# Sequential Model-based Optimization Algorithm

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A Sequential Model-based Optimization Algorithm is an Optimization Algorithm that sequentially selects the next point to evaluate based on a model of the objective function.

**Context:**- It aims to balance exploration and exploitation by using the model to guide search.
- It builds a surrogate model of the objective function and uses it to select the next point to evaluate.
- The model is updated as new points are evaluated, allowing it to improve over time.
- It can use models such as: Gaussian Processes, Random Forests, and Bayesian Neural Networks.
- It evaluates points one at a time (or in small batches), sequentially updating the model.
- It aims to minimize simple regret over the sequence of evaluations.
- It Uses a model of the objective function to guide search.
- It Balances exploration and exploitation.
- It Evaluates points sequentially and updates model.
- It Optimizes over the sequence of evaluations.
- ...

**Examples:****See:**Derivative-Free Optimization, Model-Based Optimization, Bayesian Optimization.