Too Constrained for Genetic Algorithms. Too Hard for Evolutionary Computing. The Traveling Tournament Problem.
Conference Proceeding
Verduin, K., Thomson, S. L., & van den Berg, D. (2023)
Too Constrained for Genetic Algorithms. Too Hard for Evolutionary Computing. The Traveling Tournament Problem. In Proceedings of the 15th International Joint Conference on Computational Intelligence (246-257). https://doi.org/10.5220/0012192100003595
Unlike other NP-hard problems, the constraints on the traveling tournament problem are so pressing that it’s hardly possible to randomly generate a valid solution, for example...
How Much do Robots Understand Rudeness? Challenges in Human-Robot Interaction
Conference Proceeding
Orme, M., Yu, Y., & Tan, Z. (in press)
How Much do Robots Understand Rudeness? Challenges in Human-Robot Interaction.
This paper concerns the pressing need to understand and manage inappropriate language within the evolving human-robot interaction (HRI) landscape. As intelligent systems and r...
Synthesising Diverse and Discriminatory Sets of Instances using Novelty Search in Combinatorial Domains
Journal Article
Marrero, A., Segredo, E., Leon, C., & Hart, E. (in press)
Synthesising Diverse and Discriminatory Sets of Instances using Novelty Search in Combinatorial Domains. Evolutionary Computation,
Gathering sufficient instance data to either train algorithm-selection models or understand algorithm footprints within an instance space can be challenging. We propose an app...
Learning Descriptors for Novelty-Search Based Instance Generation via Meta-evolution
Conference Proceeding
Marrero, A., Segredo, E., León, C., & Hart, E. (in press)
Learning Descriptors for Novelty-Search Based Instance Generation via Meta-evolution. In Genetic and Evolutionary Computation Conference (GECCO ’24), July 14–18, 2024, Melbourne, VIC, Australia. https://doi.org/10.1145/3638529.3654028
The ability to generate example instances from a domain is important in order to benchmark algorithms and to generate data that covers an instance-space in order to train mach...
Buried pipe localization using an iterative geometric clustering on GPR data
Journal Article
Janning, R., Busche, A., Horváth, T., & Schmidt-Thieme, L. (2014)
Buried pipe localization using an iterative geometric clustering on GPR data. Artificial Intelligence Review, 42(3), 403-425. https://doi.org/10.1007/s10462-013-9410-2
Ground penetrating radar is a non-destructive method to scan the shallow subsurface for detecting buried objects like pipes, cables, ducts and sewers. Such buried objects caus...
PMNet: A Multi-branch and Multi-scale Fusion Convolutional Neural Network for Water Body Extraction of High-resolution Remote Sensing Images
Journal Article
Liu, Q., Zhang, Z., Liu, X., Zhang, Y., & Du, Z. (in press)
PMNet: A Multi-branch and Multi-scale Fusion Convolutional Neural Network for Water Body Extraction of High-resolution Remote Sensing Images. Intelligent Automation and Soft Computing,
Automatic extraction of water body information from high-resolution remote sensing images is one of the core tasks of remote sensing image interpretation. Since the complex mu...
On the Utility of Probing Trajectories for Algorithm-Selection
Conference Proceeding
Renau, Q., & Hart, E. (2024)
On the Utility of Probing Trajectories for Algorithm-Selection. In Applications of Evolutionary Computation. EvoApplications 2024 (98-114). https://doi.org/10.1007/978-3-031-56852-7_7
Machine-learning approaches to algorithm-selection typically take data describing an instance as input. Input data can take the form of features derived from the instance desc...
A Comparative Study of Assessment Metrics for Imbalanced Learning
Conference Proceeding
Farou, Z., Aharrat, M., & Horváth, T. (2023)
A Comparative Study of Assessment Metrics for Imbalanced Learning. In New Trends in Database and Information Systems: ADBIS 2023 Short Papers, Doctoral Consortium and Workshops: AIDMA, DOING, K-Gals, MADEISD, PeRS, Barcelona, Spain, September 4–7, 2023, Proceedings (119-129). https://doi.org/10.1007/978-3-031-42941-5_11
There are several machine learning algorithms addressing class imbalance problem, requiring standardized metrics for adequete performance evaluation. This paper reviews severa...
Integration of two fuzzy data mining methods
Journal Article
Horvath, T., & Krajči, S. (2004)
Integration of two fuzzy data mining methods. Neural Network World, 14(5), 391-402
The cluster analysis and the formal concept analysis are both used to identify significiant groups of similar objects. Rice & Siff's algorithm for the clustering joins these t...
Image Forgery Detection using Cryptography and Deep Learning
Conference Proceeding
Oke, A., & Babaagba, K. O. (2024)
Image Forgery Detection using Cryptography and Deep Learning. In Big Data Technologies and Applications. BDTA 2023 (62-78). https://doi.org/10.1007/978-3-031-52265-9_5
The advancement of technology has undoubtedly exposed everyone to a remarkable array of visual imagery. Nowadays, digital technology is eating away the trust and historical co...