A Continuous Optimisation Benchmark Suite from Neural Network Regression

Understand

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 gained prominence with the recent successes of deep learning.
• Although evolutionary algorithms have been used for training neural networks, gradient descent variants are by far the most common choice with their trusted good performance on large-scale machine learning tasks.
• With this paper we contribute CORNN (Continuous Optimisation of Regression tasks using Neural Networks), a large suite for benchmarking the performance of any continuous black-box algorithm on neural network training problems.