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

Packages that use AggarwalYuEvolutionary.Individuum

Package	Description
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

Modifier and Type	Method and Description
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

Modifier and Type	Method and Description
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

Modifier and Type	Method and Description
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

Modifier and Type	Method and Description
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