Learning Descriptors for Novelty-Search Based Instance Generation via Meta-evolution (2024)
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 machine-learning models for algorithm selection. Quality-Diversity (QD) algorit... Read More about Learning Descriptors for Novelty-Search Based Instance Generation via Meta-evolution.

A Hierarchical Approach to Evolving Behaviour-Trees for Swarm Control (2024)
Conference Proceeding
Montague, K., Hart, E., & Paechter, B. (2024). A Hierarchical Approach to Evolving Behaviour-Trees for Swarm Control. In S. Smith, J. Correia, & C. Cintrano (Eds.), Applications of Evolutionary Computation: 27th European Conference, EvoApplications 2024, Held as Part of EvoStar 2024, Aberystwyth, UK, April 3–5, 2024, Proceedings, Part I (178-193). https://doi.org/10.1007/978-3-031-56852-7_12

Behaviour trees (BTs) are commonly used as controllers in robotic swarms due their modular composition and to the fact that they can be easily interpreted by humans. From an algorithmic perspective, an additional advantage is that extra modules can e... Read More about A Hierarchical Approach to Evolving Behaviour-Trees for Swarm Control.

On the Utility of Probing Trajectories for Algorithm-Selection (2024)
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 description or fitness landscape , or can be a direct representation of the ins... Read More about On the Utility of Probing Trajectories for Algorithm-Selection.

Generalized Early Stopping in Evolutionary Direct Policy Search (2024)
Journal Article
Arza, E., Le Goff, L. K., & Hart, E. (in press). Generalized Early Stopping in Evolutionary Direct Policy Search. ACM Transactions on Evolutionary Learning and Optimization, https://doi.org/10.1145/3653024

Lengthy evaluation times are common in many optimization problems such as direct policy search tasks, especially when they involve conducting evaluations in the physical world, e.g. in robotics applications. Often when evaluating solution over a fixe... Read More about Generalized Early Stopping in Evolutionary Direct Policy Search.

Cluster-based oversampling with area extraction from representative points for class imbalance learning (2024)
Journal Article
Farou, Z., Wang, Y., & Horváth, T. (2024). Cluster-based oversampling with area extraction from representative points for class imbalance learning. Intelligent Systems with Applications, 22, Article 200357. https://doi.org/10.1016/j.iswa.2024.200357

Class imbalance learning is challenging in various domains where training datasets exhibit disproportionate samples in a specific class. Resampling methods have been used to adjust the class distribution, but they often have limitations for small dis... Read More about Cluster-based oversampling with area extraction from representative points for class imbalance learning.

Synthesising Diverse and Discriminatory Sets of Instances using Novelty Search in Combinatorial Domains (2024)
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 approach to generating synthetic instances that are tailored to perform well w... Read More about Synthesising Diverse and Discriminatory Sets of Instances using Novelty Search in Combinatorial Domains.

A spatio-temporal graph convolutional approach to real-time load forecasting in an edge-enabled distributed Internet of Smart Grids energy system (2024)
Journal Article
Liu, Q., Pan, L., Cao, X., Gan, J., Huang, X., & Liu, X. (in press). A spatio-temporal graph convolutional approach to real-time load forecasting in an edge-enabled distributed Internet of Smart Grids energy system. Concurrency and Computation: Practice and Experience, Article e8060. https://doi.org/10.1002/cpe.8060

As the edge nodes of the Internet of Smart Grids (IoSG), smart sockets enable all kinds of power load data to be analyzed at the edge, which create conditions for edge calculation and real-time (RT) load forecasting. In this article, an edge-cloud co... Read More about A spatio-temporal graph convolutional approach to real-time load forecasting in an edge-enabled distributed Internet of Smart Grids energy system.

Utilizing the Ensemble Learning and XAI for Performance Improvements in IoT Network Attack Detection (2024)
Conference Proceeding
Kalutharage, C. S., Liu, X., Chrysoulas, C., & Bamgboye, O. (2024). Utilizing the Ensemble Learning and XAI for Performance Improvements in IoT Network Attack Detection. In Computer Security. ESORICS 2023 International Workshops (125-139). https://doi.org/10.1007/978-3-031-54129-2_8

Expressive Talking Avatars (2024)
Journal Article
Pan, Y., Tan, S., Cheng, S., Lin, Q., Zeng, Z., & Mitchell, K. (in press). Expressive Talking Avatars. IEEE Transactions on Visualization and Computer Graphics, https://doi.org/10.1109/TVCG.2024.3372047

Stylized avatars are common virtual representations used in VR to support interaction and communication between remote collaborators. However, explicit expressions are notoriously difficult to create, mainly because most current methods rely on geome... Read More about Expressive Talking Avatars.

How Much do Robots Understand Rudeness? Challenges in Human-Robot Interaction (2024)
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 robots transition from controlled laboratory settings to everyday households... Read More about How Much do Robots Understand Rudeness? Challenges in Human-Robot Interaction.

An Entity Ontology-Based Knowledge Graph Embedding Approach to News Credibility Assessment (2024)
Journal Article
Liu, Q., Jin, Y., Cao, X., Liu, X., Zhou, X., Zhang, Y., …Qi, L. (in press). An Entity Ontology-Based Knowledge Graph Embedding Approach to News Credibility Assessment. IEEE Transactions on Computational Social Systems, https://doi.org/10.1109/TCSS.2023.3342873

Fake news is a prevalent issue in modern society, leading to misinformation and societal harm. News credibility assessment is a crucial approach for evaluating the accuracy and authenticity of news. It plays a significant role in enhancing public awa... Read More about An Entity Ontology-Based Knowledge Graph Embedding Approach to News Credibility Assessment.

DenMerD: a feature enhanced approach to radar beam blockage correction with edge-cloud computing (2024)
Journal Article
Liu, Q., Sun, J., Zhang, Y., & Liu, X. (2024). DenMerD: a feature enhanced approach to radar beam blockage correction with edge-cloud computing. Journal of cloud computing: advances, systems and applications, 13, Article 32. https://doi.org/10.1186/s13677-024-00607-x

In the field of meteorology, the global radar network is indispensable for detecting weather phenomena and offering early warning services. Nevertheless, radar data frequently exhibit anomalies, including gaps and clutter, arising from atmospheric re... Read More about DenMerD: a feature enhanced approach to radar beam blockage correction with edge-cloud computing.

Better trees: an empirical study on hyperparameter tuning of classification decision tree induction algorithms (2024)
Journal Article
Mantovani, R. G., Horváth, T., Rossi, A. L. D., Cerri, R., Barbon Junior, S., Vanschoren, J., & de Carvalho, A. C. P. L. F. (in press). Better trees: an empirical study on hyperparameter tuning of classification decision tree induction algorithms. Data Mining and Knowledge Discovery, https://doi.org/10.1007/s10618-024-01002-5

Machine learning algorithms often contain many hyperparameters whose values affect the predictive performance of the induced models in intricate ways. Due to the high number of possibilities for these hyperparameter configurations and their complex i... Read More about Better trees: an empirical study on hyperparameter tuning of classification decision tree induction algorithms.

Image Forgery Detection using Cryptography and Deep Learning (2024)
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 confidence people have in the integrity of imagery. Deep learning is often us... Read More about Image Forgery Detection using Cryptography and Deep Learning.

DanceMark: An open telemetry framework for latency sensitive real-time networked immersive experiences (2024)
Conference Proceeding
Koniaris, B., Sinclair, D., & Mitchell, K. (in press). DanceMark: An open telemetry framework for latency sensitive real-time networked immersive experiences.

DanceMark is an open telemetry framework designed for latency-sensitive real-time networked immersive experiences, focusing on online dancing in virtual reality within the DanceGraph platform. The goal is to minimize end-to-end latency and enhance us... Read More about DanceMark: An open telemetry framework for latency sensitive real-time networked immersive experiences.

Evolving Behavior Allocations in Robot Swarms (2024)
Conference Proceeding
Hallauer, S., Nitschke, G., & Hart, E. (2024). Evolving Behavior Allocations in Robot Swarms. In 2023 IEEE Symposium Series on Computational Intelligence (SSCI) (1526-1531). https://doi.org/10.1109/SSCI52147.2023.10371934

Behavioral diversity is known to benefit problem-solving in biological social systems such as insect colonies and human societies, as well as in artificial distributed systems including large-scale software and swarm-robotics systems. We investigate... Read More about Evolving Behavior Allocations in Robot Swarms.

The stuff we swim in: Regulation alone will not lead to justifiable trust in AI (2023)
Journal Article
Powers, S. T., Linnyk, O., Guckert, M., Hannig, J., Pitt, J., Urquhart, N., …Weber, T. (2023). The stuff we swim in: Regulation alone will not lead to justifiable trust in AI. IEEE technology & society magazine, 42(4), 95-106. https://doi.org/10.1109/MTS.2023.3341463

Information technology is used ubiquitously and has become an integral part of everyday life. With the ever increasing pervasiveness and persuasiveness of Artificial Intelligence (AI), the function of socio-technical systems changes and must be consi... Read More about The stuff we swim in: Regulation alone will not lead to justifiable trust in AI.

Evaluation of Frameworks That Combine Evolution and Learning to Design Robots in Complex Morphological Spaces (2023)
Journal Article
Li, W., Buchanan, E., Goff, L. K. L., Hart, E., Hale, M. F., Wei, B., …Tyrrell, A. M. (in press). Evaluation of Frameworks That Combine Evolution and Learning to Design Robots in Complex Morphological Spaces. IEEE Transactions on Evolutionary Computation, https://doi.org/10.1109/tevc.2023.3316363

Jointly optimising both the body and brain of a robot is known to be a challenging task, especially when attempting to evolve designs in simulation that will subsequently be built in the real world. To address this, it is increasingly common to combi... Read More about Evaluation of Frameworks That Combine Evolution and Learning to Design Robots in Complex Morphological Spaces.

Can HP-protein folding be solved with genetic algorithms? Maybe not (2023)
Conference Proceeding
Jansen, R., Horn, R., van Eck, O., Version, K., Thomson, S. L., & van den Berg, D. (2023). Can HP-protein folding be solved with genetic algorithms? Maybe not. In Proceedings of the 15th International Joint Conference on Computational Intelligence (131-140). https://doi.org/10.5220/0012248500003595

Genetic algorithms might not be able to solve the HP-protein folding problem because creating random individuals for an initial population is very hard, if not impossible. The reason for this, is that the expected number of constraint violations incr... Read More about Can HP-protein folding be solved with genetic algorithms? Maybe not.

Too Constrained for Genetic Algorithms. Too Hard for Evolutionary Computing. The Traveling Tournament Problem. (2023)
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, to use in a genetic algorithm’s initial population. In this study, we ran... Read More about Too Constrained for Genetic Algorithms. Too Hard for Evolutionary Computing. The Traveling Tournament Problem..