Auswahl der wissenschaftlichen Literatur zum Thema „Fair combinatorial optimization“
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Zeitschriftenartikel zum Thema "Fair combinatorial optimization"
Bourdache, Nadjet, und Patrice Perny. „Active Preference Learning Based on Generalized Gini Functions: Application to the Multiagent Knapsack Problem“. Proceedings of the AAAI Conference on Artificial Intelligence 33 (17.07.2019): 7741–48. http://dx.doi.org/10.1609/aaai.v33i01.33017741.
Der volle Inhalt der QuelleWang, Kai, Haoyu Liu, Zhipeng Hu, Xiaochuan Feng, Minghao Zhao, Shiwei Zhao, Runze Wu, Xudong Shen, Tangjie Lv und Changjie Fan. „EnMatch: Matchmaking for Better Player Engagement via Neural Combinatorial Optimization“. Proceedings of the AAAI Conference on Artificial Intelligence 38, Nr. 8 (24.03.2024): 9098–106. http://dx.doi.org/10.1609/aaai.v38i8.28760.
Der volle Inhalt der QuelleMOULIN, HERVÉ. „COST SHARING IN NETWORKS: SOME OPEN QUESTIONS“. International Game Theory Review 15, Nr. 02 (Juni 2013): 1340001. http://dx.doi.org/10.1142/s021919891340001x.
Der volle Inhalt der QuelleAdubi, Stephen A., Olufunke O. Oladipupo und Oludayo O. Olugbara. „Evolutionary Algorithm-Based Iterated Local Search Hyper-Heuristic for Combinatorial Optimization Problems“. Algorithms 15, Nr. 11 (31.10.2022): 405. http://dx.doi.org/10.3390/a15110405.
Der volle Inhalt der QuelleMaleš, Uroš, Dušan Ramljak, Tatjana Jakšić Krüger, Tatjana Davidović, Dragutin Ostojić und Abhay Haridas. „Controlling the Difficulty of Combinatorial Optimization Problems for Fair Proof-of-Useful-Work-Based Blockchain Consensus Protocol“. Symmetry 15, Nr. 1 (03.01.2023): 140. http://dx.doi.org/10.3390/sym15010140.
Der volle Inhalt der QuelleWang, Zhenzhong, Qingyuan Zeng, Wanyu Lin, Min Jiang und Kay Chen Tan. „Generating Diagnostic and Actionable Explanations for Fair Graph Neural Networks“. Proceedings of the AAAI Conference on Artificial Intelligence 38, Nr. 19 (24.03.2024): 21690–98. http://dx.doi.org/10.1609/aaai.v38i19.30168.
Der volle Inhalt der QuelleRokbani, Nizar, Pavel Kromer, Ikram Twir und Adel M. Alimi. „A Hybrid Hierarchical Heuristic-ACO With Local Search Applied to Travelling Salesman Problem, AS-FA-Ls“. International Journal of System Dynamics Applications 9, Nr. 3 (Juli 2020): 58–73. http://dx.doi.org/10.4018/ijsda.2020070104.
Der volle Inhalt der QuelleLujak, Marin, Stefano Giordani, Andrea Omicini und Sascha Ossowski. „Decentralizing Coordination in Open Vehicle Fleets for Scalable and Dynamic Task Allocation“. Complexity 2020 (16.07.2020): 1–21. http://dx.doi.org/10.1155/2020/1047369.
Der volle Inhalt der QuelleLi, Xia, und Buhong Wang. „Thinned Virtual Array for Cramer Rao Bound Optimization in MIMO Radar“. International Journal of Antennas and Propagation 2021 (15.01.2021): 1–13. http://dx.doi.org/10.1155/2021/1408498.
Der volle Inhalt der QuelleKhaled, Smail, und Djebbar Bachir. „Electromagnetism-like mechanism algorithm for hybrid flow-shop scheduling problems“. Indonesian Journal of Electrical Engineering and Computer Science 32, Nr. 3 (01.12.2023): 1614. http://dx.doi.org/10.11591/ijeecs.v32.i3.pp1614-1620.
Der volle Inhalt der QuelleDissertationen zum Thema "Fair combinatorial optimization"
Vo, Thi Quynh Trang. „Algorithms and Machine Learning for fair and classical combinatorial optimization“. Electronic Thesis or Diss., Université Clermont Auvergne (2021-...), 2024. http://www.theses.fr/2024UCFA0035.
Der volle Inhalt der QuelleCombinatorial optimization is a field of mathematics that searches for an optimal solution in a finite set of objects. It has crucial applications in many fields, including applied mathematics, software engineering, theoretical computer science, and machine learning. extit{Branch-and-cut} is one of the most widely-used algorithms for solving combinatorial optimization problems exactly. In this thesis, we focus on the computational aspects of branch-and-cut when studying two critical dimensions of combinatorial optimization: extit{the fairness of solutions} and extit{the integration of machine learning}.In Partef{part:1} (Chaptersef{chap:bnc-btsp} andef{chap:owa}), we study two common approaches to deal with the issue of fairness in combinatorial optimization, which has gained significant attention in the past decades. The first approach is extit{balanced combinatorial optimization}, which finds a fair solution by minimizing the difference between the largest and smallest components used. Due to the difficulties in bounding these components, to the best of our knowledge, no general exact framework based on mixed-integer linear programming (MILP) has been proposed for balanced combinatorial optimization. To address this gap, in Chapteref{chap:bnc-btsp}, we present a branch-and-cut algorithm and a novel class of local cutting planes tailored for balanced combinatorial optimization problems. We demonstrate the effectiveness of the proposed framework in the Balanced Traveling Salesman Problem. Additionally, we introduce bounding algorithms and mechanisms to fix variables to accelerate performance further.The second approach to handling the issue of fairness is extit{Ordered Weighted Average (OWA) combinatorial optimization}, which integrates the OWA operator into the objective function. Due to the ordering operator, OWA combinatorial optimization is nonlinear, even if its original constraints are linear. Two MILP formulations of different sizes have been introduced in the literature to linearize the OWA operator. However, which formulation performs best for OWA combinatorial optimization remains uncertain, as integrating the linearization methods may introduce additional difficulties. In Chapteref{chap:owa}, we provide theoretical and empirical comparisons of the two MILP formulations for OWA combinatorial optimization. In particular, we prove that the formulations are equivalent in terms of the linear programming relaxation. We empirically show that for OWA combinatorial optimization problems, the formulation with more variables can be solved faster with branch-and-cut.In Partef{part:2} (Chapteref{chap:mlbnc}), we develop methods for applying machine learning to enhance fundamental decision problems in branch-and-cut, with a focus on cut generation. Cut generation refers to the decision of whether to generate cuts or to branch at each node of the search tree. We empirically demonstrate that this decision significantly impacts branch-and-cut performance, especially for combinatorial cuts that exploit the facial structure of the convex hull of feasible solutions. We then propose a general framework combining supervised and reinforcement learning to learn effective strategies for generating combinatorial cuts in branch-and-cut. Our framework has two components: a cut detector to predict cut existence and a cut evaluator to choose between generating cuts and branching. Finally, we provide experimental results showing that the proposed method outperforms commonly used strategies for cut generation, even on instances larger than those used for training
Gliesch, Alex Zoch. „A genetic algorithm for fair land allocation“. reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2018. http://hdl.handle.net/10183/174950.
Der volle Inhalt der QuelleThe goal of agrarian reform projects is the redistribution of farmland from large latifundia to smaller, often family farmers. One of the main problems the Brazilian National Institute of Colonization and Agrarian Reform (INCRA) has to solve is to subdivide a large parcel of land into smaller lots that are balanced with respect to certain attributes. This problem is difficult since it considers several constraints originating from legislation as well as ethical considerations. Current solutions are computer-assisted, but manual, time-consuming and error-prone, leading to rectangular lots of similar areas which are unfair with respect to soil aptitude and access to hydric resources. In this thesis, we propose a genetic algorithm to produce fair land subdivisions automatically. We present a greedy randomized constructive heuristic based on location-allocation to generate initial solutions, as well as mutation and recombination operators that consider specifics of the problem. Experiments on 5 real-world and 25 artificial instances confirm the effectiveness of the different components of our method, and show that it leads to fairer solutions than those currently applied in practice.
Buchteile zum Thema "Fair combinatorial optimization"
Armaselu, Bogdan, und Ovidiu Daescu. „Algorithms for Fair Partitioning of Convex Polygons“. In Combinatorial Optimization and Applications, 53–65. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12691-3_5.
Der volle Inhalt der QuelleJia, Xinrui, Kshiteej Sheth und Ola Svensson. „Fair Colorful k-Center Clustering“. In Integer Programming and Combinatorial Optimization, 209–22. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45771-6_17.
Der volle Inhalt der QuelleNguyen, Viet Hung, und Paul Weng. „An Efficient Primal-Dual Algorithm for Fair Combinatorial Optimization Problems“. In Combinatorial Optimization and Applications, 324–39. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-71150-8_28.
Der volle Inhalt der QuelleHansen, Thomas Dueholm, und Orestis A. Telelis. „Improved Bounds for Facility Location Games with Fair Cost Allocation“. In Combinatorial Optimization and Applications, 174–85. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02026-1_16.
Der volle Inhalt der QuelleBlum, Christian, und Pedro Pinacho-Davidson. „Application of Negative Learning Ant Colony Optimization to the Far from Most String Problem“. In Evolutionary Computation in Combinatorial Optimization, 82–97. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-30035-6_6.
Der volle Inhalt der QuelleBlum, Christian, und Paola Festa. „A Hybrid Ant Colony Optimization Algorithm for the Far From Most String Problem“. In Evolutionary Computation in Combinatorial Optimisation, 1–12. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-44320-0_1.
Der volle Inhalt der Quelle„Combinatorial Materials and Catalysts Development: Where Are We and How Far Can We Go?“ In Combinatorial and High-Throughput Discovery and Optimization of Catalysts and Materials, 23–36. CRC Press, 2006. http://dx.doi.org/10.1201/9781420005387-7.
Der volle Inhalt der QuelleLi, Chu Min, und Felip Manyà. „Chapter 23. MaxSAT, Hard and Soft Constraints“. In Frontiers in Artificial Intelligence and Applications. IOS Press, 2021. http://dx.doi.org/10.3233/faia201007.
Der volle Inhalt der QuelleKaiwartya, Omprakash, Pawan Kumar Tiwari, Sushil Kumar und Mukesh Prasad. „Dynamic Vehicle Routing Solution in the Framework of Nature-Inspired Algorithms“. In Designing and Implementing Global Supply Chain Management, 36–50. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9720-1.ch003.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Fair combinatorial optimization"
Golrezaei, Negin, Rad Niazadeh, Kumar Kshitij Patel und Fransisca Susan. „Online Combinatorial Optimization with Group Fairness Constraints“. In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. California: International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/44.
Der volle Inhalt der QuelleMartin, Hugo, und Patrice Perny. „BiOWA for Preference Aggregation with Bipolar Scales: Application to Fair Optimization in Combinatorial Domains“. In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/252.
Der volle Inhalt der QuelleXu, Huanle, Yang Liu, Wing Cheong Lau und Rui Li. „Combinatorial Multi-Armed Bandits with Concave Rewards and Fairness Constraints“. In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/354.
Der volle Inhalt der QuelleComlek, Yigitcan, Liwei Wang und Wei Chen. „Mixed-Variable Global Sensitivity Analysis With Applications to Data-Driven Combinatorial Materials Design“. In ASME 2023 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2023. http://dx.doi.org/10.1115/detc2023-110756.
Der volle Inhalt der QuelleDai, Zuo, und Jianzhong Cha. „A Hybrid Approach of Heuristic and Neural Network for Packing Problems“. In ASME 1994 Design Technical Conferences collocated with the ASME 1994 International Computers in Engineering Conference and Exhibition and the ASME 1994 8th Annual Database Symposium. American Society of Mechanical Engineers, 1994. http://dx.doi.org/10.1115/detc1994-0119.
Der volle Inhalt der QuellePetkov, Hristo, Colin Hanley und Feng Dong. „DAG-WGAN: Causal Structure Learning with Wasserstein Generative Adversarial Networks“. In 11th International Conference on Embedded Systems and Applications (EMSA 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.120611.
Der volle Inhalt der QuelleHuang, Mingyu, und Ke Li. „Exploring Structural Similarity in Fitness Landscapes via Graph Data Mining: A Case Study on Number Partitioning Problems“. In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/621.
Der volle Inhalt der QuelleJiang, Chunheng, Jianxi Gao und Malik Magdon-Ismail. „Inferring Degrees from Incomplete Networks and Nonlinear Dynamics“. In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/457.
Der volle Inhalt der QuelleLiao, Yanfen, Jiejin Cai und Xiaoqian Ma. „Study and Application on Real Time Optimum Operation for Plant Units“. In ASME 2005 Power Conference. ASMEDC, 2005. http://dx.doi.org/10.1115/pwr2005-50311.
Der volle Inhalt der QuelleBarros, E. G. D., S. P. Szklarz, J. Hopman, K. Hopstaken, J. P. Gonçalves da Silva, O. P. Bjørlykke, V. Rios, J. Videla, R. Oliveira und R. G. Hanea. „Well Swapping and Conversion Optimization Under Uncertainty Based on Extended Well Priority Parametrization“. In Offshore Technology Conference Brasil. OTC, 2023. http://dx.doi.org/10.4043/32960-ms.
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