|  | ## Optimize Selection (Weight-Guided)

#### Synopsis

Adds iteratively features according to input attribute weights

#### Description

This operator uses input attribute weights to determine the order of features added to the feature set starting with the feature set containing only the feature with highest weight. The inner operators must provide a performance vector to determine the fitness of the current feature set, e.g. a cross validation of a learning scheme for a wrapper evaluation. Stops if adding the last `k` features does not increase the performance or if all features were added. The value of `k` can be set with the parameter `generations_without_improval`.

#### Input

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

#### Output

- example set out:
- weights:
- performance:

#### Parameters

- use early stopping: Enables early stopping. If unchecked, always the maximum number of generations is performed.
- generations without improval: Stop criterion: Stop after n generations without improval of the performance.
- use absolute weights: Indicates that the absolute values of the input weights should be used to determine the feature adding order.
- 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