Batch Bayesian Optimization Early Stopping for Industrial Experiments
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Solution Overview
Problem
Batch Bayesian Optimization (BBO) often continues beyond the point of finding the optimal candidate, leading to unnecessary expenses in evaluating multiple candidates simultaneously, as it lacks a mechanism for early stopping based on significant value determination.
Innovation Solution
Implementing a real-time intervention method that uses a BBO acquisition score with a target variable and batch percentage variable to determine whether to terminate the search early, by applying a function that considers statistical significance and batch failure criteria, allowing for early stopping when the next batch is deemed not to provide significant value.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If BBO continues evaluating candidates until budget exhaustion, then the optimization completeness is improved, but the expense increases unnecessarily
Solution Approach 1:
The patent applies preliminary action by establishing stopping criteria before the optimization process begins. The system pre-defines conditions under which the optimization should terminate early, such as when the acquisition score falls below a threshold or when no improvement is observed for a certain number of iterations. This allows the system to avoid unnecessary evaluations and reduce expenses while maintaining optimization effectiveness.
Solution Approach 2:
The patent implements feedback mechanisms by continuously monitoring the acquisition score and optimization progress during the BBO process. The system uses this feedback to dynamically adjust the optimization trajectory and determine when to stop early. The feedback loop compares current performance against predefined thresholds and historical bests, enabling the system to terminate evaluations when further searches are unlikely to yield significant improvements.
2Productivity
If BBO evaluates more candidates simultaneously, then the productivity is improved, but the cost of evaluation increases
Solution Approach 1:
The patent applies partial action by evaluating only the most promising candidates in each batch rather than exhaustively evaluating all possible candidates. The system uses the acquisition function to prioritize candidates that are most likely to improve the objective, evaluating a selective subset that provides the best trade-off between productivity and cost. This avoids the excessive action of evaluating all candidates while maintaining high productivity through intelligent selection.
3Loss of energy
If early stopping criterion is implemented, then the expense is reduced, but the risk of stopping before finding optimal candidate increases
Solution Approach 1:
The patent implements robust feedback mechanisms that continuously monitor optimization progress and compare current results against historical performance. The stopping criterion uses feedback from the acquisition score trends and improvement rates to make informed decisions about early termination. This feedback system reduces the risk of premature stopping by ensuring that termination only occurs when confidence is high that further evaluations won't yield significant improvements.
Solution Approach 2:
The patent applies preliminary action by pre-defining multiple stopping criteria and thresholds before optimization begins. These include acquisition score thresholds, improvement rate thresholds, and maximum iteration limits. By establishing these criteria in advance, the system reduces the risk of ad-hoc premature termination while maintaining the ability to stop early when conditions warrant it.
Data Source
AI summary
Real-time intervention of an industrial process can include searching for a batch of candidate configurations for use by the industrial process, the batch of candidate configurations searched for by performing a batch Bayesian optimization (BBO). The batch of candidate configurations is transmitted to the industrial process to use in running the industrial process. A result of the run is received from the industrial process. Using the result in the BBO, a next batch of candidate configurations is searched. Whether a stopping criterion is met is determined, based on the next batch of candidate configurations and by applying a function to a BBO acquisition score. Responsive to determining that the stopping criterion is met, searching for the next batch of candidates is terminated.