|  | ## Optimize by Generation (Evolutionary Aggregation)

#### Synopsis

A generating genetic algorithm for unsupervised learning (experimental).

#### Description

Performs an evolutionary feature aggregation. Each base feature is only allowed to be used as base feature, in one merged feature, or it may not be used at all.

#### Input

- example set in: expects: ExampleSet

#### Output

- example set out:
- performance vector out:

#### Parameters

- aggregation function: The aggregation function which is used for feature aggregations.
- population size: Number of individuals per generation.
- maximum number of generations: Number of generations after which to terminate the algorithm.
- selection type: The type of selection.
- tournament fraction: The fraction of the population which will participate in each tournament.
- crossover type: The type of crossover.
- p crossover: Probability for an individual to be selected for crossover.
- population criteria data file: The path to the file in which the criteria data of the final population should be saved.
- use local random seed: Indicates if a local random seed should be used.
- local random seed: Specifies the local random seed

#### ExampleProcess