82 citations · 112 across the 5 of their papers we have counts for
5 papers
A New Approach to Characterising Optimisation Problems Using Programmatic Representation and Complexity Measures
Marcus Gallagher, Katherine M. Malan
Characterising optimisation problem instances is a fundamental part of understanding the behaviour and performance of different algorithms as well as providing information for algo…
Hybridised Loss Functions for Improved Neural Network Generalisation
Matthew C. Dickson, Anna S. Bosman, Katherine M. Malan
Loss functions play an important role in the training of artificial neural networks (ANNs), and can affect the generalisation ability of the ANN model, among other properties. Spec…
A Continuous Optimisation Benchmark Suite from Neural Network Regression
Katherine M. Malan, Christopher W. Cleghorn
Designing optimisation algorithms that perform well in general requires experimentation on a range of diverse problems. Training neural networks is an optimisation task that has ga…
A survey of benchmarking frameworks for reinforcement learning
Belinda Stapelberg, Katherine M. Malan
Reinforcement learning has recently experienced increased prominence in the machine learning community. There are many approaches to solving reinforcement learning problems with ne…
Benchmarking in Optimization: Best Practice and Open Issues
Thomas Bartz-Beielstein, Carola Doerr, Daan van den Berg +14
This survey compiles ideas and recommendations from more than a dozen researchers with different backgrounds and from different institutes around the world. Promoting best practice…