Literatura científica selecionada sobre o tema "Landscape-aware algorithm selection"
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Artigos de revistas sobre o assunto "Landscape-aware algorithm selection"
Škvorc, Urban, Tome Eftimov e Peter Korošec. "Transfer Learning Analysis of Multi-Class Classification for Landscape-Aware Algorithm Selection". Mathematics 10, n.º 3 (29 de janeiro de 2022): 432. http://dx.doi.org/10.3390/math10030432.
Texto completo da fonteAverianov, А. А., E. D. Androsova e A. V. Rusakov. "Winemaking terroir – the guideline for choosing of grape rootstocks for soils with different characteristics". Dokuchaev Soil Bulletin, n.º 116 (25 de setembro de 2023): 155–87. http://dx.doi.org/10.19047/0136-1694-2023-116-155-187.
Texto completo da fonteHamzei, Marzieh, Saeed Khandagh e Nima Jafari Navimipour. "A Quality-of-Service-Aware Service Composition Method in the Internet of Things Using a Multi-Objective Fuzzy-Based Hybrid Algorithm". Sensors 23, n.º 16 (17 de agosto de 2023): 7233. http://dx.doi.org/10.3390/s23167233.
Texto completo da fonteLiu, Xiao, e Yun Deng. "A new QoS-aware service discovery technique in the Internet of Things using whale optimization and genetic algorithms". Journal of Engineering and Applied Science 71, n.º 1 (3 de janeiro de 2024). http://dx.doi.org/10.1186/s44147-023-00334-1.
Texto completo da fonteTeses / dissertações sobre o assunto "Landscape-aware algorithm selection"
Jankovic, Anja. "Towards Online Landscape-Aware Algorithm Selection in Numerical Black-Box Optimization". Electronic Thesis or Diss., Sorbonne université, 2021. http://www.theses.fr/2021SORUS302.
Texto completo da fonteBlack-box optimization algorithms (BBOAs) are conceived for settings in which exact problem formulations are non-existent, inaccessible, or too complex for an analytical solution. BBOAs are essentially the only means of finding a good solution to such problems. Due to their general applicability, BBOAs can exhibit different behaviors when optimizing different types of problems. This yields a meta-optimization problem of choosing the best suited algorithm for a particular problem, called the algorithm selection (AS) problem. By reason of inherent human bias and limited expert knowledge, the vision of automating the selection process has quickly gained traction in the community. One prominent way of doing so is via so-called landscape-aware AS, where the choice of the algorithm is based on predicting its performance by means of numerical problem instance representations called features. A key challenge that landscape-aware AS faces is the computational overhead of extracting the features, a step typically designed to precede the actual optimization. In this thesis, we propose a novel trajectory-based landscape-aware AS approach which incorporates the feature extraction step within the optimization process. We show that the features computed using the search trajectory samples lead to robust and reliable predictions of algorithm performance, and to powerful algorithm selection models built atop. We also present several preparatory analyses, including a novel perspective of combining two complementary regression strategies that outperforms any of the classical, single regression models, to amplify the quality of the final selector
Trabalhos de conferências sobre o assunto "Landscape-aware algorithm selection"
Jankovic, Anja, e Carola Doerr. "Landscape-aware fixed-budget performance regression and algorithm selection for modular CMA-ES variants". In GECCO '20: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3377930.3390183.
Texto completo da fonteJankovic, Anja, Gorjan Popovski, Tome Eftimov e Carola Doerr. "The impact of hyper-parameter tuning for landscape-aware performance regression and algorithm selection". In GECCO '21: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3449639.3459406.
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