All Outputs (8)

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.

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..

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.

Unexplained Fluctuations in Particle Swarm Optimisation Performance with Increasing Problem Dimensionality (2023)
Presentation / Conference Contribution
Graham, K. C., Thomson, S. L., & Brownlee, A. E. I. (2023). Unexplained Fluctuations in Particle Swarm Optimisation Performance with Increasing Problem Dimensionality. . https://doi.org/10.1145/3583133.3596433

We study the behaviour of particle swarm optimisation (PSO) with increasing problem dimension for the Alpine 1 function as an exploratory and preliminary case study. Performance trends are analysed and the tuned population size for PSO across dimensi... Read More about Unexplained Fluctuations in Particle Swarm Optimisation Performance with Increasing Problem Dimensionality.

From Fitness Landscapes to Explainable AI and Back (2023)
Presentation / Conference Contribution
Thomson, S. L., Adair, J., Brownlee, A. E. I., & van den Berg, D. (2023, July). From Fitness Landscapes to Explainable AI and Back. Presented at GECCO '23, Lisbon, Portugal

We consider and discuss the ways in which search landscapes might contribute to the future of explainable artificial intelligence (XAI), and vice versa. Landscapes are typically used to gain insight into algorithm search dynamics on optimisation prob... Read More about From Fitness Landscapes to Explainable AI and Back.

Randomness in Local Optima Network Sampling (2023)
Presentation / Conference Contribution
Thomson, S. L., Veerapen, N., Ochoa, G., & van den Berg, D. (2023, July). Randomness in Local Optima Network Sampling. Presented at GECCO '23, Lisbon, Portugal

We consider statistical randomness in the construction of local optima networks (LONs) and conduct a preliminary and exploratory study into this. LONs capture a fitness landscape into network format: the nodes are local optima, and edges are heuristi... Read More about Randomness in Local Optima Network Sampling.

Channel Configuration for Neural Architecture: Insights from the Search Space (2023)
Presentation / Conference Contribution
Thomson, S. L., Ochoa, G., Veerapen, N., & Michalak, K. (2023, July). Channel Configuration for Neural Architecture: Insights from the Search Space. Presented at GECCO '23, Lisbon, Portugal

We consider search spaces associated with neural network channel configuration. Architectures and their accuracy are visualised using low-dimensional Euclidean embedding (LDEE). Optimisation dynamics are captured using local optima networks (LONs). L... Read More about Channel Configuration for Neural Architecture: Insights from the Search Space.

Frequency Fitness Assignment on JSSP: A Critical Review (2023)
Presentation / Conference Contribution
de Bruin, E., Thomson, S. L., & Berg, D. V. D. (2023, April). Frequency Fitness Assignment on JSSP: A Critical Review. Presented at EvoApplications 2023: Applications of Evolutionary Computation, Brno, Czech Republic

Metaheuristic navigation towards rare objective values instead of good objective values: is it a good idea? We will discuss the closed and open ends after presenting a successful replication study of Weise et al.’s ‘frequency fitness assignment’ for... Read More about Frequency Fitness Assignment on JSSP: A Critical Review.