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.
This interface extends the Codec and allows to encode an object from the problem space to a corresponding Genotype, which is the inverse functionality of the codec.
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.