|  | ## Optimize Weights (Backward)

#### Synopsis

Assumes that features are independent and optimizes the weights of the attributes with a linear search.

#### Description

Uses the backward selection idea for the weighting of features.

#### Input

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

#### Output

- example set out:
- weights:
- performance:

#### Parameters

- keep best: Keep the best n individuals in each generation.
- generations without improval: Stop after n generations without improvement of the performance.
- weights: Use these weights for the creation of individuals in each generation.
- 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