All Classes and Interfaces
Class
Description
Abstract implementation of the alterer interface.
The abstract base implementation of the Chromosome interface.
This interface lets you accumulate elements of type
T to a result of
type R.The Alterer is responsible for the changing/recombining the Population.
Represents the result pair of a
Alterer.alter(Seq, long) call, which
consists of the altered population and the number of altered individuals.Chromosome implementation, which allows creating genes without
explicit implementing the Chromosome interface.Gene implementation, which allows creating genes without explicit
implementing the Gene interface.General base interface for an ordered, fixed sized, object sequence.
Batch executor interface, which is used for evaluating a batch of
runnables.
Implementation of the classical BitChromosome.
Implementation of a BitGene.
In this
Selector, the probability for selection is defined as.Chromosome interface for
BoundedGenes.Base interface for genes where the alleles are bound by a minimum and a
maximum value.
Character chromosome which represents character sequences.
Character gene implementation.
This class is used for holding the valid characters of an
CharacterGene.A chromosome consists of one or more genes.
A problem
Codec contains the information about how to encode a given
argument type into a Genotype.This class contains factory methods for creating common problem encodings.
Alters a chromosome by replacing two genes by the result of a given
combiner function.
This interface allows you to define constraints on single phenotypes.
This class contains methods for converting from and to the primitive arrays
int[], long[] and double[].This interface indicates that a class can create a copy of type
T.
Performs a
Crossover of two
Chromosome.Numeric chromosome implementation which holds 64-bit floating point numbers.
Implementation of the NumericGene which holds a 64-bit floating point number.
Value objects which contains statistical moments.
A state object for collecting statistics such as count, min, max, sum, mean,
variance, skewness and kurtosis.
Double range class.
Value objects which contains statistical summary information.
The
EliteSelector copies a small proportion of the fittest candidates,
without changes, into the next generation.Genetic algorithm engine which is the main class.
Builder class for building GA
Engine instances.This interface represents a recipe for configuring (setup) a given
Engine.Builder.
Gene which holds enumerable (countable) genes.
This interface allows defining different strategies for evaluating the
fitness functions of a given population.
This class contains factory methods for creating commonly usable
Evaluator implementations.This functional interface defines the evolution function, which takes an
EvolutionStart object, evolves the population, and returns an
EvolutionResult object.This class contains timing information about one evolution step.
Represents the initialization value of an evolution stream/iterator.
The evolution interceptor allows updating the
EvolutionStart object,
before the evolution start, and update the EvolutionResult
object after the evolution.This class collects the parameters which control the behavior of the
evolution process.
Builder class for the evolution parameter.
Represents a state of the GA after an evolution step.
Represents a state of the GA at the start of an evolution step.
This class can be used to gather additional statistic information of an
evolution process.
The
EvolutionStream class extends the Java Stream and adds a
method for limiting the evolution by a given predicate.This interface defines the capability of creating
EvolutionStreams
from a given EvolutionStart object.
An alternative to the "weak"
LinearRankSelector is to assign
survival probabilities to the sorted individuals using an exponential
function.This class allows forcing a reevaluation of the fitness function.
The GaussianMutator class performs the mutation of a
NumericGene.Genes are the atoms of the Jenetics library.
The central class the GA is working with, is the
Genotype.Numeric chromosome implementation which holds 32-bit integer numbers.
NumericGene implementation which holds a 32-bit integer number.
This alterer takes two chromosomes (treating it as vectors) and creates a
linear combination of these vectors as a result.
Value objects which contains statistical moments.
A state object for collecting statistics such as count, min, max, sum, mean,
variance, skewness and kurtosis.
Integer range class.
Value objects which contains statistical summary information.
Class for object serialization.
Immutable, ordered, fixed sized sequence.
This class contains factory methods for creating predicates, which can be
used for limiting the evolution stream.
In linear-ranking selection the individuals are sorted according to their
fitness values.
This alterer takes two chromosomes (treating it as vectors) and creates a
linear combination of these vectors as a result.
Numeric chromosome implementation which holds 64-bit integer numbers.
NumericGene implementation which holds a 64-bit integer number.
Value objects which contains statistical moments.
A state object for collecting statistics such as count, min, max, sum, mean,
variance, skewness and kurtosis.
Long range class.
Value objects which contains statistical summary information.
A mixin interface for genes which can have a mean value.
Alters a chromosome by replacing two genes by its mean value.
This consumer class is used for calculating the min and max value
according to the given
Comparator.The Monte Carlo selector selects the individuals from a given population
randomly.
Mutable, ordered, fixed sized sequence.
Multiple point crossover
This class is for mutating the chromosomes of a given population.
Represents the result pair of one of the four
Mutator.mutate calls.Clock implementation with nano second precision.
Numeric chromosome interface.
Base interface for numeric genes.
This
enum determines whether the GA should maximize or minimize the
fitness function.Object wrapper, which makes the wrapped value
Comparable, by defining
a separate Comparator.This alterer wraps a given alterer which works on a given section of the
genotype's chromosomes.
The
PartiallyMatchedCrossover (PMX) guarantees that all Genes
are found exactly once in each chromosome.This chromosome can be used to model permutations of a given (sub) set of
alleles.
The
Phenotype consists of a Genotype, the current generation
and an optional fitness value.Probability selectors are a variation of fitness proportional selectors and
selects individuals from a given population based on its selection
probability P(i).
This interface describes a problem which can be solved by the GA
evolution
Engine.This sorting methods doesn't sort a given array directly; instead,
an index lookup array is returned which allows accessing the array in a
sorted order.
The comparator used for comparing two array elements at the specified
indexes.
Implementation of the quantile estimation algorithm published by
Some places in the Java API still require a
Random object instead of
the new RandomGenerator.This class holds the
RandomGenerator engine used for the GA.
An enhanced genetic algorithm (EGA) combine elements of existing solutions in
order to create a new solution, with some of the properties of each parent.
This simple
Constraint implementation repairs an invalid
phenotype by creating new individuals until a valid one has been created.The roulette-wheel selector is also known as fitness proportional selector,
but in the Jenetics library it is implemented as probability selector.
Interface for creating continuous random samples, with a given
distribution.
This class defines some default samplers.
Selectors are responsible for selecting a given number of individuals from
the population.
This interface defines a recursive generic type
S, which
represents the type of the implementing class.General interface for a ordered, fixed sized, object sequence.
The shift mutation applies mutation between two randomly chosen points.
This class defines the
Chromosome shift indexes.Functional interface for creating random shift ranges objects for
shifting sequences of a given length.
The shuffle mutation, changes the order of the genes between two randomly
chosen positions.
Represents the chromosome range which will be shuffled
Functional interface for creating random range objects for
shuffling sequences of a given length.
Single point crossover
StochasticUniversalSelector is a method for selecting a
population according to some given probability in a way that minimizes chance
fluctuations.This class allows creating a reactive
Flow.Publisher from a given
Java Stream.This class contains factory methods for (flat) mapping stream elements.
The
SwapMutation changes the order of genes in a chromosome, with the
hope of bringing related genes closer together, thereby facilitating the
production of building blocks.In tournament selection the best individual from a random sample of s
individuals is chosen from the population Pg.
In truncation selection, individuals are sorted according to their fitness.
The uniform crossover uses swaps single genes between two chromosomes, instead
of whole ranges as in single- and multipoint crossover.
The
UniformOderBasedCrossover guarantees that all Genes
are found exactly once in each chromosome.This interface lets you check an object for validity.