Using machine learning oracles as ground truth in black-box optimization experiments presents several limitations. One significant issue is the overfitting of the oracle to the training data, which can lead to poor generalization when predicting unseen designs. This can result in misleading evaluations of the optimization methods, as the oracle may not accurately reflect the true fitness landscape.
Another limitation is the uncertainty in predictions made by the oracle. Machine learning models, especially those trained on limited data, can produce predictions with high variance, leading to unreliable fitness estimates. This uncertainty can skew the optimization process, as methods may converge on suboptimal solutions based on inaccurate fitness evaluations.
To make the evaluation more robust, one approach is to use ensemble methods that combine predictions from multiple oracles or models. This can help mitigate the risk of overfitting and provide a more stable estimate of fitness. Additionally, incorporating uncertainty quantification techniques, such as Bayesian neural networks, can provide confidence intervals around predictions, allowing for more informed decision-making during the optimization process.
Another strategy is to conduct cross-validation by splitting the available data into training and validation sets, ensuring that the oracle is evaluated on unseen data. This can help assess the generalization capability of the oracle and provide a more accurate measure of its performance.
Finally, integrating real experimental data into the evaluation process can provide a reality check against the predictions of the oracle. By periodically validating the oracle's predictions with actual experimental results, one can adjust the optimization strategy to account for discrepancies between the oracle and real-world outcomes.