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evoML Yellow Paper: Evolutionary AI and Optimisation Studio
2022·arXiv
Figure 1: The studio pipeline. Data flows through automated preparation into population-based model search (a); candidates settle onto an accuracy–cost Pareto front (b) from which deployment selects.∎
The yellow paper for Artemis ML (evoML): an evolutionary AI studio that automates the machine-learning lifecycle — data preparation, feature engineering, model generation, hyperparameter optimization — under explicit multi-objective search, with the generated model code itself optimized as a final stage.
Its central workflow — candidate models scored on competing objectives, Pareto fronts instead of single winners, held-out validation before anything ships — is the exact workflow Artemis later generalized from ML models to arbitrary code.
Key results
- End-to-end automated ML with multi-objective optimization
- Model code optimization as a first-class pipeline stage
- The direct ancestor of Artemis's search-and-validate loop
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