|  | ## Optimize Selection (Brute Force)

#### Synopsis

Selects the best features for an example set by trying all possible combinations of attribute selections.

#### Description

This feature selection operator selects the best attribute set by trying all possible combinations of attribute selections. It returns the example set containing the subset of attributes which produced the best performance. As this operator works on the powerset of the attributes set it has exponential runtime.

#### Input

- example set in: expects: ExampleSetMetaData: #examples: = 0; #attributes: 0
- through 1:

#### Output

- example set out:
- weights:
- performance:

#### Parameters

- use exact number of attributes: Determines if only combinations containing this numbers of attributes should be tested.
- restrict maximum: If checked the maximal number of attributes might be restricted. Otherwise all combinations of all number of attributes are generated and tested.
- min number of attributes: Determines the minimum number of features used for the combinations.
- max number of attributes: Determines the maximum number of features used for the combinations.
- exact number of attributes: Determines the exact number of features used for the combinations.
- normalize weights: Indicates if the final weights should be normalized.
- use local random seed: Indicates if a local random seed should be used.
- local random seed: Specifies the local random seed
- show stop dialog: Determines if a dialog with a button should be displayed which stops the run: the best individual is returned.
- user result individual selection: Determines if the user wants to select the final result individual from the last population.
- show population plotter: Determines if the current population should be displayed in performance space.
- plot generations: Update the population plotter in these generations.
- constraint draw range: Determines if the draw range of the population plotter should be constrained between 0 and 1.
- draw dominated points: Determines if only points which are not Pareto dominated should be painted.
- population criteria data file: The path to the file in which the criteria data of the final population should be saved.
- maximal fitness: The optimization will stop if the fitness reaches the defined maximum.

#### ExampleProcess