Generalized Early Stopping in Evolutionary Direct Policy Search
Journal Article
Arza, E., Le Goff, L. K., & Hart, E. (in press)
Generalized Early Stopping in Evolutionary Direct Policy Search. ACM Transactions on Evolutionary Learning and Optimization, https://doi.org/10.1145/3653024
Lengthy evaluation times are common in many optimization problems such as direct policy search tasks, especially when they involve conducting evaluations in the physical world...
Evaluation of Frameworks That Combine Evolution and Learning to Design Robots in Complex Morphological Spaces
Journal Article
Li, W., Buchanan, E., Goff, L. K. L., Hart, E., Hale, M. F., Wei, B., …Tyrrell, A. M. (in press)
Evaluation of Frameworks That Combine Evolution and Learning to Design Robots in Complex Morphological Spaces. IEEE Transactions on Evolutionary Computation, 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 b...
Can HP-protein folding be solved with genetic algorithms? Maybe not
Conference Proceeding
Jansen, R., Horn, R., van Eck, O., Version, K., Thomson, S. L., & van den Berg, D. (2023)
Can HP-protein folding be solved with genetic algorithms? Maybe not. In Proceedings of the 15th International Joint Conference on Computational Intelligence (131-140). https://doi.org/10.5220/0012248500003595
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...
The Opaque Nature of Intelligence and the Pursuit of Explainable AI
Conference Proceeding
Thomson, S. L., van Stein, N., van den Berg, D., & van Leeuwen, C. (2023)
The Opaque Nature of Intelligence and the Pursuit of Explainable AI. In Proceedings of the 15th International Joint Conference on Computational Intelligence (555-564). https://doi.org/10.5220/0012249500003595
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 ...
How Much do Robots Understand Rudeness? Challenges in Human-Robot Interaction
Conference Proceeding
Orme, M., Yu, Y., & Tan, Z. (in press)
How Much do Robots Understand Rudeness? Challenges in Human-Robot Interaction.
This paper concerns the pressing need to understand and manage inappropriate language within the evolving human-robot interaction (HRI) landscape. As intelligent systems and r...
Evolving Behavior Allocations in Robot Swarms
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...
Synthesising Diverse and Discriminatory Sets of Instances using Novelty Search in Combinatorial Domains
Journal Article
Marrero, A., Segredo, E., Leon, C., & Hart, E. (in press)
Synthesising Diverse and Discriminatory Sets of Instances using Novelty Search in Combinatorial Domains. Evolutionary Computation,
Gathering sufficient instance data to either train algorithm-selection models or understand algorithm footprints within an instance space can be challenging. We propose an app...
Learning Descriptors for Novelty-Search Based Instance Generation via Meta-evolution
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 mach...
On the Utility of Probing Trajectories for Algorithm-Selection
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 desc...
Too Constrained for Genetic Algorithms. Too Hard for Evolutionary Computing. The Traveling Tournament Problem.
Conference Proceeding
Verduin, K., Thomson, S. L., & van den Berg, D. (2023)
Too Constrained for Genetic Algorithms. Too Hard for Evolutionary Computing. The Traveling Tournament Problem. In Proceedings of the 15th International Joint Conference on Computational Intelligence (246-257). https://doi.org/10.5220/0012192100003595
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...