Prof Emma Hart's Outputs (187)

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.

Artificial evolution of robot bodies and control: on the interaction between evolution, individual and cultural learning (2021)
Journal Article
Hart, E., & Le Goff, L. K. (2022). Artificial evolution of robot bodies and control: on the interaction between evolution, individual and cultural learning. Philosophical Transactions B: Biological Sciences, 377(1843), https://doi.org/10.1098/rstb.2021.0117

We survey and reflect on evolutionary approaches to the joint optimisation of the body and control of a robot, in scenarios where a the goal is to find a design that maximises performance on a specified task. The review is grounded in a general frame... Read More about Artificial evolution of robot bodies and control: on the interaction between evolution, individual and cultural learning.

Enhancing the practicality of tools to estimate the whole life embodied carbon of building structures via machine-learning models (2021)
Journal Article
Pomponi, F., Luque Anguita, M., Lange, M., D'Amico, B., & Hart, E. (2021). Enhancing the practicality of tools to estimate the whole life embodied carbon of building structures via machine-learning models. Frontiers in Built Environment, 7, Article 745598. https://doi.org/10.3389/fbuil.2021.745598

The construction and operation of buildings account for significant environmental impacts, including greenhouse gas (GHG) emissions, energy demand, resource consumption and waste generation. While the operation of buildings is fairly well regulated a... Read More about Enhancing the practicality of tools to estimate the whole life embodied carbon of building structures via machine-learning models.

A Neural Approach to Generation of Constructive Heuristics (2021)
Presentation / Conference Contribution
Alissa, M., Sim, K., & Hart, E. (2021, June). A Neural Approach to Generation of Constructive Heuristics. Presented at IEEE Congress on Evolutionary Computation 2021, Kraków, Poland (online)

Both algorithm-selection methods and hyper-heuristic methods rely on a pool of complementary heuristics. Improving the pool with new heuristics can improve performance, however, designing new heuristics can be challenging. Methods such as genetic pro... Read More about A Neural Approach to Generation of Constructive Heuristics.

A Cross-Domain Method for Generation of Constructive and Perturbative Heuristics (2021)
Book Chapter
Stone, C., Hart, E., & Paechter, B. (2021). A Cross-Domain Method for Generation of Constructive and Perturbative Heuristics. In N. Pillay, & R. Qu (Eds.), Automated Design of Machine Learning and Search Algorithms (91-107). Springer. https://doi.org/10.1007/978-3-030-72069-8_6

Hyper-heuristic frameworks, although intended to be cross-domain at the highest level, usually rely on a set of domain-specific low-level heuristics which exist below the domain-barrier and are manipulated by the hyper-heuristic itself. However, for... Read More about A Cross-Domain Method for Generation of Constructive and Perturbative Heuristics.

On the challenges of jointly optimising robot morphology and control using a hierarchical optimisation scheme (2021)
Presentation / Conference Contribution
Goff, L. K. L., & Hart, E. (2021, July). On the challenges of jointly optimising robot morphology and control using a hierarchical optimisation scheme. Presented at GECCO '21: Genetic and Evolutionary Computation Conference, Lille, France

We investigate a hierarchical scheme for the joint optimisation of robot bodies and controllers in a complex morphological space. An evolutionary algorithm optimises body-plans while a separate learning algorithm is applied to each body generated to... Read More about On the challenges of jointly optimising robot morphology and control using a hierarchical optimisation scheme.

Using novelty search to explicitly create diversity in ensembles of classifiers (2021)
Presentation / Conference Contribution
Cardoso, R. P., Hart, E., Kurka, D. B., & Pitt, J. V. (2021, July). Using novelty search to explicitly create diversity in ensembles of classifiers. Presented at GECCO '21: Genetic and Evolutionary Computation Conference, Lille, France [Online]

The diversity between individual learners in an ensemble is known to influence its performance. However, there is no standard agreement on how diversity should be defined, and thus how to exploit it to construct a high-performing classifier. We propo... Read More about Using novelty search to explicitly create diversity in ensembles of classifiers.

WILDA: Wide Learning of Diverse Architectures for Classification of Large Datasets (2021)
Presentation / Conference Contribution
Pitt, J., Burth Kurka, D., Hart, E., & Cardoso, R. P. (2021, April). WILDA: Wide Learning of Diverse Architectures for Classification of Large Datasets. Presented at 24th European Conference, EvoApplications 2021, Online

In order to address scalability issues, which can be a challenge for Deep Learning methods, we propose Wide Learning of Diverse Architectures-a model that scales horizontally rather than vertically, enabling distributed learning. We propose a distrib... Read More about WILDA: Wide Learning of Diverse Architectures for Classification of Large Datasets.

Automated, Explainable Rule Extraction from MAP-Elites archives (2021)
Presentation / Conference Contribution
Urquhart, N., Höhl, S., & Hart, E. (2021, April). Automated, Explainable Rule Extraction from MAP-Elites archives. Presented at EvoAPPs2021, Online

Quality-diversity(QD) algorithms that return a large archive of elite solutions to a problem provide insights into how high-performing solutions are distributed throughout a feature-space defined by a user — they are often described as illuminating t... Read More about Automated, Explainable Rule Extraction from MAP-Elites archives.

Hardware Design for Autonomous Robot Evolution (2020)
Presentation / Conference Contribution
Hale, M. F., Angus, M., Buchanan, E., Li, W., Woolley, R., Le Goff, L. K., De Carlo, M., Timmis, J., Winfield, A. F., Hart, E., Eiben, A. E., & Tyrrell, A. M. (2020, December). Hardware Design for Autonomous Robot Evolution. Presented at International Conference on Evolvable Hardware, Canberra Australia

The long term goal of the Autonomous Robot Evolution (ARE) project is to create populations of physical robots, in which both the controllers and body plans are evolved. The transition for evolutionary designs from purely simulation environments into... Read More about Hardware Design for Autonomous Robot Evolution.

Generating Unambiguous and Diverse Referring Expressions   (2020)
Journal Article
Panagiaris, N., Hart, E., & Gkatzia, D. (2021). Generating Unambiguous and Diverse Referring Expressions  . Computer Speech and Language, 68, Article 101184. https://doi.org/10.1016/j.csl.2020.101184

Neural Referring Expression Generation (REG) models have shown promising results in generating expressions which uniquely describe visual objects. However, current REG models still lack the ability to produce diverse and unambiguous referring express... Read More about Generating Unambiguous and Diverse Referring Expressions  .

Evolution of Diverse, Manufacturable Robot Body Plans (2020)
Presentation / Conference Contribution
Buchanan, E., Le Goff, L., Hart, E., Eiben, A. E., De Carlo, M., Li, W., Hale, M. F., Angus, M., Woolley, R., Winfield, A. F., Timmis, J., & Tyrrell, A. M. (2020, December). Evolution of Diverse, Manufacturable Robot Body Plans. Presented at International Conference on Evolvable Systems (ICES), Canberra, Australia

Advances in rapid prototyping have opened up new avenues of research within Evolutionary Robotics in which not only controllers but also the body plans (morphologies) of robots can evolve in real-time and real-space. However, this also introduces new... Read More about Evolution of Diverse, Manufacturable Robot Body Plans.

Improving the Naturalness and Diversity of Referring Expression Generation models using Minimum Risk Training (2020)
Presentation / Conference Contribution
Panagiaris, N., Hart, E., & Gkatzia, D. (2020, December). Improving the Naturalness and Diversity of Referring Expression Generation models using Minimum Risk Training. Presented at International Conference on Natural Language Generation (INLG 2020), Dublin, Ireland

In this paper we consider the problem of optimizing neural Referring Expression Generation (REG) models with sequence level objectives. Recently reinforcement learning (RL) techniques have been adopted to train deep end-to-end systems to directly opt... Read More about Improving the Naturalness and Diversity of Referring Expression Generation models using Minimum Risk Training.

Towards Autonomous Robot Evolution (2020)
Book Chapter
Eiben, A. E., Hart, E., Timmis, J., Tyrrell, A. M., & Winfield, A. F. (2021). Towards Autonomous Robot Evolution. In A. Cavalcanti, B. Dongol, R. Hierons, J. Timmis, & J. Woodcock (Eds.), Software Engineering for Robotics (29-51). Springer. https://doi.org/10.1007/978-3-030-66494-7_2

We outline a perspective on the future of evolutionary robotics and discuss a long-term vision regarding robots that evolve in the real world. We argue that such systems offer significant potential for advancing both science and engineering. For scie... Read More about Towards Autonomous Robot Evolution.

Bootstrapping artificial evolution to design robots for autonomous fabrication (2020)
Journal Article
Buchanan, E., Le Goff, L. K., Li, W., Hart, E., Eiben, A. E., De Carlo, M., Winfield, A., Hale, M. F., Woolley, R., Angus, M., Timmis, J., & Tyrrell, A. M. (2020). Bootstrapping artificial evolution to design robots for autonomous fabrication. Robotics, 9(4), Article 106. https://doi.org/10.3390/robotics9040106

A long-term vision of evolutionary robotics is a technology enabling the evolution of entire autonomous robotic ecosystems that live and work for long periods in challenging and dynamic environments without the need for direct human oversight. Evolut... Read More about Bootstrapping artificial evolution to design robots for autonomous fabrication.

Improving Classification of Metamorphic Malware by Augmenting Training Data with a Diverse Set of Evolved Mutant Samples (2020)
Presentation / Conference Contribution
Babaagba, K., Tan, Z., & Hart, E. (2020, July). Improving Classification of Metamorphic Malware by Augmenting Training Data with a Diverse Set of Evolved Mutant Samples. Presented at The 2020 IEEE Congress on Evolutionary Computation (IEEE CEC 2020), Glasgow, UK

Detecting metamorphic malware provides a challenge to machine-learning models as trained models might not generalise to future mutant variants of the malware. To address this, we explore whether machine-learning models can be improved by augmenting t... Read More about Improving Classification of Metamorphic Malware by Augmenting Training Data with a Diverse Set of Evolved Mutant Samples.

Sample and time efficient policy learning with CMA-ES and Bayesian Optimisation (2020)
Presentation / Conference Contribution
Le Goff, L. K., Buchanan, E., Hart, E., Eiben, A. E., Li, W., De Carlo, M., Hale, M. F., Angus, M., Woolley, R., Timmis, J., Winfield, A., & Tyrrell, A. M. (2020, July). Sample and time efficient policy learning with CMA-ES and Bayesian Optimisation. Presented at ALife 2020, Online

In evolutionary robot systems where morphologies and controllers of real robots are simultaneously evolved, it is clear that there is likely to be requirements to refine the inherited controller of a 'newborn' robot in order to better align it to its... Read More about Sample and time efficient policy learning with CMA-ES and Bayesian Optimisation.

On Pros and Cons of Evolving Topologies with Novelty Search (2020)
Presentation / Conference Contribution
Le Goff, L. K., Hart, E., Coninx, A., & Doncieux, S. (2020, July). On Pros and Cons of Evolving Topologies with Novelty Search. Presented at ALIFE 2020: The 2020 Conference on Artificial Life, Online

Novelty search was proposed as a means of circumventing deception and providing selective pressure towards novel behaviours to provide a path towards open-ended evolution. Initial implementations relied on neuro-evolution approaches which increased n... Read More about On Pros and Cons of Evolving Topologies with Novelty Search.

A Deep Learning Approach to Predicting Solutions in Streaming Optimisation Domains (2020)
Presentation / Conference Contribution
Alissa, M., Sim, K., & Hart, E. (2020, July). A Deep Learning Approach to Predicting Solutions in Streaming Optimisation Domains. Presented at GECCO ’20, Cancún, Mexico

In the field of combinatorial optimisation, per-instance algorithm selection still remains a challenging problem, particularly with respect to streaming problems such as packing or scheduling. Typical approaches involve training a model to predict th... Read More about A Deep Learning Approach to Predicting Solutions in Streaming Optimisation Domains.

From disorganized equality to efficient hierarchy: how group size drives the evolution of hierarchy in human societies (2020)
Journal Article
Perret, C., Hart, E., & Powers, S. T. (2020). From disorganized equality to efficient hierarchy: how group size drives the evolution of hierarchy in human societies. Proceedings of the Royal Society B: Biological Sciences, 287(1928), Article 20200693. https://doi.org/10.1098/rspb.2020.0693

A manifest trend is that larger and more productive human groups shift from distributed to centralized decision-making. Voluntary theories propose that human groups shift to hierarchy to limit scalar stress, i.e. the increase in cost of organization... Read More about From disorganized equality to efficient hierarchy: how group size drives the evolution of hierarchy in human societies.