Outputs (156)

Randomness in Local Optima Network Sampling (2023)
Presentation / Conference Contribution
Thomson, S. L., Veerapen, N., Ochoa, G., & van den Berg, D. (2023, July). Randomness in Local Optima Network Sampling. Presented at GECCO '23, Lisbon, Portugal

We consider statistical randomness in the construction of local optima networks (LONs) and conduct a preliminary and exploratory study into this. LONs capture a fitness landscape into network format: the nodes are local optima, and edges are heuristi... Read More about Randomness in Local Optima Network Sampling.

Generating diverse and discriminatory knapsack instances by searching for novelty in variable dimensions of feature-space (2023)
Presentation / Conference Contribution
Marrero, A., Segredo, E., Hart, E., Bossek, J., & Neumann, A. (2023, July). Generating diverse and discriminatory knapsack instances by searching for novelty in variable dimensions of feature-space. Presented at GECCO 2023, Lisbon, Portugal

Generating new instances via evolutionary methods is commonly used to create new benchmarking data-sets, with a focus on attempting to cover an instance-space as completely as possible. Recent approaches have exploited Quality-Diversity methods to ev... Read More about Generating diverse and discriminatory knapsack instances by searching for novelty in variable dimensions of feature-space.

Learning-Based Neural Ant Colony Optimization (2023)
Presentation / Conference Contribution
Liu, Y., Qiu, J., Hart, E., Yu, Y., Gan, Z., & Li, W. (2023, July). Learning-Based Neural Ant Colony Optimization. Presented at GECCO 2023, Lisbon, Portugal

In this paper, we propose a new ant colony optimization algorithm , called learning-based neural ant colony optimization (LN-ACO), which incorporates an "intelligent ant". This intelligent ant contains a convolutional neural network pre-trained on a... Read More about Learning-Based Neural Ant Colony Optimization.

Channel Configuration for Neural Architecture: Insights from the Search Space (2023)
Presentation / Conference Contribution
Thomson, S. L., Ochoa, G., Veerapen, N., & Michalak, K. (2023, July). Channel Configuration for Neural Architecture: Insights from the Search Space. Presented at GECCO '23, Lisbon, Portugal

We consider search spaces associated with neural network channel configuration. Architectures and their accuracy are visualised using low-dimensional Euclidean embedding (LDEE). Optimisation dynamics are captured using local optima networks (LONs). L... Read More about Channel Configuration for Neural Architecture: Insights from the Search Space.

Extending AGADE Traffic To Simulate Auctions In Shared Mobility Services (2023)
Presentation / Conference Contribution
Nguyen, J., Powers, S., Urquhart, N., Eckerle, D., Farrenkopf, T., & Guckert, M. (2023, June). Extending AGADE Traffic To Simulate Auctions In Shared Mobility Services. Presented at 37th ECMS International Conference on Modelling and Simulation, Florence, Italy

With the number of individual vehicles meeting the capacity limit of urban road infrastructure, the deployment of new mobility services may help to achieve more efficient use of available resources and prevent critical overload. It may be observed th... Read More about Extending AGADE Traffic To Simulate Auctions In Shared Mobility Services.

PMNet: A Multi-branch and Multi-scale Fusion Convolutional Neural Network for Water Body Extraction of High-resolution Remote Sensing Images (2023)
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 multi-scale characteristics of high-resolution remote sensing images, it is d... Read More about PMNet: A Multi-branch and Multi-scale Fusion Convolutional Neural Network for Water Body Extraction of High-resolution Remote Sensing Images.

Playing the political game: The co-evolution of institutions with group size and political inequality (2023)
Journal Article
Powers, S. T., Perret, C., & Currie, T. E. (2023). Playing the political game: The co-evolution of institutions with group size and political inequality. Philosophical Transactions B: Biological Sciences, 378(1883), https://doi.org/10.1098/rstb.2022.0303

All societies need to form institutional rules to regulate their social interactions. These specify what actions individuals should take in particular situations, and what sanctions will apply if individuals violate these rules. But forming these ins... Read More about Playing the political game: The co-evolution of institutions with group size and political inequality.

Review of recent advances in frequency-domain near-infrared spectroscopy technologies (2023)
Journal Article
Zhou, X., Xia, Y., Uchitel, J., Collins-Jones, L., Yang, S., Loureiro, R., Cooper, R. J., & Zhao, H. (2023). Review of recent advances in frequency-domain near-infrared spectroscopy technologies. Biomedical Optics Express, 14(7), 3234-3258. https://doi.org/10.1364/BOE.484044

Over the past several decades, near-infrared spectroscopy (NIRS) has become a popular research and clinical tool for non-invasively measuring the oxygenation of biological tissues, with particular emphasis on applications to the human brain. In most... Read More about Review of recent advances in frequency-domain near-infrared spectroscopy technologies.

Towards individualised speech enhancement: An SNR preference learning system for multi-modal hearing aids (2023)
Presentation / Conference Contribution
Kirton-Wingate, J., Ahmed, S., Gogate, M., Tsao, Y., & Hussain, A. (2023, June). Towards individualised speech enhancement: An SNR preference learning system for multi-modal hearing aids. Presented at 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW), Rhodes Island, Greece

Since the advent of deep learning (DL), speech enhancement (SE) models have performed well under a variety of noise conditions. However, such systems may still introduce sonic artefacts, sound unnatural, and restrict the ability for a user to hear am... Read More about Towards individualised speech enhancement: An SNR preference learning system for multi-modal hearing aids.

Evolved Open-Endedness in Cultural Evolution: A New Dimension in Open-Ended Evolution Research (2023)
Journal Article
Borg, J. M., Buskell, A., Kapitany, R., Powers, S. T., Reindl, E., & Tennie, C. (2024). Evolved Open-Endedness in Cultural Evolution: A New Dimension in Open-Ended Evolution Research. Artificial Life, 30(3), 417-438. https://doi.org/10.1162/artl_a_00406

The goal of Artificial Life research, as articulated by Chris Langton, is “to contribute to theoretical biology by locating life-as-we-know-it within the larger picture of life-as-it-could-be” (1989, p. 1). The study and pursuit of open-ended evoluti... Read More about Evolved Open-Endedness in Cultural Evolution: A New Dimension in Open-Ended Evolution Research.

NCC: Neural concept compression for multilingual document recommendation (2023)
Presentation / Conference Contribution
Tashu, T. M., Lenz, M., & Horváth, T. NCC: Neural concept compression for multilingual document recommendation

In this work, we propose a novel method for generating inter-lingual document representations using neural network concept compression. The presented approach is intended to improve the quality of content-based multilingual document recommendation an... Read More about NCC: Neural concept compression for multilingual document recommendation.

Frequency Fitness Assignment on JSSP: A Critical Review (2023)
Presentation / Conference Contribution
de Bruin, E., Thomson, S. L., & Berg, D. V. D. (2023, April). Frequency Fitness Assignment on JSSP: A Critical Review. Presented at EvoApplications 2023: Applications of Evolutionary Computation, Brno, Czech Republic

Metaheuristic navigation towards rare objective values instead of good objective values: is it a good idea? We will discuss the closed and open ends after presenting a successful replication study of Weise et al.’s ‘frequency fitness assignment’ for... Read More about Frequency Fitness Assignment on JSSP: A Critical Review.

DIGNEA: A tool to generate diverse and discriminatory instance suites for optimisation domains (2023)
Journal Article
Marrero, A., Segredo, E., León, C., & Hart, E. (2023). DIGNEA: A tool to generate diverse and discriminatory instance suites for optimisation domains. SoftwareX, 22, Article 101355. https://doi.org/10.1016/j.softx.2023.101355

To advance research in the development of optimisation algorithms, it is crucial to have access to large test-beds of diverse and discriminatory instances from a domain that can highlight strengths and weaknesses of different algorithms. The DIGNEA t... Read More about DIGNEA: A tool to generate diverse and discriminatory instance suites for optimisation domains.

On Optimizing the Structure of Neural Networks Through a Compact Codification of Their Architecture (2023)
Presentation / Conference Contribution
Lupión, M., Cruz, N. C., Paechter, B., & Ortigosa, P. M. (2022, July). On Optimizing the Structure of Neural Networks Through a Compact Codification of Their Architecture. Presented at Metaheuristics: 14th International Conference, MIC 2022, Syracuse, Italy

Neural networks stand out in Artificial Intelligence for their capacity of being applied to multiple challenging tasks such as image classification. However, designing a neural network to address a particular problem is also a demanding task that req... Read More about On Optimizing the Structure of Neural Networks Through a Compact Codification of Their Architecture.

Hyper-parameter initialization of classification algorithms using dynamic time warping: A perspective on PCA meta-features (2022)
Presentation / Conference Contribution
Horváth, T., Mantovani, R. G., & de Carvalho, A. C. Hyper-parameter initialization of classification algorithms using dynamic time warping: A perspective on PCA meta-features

Meta-learning, a concept from the area of automated machine learning, aims at providing decision support for data scientists by recommending a suitable setting (a machine learning algorithm or its hyper-parameters) to be used for a given dataset. Suc... Read More about Hyper-parameter initialization of classification algorithms using dynamic time warping: A perspective on PCA meta-features.

Most NLG is Low-Resource: here's what we can do about it (2022)
Presentation / Conference Contribution
Howcroft, D. M., & Gkatzia, D. (2022, December). Most NLG is Low-Resource: here's what we can do about it. Presented at Workshop on Natural Language Generation, Evaluation, and Metrics (GEM), Abu Dhabi, UAE

Many domains and tasks in natural language generation (NLG) are inherently 'low-resource', where training data, tools and linguistic analyses are scarce. This poses a particular challenge to researchers and system developers in the era of machine-lea... Read More about Most NLG is Low-Resource: here's what we can do about it.

Accelerating neural network architecture search using multi-GPU high-performance computing (2022)
Journal Article
Lupión, M., Cruz, N. C., Sanjuan, J. F., Paechter, B., & Ortigosa, P. M. (2023). Accelerating neural network architecture search using multi-GPU high-performance computing. Journal of Supercomputing, 79, 7609-7625. https://doi.org/10.1007/s11227-022-04960-z

Neural networks stand out from artificial intelligence because they can complete challenging tasks, such as image classification. However, designing a neural network for a particular problem requires experience and tedious trial and error. Automating... Read More about Accelerating neural network architecture search using multi-GPU high-performance computing.

Universally Hard Hamiltonian Cycle Problem Instances (2022)
Presentation / Conference Contribution
Sleegers, J., Thomson, S. L., & van den Berg, D. (2022, November). Universally Hard Hamiltonian Cycle Problem Instances. Presented at ECTA 2022 : 14th International Conference on Evolutionary Computation Theory and Applications, Valletta, Malta

In 2021, evolutionary algorithms found the hardest-known yes and no instances for the Hamiltonian cycle problem. These instances, which show regularity patterns, require a very high number of recursions for the best exact backtracking algorithm (Vand... Read More about Universally Hard Hamiltonian Cycle Problem Instances.

Multi-Agent Modelling Notation (MAMN): A multi-layered graphical modelling notation for agent-based simulations (2022)
Presentation / Conference Contribution
Nguyen, J., Powers, S., Urquhart, N., Farrenkopf, T., & Guckert, M. (2022, November). Multi-Agent Modelling Notation (MAMN): A multi-layered graphical modelling notation for agent-based simulations. Presented at 24th International Conference on Principles and Practice of Multi-Agent Systems, Valencia, Spain

Cause-effect graphs have been applied in non agent-based simulations, where they are used to model chained causal relations between input parameters and system behaviour measured by appropriate indicators. This can be useful for the analysis and inte... Read More about Multi-Agent Modelling Notation (MAMN): A multi-layered graphical modelling notation for agent-based simulations.

Object Detection Using Sim2Real Domain Randomization for Robotic Applications (2022)
Journal Article
Horváth, D., Erdős, G., Istenes, Z., Horváth, T., & Földi, S. (2023). Object Detection Using Sim2Real Domain Randomization for Robotic Applications. IEEE Transactions on Robotics, 39(2), 1225-1243. https://doi.org/10.1109/tro.2022.3207619

Robots working in unstructured environments must be capable of sensing and interpreting their surroundings. One of the main obstacles of deep-learning-based models in the field of robotics is the lack of domain-specific labeled data for different ind... Read More about Object Detection Using Sim2Real Domain Randomization for Robotic Applications.