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Relevant bibliographies by topics / Landscape-aware algorithm selection

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Academic literature on the topic 'Landscape-aware algorithm selection'

Author: Grafiati

Published: 25 May 2024

Last updated: 31 July 2025

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Journal articles on the topic "Landscape-aware algorithm selection"

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Škvorc, Urban, Tome Eftimov, and Peter Korošec. "Transfer Learning Analysis of Multi-Class Classification for Landscape-Aware Algorithm Selection." Mathematics 10, no. 3 (2022): 432. http://dx.doi.org/10.3390/math10030432.

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In optimization, algorithm selection, which is the selection of the most suitable algorithm for a specific problem, is of great importance, as algorithm performance is heavily dependent on the problem being solved. However, when using machine learning for algorithm selection, the performance of the algorithm selection model depends on the data used to train and test the model, and existing optimization benchmarks only provide a limited amount of data. To help with this problem, artificial problem generation has been shown to be a useful tool for augmenting existing benchmark problems. In this

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Tuncel, Yusuf Kursat, and Kasım Öztoprak. "SAFE-CAST: secure AI-federated enumeration for clustering-based automated surveillance and trust in machine-to-machine communication." PeerJ Computer Science 11 (January 2, 2025): e2551. https://doi.org/10.7717/peerj-cs.2551.

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Machine-to-machine (M2M) communication within the Internet of Things (IoT) faces increasing security and efficiency challenges as networks proliferate. Existing approaches often struggle with balancing robust security measures and energy efficiency, leading to vulnerabilities and reduced performance in resource-constrained environments. To address these limitations, we propose SAFE-CAST, a novel secure AI-federated enumeration for clustering-based automated surveillance and trust framework. This study addresses critical security and efficiency challenges in M2M communication within the context

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Averianov, А. А., E. D. Androsova, and A. V. Rusakov. "Winemaking terroir – the guideline for choosing of grape rootstocks for soils with different characteristics." Dokuchaev Soil Bulletin, no. 116 (September 25, 2023): 155–87. http://dx.doi.org/10.19047/0136-1694-2023-116-155-187.

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The selection of rootstocks is one of the first and most important stages in the establishment of grape plantations under grafted conditions, which determines the productivity of rootstock-scion combinations and the further chain of design solutions: spatial placement of rows on the land plot, accompanying the production process of agronomic and agrochemical methods. Given the high importance of terroir factors for viticulture and winemaking, we were aware of the need to consider them in detail at this design stage. The aim was to create an algorithm for selecting varieties based on local terr

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Ankita Saxena. "The Frontier of Selection Optimization: Emerging Innovations in AI-Driven Recommendation Systems." Journal of Computer Science and Technology Studies 7, no. 7 (2025): 607–14. https://doi.org/10.32996/jcsts.2025.7.7.68.

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Recent advancements in artificial intelligence have catalyzed profound transformations in recommendation systems across digital platforms. The evolution from basic collaborative filtering toward sophisticated AI-driven approaches represents a significant paradigm shift in selection optimization. As recommendation engines mature, the field transitions from traditional personalization toward context-aware, generative, and causal recommendation paradigms. Key innovations reshaping this landscape include large language models, self-supervised learning frameworks, reinforcement learning algorithms,

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Elarbi, Badidi. "A Broker-based Framework for Integrated SLA-Aware SaaS Provisioning." International Journal on Cloud Computing: Services and Architecture (IJCCSA) 6, no. 2 (2019): 1–19. https://doi.org/10.5281/zenodo.3565434.

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In the service landscape, the issues of service selection, negotiation of Service Level Agreements (SLA), and SLA-compliance monitoring have typically been used in separate and disparate ways, which affect the quality of the services that consumers obtain from their providers. In this work, we propose a broker-based framework to deal with these concerns in an integrated mannerfor Software as a Service (SaaS) provisioning. The SaaS Broker selects a suitable SaaS provider on behalf of the service consumer by using a utility-driven selection algorithm that ranks the QoS offerings of potential Saa

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Hamzei, Marzieh, Saeed Khandagh, and 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, no. 16 (2023): 7233. http://dx.doi.org/10.3390/s23167233.

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The Internet of Things (IoT) represents a cutting-edge technical domain, encompassing billions of intelligent objects capable of bridging the physical and virtual worlds across various locations. IoT services are responsible for delivering essential functionalities. In this dynamic and interconnected IoT landscape, providing high-quality services is paramount to enhancing user experiences and optimizing system efficiency. Service composition techniques come into play to address user requests in IoT applications, allowing various IoT services to collaborate seamlessly. Considering the resource

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Sharma, Garvit, Karthik Pragada, Poushali Deb Purkayastha, and Yukta Vajpayee. "Research Paper on Exploring the Landscape of Recommendation Systems: A Comparative Analysis of Techniques and Approaches." International Journal of Engineering and Computer Science 13, no. 06 (2024): 26196–218. http://dx.doi.org/10.18535/ijecs/v13i06.4827.

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The field of recommendation systems has witnessed a profound evolution since its inception with Grundy, the first computer-based librarian, in 1979. From its humble beginnings, recommendation systems have become integral to various facets of daily life, particularly in e-commerce, thanks to breakthroughs like Amazon’s Collaborative Filtering in the late 1990s. This led to widespread adoption across diverse sectors, prompting significant research interest and investment, exemplified by Netflix’s renowned recommendation system contest in 2006. Today, recommendation systems employ various techniq

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Shambour, Qusai, Mosleh Abualhaj, Ahmad Abu-Shareha, and Qasem Kharma. "Personalized Tourism Recommendations: Leveraging User Preferences and Trust Network." Interdisciplinary Journal of Information, Knowledge, and Management 19 (2024): 017. http://dx.doi.org/10.28945/5329.

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Aim/Purpose: This study aims to develop a solution for personalized tourism recommendations that addresses information overload, data sparsity, and the cold-start problem. It focuses on enabling tourists to choose the most suitable tourism-related facilities, such as restaurants and hotels, that match their individual needs and preferences. Background: The tourism industry is experiencing a significant shift towards digitalization due to the increasing use of online platforms and the abundance of user data. Travelers now heavily rely on online resources to explore destinations and associated o

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S, Sheela, and Dilip Kumar SM. "Modelling resource-aware fault tolerant task scheduling in dynamic fog environment: A tournament selection Approach." Intelligent Decision Technologies, March 18, 2025. https://doi.org/10.1177/18724981251324564.

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The contemporary computing landscape has witnessed the drift of services extended by the cloud to the network edge through the fog computing paradigm. The proximity to data sources and end users offers many advantages, including significantly reduced latency, enhanced real-time processing, and improved privacy. In this context, the orchestration of computational tasks through effective task scheduling becomes pivotal for maximizing resource utilization efficiency, latency minimization, and optimizing the overall performance of system. This work proposes a learning automata-based task schedulin

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Elarbi, Badidi. "A Broker-based Framework for Integrated SLA-Aware SaaS Provisioning." April 30, 2016. https://doi.org/10.5121/ijccsa.2016.6201.

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In the service landscape, the issues of service selection, negotiation of Service Level Agreements (SLA), and SLA-compliance monitoring have typically been used in separate and disparate ways, which affect the quality of the services that consumers obtain from their providers. In this work, we propose a broker-based framework to deal with these concerns in an integrated mannerfor Software as a Service (SaaS) provisioning. The SaaS Broker selects a suitable SaaS provider on behalf of the service consumer by using a utility-driven selection algorithm that ranks the QoS offerings of potential Saa

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Dissertations / Theses on the topic "Landscape-aware algorithm selection"

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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.

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Les algorithmes d'optimisation de boîte noire (BBOA) sont conçus pour des scénarios où les formulations exactes de problèmes sont inexistantes, inaccessibles, ou trop complexes pour la résolution analytique. Les BBOA sont le seul moyen de trouver une bonne solution à un tel problème. En raison de leur applicabilité générale, les BBOA présentent des comportements différents lors de l'optimisation de différents types de problèmes. Cela donne un problème de méta-optimisation consistant à choisir l'algorithme le mieux adapté à un problème particulier, appelé problème de sélection d'algorithmes (AS

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Book chapters on the topic "Landscape-aware algorithm selection"

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Long, Fu Xing, Moritz Frenzel, Peter Krause, Markus Gitterle, Thomas Bäck, and Niki van Stein. "Landscape-Aware Automated Algorithm Configuration Using Multi-output Mixed Regression and Classification." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-70068-2_6.

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AbstractIn landscape-aware algorithm selection problem, the effectiveness of feature-based predictive models strongly depends on the representativeness of training data for practical applications. In this work, we investigate the potential of randomly generated functions (RGF) for the model training, which cover a much more diverse set of optimization problem classes compared to the widely-used black-box optimization benchmarking (BBOB) suite. Correspondingly, we focus on automated algorithm configuration (AAC), that is, selecting the best suited algorithm and fine-tuning its hyperparameters b

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Conference papers on the topic "Landscape-aware algorithm selection"

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Jankovic, Anja, and Carola Doerr. "Landscape-aware fixed-budget performance regression and algorithm selection for modular CMA-ES variants." In GECCO '20: Genetic and Evolutionary Computation Conference. ACM, 2020. http://dx.doi.org/10.1145/3377930.3390183.

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Jankovic, Anja, Gorjan Popovski, Tome Eftimov, and Carola Doerr. "The impact of hyper-parameter tuning for landscape-aware performance regression and algorithm selection." In GECCO '21: Genetic and Evolutionary Computation Conference. ACM, 2021. http://dx.doi.org/10.1145/3449639.3459406.

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