Nils Hebbinghaus, Frank Neumann
Comparison of simple diversity mechanisms on plateau functions. Theor. Comput. Sci. 410(26): 2455-2462 (2009) - [j6]Dimo Brockhoff, Tobias Friedrich, Nils Hebbinghaus, Christian Klein, Frank Neumann
On the Effects of Adding Objectives to Plateau Functions. IEEE Trans. Evol. Comput. 13(3): 591-603 (2009) - [c40]Pietro S. Oliveto
Theoretical analysis of rank-based mutation - combining exploration and exploitation. IEEE Congress on Evolutionary Computation 2009: 1455-1462 - [c39]Surender Baswana, Somenath Biswas, Benjamin Doerr, Tobias Friedrich, Piyush P. Kurur, Frank Neumann
Computing single source shortest paths using single-objective fitness. FOGA 2009: 59-66 - [c38]Christian Horoba, Frank Neumann
Additive approximations of pareto-optimal sets by evolutionary multi-objective algorithms. FOGA 2009: 79-86 - [c37]Stefan Kratsch, Frank Neumann
Fixed-parameter evolutionary algorithms and the vertex cover problem. GECCO 2009: 293-300 - [c36]Tobias Friedrich, Christian Horoba, Frank Neumann
Multiplicative approximations and the hypervolume indicator. GECCO 2009: 571-578 - [c35]Benjamin Doerr, Anton V. Eremeev
Evolutionary algorithms and dynamic programming. GECCO 2009: 771-778 - [c34]Frank Neumann
Theoretical analysis of fitness-proportional selection: landscapes and efficiency. GECCO 2009: 835-842 - [c33]Thomas Jansen, Frank Neumann:
Computational complexity and evolutionary computation. GECCO (Companion) 2009: 3157-3184 - [p2]Frank Neumann
Computational Complexity of Ant Colony Optimization and Its Hybridization with Local Search. Innovations in Swarm Intelligence 2009: 91-120 - 2008
- [j5]Frank Neumann
Expected runtimes of evolutionary algorithms for the Eulerian cycle problem. Comput. Oper. Res. 35(9): 2750-2759 (2008) - [c32]Florian Diedrich, Britta Kehden, Frank Neumann:
Multi-objective Problems in Terms of Relational Algebra. RelMiCS 2008: 84-98 - [c31]Frank Neumann
Rigorous Analyses for the Combination of Ant Colony Optimization and Local Search. ANTS Conference 2008: 132-143 - [c30]Tobias Friedrich, Frank Neumann
When to use bit-wise neutrality. IEEE Congress on Evolutionary Computation 2008: 997-1003 - [c29]Florian Diedrich, Frank Neumann
Using fast matrix multiplication in bio-inspired computation for complex optimization problems. IEEE Congress on Evolutionary Computation 2008: 3827-3832 - [c28]Christian Horoba, Frank Neumann:
Benefits and drawbacks for the use of epsilon-dominance in evolutionary multi-objective optimization. GECCO 2008: 641-648 - [c27]Frank Neumann, Joachim Reichel, Martin Skutella:
Computing minimum cuts by randomized search heuristics. GECCO 2008: 779-786 - [c26]Edda Happ, Daniel Johannsen, Christian Klein, Frank Neumann:
Rigorous analyses of fitness-proportional selection for optimizing linear functions. GECCO 2008: 953-960 - [c25]Thomas Jansen, Frank Neumann:
Computational complexity and evolutionary computation. GECCO (Companion) 2008: 2417-2444 - [c24]Frank Neumann
Approximating Minimum Multicuts by Evolutionary Multi-objective Algorithms. PPSN 2008: 72-81 - [c23]Dimo Brockhoff, Tobias Friedrich, Frank Neumann
Analyzing Hypervolume Indicator Based Algorithms. PPSN 2008: 651-660 - [c22]Tobias Friedrich, Christian Horoba, Frank Neumann
Runtime Analyses for Using Fairness in Evolutionary Multi-Objective Optimization. PPSN 2008: 671-680 - [c21]Jens Kroeske, Adam Ghandar
Learning Fuzzy Rules with Evolutionary Algorithms - An Analytic Approach. PPSN 2008: 1051-1060 - [p1]Frank Neumann, Ingo Wegener:
Can Single-Objective Optimization Profit from Multiobjective Optimization? Multiobjective Problem Solving from Nature 2008: 115-130 - 2007
- [j4]Benjamin Doerr, Nils Hebbinghaus, Frank Neumann
Speeding Up Evolutionary Algorithms through Asymmetric Mutation Operators. Evol. Comput. 15(4): 401-410 (2007) - [j3]Frank Neumann
Expected runtimes of a simple evolutionary algorithm for the multi-objective minimum spanning tree problem. Eur. J. Oper. Res. 181(3): 1620-1629 (2007) - [j2]Frank Neumann
Randomized local search, evolutionary algorithms, and the minimum spanning tree problem. Theor. Comput. Sci. 378(1): 32-40 (2007) - [c20]Benjamin Doerr, Michael Gnewuch, Nils Hebbinghaus, Frank Neumann
A rigorous view on neutrality. IEEE Congress on Evolutionary Computation 2007: 2591-2597 - [c19]Tobias Friedrich, Jun He
On improving approximate solutions by evolutionary algorithms. IEEE Congress on Evolutionary Computation 2007: 2614-2621 - [c18]Tobias Friedrich, Nils Hebbinghaus, Frank Neumann
Plateaus can be harder in multi-objective optimization. IEEE Congress on Evolutionary Computation 2007: 2622-2629 - [c17]Benjamin Doerr, Frank Neumann
On the runtime analysis of the 1-ANT ACO algorithm. GECCO 2007: 33-40 - [c16]Dimo Brockhoff, Tobias Friedrich, Nils Hebbinghaus, Christian Klein, Frank Neumann
Do additional objectives make a problem harder? GECCO 2007: 765-772 - [c15]Tobias Friedrich, Nils Hebbinghaus, Frank Neumann
Approximating covering problems by randomized search heuristics using multi-objective models. GECCO 2007: 797-804 - [c14]Tobias Friedrich, Nils Hebbinghaus, Frank Neumann
Rigorous analyses of simple diversity mechanisms. GECCO 2007: 1219-1225 - [c13]Thomas Jansen
Computational complexity and evolutionary computation. GECCO (Companion) 2007: 3225-3250 - [c12]Frank Neumann
Ant Colony Optimization and the Minimum Spanning Tree Problem. LION 2007: 153-166 - [c11]Frank Neumann, Dirk Sudholt, Carsten Witt:
Comparing Variants of MMAS ACO Algorithms on Pseudo-Boolean Functions. SLS 2007: 61-75 - [i6]Tobias Friedrich, Jun He, Nils Hebbinghaus, Frank Neumann, Carsten Witt:
Approximating Covering Problems by Randomized Search Heuristics Using Multi-Objective Models. Electron. Colloquium Comput. Complex. TR07 (2007) - 2006
- [j1]Frank Neumann
Minimum spanning trees made easier via multi-objective optimization. Nat. Comput. 5(3): 305-319 (2006) - [c10]Britta Kehden, Frank Neumann
A Relation-Algebraic View on Evolutionary Algorithms for Some Graph Problems. EvoCOP 2006: 147-158 - [c9]Frank Neumann
Runtime Analysis of a Simple Ant Colony Optimization Algorithm. ISAAC 2006: 618-627 - [c8]Frank Neumann
Speeding up Approximation Algorithms for NP-Hard Spanning Forest Problems by Multi-objective Optimization. LATIN 2006: 745-756 - [c7]Benjamin Doerr, Nils Hebbinghaus, Frank Neumann:
Speeding Up Evolutionary Algorithms Through Restricted Mutation Operators. PPSN 2006: 978-987 - [i5]Frank Neumann, Carsten Witt:
Runtime Analysis of a Simple Ant Colony Optimization Algorithm. Theory of Evolutionary Algorithms 2006 - [i4]Nils Hebbinghaus, Benjamin Doerr, Frank Neumann:
Speeding up Evolutionary Algorithms by Restricted Mutation Operators. Electron. Colloquium Comput. Complex. TR06 (2006) - [i3]Frank Neumann, Carsten Witt:
Runtime Analysis of a Simple Ant Colony Optimization Algorithm. Electron. Colloquium Comput. Complex. TR06 (2006) - [i2]Frank Neumann, Carsten Witt:
Ant Colony Optimization and the Minimum Spanning Tree Problem. Electron. Colloquium Comput. Complex. TR06 (2006) - 2005
- [b1]Frank Neumann:
Combinatorial optimization and the analysis of randomized search heuristics. University of Kiel, Germany, 2005, pp. 1-143 - [c6]Britta Kehden, Frank Neumann
Relational Implementation of Simple Parallel Evolutionary Algorithms. RelMiCS 2005: 161-172 - [c5]Rudolf Berghammer, Frank Neumann
RelView - An OBDD-Based Computer Algebra System for Relations. CASC 2005: 40-51 - [c4]Frank Neumann
Minimum spanning trees made easier via multi-objective optimization. GECCO 2005: 763-769 - [i1]Frank Neumann, Marco Laumanns:
Speeding Up Approximation Algorithms for NP-hard Spanning Forest Problems by Multi-objective Optimization. Electron. Colloquium Comput. Complex. TR05 (2005) - 2004
- [c3]Frank Neumann:
Expected runtimes of evolutionary algorithms for the Eulerian cycle problem. IEEE Congress on Evolutionary Computation 2004: 904-910 - [c2]Frank Neumann, Ingo Wegener:
Randomized Local Search, Evolutionary Algorithms, and the Minimum Spanning Tree Problem. GECCO (1) 2004: 713-724 - [c1]Frank Neumann:
Expected Runtimes of a Simple Evolutionary Algorithm for the Multi-objective Minimum Spanning Tree Problem. PPSN 2004: 81-90
Coauthor Index
[c216] [c212] [c211] [i122] [i120] [j67] [c188] [i100] [c170] [j56] [j52] [c165] [j49] [j48] [c151] [c149] [i57] [i51] [c130] [i42] [i40] [c122] [c121] [c119] [c118] [c115] [i35] [c107] [c103] [e3] [e2] [j37] [j36] [j34] [c84] [j26] [i17] [j22] [j20] [c60] [c56] [c53] [i9] [j14] [j13] [j12] [c49] [c47] [j9] [j7] [j6] [c39] [c36] [c30] [c23] [c22] [c19] [c18] [c16] [c15] [c14] [i6]
[j68] [c249] [c247] [c246] [c245] [c244] [c243] [c242] [c241] [c239] [c238] [c236] [c235] [c234] [c233] [c231] [c230] [c229] [c227] [i143] [i142] [i141] [i139] [i138] [i137] [i136] [i133] [i132] [i131] [c226] [c224] [c223] [c220] [c218] [c215] [c214] [c213] [c212] [c211] [c210] [c209] [i129] [i128] [i127] [i126] [i125] [i121] [i120] [i119] [i118] [i117] [i116] [j67] [j65] [j64] [c208] [c205] [c204] [c203] [c202] [c201] [c200] [c199] [c197] [c195] [c194] [c193] [c192] [i112] [i110] [i109] [i107] [i106] [i105] [i104] [i102] [i99] [c185] [c184] [c181] [c180] [c179] [c178] [c177] [c176] [c175] [c174] [c172] [i97] [i96] [i95] [i94] [i92] [i91] [i90] [i88] [i87] [i83] [i81] [j54] [c169] [c166] [c165] [c164] [c162] [c161] [c157] [c156] [c155] [c152] [i76] [i75] [i74] [i73] [i70] [i68] [i67] [i60] [j49] [c149] [c147] [c142] [c140] [c139] [i55] [i51] [i50] [i49] [c134] [c128] [c127] [c123] [i47] [i41] [i40] [c120] [c116] [i37] [i35] [c105] [i32] [i30]
Christian Thyssen
aka: Christian Horoba
aka: Christian Horoba
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