Uses of Class
de.lmu.ifi.dbs.elki.algorithm.outlier.AggarwalYuEvolutionary.Individuum

Packages that use AggarwalYuEvolutionary.Individuum

de.lmu.ifi.dbs.elki.algorithm.outlier

Outlier detection algorithms

Uses of AggarwalYuEvolutionary.Individuum in de.lmu.ifi.dbs.elki.algorithm.outlier

Methods in de.lmu.ifi.dbs.elki.algorithm.outlier that return AggarwalYuEvolutionary.Individuum

private AggarwalYuEvolutionary.Individuum	AggarwalYuEvolutionary.EvolutionarySearch.combineRecursive(ArrayList<Integer> r, int i, int[] current, AggarwalYuEvolutionary.Individuum parent1, AggarwalYuEvolutionary.Individuum parent2) Recursive method to build all possible gene combinations using positions in r.
private AggarwalYuEvolutionary.Individuum	AggarwalYuEvolutionary.EvolutionarySearch.makeIndividuum(int[] gene) Make a new individuum helper, computing sparsity=fitness
static AggarwalYuEvolutionary.Individuum	AggarwalYuEvolutionary.Individuum.nullIndividuum(int dim) Create a "null" individuum (full space).

Methods in de.lmu.ifi.dbs.elki.algorithm.outlier that return types with arguments of type AggarwalYuEvolutionary.Individuum

private ArrayList<AggarwalYuEvolutionary.Individuum>	AggarwalYuEvolutionary.EvolutionarySearch.crossoverOptimized(ArrayList<AggarwalYuEvolutionary.Individuum> population) method implements the crossover algorithm
private ArrayList<AggarwalYuEvolutionary.Individuum>	AggarwalYuEvolutionary.EvolutionarySearch.initialPopulation(int popsize) Produce an initial (random) population.
private ArrayList<AggarwalYuEvolutionary.Individuum>	AggarwalYuEvolutionary.EvolutionarySearch.mutation(ArrayList<AggarwalYuEvolutionary.Individuum> population, double perc1, double perc2) method implements the mutation algorithm
private Pair<AggarwalYuEvolutionary.Individuum,AggarwalYuEvolutionary.Individuum>	AggarwalYuEvolutionary.EvolutionarySearch.recombineOptimized(AggarwalYuEvolutionary.Individuum parent1, AggarwalYuEvolutionary.Individuum parent2) Recombination method.
private Pair<AggarwalYuEvolutionary.Individuum,AggarwalYuEvolutionary.Individuum>	AggarwalYuEvolutionary.EvolutionarySearch.recombineOptimized(AggarwalYuEvolutionary.Individuum parent1, AggarwalYuEvolutionary.Individuum parent2) Recombination method.
private ArrayList<AggarwalYuEvolutionary.Individuum>	AggarwalYuEvolutionary.EvolutionarySearch.rouletteRankSelection(ArrayList<AggarwalYuEvolutionary.Individuum> population) the selection criterion for the genetic algorithm: roulette wheel mechanism: where the probability of sampling an individual of the population was proportional to p - r(i), where p is the size of population and r(i) the rank of i-th individual
Collection<AggarwalYuEvolutionary.Individuum>	AggarwalYuEvolutionary.EvolutionarySearch.run()

Methods in de.lmu.ifi.dbs.elki.algorithm.outlier with parameters of type AggarwalYuEvolutionary.Individuum

private AggarwalYuEvolutionary.Individuum	AggarwalYuEvolutionary.EvolutionarySearch.combineRecursive(ArrayList<Integer> r, int i, int[] current, AggarwalYuEvolutionary.Individuum parent1, AggarwalYuEvolutionary.Individuum parent2) Recursive method to build all possible gene combinations using positions in r.
private Pair<AggarwalYuEvolutionary.Individuum,AggarwalYuEvolutionary.Individuum>	AggarwalYuEvolutionary.EvolutionarySearch.recombineOptimized(AggarwalYuEvolutionary.Individuum parent1, AggarwalYuEvolutionary.Individuum parent2) Recombination method.

Method parameters in de.lmu.ifi.dbs.elki.algorithm.outlier with type arguments of type AggarwalYuEvolutionary.Individuum

private boolean	AggarwalYuEvolutionary.EvolutionarySearch.checkConvergence(Collection<AggarwalYuEvolutionary.Individuum> pop) check the termination criterion
private ArrayList<AggarwalYuEvolutionary.Individuum>	AggarwalYuEvolutionary.EvolutionarySearch.crossoverOptimized(ArrayList<AggarwalYuEvolutionary.Individuum> population) method implements the crossover algorithm
private ArrayList<AggarwalYuEvolutionary.Individuum>	AggarwalYuEvolutionary.EvolutionarySearch.mutation(ArrayList<AggarwalYuEvolutionary.Individuum> population, double perc1, double perc2) method implements the mutation algorithm
private ArrayList<AggarwalYuEvolutionary.Individuum>	AggarwalYuEvolutionary.EvolutionarySearch.rouletteRankSelection(ArrayList<AggarwalYuEvolutionary.Individuum> population) the selection criterion for the genetic algorithm: roulette wheel mechanism: where the probability of sampling an individual of the population was proportional to p - r(i), where p is the size of population and r(i) the rank of i-th individual