The selection of solver parameters or initial guesses can be determined by another optimization algorithm to search in among categorical or continuous parameters. These solver parameters are called hyperparameters in Machine Learning. This tutorial is an introduction to hyperparameter optimization and the application for global optimization. A simple test optimization case with two local minima demonstrates the approach.
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There are several common methods for global hyperparameter optimization, each with its own strengths and weaknesses:
1️⃣ Grid search: A technique where a set of possible values for each hyperparameter is specified, and the algorithm will train and evaluate a model for each combination of hyperparameter values. Grid search can be computationally expensive, particularly when searching over many hyperparameters or a large range of values for each hyperparameter.
2️⃣ Random search: A technique where a random set of hyperparameter values is sampled from a predefined distribution for each hyperparameter. Random search is less computationally expensive than grid search, but still has a higher chance of finding a good set of hyperparameters than a simple grid search.
3️⃣ Bayesian optimization: A probabilistic model-based approach that uses Bayesian inference to model the function that maps the hyperparameters to the performance of the model. It uses the acquired knowledge to direct the search to the regions where it expects to find the best performance. Bayesian optimization cannot be parallelized and requires continuous hyperparameters (not categorical). It quickly converges to an optimal solution when there are few hyperparameters, but this efficiency degrades when the search dimension increases.
4️⃣ Genetic Algorithm: A evolutionary based algorithm that uses concepts of natural selection and genetics to optimize the parameters.
5️⃣ Gradient-based optimization: A method that uses gradient information to optimize the hyperparameters. This can be done using optimization algorithms such as gradient descent or Adam.
6️⃣ Hyperband: An algorithm that uses the idea of early stopping to decide when to stop training a model, which reduces the number of models that need to be trained and evaluated, making it faster than grid search or random search.
Which method to use depends on the problem, the complexity of the model, the computational resources available, and the desired trade-off between computation time and optimization quality.