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IEO: Intelligent Evolutionary Optimisation for Hyperparameter Tuning
2020·arXiv
Figure 1: Learning inside the loop. A surrogate model predicts which offspring merit real evaluation (a), shortcutting the evolutionary cycle without surrendering the final measurement (b).∎
Evolutionary hyperparameter search is robust but spendthrift: most evaluations are wasted on candidates a model could have predicted were poor. IEO puts a learned model inside the loop to steer it — predicting promising regions and skipping evaluations the surrogate is confident about.
On classification tasks this hybrid reached comparable configurations substantially faster than conventional evolutionary search — an early instance of the pattern that recurs through this research line: learning guides the search, measurement still decides.
Key results
- Substantially faster hyperparameter search on classification tasks
- Learned guidance reduces wasted evaluations
- Search quality preserved under the accelerated loop
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