Synopsis
Weight the features with an evolutionary approach.
Description
This operator performs the weighting of features with an evolutionary strategies approach. The variance of the gaussian additive mutation can be adapted by a 1/5-rule.
Input
- example set in: expects: ExampleSetMetaData: #examples: = 0; #attributes: 0
- attribute weights in: optional: AttributeWeights
- through 1:
Output
- example set out:
- weights:
- performance:
Parameters
- 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.
- 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.
- selection scheme: The selection scheme of this EA.
- tournament size: The fraction of the current population which should be used as tournament members.
- start temperature: The scaling temperature .
- dynamic selection pressure: If set to true the selection pressure is increased to maximum during the complete optimization run.
- keep best individual: If set to true, the best individual of each generations is guaranteed to be selected for the next generation (elitist selection).
- save intermediate weights: Determines if the intermediate best results should be saved.
- intermediate weights generations: Determines if the intermediate best results should be saved. Will be performed every k generations for a specified value of k.
- intermediate weights file: The file into which the intermediate weights will be saved.
- mutation variance: The (initial) variance for each mutation.
- 1 5 rule: If set to true, the 1/5 rule for variance adaption is used.
- bounded mutation: If set to true, the weights are bounded between 0 and 1.
- p crossover: Probability for an individual to be selected for crossover.
- crossover type: Type of the crossover.
- use default mutation rate: Use the default mutation rate for nominal attributes.
- nominal mutation rate: The probability to switch nominal attributes between 0 and 1.
- initialize with input weights: Indicates if this operator should look for attribute weights in the given input and use the input weights of all known attributes as starting point for the optimization.