smile.gap

Enum GeneticAlgorithm.Selection

java.lang.Object

java.lang.Enum<GeneticAlgorithm.Selection>

smile.gap.GeneticAlgorithm.Selection

All Implemented Interfaces:

Serializable, Comparable<GeneticAlgorithm.Selection>

Enclosing class:

GeneticAlgorithm<T extends Chromosome>

public static enum GeneticAlgorithm.Selection
extends Enum<GeneticAlgorithm.Selection>

The way to select chromosomes from the population as parents to crossover.

Enum Constant Summary

Enum Constants

Enum Constant and Description
RANK Rank Selection.
ROULETTE_WHEEL Roulette Wheel Selection, also called fitness proportionate selection.
SCALED_ROULETTE_WHEEL Scaled Roulette Wheel Selection.
TOURNAMENT Tournament Selection.

Method Summary

Methods

Modifier and Type	Method and Description
static GeneticAlgorithm.Selection	valueOf(String name) Returns the enum constant of this type with the specified name.
static GeneticAlgorithm.Selection[]	values() Returns an array containing the constants of this enum type, in the order they are declared.

Methods inherited from class java.lang.Enum

clone, compareTo, equals, finalize, getDeclaringClass, hashCode, name, ordinal, toString, valueOf

Methods inherited from class java.lang.Object

getClass, notify, notifyAll, wait, wait, wait

Enum Constant Detail

ROULETTE_WHEEL

public static final GeneticAlgorithm.Selection ROULETTE_WHEEL

Roulette Wheel Selection, also called fitness proportionate selection. Parents are selected by the ratio of its fitness to the fitness of other members of the current population.

SCALED_ROULETTE_WHEEL

public static final GeneticAlgorithm.Selection SCALED_ROULETTE_WHEEL

Scaled Roulette Wheel Selection. As the average fitness of chromosomes in the population increases, the variance of fitness decreases in the population. There may be little difference between the best and worst chromosome in the population after several generations, and the selective pressure based on fitness is corresponding reduced. This problem can partially be addressed by using some form of fitness scaling. In the simplest case, on can subtract the fitness of the worst chromosome in the population from the fitnesses of all chromosomes in the population. Alternatively, one may use rank based selection.

RANK

public static final GeneticAlgorithm.Selection RANK

Rank Selection. The Roulette Wheel Selection will have problems when the fitnesses differs very much. For example, if the best chromosome fitness is 90% of all the roulette wheel then the other chromosomes will have very few chances to be selected. Rank selection first ranks the population and then every chromosome receives fitness from this ranking. The worst will have fitness 1 and the best will have fitness N (number of chromosomes in population). After this all the chromosomes have a chance to be selected. But this method can lead to slower convergence, because the best chromosomes do not differ so much from other ones.

TOURNAMENT

public static final GeneticAlgorithm.Selection TOURNAMENT

Tournament Selection. Tournament selection returns the fittest individual of some t individuals picked at random, with replacement, from the population. First choose t (the tournament size) individuals from the population at random. Then choose the best individual from tournament with probability p, choose the second best individual with probability p*(1-p), choose the third best individual with probability p*((1-p)^2), and so on... Tournament Selection has become the primary selection technique used for the Genetic Algorithm. First, it's not sensitive to the particulars of the fitness function. Second, it's very simple, requires no preprocessing, and works well with parallel algorithms. Third, it's tunable: by setting the tournament size t, you can change how selective the technique is. At the extremes, if t = 1, this is just random search. If t is very large (much larger than the population size itself), then the probability that the fittest individual in the population will appear in the tournament approaches 1.0, and so Tournament Selection just picks the fittest individual each time.

Method Detail

values

public static GeneticAlgorithm.Selection[] values()

Returns an array containing the constants of this enum type, in the order they are declared. This method may be used to iterate over the constants as follows:

for (GeneticAlgorithm.Selection c : GeneticAlgorithm.Selection.values())
    System.out.println(c);

Returns:

an array containing the constants of this enum type, in the order they are declared

valueOf

public static GeneticAlgorithm.Selection valueOf(String name)

Returns the enum constant of this type with the specified name. The string must match exactly an identifier used to declare an enum constant in this type. (Extraneous whitespace characters are not permitted.)

Parameters:

name - the name of the enum constant to be returned.

Returns:

the enum constant with the specified name

Throws:

IllegalArgumentException - if this enum type has no constant with the specified name

NullPointerException - if the argument is null