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Improving Algorithm-Selection and Performance-Prediction via Learning Discriminating Training Samples (2024)
Presentation / Conference Contribution
Renau, Q., & Hart, E. (2024, July). Improving Algorithm-Selection and Performance-Prediction via Learning Discriminating Training Samples. Presented at GECCO 2024, Melbourne, USA

The choice of input-data used to train algorithm-selection models is recognised as being a critical part of the model success. Recently, feature-free methods for algorithm-selection that use short trajec-tories obtained from running a solver as input... Read More about Improving Algorithm-Selection and Performance-Prediction via Learning Discriminating Training Samples.

Understanding fitness landscapes in morpho-evolution via local optima networks (2024)
Presentation / Conference Contribution
Thomson, S. L., Le Goff, L., Hart, E., & Buchanan, E. (2024, July). Understanding fitness landscapes in morpho-evolution via local optima networks. Presented at Genetic and Evolutionary Computation Conference (GECCO 2024), Melbourne, Australia

Morpho-Evolution (ME) refers to the simultaneous optimisation of a robot's design and controller to maximise performance given a task and environment. Many genetic encodings have been proposed which are capable of representing design and control. Pre... Read More about Understanding fitness landscapes in morpho-evolution via local optima networks.

DIBNN: A Dual-Improved-BNN Based Algorithm for Multi-Robot Cooperative Area Search in Complex Obstacle Environments (2024)
Journal Article
Chen, B., Zhang, H., Zhang, F., Jiang, Y., Miao, Z., Yu, H., & Wang, Y. (online). DIBNN: A Dual-Improved-BNN Based Algorithm for Multi-Robot Cooperative Area Search in Complex Obstacle Environments. IEEE Transactions on Automation Science and Engineering, https://doi.org/10.1109/tase.2024.3379166

Aiming at the area search task of a multi-robot system in an unknown complex obstacle environment, we propose a cooperative area search algorithm based on a dual improved bio-inspired neural network (DIBNN). First, we improve the BNN model to reduce... Read More about DIBNN: A Dual-Improved-BNN Based Algorithm for Multi-Robot Cooperative Area Search in Complex Obstacle Environments.

Wide-Scan/High-Gain Phased Array Antenna for 5G/6G Cellular Networks (2024)
Presentation / Conference Contribution
Basherlou, H. J., Ojaroudi Parchin, N., Alibakhshikenari, M., Kouhalvandi, L., & See, C. H. (2024, June). Wide-Scan/High-Gain Phased Array Antenna for 5G/6G Cellular Networks. Presented at 2024 IEEE 22nd Mediterranean Electrotechnical Conference- IEEE MELECON 2024, Porto, Portugal

A Hierarchical Approach to Evolving Behaviour-Trees for Swarm Control (2024)
Presentation / Conference Contribution
Montague, K., Hart, E., & Paechter, B. (2024, April). A Hierarchical Approach to Evolving Behaviour-Trees for Swarm Control. Presented at EvoStar 2024, Aberystwyth

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)
Presentation / Conference Contribution
Renau, Q., & Hart, E. (2024, April). On the Utility of Probing Trajectories for Algorithm-Selection. Presented at EvoStar 2024, Aberystwyth, UK

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.

Evolving Staff Training Schedules using an Extensible Fitness Function and a Domain Specific Language (2024)
Presentation / Conference Contribution
Urquhart, N., & Hunter, K. (2024, April). Evolving Staff Training Schedules using an Extensible Fitness Function and a Domain Specific Language. Presented at 27th European Conference, EvoApplications 2024, Aberystwyth, UK

When using a meta-heuristic based optimiser in some industrial scenarios, there may be a need to amend the objective function as time progresses to encompass constraints that did not exist during the development phase of the software. We propose a me... Read More about Evolving Staff Training Schedules using an Extensible Fitness Function and a Domain Specific Language.

PULRAS: A Novel PUF-Based Lightweight Robust Authentication Scheme (2024)
Presentation / Conference Contribution
Yaqub, Z., Yigit, Y., Maglaras, L., Tan, Z., & Wooderson, P. (2024, April). PULRAS: A Novel PUF-Based Lightweight Robust Authentication Scheme. Presented at The 20th Annual International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT 2024), Abu Dhabi, UAE

In the rapidly evolving landscape of Intelligent Transportation Systems (ITS), Vehicular Ad-hoc Networks (VANETs) play a critical role in enhancing road safety and traffic flow. However, VANETs face significant security and privacy challenges due to... Read More about PULRAS: A Novel PUF-Based Lightweight Robust Authentication Scheme.

Temporal True and Surrogate Fitness Landscape Analysis for Expensive Bi-Objective Optimisation Expensive Bi-Objective (2024)
Presentation / Conference Contribution
Rodriguez, C. J., Thomson, S. L., Alderliesten, T., & Bosman, P. A. N. (2024, July). Temporal True and Surrogate Fitness Landscape Analysis for Expensive Bi-Objective Optimisation Expensive Bi-Objective. Presented at Genetic and Evolutionary Computation Conference (GECCO 2024), Melbourne, Australia

Many real-world problems have expensive-to-compute fitness functions and are multi-objective in nature. Surrogate-assisted evolutionary algorithms are often used to tackle such problems. Despite this, literature about analysing the fitness landscapes... Read More about Temporal True and Surrogate Fitness Landscape Analysis for Expensive Bi-Objective Optimisation Expensive Bi-Objective.

Learning Descriptors for Novelty-Search Based Instance Generation via Meta-evolution (2024)
Presentation / Conference Contribution
Marrero, A., Segredo, E., León, C., & Hart, E. (2024, July). Learning Descriptors for Novelty-Search Based Instance Generation via Meta-evolution. Presented at ACM GECCO 2024, Melbourne, Australia

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.

Generalized Early Stopping in Evolutionary Direct Policy Search (2024)
Journal Article
Arza, E., Le Goff, L. K., & Hart, E. (online). 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.

DBMA-Net: A Dual-Branch Multi-Attention Network for Polyp Segmentation (2024)
Journal Article
Zhai, C., Yang, L., Liu, Y., & Yu, H. (2024). DBMA-Net: A Dual-Branch Multi-Attention Network for Polyp Segmentation. IEEE Transactions on Instrumentation and Measurement, 73, Article 2512316. https://doi.org/10.1109/tim.2024.3379418

In the early prevention stage of colorectal cancer, the utilization of automatic polyp segmentation techniques from colonoscopy images has demonstrated efficacy in mitigating the misdiagnosis rate. Nonetheless, accurate polyp segmentation is always a... Read More about DBMA-Net: A Dual-Branch Multi-Attention Network for Polyp Segmentation.

Design and Evaluation of Memory Efficient Data Structure Scheme for Energy Drainage Attacks in Wireless Sensor Networks (2024)
Journal Article
Bhatti, D. S., Saleem, S., Ali, Z., Park, T., Suh, B., Kamran, A., Buchanan, W. J., & Kim, K. (2024). Design and Evaluation of Memory Efficient Data Structure Scheme for Energy Drainage Attacks in Wireless Sensor Networks. IEEE Access, 12, 41499-41516. https://doi.org/10.1109/access.2024.3377144

Wireless Sensor Networks (WSN) are deployed on a large scale and require protection from malicious energy drainage attacks, particularly those directed at the routing layer. The complexity increases during critical operations like cluster head select... Read More about Design and Evaluation of Memory Efficient Data Structure Scheme for Energy Drainage Attacks in Wireless Sensor Networks.

Using Frequency B-Splines for an accurate and faster calculation of adaptive transforms for electric machines diagnosis (2024)
Presentation / Conference Contribution
Pons-Llinares, J., Quijano-López, A., Bonet-Jara, J., & Vedreño-Santos, F. (2024, May). Using Frequency B-Splines for an accurate and faster calculation of adaptive transforms for electric machines diagnosis. Presented at Electrimacs 2024, Castellón de la Plana, Spain

Early detection of faults in electric motors is crucial to prevent unplanned downtime and expensive repairs. Transient analysis through time-frequency transforms reveals important information on the motor condition. Computational time of these transf... Read More about Using Frequency B-Splines for an accurate and faster calculation of adaptive transforms for electric machines diagnosis.

Gain-Enhanced/End-Fire Phased Array Antenna for Future Cellular Networks (2024)
Presentation / Conference Contribution
Basherlou, H. J., Ojaroudi Parchin, N., Manjakkal, L., See, C. H., Amar, A., & Salama, A. (2024, May). Gain-Enhanced/End-Fire Phased Array Antenna for Future Cellular Networks. Presented at 14th International Conference on Electrical Engineering (ICEENG), Cairo, Egypt

A Novel RFID Tag's Antenna for Mounting on Metallic Objects (2024)
Presentation / Conference Contribution
Gharbia, I., Aldelemy, A., Elmegri, F., Ismail, . A. S., Darwish, M., See, C. H., & Abd-Alhameed, R. A. (2024, May). A Novel RFID Tag's Antenna for Mounting on Metallic Objects. Presented at 14th International Conference on Electrical Engineering (ICEENG), Cairo, Egypt

Novel Aperture Antenna Array Design for Indoor Localisation UWB Radar Technologies (2024)
Presentation / Conference Contribution
Ahmed, I., Aldelemy, A., Ismail, A. S., See, C. H., Ghareeb, M. F., Nafea,, S., Oda, E., & Abd-Alhameed, R. A. (2024, May). Novel Aperture Antenna Array Design for Indoor Localisation UWB Radar Technologies. Presented at 14th International Conference on Electrical Engineering (ICEENG), Cairo, Egypt