(AI Studio Core)
Synopsis
Another (improved) genetic algorithm for feature selection and feature generation (AGA).Description
Basically the same operator as the GeneratingGeneticAlgorithm operator. This version adds additional generators and improves the simple GGA approach by providing some basic intron prevention techniques. In general, this operator seems to work better than the original approach but frequently deliver inferior results compared to the operator YAGGA2 .
Input
- example set (IOObject)
This is an example set input port
Output
- example set (Data table)
This is an example set output port
- attribute weights out (Attribute Weights)
- performance out (Performance Vector)
Parameters
- max number of new attributesMax number of attributes to generate for an individual in one generation.
- limit max total number of attributesIndicates if the total number of attributes in all generations should be limited.
- max total number of attributesMax total number of attributes in all generations.
- use local random seedIndicates if a local random seed should be used.
- local random seedSpecifies the local random seed
- maximal fitnessThe optimization will stop if the fitness reaches the defined maximum.
- population sizeNumber of individuals per generation.
- maximum number of generationsNumber of generations after which to terminate the algorithm.
- use plusGenerate sums.
- use diffGenerate differences.
- use multGenerate products.
- use divGenerate quotients.
- reciprocal valueGenerate reciprocal values.
- use early stoppingEnables early stopping. If unchecked, always the maximum number of generations is performed.
- generations without improvalStop criterion: Stop after n generations without improval of the performance.
- tournament sizeThe fraction of the current population which should be used as tournament members (only tournament selection).
- start temperatureThe scaling temperature (only Boltzmann selection).
- dynamic selection pressureIf set to true the selection pressure is increased to maximum during the complete optimization run (only Boltzmann and tournament selection).
- keep best individualIf set to true, the best individual of each generations is guaranteed to be selected for the next generation (elitist selection).
- p initializeInitial probability for an attribute to be switched on.
- p crossoverProbability for an individual to be selected for crossover.
- crossover typeType of the crossover.
- p generateProbability for an individual to be selected for generation.
- use heuristic mutation probabilityIf checked the probability for mutations will be chosen as 1/number of attributes.
- p mutationProbability for mutation.
- use square rootsGenerate square root values.
- use power functionsGenerate the power of one attribute and another.
- use sinGenerate sinus.
- use cosGenerate cosinus.
- use tanGenerate tangens.
- use atanGenerate arc tangens.
- use expGenerate exponential functions.
- use logGenerate logarithmic functions.
- use absolute valuesGenerate absolute values.
- use minGenerate minimum values.
- use maxGenerate maximum values.
- use sgnGenerate signum values.
- use floor ceil functionsGenerate floor, ceil, and rounded values.
- restrictive selectionUse restrictive generator selection (faster).
- remove uselessRemove useless attributes.
- remove equivalentRemove equivalent attributes.
- equivalence samplesCheck this number of samples to prove equivalency.
- equivalence epsilonConsider two attributes equivalent if their difference is not bigger than epsilon.
- equivalence use statisticsRecalculates attribute statistics before equivalence check.
- search fourier peaksUse this number of highest frequency peaks for sinus generation.
- attributes per peakUse this number of additional peaks for each found peak.
- epsilonUse this range for additional peaks for each found peak.
- adaption typeUse this adaption type for additional peaks.