|  | ## Optimize Weights (PSO)

#### Synopsis

Weight the features with a particle swarm optimization approach.

#### Description

This operator performs the weighting of features with a particle swarm approach.

#### Input

- example set: expects: ExampleSet
- input 1:

#### Output

- weights:
- example set:
- performance:

#### Parameters

- normalize weights: Activates the normalization of all weights.
- population size: Number of individuals per generation.
- maximum number of generations: Number of generations after which to terminate the algorithm.
- 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.
- inertia weight: The (initial) weight for the old weighting.
- local best weight: The weight for the individual's best position during run.
- global best weight: The weight for the population's best position during run.
- dynamic inertia weight: If set to true the inertia weight is improved during run.
- min weight: The lower bound for the weights.
- max weight: The upper bound for the weights.
- use local random seed: Indicates if a local random seed should be used.
- local random seed: Specifies the local random seed

#### ExampleProcess