Gradient-Free Optimizers#
The GFO backend provides optimization algorithms from the gradient-free-optimizers package. These implement various metaheuristic and numerical optimization algorithms.
Local Search#
Local search algorithms explore the neighborhood of the current position to find improving solutions.
|
Hill climbing optimizer. |
|
Stochastic hill climbing optimizer. |
|
Repulsing hill climbing optimizer. |
|
Random restart hill climbing optimizer. |
Simulated Annealing#
Probabilistic technique for approximating the global optimum by allowing occasional moves to worse solutions to escape local optima.
|
Simulated annealing optimizer. |
Global Search#
Random and systematic search strategies that explore the search space broadly.
|
Random search optimizer. |
|
Grid search optimizer. |
Direct Methods#
Direct search methods that do not require gradient information and work directly with function evaluations.
|
Downhill simplex optimizer. |
|
Powell's method optimizer. |
|
Pattern search optimizer. |
|
Lipschitz optimizer. |
|
Direct optimizer. |
Population-Based#
Optimization algorithms that maintain and evolve populations of candidate solutions.
|
Parallel tempering optimizer. |
|
Particle swarm optimizer. |
|
Spiral optimizer. |
|
Genetic algorithm optimizer. |
|
Evolution strategy optimizer. |
|
Differential evolution optimizer. |
Sequential Model-Based#
Algorithms that build surrogate models of the objective function to guide the search towards promising regions.
|
Bayesian optimizer. |
Tree structured parzen estimators optimizer. |
|
|
Forest optimizer. |