Outputs (94)

Directed Perturbations for Efficient Learning of Surrogate Losses (2025)
Presentation / Conference Contribution
Cargan, T. R., Landa-Silva, D., & Triguero, I. (2025, June). Directed Perturbations for Efficient Learning of Surrogate Losses. Presented at International Joint Conference on Neural Networks (IJCNN 2025), Rome, Italy

Decision-Focused Learning (DFL) is a paradigm to learn neural network-based predictive models tailored to a specific optimisation problem. A key challenge for DFL methods lies in the non-differentiable nature of most optimisation problems. Recent sol... Read More about Directed Perturbations for Efficient Learning of Surrogate Losses.

Local-global methods for generalised solar irradiance forecasting (2024)
Journal Article
Cargan, T. R., Landa-Silva, D., & Triguero, I. (2024). Local-global methods for generalised solar irradiance forecasting. Applied Intelligence, 54(2), 2225-2247. https://doi.org/10.1007/s10489-024-05273-9

For efficient operation, solar power operators often require generation forecasts for multiple sites with varying data availability. Many proposed methods for forecasting solar irradiance / solar power production formulate the problem as a time-serie... Read More about Local-global methods for generalised solar irradiance forecasting.

UAV Path Planning for Area Coverage and Energy Consumption in Oil and Gas Exploration Environment (2023)
Presentation / Conference Contribution
Maaji, S. S., & Landa-Silva, D. (2023, September). UAV Path Planning for Area Coverage and Energy Consumption in Oil and Gas Exploration Environment. Presented at 14th International Conference on Computational Logistics, ICCL 2023, Berlin, Germany

This paper proposes a model for unmanned aerial vehicles (UAV) grid-based coverage path planning, considering coverage completeness and energy consumption in complex environments with multiple obstacles. The work is inspired by the need for more effi... Read More about UAV Path Planning for Area Coverage and Energy Consumption in Oil and Gas Exploration Environment.

Accelerated pattern search with variable solution size for simultaneous instance selection and generation (2022)
Presentation / Conference Contribution
Le, H. L., Neri, F., Landa-Silva, D., & Triguero, I. (2022, July). Accelerated pattern search with variable solution size for simultaneous instance selection and generation. Poster presented at Genetic and Evolutionary Computation Conference Companion (GECCO 2022), Boston, USA and online

The search for the optimum in a mixed continuous-combinatorial space is a challenging task since it requires operators that handle both natures of the search domain. Instance reduction (IR), an important pre-processing technique in data science, is o... Read More about Accelerated pattern search with variable solution size for simultaneous instance selection and generation.

Towards Blockchain-based Ride-sharing Systems (2021)
Presentation / Conference Contribution
Vazquez, E., & Landa-Silva, D. (2021, February). Towards Blockchain-based Ride-sharing Systems. Presented at ICORES 2021 - Proceedings of the 10th International Conference on Operations Research and Enterprise Systems, Online

Blockchain technology has been used in finance, health care, supply chain, and transport, with the main goals of improving security and eliminating the need for a third party to manage transactions in the system. In blockchain, smart contracts are us... Read More about Towards Blockchain-based Ride-sharing Systems.

Towards Blockchain-based Ride-sharing Systems (2021)
Presentation / Conference Contribution
Vazquez, E., & Landa-Silva, D. (2021, February). Towards Blockchain-based Ride-sharing Systems. Presented at 10th International Conference on Operations Research and Enterprise Systems, Online

Blockchain technology has been used in finance, health care, supply chain, and transport, with the main goals of improving security and eliminating the need for a third party to manage transactions in the system. In blockchain, smart contracts are us... Read More about Towards Blockchain-based Ride-sharing Systems.

Towards Blockchain-based Ride-sharing Systems (2021)
Presentation / Conference Contribution
Vazquez, E., & Landa-Silva, D. (2021, February). Towards Blockchain-based Ride-sharing Systems. Presented at ICORES 2021 - Proceedings of the 10th International Conference on Operations Research and Enterprise Systems, Online

Blockchain technology has been used in finance, health care, supply chain, and transport, with the main goals of improving security and eliminating the need for a third party to manage transactions in the system. In blockchain, smart contracts are us... Read More about Towards Blockchain-based Ride-sharing Systems.

EUSC: A clustering-based surrogate model to accelerate evolutionary undersampling in imbalanced classification (2020)
Journal Article
Le, H. L., Landa-Silva, D., Galar, M., Garcia, S., & Triguero, I. (2021). EUSC: A clustering-based surrogate model to accelerate evolutionary undersampling in imbalanced classification. Applied Soft Computing, 101, Article 107033. https://doi.org/10.1016/j.asoc.2020.107033

© 2020 Learning from imbalanced datasets is highly demanded in real-world applications and a challenge for standard classifiers that tend to be biased towards the classes with the majority of the examples. Undersampling approaches reduce the size of... Read More about EUSC: A clustering-based surrogate model to accelerate evolutionary undersampling in imbalanced classification.

Soft clustering-based scenario bundling for a progressive hedging heuristic in stochastic service network design (2020)
Journal Article
Jiang, X., Bai, R., Wallace, S. W., Kendall, G., & Landa-Silva, D. (2021). Soft clustering-based scenario bundling for a progressive hedging heuristic in stochastic service network design. Computers and Operations Research, 128, Article 105182. https://doi.org/10.1016/j.cor.2020.105182

© 2020 Elsevier Ltd We present a method for bundling scenarios in a progressive hedging heuristic (PHH) applied to stochastic service network design, where the uncertain demand is represented by a finite number of scenarios. Given the number of scena... Read More about Soft clustering-based scenario bundling for a progressive hedging heuristic in stochastic service network design.

A Hybrid Surrogate Model for Evolutionary Undersampling in Imbalanced Classification (2020)
Presentation / Conference Contribution
Le, H. L., Landa-Silva, D., Galar, M., Garcia, S., & Triguero, I. (2020, July). A Hybrid Surrogate Model for Evolutionary Undersampling in Imbalanced Classification. Presented at 2020 IEEE Congress on Evolutionary Computation, CEC 2020, Glasgow, UK

© 2020 IEEE. Data preprocessing is a key stage in data mining that allows machine learning algorithms to obtain meaningful insights. Many preprocessing problems such as feature selection or instance selection can be modelled as optimisation/search pr... Read More about A Hybrid Surrogate Model for Evolutionary Undersampling in Imbalanced Classification.