All Outputs (8)

Towards optimisers that `Keep Learning' (2023)
Presentation / Conference Contribution
Hart, E., Miguel, I., Stone, C., & Renau, Q. (2023, July). Towards optimisers that `Keep Learning'. Presented at Companion Conference on Genetic and Evolutionary Computation, Lisbon, Portugal

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'.

A Feature-Free Approach to Automated Algorithm Selection (2023)
Presentation / Conference Contribution
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)
Presentation / Conference Contribution
Nitschke, G., Hallauer, S., & Hart, E. (2023, July). Evolving Herding Behaviour Diversity in Robot Swarms. Presented at Proceedings of the Companion Conference on Genetic and Evolutionary Computation, Lisbon, Portugal

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.

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.

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.

To Switch or not to Switch: Predicting the Benefit of Switching between Algorithms based on Trajectory Features (2023)
Presentation / Conference Contribution
Vermetten, D., Wang, H., Sim, K., & Hart, E. (2023, April). To Switch or not to Switch: Predicting the Benefit of Switching between Algorithms based on Trajectory Features. Presented at Evo Applications 2023, Brno, Czech Republic

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.

Improving the size and quality of MAP-Elites containers via multiple emitters and decoders for urban logistics (2023)
Presentation / Conference Contribution
Urquhart, N., & Hart, E. (2023, April). Improving the size and quality of MAP-Elites containers via multiple emitters and decoders for urban logistics. Presented at Evo Applications 2023, Brno, Czech Republic

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.