All Outputs (74)

Understanding fitness landscapes in morpho-evolution via local optima networks (2024)
Conference Proceeding
Thomson, S. L., Le Goff, L., Hart, E., & Buchanan, E. (in press). Understanding fitness landscapes in morpho-evolution via local optima networks. . https://doi.org/10.1145/3638529.3654059

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.

Learning Descriptors for Novelty-Search Based Instance Generation via Meta-evolution (2024)
Conference Proceeding
Marrero, A., Segredo, E., León, C., & Hart, E. (in press). Learning Descriptors for Novelty-Search Based Instance Generation via Meta-evolution. In Genetic and Evolutionary Computation Conference (GECCO ’24), July 14–18, 2024, Melbourne, VIC, Australia. https://doi.org/10.1145/3638529.3654028

The ability to generate example instances from a domain is important in order to benchmark algorithms and to generate data that covers an instance-space in order to train machine-learning models for algorithm selection. Quality-Diversity (QD) algorit... Read More about Learning Descriptors for Novelty-Search Based Instance Generation via Meta-evolution.

A Hierarchical Approach to Evolving Behaviour-Trees for Swarm Control (2024)
Conference Proceeding
Montague, K., Hart, E., & Paechter, B. (2024). A Hierarchical Approach to Evolving Behaviour-Trees for Swarm Control. In S. Smith, J. Correia, & C. Cintrano (Eds.), Applications of Evolutionary Computation: 27th European Conference, EvoApplications 2024, Held as Part of EvoStar 2024, Aberystwyth, UK, April 3–5, 2024, Proceedings, Part I (178-193). https://doi.org/10.1007/978-3-031-56852-7_12

Behaviour trees (BTs) are commonly used as controllers in robotic swarms due their modular composition and to the fact that they can be easily interpreted by humans. From an algorithmic perspective, an additional advantage is that extra modules can e... Read More about A Hierarchical Approach to Evolving Behaviour-Trees for Swarm Control.

On the Utility of Probing Trajectories for Algorithm-Selection (2024)
Conference Proceeding
Renau, Q., & Hart, E. (2024). On the Utility of Probing Trajectories for Algorithm-Selection. In Applications of Evolutionary Computation. EvoApplications 2024 (98-114). https://doi.org/10.1007/978-3-031-56852-7_7

Machine-learning approaches to algorithm-selection typically take data describing an instance as input. Input data can take the form of features derived from the instance description or fitness landscape , or can be a direct representation of the ins... Read More about On the Utility of Probing Trajectories for Algorithm-Selection.

Evolving Behavior Allocations in Robot Swarms (2024)
Conference Proceeding
Hallauer, S., Nitschke, G., & Hart, E. (2024). Evolving Behavior Allocations in Robot Swarms. In 2023 IEEE Symposium Series on Computational Intelligence (SSCI) (1526-1531). https://doi.org/10.1109/SSCI52147.2023.10371934

Behavioral diversity is known to benefit problem-solving in biological social systems such as insect colonies and human societies, as well as in artificial distributed systems including large-scale software and swarm-robotics systems. We investigate... Read More about Evolving Behavior Allocations in Robot Swarms.

A Feature-Free Approach to Automated Algorithm Selection (2023)
Conference Proceeding
Alissa, M., Sim, K., & Hart, E. (2023). A Feature-Free Approach to Automated Algorithm Selection. In GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary Computation (9-10). https://doi.org/10.1145/3583133.3595832

This article summarises recent work in the domain of feature-free algorithm selection that was published in the Journal of Heuristics in January 2023, with the title 'Automated Algorithm Selection: from Feature-Based to Feature-Free Approaches'. Spec... Read More about A Feature-Free Approach to Automated Algorithm Selection.

Evolving Herding Behaviour Diversity in Robot Swarms (2023)
Conference Proceeding
Nitschke, G., Hallauer, S., & Hart, E. (2023). Evolving Herding Behaviour Diversity in Robot Swarms. In GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary Computation (95-98). https://doi.org/10.1145/3583133.3590528

Behavioural diversity has been demonstrated as beneficial in biological social systems, such as insect colonies and human societies, as well as artificial systems such as large-scale software and swarm-robotics systems. Evolutionary swarm robotics is... Read More about Evolving Herding Behaviour Diversity in Robot Swarms.

Towards optimisers that `Keep Learning' (2023)
Conference Proceeding
Hart, E., Miguel, I., Stone, C., & Renau, Q. (2023). Towards optimisers that `Keep Learning'. In GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary Computation (1636-1638). https://doi.org/10.1145/3583133.3596344

We consider optimisation in the context of the need to apply an optimiser to a continual stream of instances from one or more domains, and consider how such a system might 'keep learning': by drawing on past experience to improve performance and lear... Read More about Towards optimisers that `Keep Learning'.

Learning-Based Neural Ant Colony Optimization (2023)
Conference Proceeding
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.

Generating diverse and discriminatory knapsack instances by searching for novelty in variable dimensions of feature-space (2023)
Conference Proceeding
Marrero, A., Segredo, E., Hart, E., Bossek, J., & Neumann, A. (2023). Generating diverse and discriminatory knapsack instances by searching for novelty in variable dimensions of feature-space. In GECCO '23: Proceedings of the Genetic and Evolutionary Computation Conference (312-320). https://doi.org/10.1145/3583131.3590504

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.

To Switch or not to Switch: Predicting the Benefit of Switching between Algorithms based on Trajectory Features (2023)
Conference Proceeding
Vermetten, D., Wang, H., Sim, K., & Hart, E. (2023). To Switch or not to Switch: Predicting the Benefit of Switching between Algorithms based on Trajectory Features. In J. Correia, S. Smith, & R. Qaddoura (Eds.), Applications of Evolutionary Computation: 26th International Conference, EvoApplications 2023 (335-350). https://doi.org/10.1007/978-3-031-30229-9_22

Dynamic algorithm selection aims to exploit the complementarity of multiple optimization algorithms by switching between them during the search. While these kinds of dynamic algorithms have been shown to have potential to outperform their component a... Read More about To Switch or not to Switch: Predicting the Benefit of Switching between Algorithms based on Trajectory Features.

A Quality-Diversity Approach to Evolving a Repertoire of Diverse Behaviour-Trees in Robot Swarms (2023)
Conference Proceeding
Montague, K., Hart, E., Paechter, B., & Nitschke, G. (2023). A Quality-Diversity Approach to Evolving a Repertoire of Diverse Behaviour-Trees in Robot Swarms. In J. Correia, S. Smith, & R. Qaddoura (Eds.), Applications of Evolutionary Computation: 26th European Conference, EvoApplications 2023, Held as Part of EvoStar 2023, Brno, Czech Republic, April 12–14, 2023, Proceedings (145-160). https://doi.org/10.1007/978-3-031-30229-9_10

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.

Improving the size and quality of MAP-Elites containers via multiple emitters and decoders for urban logistics (2023)
Conference Proceeding
Urquhart, N., & Hart, E. (2023). Improving the size and quality of MAP-Elites containers via multiple emitters and decoders for urban logistics. In Applications of Evolutionary Computation – 26th International Conference, EvoApplications 2023 (35-52). https://doi.org/10.1007/978-3-031-30229-9

Quality-diversity (QD) methods such as MAP-Elites have been demonstrated to be useful in the domain of combinatorial optimisation due to their ability to generate a large set of solutions to a single-objective problem that are diverse with respect to... Read More about Improving the size and quality of MAP-Elites containers via multiple emitters and decoders for urban logistics.

A Novelty-Search Approach to Filling an Instance-Space with Diverse and Discriminatory Instances for the Knapsack Problem (2022)
Conference Proceeding
Marrero, A., Segredo, E., León, C., & Hart, E. (2022). A Novelty-Search Approach to Filling an Instance-Space with Diverse and Discriminatory Instances for the Knapsack Problem. In Parallel Problem Solving from Nature – PPSN XVII. PPSN 2022 (223-236). https://doi.org/10.1007/978-3-031-14714-2_16

We propose a new approach to generating synthetic instances in the knapsack domain in order to fill an instance-space. The method uses a novelty-search algorithm to search for instances that are diverse with respect to a feature-space but also elicit... Read More about A Novelty-Search Approach to Filling an Instance-Space with Diverse and Discriminatory Instances for the Knapsack Problem.

Evolutionary Approaches to Improving the Layouts of Instance-Spaces (2022)
Conference Proceeding
Sim, K., & Hart, E. (2022). Evolutionary Approaches to Improving the Layouts of Instance-Spaces. In Parallel Problem Solving from Nature – PPSN XVII. PPSN 2022 (207-219). https://doi.org/10.1007/978-3-031-14714-2_15

We propose two new methods for evolving the layout of an instance-space. Specifically we design three different fitness metrics that seek to: (i) reward layouts which place instances won by the same solver close in the space; (ii) reward layouts that... Read More about Evolutionary Approaches to Improving the Layouts of Instance-Spaces.

Augmenting Novelty Search with a Surrogate Model to Engineer Meta-Diversity in Ensembles of Classifiers (2022)
Conference Proceeding
Cardoso, R. P., Hart, E., Burth Kurka, D., & Pitt, J. (2022). Augmenting Novelty Search with a Surrogate Model to Engineer Meta-Diversity in Ensembles of Classifiers. In Applications of Evolutionary Computation: EvoApplications 2022 (418-434). https://doi.org/10.1007/978-3-031-02462-7_27

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.

A Neural Approach to Generation of Constructive Heuristics (2021)
Conference Proceeding
Alissa, M., Sim, K., & Hart, E. (2021). A Neural Approach to Generation of Constructive Heuristics. In 2021 IEEE Congress on Evolutionary Computation (CEC) (1147-1154). https://doi.org/10.1109/CEC45853.2021.9504989

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.

On the challenges of jointly optimising robot morphology and control using a hierarchical optimisation scheme (2021)
Conference Proceeding
Goff, L. K. L., & Hart, E. (2021). On the challenges of jointly optimising robot morphology and control using a hierarchical optimisation scheme. In GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference Companion (1498-1502). https://doi.org/10.1145/3449726.3463156

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)
Conference Proceeding
Cardoso, R. P., Hart, E., Kurka, D. B., & Pitt, J. V. (2021). Using novelty search to explicitly create diversity in ensembles of classifiers. In GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference (849-857). https://doi.org/10.1145/3449639.3459308

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.

Automated, Explainable Rule Extraction from MAP-Elites archives (2021)
Conference Proceeding
Urquhart, N., Höhl, S., & Hart, E. (2021). Automated, Explainable Rule Extraction from MAP-Elites archives. In Applications of Evolutionary Computation: 24th International Conference, EvoApplications 2021 (258-272). https://doi.org/10.1007/978-3-030-72699-7_17

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.