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 to create genes without
explicit implementing the Chromosome interface.Gene implementation, which allows to create genes without explicit
implementing the Gene interface.Helper class for parsing command line arguments.
Array implementation class.
Minimal interface for accessing an underlying array structure.
Mutable reference of an underlying array
Store.Helper class which contains array helper methods.
General base interface for a ordered, fixed sized, mutable, object sequence.
General base interface for a ordered, fixed sized, object sequence.
This object contains mathematical helper functions.
This class represents a fixed sized array of bit or boolean
values, backed by a
byte[] array.Implementation of the classical BitChromosome.
Implementation of a BitGene.
Some bit utils.
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.
CharacterChromosome 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 interface indicates, that a class can create a copy of type
T.
Performs a
Crossover of two
Chromosome.This class implements the the
Kahan
summation algorithm, which significantly reduces the numerical error when
adding double values.
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.Contains static
Seq definitions.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 to update the
EvolutionStart object,
before the evolution start, and update the EvolutionResult
object after the evolution.This class collects the parameters which control the behaviour of the
evolution process.
Builder class for the evolution parameter.
Represents a state of the GA after an evolution step.
The
Spliterator implementation of the EvolutionStream.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 to force 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.Static methods for simple and efficient hash-code calculation.
Comparator for int values.
Numeric chromosome implementation which holds 32-bit integer numbers.
NumericGene implementation which holds a 32-bit integer number.
This alterer takes two chromosome (treating it as vectors) and creates a
linear combination of these vectors as 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.
Int reference class, which allows the usage in an lambda expression.
Value objects which contains statistical summary information.
Class for object serialization.
Immutable, ordered, fixed sized sequence.
Class for lazy value initialization.
Interfaces and classes for handling resource (
AutoCloseable) objects.Extends the
AutoCloseable with methods for wrapping the thrown
exception into unchecked exceptions or ignoring them.This class allows to collect one or more
AutoCloseable objects
into one.A method which takes an argument and can throw an exception.
A function which takes an argument and can throw an exception.
Runnable task/method, which might throw an exception
E.This class represents a closeable value.
This class contains factory methods for creating predicates, which can be
used for limiting the evolution stream.
Extends the
Spliterator interface by an additional proceed
predicate.
In linear-ranking selection the individuals are sorted according to their
fitness values.
This alterer takes two chromosome (treating it as vectors) and creates a
linear combination of this vectors as 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 a chromosomes of an 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.This class contains some common predicates
Mathematical functions regarding probabilities.
Probability selectors are a variation of fitness proportional selectors and
selects individuals from a given population based on it's 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 to access 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.Some random helper functions.
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.
Some helper methods for creating hash codes and comparing values.
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.
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.
Helper methods needed for implementing the Java serializations.
Object reader interface
Object writer interface.
Single point crossover
StochasticUniversalSelector is a method for selecting a
population according to some given probability in a way that minimize chance
fluctuations.This class allows to create a reactive
Flow.Publisher from a given
Java Stream.This class contains factory methods for (flat) mapping stream elements.
This class creates random subsets of size
k from a set of n
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 multi-point crossover.