All Outputs (5)

Evolutionary Approaches to Improving the Layouts of Instance-Spaces (2022)
Presentation / Conference Contribution
Sim, K., & Hart, E. (2022, September). Evolutionary Approaches to Improving the Layouts of Instance-Spaces. Presented at 17th International Conference, PPSN 2022, Dortmund, Germany

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.

A Novelty-Search Approach to Filling an Instance-Space with Diverse and Discriminatory Instances for the Knapsack Problem (2022)
Presentation / Conference Contribution
Marrero, A., Segredo, E., León, C., & Hart, E. (2022, September). A Novelty-Search Approach to Filling an Instance-Space with Diverse and Discriminatory Instances for the Knapsack Problem. Presented at Parallel Problem Solving from Nature – PPSN

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.

Augmenting Novelty Search with a Surrogate Model to Engineer Meta-Diversity in Ensembles of Classifiers (2022)
Presentation / Conference Contribution
Cardoso, R. P., Hart, E., Burth Kurka, D., & Pitt, J. (2022, April). Augmenting Novelty Search with a Surrogate Model to Engineer Meta-Diversity in Ensembles of Classifiers. Presented at EvoSTAR, Madrid

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.

Lifelong Learning Machines: Towards Developing Optimisation Systems That Continually Learn (2022)
Book Chapter
Hart, E. (2022). Lifelong Learning Machines: Towards Developing Optimisation Systems That Continually Learn. In A. E. Smith (Ed.), Women in Computational Intelligence: Key Advances and Perspectives on Emerging Topics (187-203). Springer. https://doi.org/1

Standard approaches to developing optimisation algorithms tend to involve selecting an algorithm and tuning it to work well on a large set of problem instances from the domain of interest. Once deployed, the algorithm remains static, failing to impro... Read More about Lifelong Learning Machines: Towards Developing Optimisation Systems That Continually Learn.

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. I

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.