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Related Questions
- How does the acquisition function update its estimate of the objective function value when there's a sudden change, such as a local optimum or a saddle point?
- How can the acquisition function balance exploration and exploitation when the objective function changes rapidly?
- What strategies can be employed to prevent the acquisition function from getting stuck in a local optimum when the objective function has a complex landscape?
- How does the acquisition function handle sudden changes in the objective function due to factors like noisy evaluations or changes in the problem's constraints?
- What techniques can be used to improve the robustness of the acquisition function in the presence of sudden changes in the objective function?
- Can you discuss the implications of using different types of acquisition functions (e.g., expected improvement, probability of improvement, or upper confidence bound) in the presence of sudden changes in the objective function?
- How can the acquisition function be modified to adapt to changing objective functions, such as when new information becomes available or when the problem's constraints change?
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