Hybridised Loss Functions for Improved Neural Network Generalisation

no code implementations • 26 Apr 2022 • Matthew C. Dickson, Anna S. Bosman, Katherine M. Malan

It is shown that hybridisation of the two loss functions improves the generalisation ability of the ANNs on all problems considered.

A Continuous Optimisation Benchmark Suite from Neural Network Regression

1 code implementation • 12 Sep 2021 • Katherine M. Malan, Christopher W. Cleghorn

Designing optimisation algorithms that perform well in general requires experimentation on a range of diverse problems.

Benchmarking Evolutionary Algorithms +1

A survey of benchmarking frameworks for reinforcement learning

no code implementations • 27 Nov 2020 • Belinda Stapelberg, Katherine M. Malan

There are many approaches to solving reinforcement learning problems with new techniques developed constantly.

Benchmarking reinforcement-learning +1

Benchmarking in Optimization: Best Practice and Open Issues

no code implementations • 7 Jul 2020 • Thomas Bartz-Beielstein, Carola Doerr, Daan van den Berg, Jakob Bossek, Sowmya Chandrasekaran, Tome Eftimov, Andreas Fischbach, Pascal Kerschke, William La Cava, Manuel Lopez-Ibanez, Katherine M. Malan, Jason H. Moore, Boris Naujoks, Patryk Orzechowski, Vanessa Volz, Markus Wagner, Thomas Weise

This survey compiles ideas and recommendations from more than a dozen researchers with different backgrounds and from different institutes around the world.

Benchmarking