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Related Questions
- Can the acquisition function be designed to incorporate a 'stop-and-learn' strategy, where it temporarily suspends the search process when a significant improvement is detected, allowing the model to adapt and learn from the new information?
- How can the acquisition function be modified to weigh the trade-off between exploration and exploitation more dynamically in response to changes in the search space?
- Would incorporating a moving average or exponential smoothing component into the acquisition function help it respond more effectively to sudden improvements in the objective function value?
- Can the acquisition function be adapted to use a 'look-ahead' or ' lookahead' strategy, where it simulates multiple possible next steps and chooses the one that is likely to result in the largest improvement?
- How can the acquisition function be modified to incorporate information from multiple sources, such as multiple objective functions or constraints, to better capture the nuances of the search space?
- Would using a more sophisticated optimization algorithm, such as a trust-region or line-search method, help the acquisition function respond more effectively to sudden improvements in the objective function value?
- Can the acquisition function be designed to adapt to changes in the search space by using a Gaussian process or other probabilistic model to represent the uncertainty in the objective function value?
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