Search Results - "Per-Instance Algorithm Configuration"

Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP

Authors: Seiler, Moritz Vinzent Rook, Jeroen Heins, Jonathan et al.

Source: 2023 IEEE Symposium Series on Computational Intelligence (SSCI). :361-368

Subject Terms: Per-Instance algorithm configuration, 2024 OA procedure, Traveling salesperson problem, Reinforcement learning, 0211 other engineering and technologies, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Per-instance algorithm configuration for production planning in a reconfigurable assembly system

Authors: Daniel Guzman Vargas Sidharta Gautama Mehmet Uzunosmanoglu et al.

Source: 2024 IEEE 22nd Mediterranean Electrotechnical Conference (MELECON). :62-67

Per-Instance Algorithm Configuration in Homogeneous Instance Spaces: A Use Case in Reconfigurable Assembly Systems

Authors: Daniel Guzman Vargas Sidharta Gautama Mehmet Uzunosmanoglu et al.

Source: Applied Sciences, Vol 14, Iss 14, p 6035 (2024)
APPLIED SCIENCES-BASEL

Subject Terms: Technology, Technology and Engineering, QH301-705.5, Physics, QC1-999, 02 engineering and technology, Engineering (General). Civil engineering (General), 01 natural sciences, homogeneous instance spaces, integer quadratic programming, Business and Economics, Chemistry, Mathematics and Statistics, per-instance algorithm configuration, 0202 electrical engineering, electronic engineering, information engineering, reconfigurable assembly systems, NORMALITY, TA1-2040, Biology (General), 0101 mathematics, QD1-999

File Description: application/pdf

Access URL: https://doaj.org/article/4f158222f680466480274445a59fb715
https://biblio.ugent.be/publication/01J4KB37PN3W3RA9E84MG8ZTVN
http://hdl.handle.net/1854/LU-01J4KB37PN3W3RA9E84MG8ZTVN
https://biblio.ugent.be/publication/01J4KB37PN3W3RA9E84MG8ZTVN/file/01J8M91J9EF3GSDR81SVWBDAE6
http://doi.org/10.3390/app14146035

Per instance algorithm configuration of CMA-ES with limited budget

Authors: Belkhir, Nacim Dreo, Johann Savéant, Pierre et al.

Contributors: Belkhir, Nacim Dreo, Johann Savéant, Pierre et al.

Source: Proceedings of the Genetic and Evolutionary Computation Conference. :681-688

Subject Terms: [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], [MATH.MATH-OC] Mathematics [math]/Optimization and Control [math.OC], 0102 computer and information sciences, 02 engineering and technology, 01 natural sciences, CCS CONCEPTS • General and reference → Empirical studies, [STAT.ML] Statistics [stat]/Machine Learning [stat.ML], numerical black box optimization, empirical study, fitness landscape analysis, 0202 electrical engineering, electronic engineering, information engineering, Algorithm Configuration, • Theory of computation → Continuous optimization, Bio-inspired optimization, problem features

File Description: application/pdf

Access URL: https://hal.inria.fr/hal-01613753/file/GECCO2017_nacimbelkhir.pdf
https://inria.hal.science/hal-01613753v1
https://dl.acm.org/citation.cfm?id=3071343
https://dl.acm.org/doi/10.1145/3071178.3071343
https://doi.org/10.1145/3071178.3071343
https://hal.inria.fr/hal-01613753/document
https://hal.inria.fr/hal-01613753
https://dblp.uni-trier.de/db/conf/gecco/gecco2017.html#BelkhirDSS17

Per-Instance Algorithm Configuration in Homogeneous Instance Spaces: A Use Case in Reconfigurable Assembly Systems.

Authors: Guzman Vargas, Daniel Gautama, Sidharta Uzunosmanoglu, Mehmet et al.

Source: Applied Sciences (2076-3417); Jul2024, Vol. 14 Issue 14, p6035, 28p

Subject Terms: MARKET volatility, HOMOGENEOUS spaces, CASE-based reasoning, HYDRA (Marine life), QUADRATIC programming

Per Instance Algorithm Configuration for Continuous Black Box Optimization

Paramétrage automatisé d'algorithme par instance pour l'optimisation numérique boite noire

Authors: Belkhir, Nacim

Thesis Advisors: Université Paris-Saclay (ComUE), Schoenauer, Marc

Subject Terms: Apprentissage statistique, Optimisation, Intelligence artificielle, Machine learning, Artificial intelligence, Optimization

Availability: http://www.theses.fr/2017SACLS455/document

Per-instance algorithm configuration: from meta-learning to multi-objective decomposition ; Configuração de algoritmos baseada em instância: do meta-aprendizado à decomposição multiobjetivo

Authors: Pavelski, Lucas Marcondes Delgado, Myriam Regattieri de Biase da Silva orcid:0000-0002-2791-174X et al.

Contributors: Pavelski, Lucas Marcondes Delgado, Myriam Regattieri de Biase da Silva orcid:0000-0002-2791-174X et al.

Subject Terms: Algoritmos, Otimização matemática, Heurística, Aprendizado de máquinas, Algorítmos genéticos, Otimização combinatória, Algorithms, Mathematical optimization, Heuristic, Machine learning, Genetic algorithms, Combinatorial optimization, CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO, Engenharia Elétrica

File Description: application/pdf

Relation: http://repositorio.utfpr.edu.br/jspui/handle/1/27906

Availability: http://repositorio.utfpr.edu.br/jspui/handle/1/27906

Per Instance Algorithm Configuration for Continuous Black Box Optimization ; Paramétrage automatisé d'algorithme par instance pour l'optimisation numérique boite noire

Authors: Belkhir, Nacim Centre Inria de Saclay Institut National de Recherche en Informatique et en Automatique (Inria) et al.

Contributors: Belkhir, Nacim Centre Inria de Saclay Institut National de Recherche en Informatique et en Automatique (Inria) et al.

Source: https://inria.hal.science/tel-01669527 ; Artificial Intelligence [cs.AI]. Université Paris Saclay (COmUE), 2017. English. ⟨NNT : 2017SACLS455⟩.

Subject Terms: Optimization, Artificial intelligence, Machine learning, Apprentissage statistique, Optimisation, Intelligence artificielle, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [INFO.INFO-RO]Computer Science [cs]/Operations Research [math.OC], [MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC]

Relation: NNT: 2017SACLS455

Availability: https://inria.hal.science/tel-01669527
https://inria.hal.science/tel-01669527v2/document
https://inria.hal.science/tel-01669527v2/file/75926_BELKHIR_2017_diffusion.pdf

Stochastic local search and parameters recommendation: a case study on flowshop problems.

Authors: Pavelski, Lucas M. Delgado, Myriam Kessaci, Marie-Éléonore et al.

Source: International Transactions in Operational Research; Mar2023, Vol. 30 Issue 2, p774-799, 26p

Subject Terms: BUILDING performance, MACHINE learning, ALGORITHMS

Preface: Special issue on "Understanding of Evolutionary Optimization Behavior", Part 2.

Authors: Blum, Christian Eftimov, Tome Korošec, Peter

Source: Natural Computing; Jun2022, Vol. 21 Issue 2, p127-130, 4p

Subject Terms: SAMPLING errors, QUADRATIC assignment problem, ANT algorithms, EVOLUTIONARY algorithms, TRAVELING salesman problem

Accelerating process control and optimization via machine learning: a review.

Authors: Mitrai, Ilias Daoutidis, Prodromos

Source: Reviews in Chemical Engineering; May2025, Vol. 41 Issue 4, p401-418, 18p

Subject Terms: MACHINE learning, OPTIMIZATION algorithms, MATHEMATICAL optimization, PROCESS control systems, AUTOMATION, STATISTICAL decision making, CHEMICAL engineering

The Importance of Being Constrained: Dealing with Infeasible Solutions in Differential Evolution and Beyond.

Authors: Kononova, Anna V. Vermetten, Diederick Caraffini, Fabio et al.

Source: Evolutionary Computation; Spring2024, Vol. 32 Issue 1, p3-48, 46p

Subject Terms: DIFFERENTIAL evolution, OPTIMIZATION algorithms, HEURISTIC algorithms, SPECIAL functions

Learning Individualized Hyperparameter Settings.

Authors: Maniezzo, Vittorio Zhou, Tingting

Source: Algorithms; Jun2023, Vol. 16 Issue 6, p267, 15p

Subject Terms: QUADRATIC assignment problem, OPTIMIZATION algorithms, ARTIFICIAL neural networks, ROBUST optimization, ASSIGNMENT problems (Programming), TABU search algorithm

Speeding up neural network robustness verification via algorithm configuration and an optimised mixed integer linear programming solver portfolio.

Authors: König, Matthias Hoos, Holger H. Rijn, Jan N. van

Source: Machine Learning; Dec2022, Vol. 111 Issue 12, p4565-4584, 20p

Subject Terms: LINEAR programming, MIXED integer linear programming, ALGORITHMS

From fitness landscapes evolution to automatic local search algorithm generation.

Authors: Hénaux, Vincent Goëffon, Adrien Saubion, Frédéric

Source: International Transactions in Operational Research; Sep2022, Vol. 29 Issue 5, p2737-2760, 24p, 2 Charts, 7 Graphs

Subject Terms: SEARCH algorithms, PROBLEM solving

Analyzing randomness effects on the reliability of exploratory landscape analysis.

Authors: Muñoz, Mario Andrés Kirley, Michael Smith-Miles, Kate

Source: Natural Computing; Jun2022, Vol. 21 Issue 2, p131-154, 24p

Subject Terms: STATISTICAL hypothesis testing, RESAMPLING (Statistics), SAMPLING (Process), SAMPLE size (Statistics), RELIABILITY in engineering, DATA libraries

Black-Box Optimization Revisited: Improving Algorithm Selection Wizards Through Massive Benchmarking.

Authors: Meunier, Laurent Rakotoarison, Herilalaina Wong, Pak Kan et al.

Source: IEEE Transactions on Evolutionary Computation; Jun2022, Vol. 26 Issue 3, p490-500, 11p

Subject Terms: ALGORITHMS, GLOBAL optimization, MATHEMATICAL optimization, REPRODUCIBLE research, OPEN source software

Automated Reinforcement Learning (AutoRL): A Survey and Open Problems.

Authors: Parker-Holder, Jack Rajan, Raghu Xingyou Song et al.

Source: Journal of Artificial Intelligence Research; 2022, Vol. 74, p517-568, 52p

Subject Terms: REINFORCEMENT learning, DEEP learning, MACHINE learning, ARTIFICIAL intelligence, ARTIFICIAL neural networks

Flowshop NEH-Based Heuristic Recommendation

Authors: Pavelski, Lucas Marcondes Kessaci, Marie-Eléonore Delgado, Myriam

Contributors: Pavelski, Lucas Marcondes Kessaci, Marie-Eléonore Delgado, Myriam

Source: Lecture Notes in Computer Science ISBN: 9783030729035

Subject Terms: [INFO.INFO-DM] Computer Science [cs]/Discrete Mathematics [cs.DM], 0211 other engineering and technologies, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, [INFO.INFO-RO] Computer Science [cs]/Operations Research [math.OC]

Access URL: https://rd.springer.com/chapter/10.1007/978-3-030-72904-2_9
https://link.springer.com/chapter/10.1007/978-3-030-72904-2_9

Gaussian Process Surrogate Models for the CMA Evolution Strategy.

Authors: Bajer, Lukáš Pitra, Zbyněk Repický, Jakub et al.

Source: Evolutionary Computation; Winter2019, Vol. 27 Issue 4, p665-697, 33p

Subject Terms: GAUSSIAN processes, COVARIANCE matrices, BIOLOGICAL evolution