Outputs (121)

Compounding barriers to fairness in the digital technology ecosystem (2021)
Presentation / Conference Contribution
Woolley, S. I., Collins, T., Andras, P., Gardner, A., Ortolani, M., & Pitt, J. (2021, October). Compounding barriers to fairness in the digital technology ecosystem. Presented at 2021 IEEE International Symposium on Technology and Society (ISTAS), Waterloo, ON, Canada

A growing sense of unfairness permeates our quasi-digital society. Despite drivers supporting and motivating ethical practice in the digital technology ecosystem, there are compounding barriers to fairness that, at every level, impact technology inno... Read More about Compounding barriers to fairness in the digital technology ecosystem.

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.

Federated Learning for Short-term Residential Load Forecasting (2022)
Journal Article
Briggs, C., Fan, Z., & Andras, P. (2022). Federated Learning for Short-term Residential Load Forecasting. IEEE Open Access Journal of Power and Energy, 9, 573-583. https://doi.org/10.1109/oajpe.2022.3206220

Load forecasting is an essential task performed within the energy industry to help balance supply with demand and maintain a stable load on the electricity grid. As supply transitions towards less reliable renewable energy generation, smart meters wi... Read More about Federated Learning for Short-term Residential Load Forecasting.

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.

Structural Complexity and Performance of Support Vector Machines (2022)
Presentation / Conference Contribution
Olorisade, B. K., Brereton, P., & Andras, P. (2022). Structural Complexity and Performance of Support Vector Machines. In 2022 International Joint Conference on Neural Networks (IJCNN). https://doi.org/10.1109/ijcnn55064.2022.9892368

Support vector machines (SVM) are often applied in the context of machine learning analysis of various data. Given the nature of SVMs, these operate always in the sub-interpolation range as a machine learning method. Here we explore the impact of str... Read More about Structural Complexity and Performance of Support Vector Machines.

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.

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.

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.

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). Learning-Based Neural Ant Colony Optimization. In GECCO 2023: Proceedings of the Genetic and Evolutionary Computation Conference (47-55). https://doi.org/10.1145/3583131.3590483

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.

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. (in press). Evolved Open-Endedness in Cultural Evolution: A New Dimension in Open-Ended Evolution Research. Artificial Life, 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.

Most NLG is Low-Resource: here's what we can do about it (2022)
Presentation / Conference Contribution
Howcroft, D. M., & Gkatzia, D. (2022). Most NLG is Low-Resource: here's what we can do about it. In Proceedings of the 2nd Workshop on Natural Language Generation, Evaluation, and Metrics (GEM) (336-350)

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.

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.

Selective Query Processing: A Risk-Sensitive Selection of Search Configurations (2023)
Journal Article
Mothe, J., & Ullah, M. Z. (2024). Selective Query Processing: A Risk-Sensitive Selection of Search Configurations. ACM transactions on information systems, 42(1), https://doi.org/10.1145/3608474

In information retrieval systems, search parameters are optimized to ensure high effectiveness based on a set of past searches and these optimized parameters are then used as the system configuration for all subsequent queries. A better approach, how... Read More about Selective Query Processing: A Risk-Sensitive Selection of Search Configurations.

Comparing communities of optima with funnels in combinatorial fitness landscapes (2017)
Presentation / Conference Contribution
Thomson, S. L., Daolio, F., & Ochoa, G. (2017, July). Comparing communities of optima with funnels in combinatorial fitness landscapes. Presented at GECCO '17: Genetic and Evolutionary Computation Conference, Berlin, Germany

The existence of sub-optimal funnels in combinatorial fitness landscapes has been linked to search difficulty. The exact nature of these structures --- and how commonly they appear --- is not yet fully understood. Improving our understanding of funne... Read More about Comparing communities of optima with funnels in combinatorial fitness landscapes.

Multifractality and dimensional determinism in local optima networks (2018)
Presentation / Conference Contribution
Thomson, S. L., Verel, S., Ochoa, G., Veerapen, N., & Cairns, D. (2018, July). Multifractality and dimensional determinism in local optima networks. Presented at GECCO '18: Genetic and Evolutionary Computation Conference, Kyoto, Japan

We conduct a study of local optima networks (LONs) in a search space using fractal dimensions. The fractal dimension (FD) of these networks is a complexity index which assigns a non-integer dimension to an object. We propose a fine-grained approach t... Read More about Multifractality and dimensional determinism in local optima networks.

On the Fractal Nature of Local Optima Networks (2018)
Presentation / Conference Contribution
Thomson, S. L., Verel, S., Ochoa, G., Veerapen, N., & McMenemy, P. (2018). On the Fractal Nature of Local Optima Networks. In Evolutionary Computation in Combinatorial Optimization. EvoCOP 2018 (18-33). https://doi.org/10.1007/978-3-319-77449-7_2

A Local Optima Network represents fitness landscape connectivity within the space of local optima as a mathematical graph. In certain other complex networks or graphs there have been recent observations made about inherent self-similarity. An object... Read More about On the Fractal Nature of Local Optima Networks.

The effect of landscape funnels in QAPLIB instances (2017)
Presentation / Conference Contribution
Thomson, S. L., Ochoa, G., Daolio, F., & Veerapen, N. (2017, July). The effect of landscape funnels in QAPLIB instances. Presented at GECCO '17: Genetic and Evolutionary Computation Conference, Berlin, Germany

The effectiveness of common metaheuristics on combinatorial optimisation problems can be limited by certain characteristics of the fitness landscape. We use the local optima network model to compress the 'inherent structure' of a problem space into a... Read More about The effect of landscape funnels in QAPLIB instances.

Unexplained Fluctuations in Particle Swarm Optimisation Performance with Increasing Problem Dimensionality (2023)
Presentation / Conference Contribution
Graham, K. C., Thomson, S. L., & Brownlee, A. E. I. (2023). Unexplained Fluctuations in Particle Swarm Optimisation Performance with Increasing Problem Dimensionality. . https://doi.org/10.1145/3583133.3596433

We study the behaviour of particle swarm optimisation (PSO) with increasing problem dimension for the Alpine 1 function as an exploratory and preliminary case study. Performance trends are analysed and the tuned population size for PSO across dimensi... Read More about Unexplained Fluctuations in Particle Swarm Optimisation Performance with Increasing Problem Dimensionality.

Image Forgery Detection using Cryptography and Deep Learning (2024)
Presentation / Conference Contribution
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.