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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., Carlo, M. D., Angus, M., Woolley, R., Gan, Z., Winfield, A. F., Timmis, J., Eiben, A. E., & Tyrrell, A. M. (2024). Evaluation of Frameworks That Combine Evolution and Learning to Design Robots in Complex Morphological Spaces. IEEE Transactions on Evolutionary Computation, 28(6), 1561 - 1574. 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)
Presentation / Conference Contribution
Jansen, R., Horn, R., van Eck, O., Version, K., Thomson, S. L., & van den Berg, D. (2023, November). Can HP-protein folding be solved with genetic algorithms? Maybe not. Presented at ECTA 2023 15th International Conference on Evolutionary Computation Theory and Applications, Rome, Italy

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)
Presentation / Conference Contribution
Verduin, K., Thomson, S. L., & van den Berg, D. (2023, November). Too Constrained for Genetic Algorithms. Too Hard for Evolutionary Computing. The Traveling Tournament Problem. Presented at ECTA 2023 15th International Conference on Evolutionary Computation Theory and Applications, Rome, Italy

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..

The Opaque Nature of Intelligence and the Pursuit of Explainable AI (2023)
Presentation / Conference Contribution
Thomson, S. L., van Stein, N., van den Berg, D., & van Leeuwen, C. (2023, November). The Opaque Nature of Intelligence and the Pursuit of Explainable AI. Presented at NCTA 2023: 15th International Conference on Neural Computation Theory and Applications, Rome, Italy

When artificial intelligence is used for making decisions, people are more likely to accept those decisions if they can be made intelligible to the public. This understanding has led to the emerging field of explainable artificial intelligence. We re... Read More about The Opaque Nature of Intelligence and the Pursuit of Explainable AI.

Robotics and Autonomous Systems for Environmental Sustainability: Monitoring Terrestrial Biodiversity (2023)
Preprint / Working Paper
Pringle, S., Davies, Z. G., Goddard, M. A., Dallimer, M., Hart, E., Le Goff, L., & Langdale, S. J. Robotics and Autonomous Systems for Environmental Sustainability: Monitoring Terrestrial Biodiversity

Welcome to the UK-RAS White paper Series on Robotics and Autonomous Systems (RAS). This is one of the core activities of UK-RAS Network, funded by the Engineering and Physical Sciences Research Council (EPSRC). By Bringing together academic centres o... Read More about Robotics and Autonomous Systems for Environmental Sustainability: Monitoring Terrestrial Biodiversity.

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.

MedOptNet: Meta-Learning Framework for Few-shot Medical Image Classification (2023)
Journal Article
Lu, L., Cui, X., Tan, Z., & Wu, Y. (2024). MedOptNet: Meta-Learning Framework for Few-shot Medical Image Classification. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 21(4), 725-736. https://doi.org/10.1109/TCBB.2023.3284846

In the medical research domain, limited data and high annotation costs have made efficient classification under few-shot conditions a popular research area. This paper proposes a meta-learning framework, termed MedOptNet, for few-shot medical image c... Read More about MedOptNet: Meta-Learning Framework for Few-shot Medical Image Classification.

A Quality-Diversity Approach to Evolving a Repertoire of Diverse Behaviour-Trees in Robot Swarms (2023)
Presentation / Conference Contribution
Montague, K., Hart, E., Paechter, B., & Nitschke, G. (2023, April). A Quality-Diversity Approach to Evolving a Repertoire of Diverse Behaviour-Trees in Robot Swarms. Presented at EVOStar 2023, Brno, Czechia

Designing controllers for a swarm of robots such that collabo-rative behaviour emerges at the swarm level is known to be challenging. Evolutionary approaches have proved promising, with attention turning more recently to evolving repertoires of dive... Read More about A Quality-Diversity Approach to Evolving a Repertoire of Diverse Behaviour-Trees in Robot Swarms.

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.

Automated Algorithm Selection: from Feature-Based to Feature-Free Approaches (2023)
Journal Article
Alissa, M., Sim, K., & Hart, E. (2023). Automated Algorithm Selection: from Feature-Based to Feature-Free Approaches. Journal of Heuristics, 29(1), 1-38. https://doi.org/10.1007/s10732-022-09505-4

We propose a novel technique for algorithm-selection, applicable to optimisation domains in which there is implicit sequential information encapsulated in the data, e.g., in online bin-packing. Specifically we train two types of recurrent neural netw... Read More about Automated Algorithm Selection: from Feature-Based to Feature-Free Approaches.

Nature Inspired Optimisation for Delivery Problems: From Theory to the Real World (2022)
Book
Urquhart, N. (2022). Nature Inspired Optimisation for Delivery Problems: From Theory to the Real World. Springer. https://doi.org/10.1007/978-3-030-98108-2

This book explains classic routing and transportation problems and solutions, before offering insights based on successful real-world solutions. The chapters in Part I introduce and explain the traveling salesperson problem (TSP), vehicle routing pro... Read More about Nature Inspired Optimisation for Delivery Problems: From Theory to the Real World.

A Compact Wideband Circularly Polarized Planar Monopole Antenna with Axial Ratio Bandwidth Entirely Encompassing the Antenna Bandwidth (2022)
Journal Article
Mousa, F. M., Al-Yasir, Y. I. A., Ali, N. T., Gharbia, I., See, C. H., & Abd-Alhameed, R. (2022). A Compact Wideband Circularly Polarized Planar Monopole Antenna with Axial Ratio Bandwidth Entirely Encompassing the Antenna Bandwidth. IEEE Access, 10, 81828-81835. https://doi.org/10.1109/ACCESS.2022.3196610

The antenna presented in this study is a compact wideband monopole with wideband circular polarization that can be used across the whole antenna bandwidth. A rectangular C-shaped patch is partially covered by a ground plane in the proposed planar mon... Read More about A Compact Wideband Circularly Polarized Planar Monopole Antenna with Axial Ratio Bandwidth Entirely Encompassing the Antenna Bandwidth.

Machine learning-enabled quantitative ultrasound techniques for tissue differentiation (2022)
Journal Article
Thomson, H., Yang, S., & Cochran, S. (2022). Machine learning-enabled quantitative ultrasound techniques for tissue differentiation. Journal of Medical Ultrasonics, 49, 517-528. https://doi.org/10.1007/s10396-022-01230-6

Purpose: Quantitative ultrasound (QUS) infers properties about tissue microstructure from backscattered radio-frequency ultrasound data. This paper describes how to implement the most practical QUS parameters using an ultrasound research system for t... Read More about Machine learning-enabled quantitative ultrasound techniques for tissue differentiation.

Single-Element and MIMO Circularly Polarized Microstrip Antennas with Negligible Back Radiation for 5G Mid-Band Handsets (2022)
Journal Article
Alnahwi, F. M., Al-Yasir, Y. I. A., See, C. H., & Abd-Alhameed, R. A. (2022). Single-Element and MIMO Circularly Polarized Microstrip Antennas with Negligible Back Radiation for 5G Mid-Band Handsets. Sensors, 22(8), Article 3067. https://doi.org/10.3390/s22083067

In this paper, single-element and MIMO microstrip antenna with two pairs of unequal slits is proposed as a circularly polarized antenna with negligible back radiation for 5G mid-band handsets. The unequal pairs of slits are engraved on the antenna pa... Read More about Single-Element and MIMO Circularly Polarized Microstrip Antennas with Negligible Back Radiation for 5G Mid-Band Handsets.

Augmenting Novelty Search with a Surrogate Model to Engineer Meta-Diversity in Ensembles of Classifiers (2022)
Presentation / Conference Contribution
Cardoso, R. P., Hart, E., Burth Kurka, D., & Pitt, J. (2022, April). Augmenting Novelty Search with a Surrogate Model to Engineer Meta-Diversity in Ensembles of Classifiers. Presented at EvoSTAR, Madrid

Using Neuroevolution combined with Novelty Search to promote behavioural diversity is capable of constructing high-performing ensembles for classification. However, using gradient descent to train evolved architectures during the search can be comput... Read More about Augmenting Novelty Search with a Surrogate Model to Engineer Meta-Diversity in Ensembles of Classifiers.

Lifelong Learning Machines: Towards Developing Optimisation Systems That Continually Learn (2022)
Book Chapter
Hart, E. (2022). Lifelong Learning Machines: Towards Developing Optimisation Systems That Continually Learn. In A. E. Smith (Ed.), Women in Computational Intelligence: Key Advances and Perspectives on Emerging Topics (187-203). Springer. https://doi.org/10.1007/978-3-030-79092-9_9

Standard approaches to developing optimisation algorithms tend to involve selecting an algorithm and tuning it to work well on a large set of problem instances from the domain of interest. Once deployed, the algorithm remains static, failing to impro... Read More about Lifelong Learning Machines: Towards Developing Optimisation Systems That Continually Learn.

A Novel Nomad Migration-Inspired Algorithm for Global Optimization (2022)
Journal Article
Lin, N., Fu, L., Zhao, L., Hawbani, A., Tan, Z., Al-Dubai, A., & Min, G. (2022). A Novel Nomad Migration-Inspired Algorithm for Global Optimization. Computers and Electrical Engineering, 100, Article 107862. https://doi.org/10.1016/j.compeleceng.2022.107862

Nature-inspired computing (NIC) has been widely studied for many optimization scenarios. However, miscellaneous solution space of real-world problem causes it is challenging to guarantee the global optimum. Besides, cumbersome structure and complex p... Read More about A Novel Nomad Migration-Inspired Algorithm for Global Optimization.

Morpho-evolution with learning using a controller archive as an inheritance mechanism (2022)
Journal Article
Le Goff, L. K., Buchanan, E., Hart, E., Eiben, A. E., Li, W., De Carlo, M., Winfield, A. F., Hale, M. F., Woolley, R., Angus, M., Timmis, J., & Tyrrell, A. M. (2023). Morpho-evolution with learning using a controller archive as an inheritance mechanism. IEEE Transactions on Cognitive and Developmental Systems, 15(2), 507-517. https://doi.org/10.1109/tcds.2022.3148543

Most work in evolutionary robotics centres on evolving a controller for a fixed body-plan. However, previous studiessuggest that simultaneously evolving both controller and body-plan could open up many interesting possibilities. However... Read More about Morpho-evolution with learning using a controller archive as an inheritance mechanism.