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Outputs (156)

The Performance of Frequency Fitness Assignment on JSSP for Different Problem Instance Sizes (2024)
Presentation / Conference Contribution
Pijning, I., Koppenhol, L., Dijkzeul, D., Brouwer, N., Thomson, S. L., & van den Berg, D. (2024, November). The Performance of Frequency Fitness Assignment on JSSP for Different Problem Instance Sizes. Presented at ECTA 2024: 16th International Conference on Evolutionary Computation Theory and Applications, Porto, Portugal

The Frequency Fitness Assignment (FFA) method steers evolutionary algorithms by objective rareness instead of objective goodness. Does this mean the size of the combinatorial search space influences its performance when compared to more traditional e... Read More about The Performance of Frequency Fitness Assignment on JSSP for Different Problem Instance Sizes.

FedCST: Federated Learning on Heterogeneous Resource-constrained Devices Using Clustering and Split Training (2024)
Presentation / Conference Contribution
Wang, Z., Lin, H., Liu, Q., Zhang, Y., & Liu, X. (2024, July). FedCST: Federated Learning on Heterogeneous Resource-constrained Devices Using Clustering and Split Training. Presented at The 24th International Conference on Software Quality, Reliability, and Security Companion (QRS-C), Cambridge, UK

With the rapid development of 5G and Internet of Things (IoT) technologies, edge devices such as sensors, smartphones, and wearable devices have become increasingly prevalent. The massive amount of distributed data generated by these devices offers u... Read More about FedCST: Federated Learning on Heterogeneous Resource-constrained Devices Using Clustering and Split Training.

A New Improved Method of Recurrent Memory Perception for Radar Echo Extrapolation (2024)
Presentation / Conference Contribution
Ji, R., Liu, Q., Zhang, Y., & Liu, X. (2024, July). A New Improved Method of Recurrent Memory Perception for Radar Echo Extrapolation. Presented at 2024 IEEE 24th International Conference on Software Quality, Reliability, and Security Companion (QRS-C), Cambridge, United Kingdom

Precipitation forecasting has long been a prominent topic in meteorology, as accurate predictions of impending rainfall are crucial for daily life and travel planning. Currently, radar echo extrapolation serves as the primary method for precipitation... Read More about A New Improved Method of Recurrent Memory Perception for Radar Echo Extrapolation.

High Intensity Radar Echo Extrapolation Based on Stacked Generative Structure (2024)
Presentation / Conference Contribution
Tao, S., Liu, Q., Zhang, Y., & Liu, X. (2024, July). High Intensity Radar Echo Extrapolation Based on Stacked Generative Structure. Presented at 2024 IEEE 24th International Conference on Software Quality, Reliability, and Security Companion (QRS-C), Cambridge, UK

In recent years, the phenomenon of severe convective disaster weather has increased significantly in the world. Due to the characteristics of small spatial scale, short occurrence period, great destructiveness and drastic changes, severe convective d... Read More about High Intensity Radar Echo Extrapolation Based on Stacked Generative Structure.

Automated Human-Readable Label Generation in Open Intent Discovery (2024)
Presentation / Conference Contribution
Anderson, G., Hart, E., Gkatzia, D., & Beaver, I. (2024, September). Automated Human-Readable Label Generation in Open Intent Discovery. Presented at Interspeech 2024, Kos, Greece

The correct determination of user intent is key in dialog systems. However, an intent classifier often requires a large, labelled training dataset to identify a set of known intents. The creation of such a dataset is a complex and time-consuming task... Read More about Automated Human-Readable Label Generation in Open Intent Discovery.

DeFT-Net: Dual-Window Extended Frequency Transformer for Rhythmic Motion Prediction (2024)
Presentation / Conference Contribution
Ademola, A., Sinclair, D., Koniaris, B., Hannah, S., & Mitchell, K. (2024, September). DeFT-Net: Dual-Window Extended Frequency Transformer for Rhythmic Motion Prediction. Presented at EG UK Computer Graphics & Visual Computing (2024), London, UK

Enabling online virtual reality (VR) users to dance and move in a way that mirrors the real-world necessitates improvements in the accuracy of predicting human motion sequences paving way for an immersive and connected experience. However, the drawba... Read More about DeFT-Net: Dual-Window Extended Frequency Transformer for Rhythmic Motion Prediction.

Evaluating the Robustness of Deep-Learning Algorithm-Selection Models by Evolving Adversarial Instances (2024)
Presentation / Conference Contribution
Hart, E., Renau, Q., Sim, K., & Alissa, M. (2024, September). Evaluating the Robustness of Deep-Learning Algorithm-Selection Models by Evolving Adversarial Instances. Presented at 18th International Conference on Parallel Problem Solving From Nature PPSN 2024, Hagenburg, Austria

Deep neural networks (DNN) are increasingly being used to perform algorithm-selection in combinatorial optimisation domains, particularly as they accommodate input representations which avoid designing and calculating features. Mounting evidence fro... Read More about Evaluating the Robustness of Deep-Learning Algorithm-Selection Models by Evolving Adversarial Instances.

Entropy, Search Trajectories, and Explainability for Frequency Fitness Assignment (2024)
Presentation / Conference Contribution
Thomson, S. L., Ochoa, G., van den Berg, D., Liang, T., & Weise, T. (2024, September). Entropy, Search Trajectories, and Explainability for Frequency Fitness Assignment. Presented at Parallel Problem Solving from Nature (PPSN 2024), Hagenberg, Austria

Local optima are a menace that can trap optimisation processes. Frequency fitness assignment (FFA) is an concept aiming to overcome this problem. It steers the search towards solutions with rare fitness instead of high-quality fitness. FFA-based algo... Read More about Entropy, Search Trajectories, and Explainability for Frequency Fitness Assignment.

A Deep Dive into Effects of Structural Bias on CMA-ES Performance along Affine Trajectories (2024)
Presentation / Conference Contribution
van Stein, N., Thomson, S. L., & Kononova, A. V. (2024, September). A Deep Dive into Effects of Structural Bias on CMA-ES Performance along Affine Trajectories. Presented at Parallel Problem Solving from Nature (PPSN) 2024, Hagenberg, Austria

To guide the design of better iterative optimisation heuristics, it is imperative to understand how inherent structural biases within algorithm components affect the performance on a wide variety of search landscapes. This study explores the impact o... Read More about A Deep Dive into Effects of Structural Bias on CMA-ES Performance along Affine Trajectories.

Information flow and Laplacian dynamics on local optima networks (2024)
Presentation / Conference Contribution
Richter, H., & Thomson, S. L. (2024, June). Information flow and Laplacian dynamics on local optima networks. Presented at IEEE Congress on Evolutionary Computation (IEEE CEC), Yokohama, Japan

We propose a new way of looking at local optima networks (LONs). LONs represent fitness landscapes; the nodes are local optima, and the edges are search transitions between them. Many metrics computed on LONs have been proposed and shown to be linked... Read More about Information flow and Laplacian dynamics on local optima networks.

The Easiest Hard Problem: Now Even Easier (2024)
Presentation / Conference Contribution
Horn, R., Thomson, S. L., van den Berg, D., & Adriaans, P. (2024, July). The Easiest Hard Problem: Now Even Easier. Presented at ACM Genetic and Evolutionary Computation Conference (GECCO) 2024, Melbourne, Australia

We present an exponential decay function that characterizes the number of solutions to instances of the Number Partitioning Problem (NPP) with uniform distribution of bits across the integers. This function is fitted on the number of optimal solution... Read More about The Easiest Hard Problem: Now Even Easier.

Exploring the use of fitness landscape analysis for understanding malware evolution (2024)
Presentation / Conference Contribution
Babaagba, K., Murali, R., & Thomson, S. L. (2024, July). Exploring the use of fitness landscape analysis for understanding malware evolution. Presented at ACM Genetic and Evolutionary Computation Conference (GECCO) 2024, Melbourne, Australia

We conduct a preliminary study exploring the potential of using fitness landscape analysis for understanding the evolution of malware. This type of optimisation is fairly new and has not previously been studied through the lens of landscape analysis.... Read More about Exploring the use of fitness landscape analysis for understanding malware evolution.

Explaining evolutionary feature selection via local optima networks (2024)
Presentation / Conference Contribution
Adair, J., Thomson, S. L., & Brownlee, A. E. I. (2024, July). Explaining evolutionary feature selection via local optima networks. Presented at ACM Genetic and Evolutionary Computation Conference (GECCO) 2024, Melbourne, Australia

We analyse fitness landscapes of evolutionary feature selection to obtain information about feature importance in supervised machine learning. Local optima networks (LONs) are a compact representation of a landscape, and can potentially be adapted fo... Read More about Explaining evolutionary feature selection via local optima networks.

Improving Efficiency of Evolving Robot Designs via Self-Adaptive Learning Cycles and an Asynchronous Architecture (2024)
Presentation / Conference Contribution
Le Goff, L., & Hart, E. (2024, July). Improving Efficiency of Evolving Robot Designs via Self-Adaptive Learning Cycles and an Asynchronous Architecture. Presented at GECCO 2024 Embodied and Evolved Artificial Intelligence Workshop, Melbourne, Australia

Algorithmic frameworks for the joint optimisation of a robot's design and controller often utilise a learning loop nested within an evolutionary algorithm to refine the controller associated with a newly generated robot design. Intuitively, it is rea... Read More about Improving Efficiency of Evolving Robot Designs via Self-Adaptive Learning Cycles and an Asynchronous Architecture.

Neurosymbolic Learning in the XAI Framework for Enhanced Cyberattack Detection with Expert Knowledge Integration (2024)
Presentation / Conference Contribution
Kalutharage, C. S., Liu, X., Chrysoulas, C., & Bamgboye, O. (2024, June). Neurosymbolic Learning in the XAI Framework for Enhanced Cyberattack Detection with Expert Knowledge Integration. Presented at The 39th International Conference on ICT Systems Security and Privacy Protection (SEC 2024), Edinburgh

The perpetual evolution of cyberattacks, especially in the realm of Internet of Things (IoT) networks, necessitates advanced, adaptive, and intelligent defence mechanisms. The integration of expert knowledge can drastically enhance the efficacy of Io... Read More about Neurosymbolic Learning in the XAI Framework for Enhanced Cyberattack Detection with Expert Knowledge Integration.

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.

A Bi-Level Approach to Vehicle Fleet Reduction: Successful Case Study in Community Healthcare (2024)
Presentation / Conference Contribution
Brownlee, A., Thomson, S., & Oladapo, R. (2024, July). A Bi-Level Approach to Vehicle Fleet Reduction: Successful Case Study in Community Healthcare. Presented at Proceedings of the Genetic and Evolutionary Computation Conference Companion, Melbourne, Australia

We report on a case study application of metaheuristics with Argyll and Bute Health and Social Care Partnership in the West of Scotland. The Partnership maintains a fleet of pool vehicles that are available to service visits of staff to locations acr... Read More about A Bi-Level Approach to Vehicle Fleet Reduction: Successful Case Study in Community Healthcare.

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.