All Classes and Interfaces

Class	Description
AbstractAlterer<G,C>	Abstract implementation of the alterer interface.
AbstractChromosome<G>	The abstract base implementation of the Chromosome interface.
AbstractTreeChromosome<A,G>	Abstract base implementation of a TreeChromosome.
AbstractTreeGene<A,G>	Abstract implementation of the TreeGene interface..
Accumulator<T,A,R>	This interface lets you accumulate elements of type T to a result of type R.
Alterer<G,C>	The Alterer is responsible for the changing/recombining the Population.
AltererResult<G,C>	Represents the result pair of a Alterer.alter(Seq, long) call, which consists of the altered population and the number of altered individuals.
AnyChromosome<A>	Chromosome implementation, which allows creating genes without explicit implementing the Chromosome interface.
AnyGene<A>	Gene implementation, which allows creating genes without explicit implementing the Gene interface.
AutoCloseableXMLStreamReader	Makes the XMLStreamReader interface AutoCloseable.
AutoCloseableXMLStreamWriter	Makes the XMLStreamWriter interface AutoCloseable.
BaseSeq<T>	General base interface for an ordered, fixed sized, object sequence.
BatchExecutor	Batch executor interface, which is used for evaluating a batch of runnables.
BigIntegerChromosome	Numeric chromosome implementation which holds arbitrary sized integer numbers.
BigIntegerGene	Numeric chromosome implementation which holds an arbitrary sized integer number.
BitChromosome	Implementation of the classical BitChromosome.
BitGene	Implementation of a BitGene.
Bnf	This class contains methods for parsing and formatting context-free grammars in BNF format.
BoltzmannSelector<G,N>	In this Selector, the probability for selection is defined as.
BoolOp	This class contains basic and secondary boolean operations.
BoundedChromosome<A,G>	Chromosome interface for BoundedGenes.
BoundedGene<A,G>	Base interface for genes where the alleles are bound by a minimum and a maximum value.
BoundedGeneCreator<A,G>	Functional interface for creating bounded genes.
Cfg<T>	Represents a context-free grammar (CFG).
Cfg.Annotatable<T>	Interface for annotatable CFG types.
Cfg.Builder<T>	Builder class for building Cfg objects.
Cfg.Element<T>	Represents an element of a CFG
Cfg.Expression<T>	Represents one expression (list of alternative symbols) a production rule consists of.
Cfg.Expression.Builder<T>	Builder class for building rule expressions.
Cfg.NonTerminal<T>	Represents the non-terminal symbols of the grammar (NT).
Cfg.Rule<T>	Represents a production rule of the grammar (R).
Cfg.Rule.Builder<T>	Builder class for building CFG rules.
Cfg.Symbol<T>	Represents the symbols the BNF grammar consists.
Cfg.Terminal<T>	Represents a terminal symbols of the grammar (T).
CharacterChromosome	Character chromosome which represents character sequences.
CharacterGene	Character gene implementation.
CharSeq	This class is used for holding the valid characters of an CharacterGene.
Chromosome<G>	A chromosome consists of one or more genes.
Codec<T,G>	A problem Codec contains the information about how to encode a given argument type into a Genotype.
Codecs	This class contains factory methods for creating common problem encodings.
Codons	Represents a mapping of a finite set of integers to symbol indexes.
CombineAlterer<G,C>	Alters a chromosome by replacing two genes by the result of a given combiner function.
Complexity<T>	Represents a complexity measure if a given program tree.
ConcatEngine<G,C>	The ConcatEngine lets you concatenate two (or more) evolution Engine, with different configurations, and let it use as one engine EvolutionStreamable.
Const<T>	Represents an operation which always returns the same, constant, value.
Constraint<G,C>	This interface allows you to define constraints on single phenotypes.
ConstRewriter<T>	This class rewrites constant expressions to its single value.
Conversions	This class contains methods for converting from and to the primitive arrays int[], long[] and double[].
Copyable<T>	This interface indicates that a class can create a copy of type T.
Crossover<G,C>	Performs a Crossover of two Chromosome.
CsvSupport	This class contains helper classes, which are the building blocks for handling CSV files.
CsvSupport.ColumnIndexes	Holds the column indexes, which should be part of the split or join operation.
CsvSupport.ColumnJoiner	This class joins an array of columns into one CSV line.
CsvSupport.LineReader	This class reads CSV files and splits it into lines.
CsvSupport.LineSplitter	Splitting a CSV line into columns (records).
CsvSupport.Quote	Holds the CSV column quote character.
CsvSupport.Separator	Holds the CSV column separator character.
CyclicEngine<G,C>	The CyclicEngine lets you concatenate two (or more) evolution Engine, with different configurations, and let it use as one engine EvolutionStreamable.
DerivationTreeGenerator<T>	Standard implementation of a derivation-tree generator.
DoubleChromosome	Numeric chromosome implementation which holds 64-bit floating point numbers.
DoubleGene	Implementation of the NumericGene which holds a 64-bit floating point number.
DoubleMoments	Value objects which contains statistical moments.
DoubleMomentStatistics	A state object for collecting statistics such as count, min, max, sum, mean, variance, skewness and kurtosis.
DoubleRange	Double range class.
DoubleSummary	Value objects which contains statistical summary information.
ElementComparator<V>	Defines the order of two elements of a given vector type V.
ElementDistance<V>	Defines the distance of two elements of a given vector type V.
EliteSelector<G,C>	The EliteSelector copies a small proportion of the fittest candidates, without changes, into the next generation.
Engine<G,C>	Genetic algorithm engine which is the main class.
Engine.Builder<G,C>	Builder class for building GA Engine instances.
Engine.Setup<G,C>	This interface represents a recipe for configuring (setup) a given Engine.Builder.
EnumGene<A>	Gene which holds enumerable (countable) genes.
EphemeralConst<T>	Implementation of an ephemeral constant.
Error<T>	This function calculates the overall error of a given program tree.
Evaluator<G,C>	This interface allows defining different strategies for evaluating the fitness functions of a given population.
Evaluators	This class contains factory methods for creating commonly usable Evaluator implementations.
Evolution<G,C>	This functional interface defines the evolution function, which takes an EvolutionStart object, evolves the population, and returns an EvolutionResult object.
EvolutionDurations	This class contains timing information about one evolution step.
EvolutionInit<G>	Represents the initialization value of an evolution stream/iterator.
EvolutionInterceptor<G,C>	The evolution interceptor allows updating the EvolutionStart object, before the evolution start, and update the EvolutionResult object after the evolution.
EvolutionParams<G,C>	This class collects the parameters which control the behavior of the evolution process.
EvolutionParams.Builder<G,C>	Builder class for the evolution parameter.
EvolutionResult<G,C>	Represents a state of the GA after an evolution step.
EvolutionStart<G,C>	Represents a state of the GA at the start of an evolution step.
EvolutionStatistics<C,FitnessStatistics>	This class can be used to gather additional statistic information of an evolution process.
EvolutionStream<G,C>	The EvolutionStream class extends the Java Stream and adds a method for limiting the evolution by a given predicate.
EvolutionStreamable<G,C>	This interface defines the capability of creating EvolutionStreams from a given EvolutionStart object.
ExponentialRankSelector<G,C>	An alternative to the "weak" LinearRankSelector is to assign survival probabilities to the sorted individuals using an exponential function.
Factory<T>
FitnessNullifier<G,C>	This class allows forcing a reevaluation of the fitness function.
FlatTree<V,T>	Tree specification, where the nodes of the whole tree are stored in an array.
FlatTreeNode<V>	Default implementation of the FlatTree interface.
GaussianMutator<G,C>	The GaussianMutator class performs the mutation of a NumericGene.
Gene<A,G>	Genes are the atoms of the Jenetics library.
Generator<T,R>	Generator interface for generating sentences/derivation trees from a given grammar.
Genotype<G>	The central class the GA is working with, is the Genotype.
HPRMutator<G,C>	The Hybridizing PSM and RSM Operator (HPRM) constructs an offspring from a pair of parents by hybridizing two mutation operators, PSM and RSM.
IntegerChromosome	Numeric chromosome implementation which holds 32-bit integer numbers.
IntegerGene	NumericGene implementation which holds a 32-bit integer number.
IntermediateCrossover<G,C>	This alterer takes two chromosomes (treating it as vectors) and creates a linear combination of these vectors as a result.
IntMoments	Value objects which contains statistical moments.
IntMomentStatistics	A state object for collecting statistics such as count, min, max, sum, mean, variance, skewness and kurtosis.
IntRange	Integer range class.
IntSummary	Value objects which contains statistical summary information.
InvertibleCodec<T,G>	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.
IO	Class for object serialization.
ISeq<T>	Immutable, ordered, fixed sized sequence.
Limits	This class contains factory methods for creating predicates, which can be used for limiting the evolution stream.
LinearRankSelector<G,C>	In linear-ranking selection the individuals are sorted according to their fitness values.
LineCrossover<G,C>	This alterer takes two chromosomes (treating it as vectors) and creates a linear combination of these vectors as a result.
LongChromosome	Numeric chromosome implementation which holds 64-bit integer numbers.
LongGene	NumericGene implementation which holds a 64-bit integer number.
LongMoments	Value objects which contains statistical moments.
LongMomentStatistics	A state object for collecting statistics such as count, min, max, sum, mean, variance, skewness and kurtosis.
LongRange	Long range class.
LongSummary	Value objects which contains statistical summary information.
LossFunction<T>	This function evaluates how well an evolved program tree fits the given sample data set.
Mappers	This class defines factories for different CFG ↔ Chromosome mappings (encodings).
MathExpr	Contains methods for parsing mathematical expression.
MathOp	This class contains operations for performing basic numeric operations.
MathRewriteAlterer<G,C>	Prunes a given mathematical tree with the given alterer probability.
Mean<T>	A mixin interface for genes which can have a mean value.
MeanAlterer<G,C>	Alters a chromosome by replacing two genes by its mean value.
MinMax<C>	This consumer class is used for calculating the min and max value according to the given Comparator.
MLEvolutionStrategy<G,C>	Setup for a (μ, λ)-Evolution Strategy.
MOEA	Collectors for collecting final pareto-set for multi-objective optimization.
MonteCarloSelector<G,C>	The Monte Carlo selector selects the individuals from a given population randomly.
MpLEvolutionStrategy<G,C>	Setup for a (μ + λ)-Evolution Strategy.
MSeq<T>	Mutable, ordered, fixed sized sequence.
MultiPointCrossover<G,C>	Multiple point crossover
Mutator<G,C>	This class is for mutating the chromosomes of a given population.
MutatorResult<T>	Represents the result pair of one of the four Mutator.mutate calls.
NanoClock	Clock implementation with nano second precision.
NSGA2Selector<G,C>	This selector selects the first count elements of the population, which has been sorted by the Crowded-Comparison Operator, as described in A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II
NumericChromosome<N,G>	Numeric chromosome interface.
NumericGene<N,G>	Base interface for numeric genes.
Op<T>	Operation interface.
Optimize	This enum determines whether the GA should maximize or minimize the fitness function.
Ordered<T>	Object wrapper, which makes the wrapped value Comparable, by defining a separate Comparator.
Pareto	Low-level utility methods for doing pareto-optimal calculations.
ParetoFront<T>	This class only contains non-dominate (Pareto-optimal) elements according to a given dominance measure.
PartialAlterer<G,C>	This alterer wraps a given alterer which works on a given section of the genotype's chromosomes.
PartiallyMatchedCrossover<T,C>	The PartiallyMatchedCrossover (PMX) guarantees that all Genes are found exactly once in each chromosome.
PermutationChromosome<T>	This chromosome can be used to model permutations of a given (sub) set of alleles.
Phenotype<G,C>	The Phenotype consists of a Genotype, the current generation and an optional fitness value.
ProbabilitySelector<G,C>	Probability selectors are a variation of fitness proportional selectors and selects individuals from a given population based on its selection probability P(i).
Problem<T,G,C>	This interface describes a problem which can be solved by the GA evolution Engine.
Program<T>	This class composes a given operation tree to a new operation, which can serve as a sub program in another operation tree.
ProgramChromosome<A>	Holds the nodes of the operation tree.
ProgramGene<A>	This gene represents a program, build upon an AST of Op functions.
ProxySorter	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.
ProxySorter.Comparator<T>	The comparator used for comparing two array elements at the specified indexes.
Quantile	Implementation of the quantile estimation algorithm published by
RandomAdapter	Some places in the Java API still require a Random object instead of the new RandomGenerator.
RandomRegistry	This class holds the RandomGenerator engine used for the GA.
Reader<T>	XML reader class, used for reading objects in XML format.
Readers	This class contains static fields and methods, for creating chromosome- and genotype readers for different gene types.
Readers.BitChromosome	Bit chromosome reader methods, which reads XML-representations of bit-chromosomes.
Readers.BoundedChromosome	Reader methods for BoundedChromosome objects.
Readers.CharacterChromosome	Reader methods for CharacterChromosome objects.
Readers.DoubleChromosome	Reader methods for DoubleChromosome objects.
Readers.Genotype	Writer methods for Genotype objects.
Readers.Genotypes	This class contains static reader methods for Genotype objects.
Readers.IntegerChromosome	Reader methods for IntegerChromosome objects.
Readers.LongChromosome	Reader methods for LongChromosome objects.
Readers.PermutationChromosome	Reader methods for PermutationChromosome objects.
Recombinator<G,C>	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.
Regression<T>	This class implements a symbolic regression problem.
RetryConstraint<G,C>	This simple Constraint implementation repairs an invalid phenotype by creating new individuals until a valid one has been created.
RouletteWheelSelector<G,N>	The roulette-wheel selector is also known as fitness proportional selector, but in the Jenetics library it is implemented as probability selector.
RSMutator<G,C>	The reverse sequence mutation, two positions i and j are randomly chosen The gene order in a chromosome will then be reversed between this two points.
Sample<T>	Represents a sample point used for the symbolic regression task.
SampleBuffer<T>	This class holds the actual sample values which are used for the symbolic regression example.
Sampler	Interface for creating continuous random samples, with a given distribution.
Samplers	This class defines some default samplers.
Sampling<T>	This interface represents a set of sample points, which can be evaluated with a given evolved program.
Sampling.Result<T>	This class represents the result of a sample calculation, which contains the array of calculated values and a corresponding array with expected sample values.
Selector<G,C>	Selectors are responsible for selecting a given number of individuals from the population.
Self<S>	This interface defines a recursive generic type S, which represents the type of the implementing class.
SentenceGenerator<T>	Standard implementation of a sentence generator.
SentenceGenerator.Expansion	Defines the expansion strategy used when generating the sentences.
Seq<T>	General interface for a ordered, fixed sized, object sequence.
ShiftMutator<G,C>	The shift mutation applies mutation between two randomly chosen points.
ShiftMutator.Range	This class defines the Chromosome shift indexes.
ShiftMutator.RangeRandom	Functional interface for creating random shift ranges objects for shifting sequences of a given length.
ShuffleMutator<G,C>	The shuffle mutation, changes the order of the genes between two randomly chosen positions.
ShuffleMutator.Range	Represents the chromosome range which will be shuffled
ShuffleMutator.RangeRandom	Functional interface for creating random range objects for shuffling sequences of a given length.
SimulatedBinaryCrossover<G,C>	Performs the simulated binary crossover (SBX) on a Chromosome of NumericGenes such that each position is either crossed contracted or expanded with a certain probability.
SingleNodeCrossover<G,C>	Swaps two, randomly chosen, nodes (subtrees) from two given trees.
SinglePointCrossover<G,C>	Single point crossover
StochasticUniversalSelector<G,N>	StochasticUniversalSelector is a method for selecting a population according to some given probability in a way that minimizes chance fluctuations.
StreamPublisher<T>	This class allows creating a reactive Flow.Publisher from a given Java Stream.
Streams	This class contains factory methods for (flat) mapping stream elements.
SwapMutator<G,C>	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.
SymbolIndex	Functional interface for selecting a Cfg.Symbol by its index within a rule.
TournamentSelector<G,C>	In tournament selection the best individual from a random sample of s individuals is chosen from the population Pg.
Tree<V,T>	General purpose tree structure.
Tree.Path	This class represents the path to child within a given tree.
TreeChromosome<A,G>	Chromosome for tree shaped genes.
TreeCrossover<G,C>	Abstract implementation of tree base crossover recombinator.
TreeFormatter	Definition of different tree formatter strategies.
TreeGene<A,G>	Representation of tree shaped gene.
TreeMatcher<V>	Implementation of a pattern-based tree matcher.
TreeMatchResult<V>	The result of a tree match operation.
TreeMutator<A,G,C>	Abstract class for mutating tree chromosomes.
TreeNode<T>	A general purpose node in a tree data-structure.
TreePattern<V>	This class serves two purposes.
TreePattern.Decl<V>	A sealed interface, which constitutes the nodes of a pattern tree.
TreePattern.Val<V>	This class represents a constant pattern value, which can be part of a whole subtree.
TreePattern.Var<V>	Represents a placeholder (variable) for an arbitrary subtree.
TreeRewriteAlterer<A,G,C>	This alterer uses a TreeRewriter for altering the TreeChromosome.
TreeRewriter<V>	Interface for rewriting a given tree.
TreeRewriteRule<V>	Represents a tree rewrite rule.
TRS<V>	This class represents a Tree Rewrite System, which consists of a set of Tree Rewrite Rules.
TruncationSelector<G,C>	In truncation selection, individuals are sorted according to their fitness.
UFTournamentSelector<G,C>	Unique fitness based tournament selection.
UniformCrossover<G,C>	The uniform crossover uses swaps single genes between two chromosomes, instead of whole ranges as in single- and multipoint crossover.
UniformOderBasedCrossover<T,C>	The UniformOderBasedCrossover guarantees that all Genes are found exactly once in each chromosome.
Val<T>	This is the sealed base class for unmodifiable values.
Var<T>	Represents the program variables.
Vec<T>	The Vec interface represents the fitness result of a multi-objective fitness function.
VecFactory<T>	This interface allows creating a vector object from a given array type T.
Verifiable	This interface lets you check an object for validity.
WeaselMutator<G,C>	Mutator implementation which is part of the Weasel program algorithm.
WeaselProgram<G,C>	Configures the evolution engine to execute the Weasel program algorithm.
WeaselSelector<G,C>	Selector implementation which is part of the Weasel program algorithm.
Writer<T>	XML writer interface, used for writing objects in XML format.
Writers	This class contains static fields and methods, for creating chromosome- and genotype writers for different gene types.
Writers.BitChromosome	This class contains static writer methods for BitChromosome objects.
Writers.BoundedChromosome	This class contains static writer methods for BoundedChromosome objects.
Writers.CharacterChromosome	This class contains static writer methods for CharacterChromosome objects.
Writers.DoubleChromosome	This class contains static writer methods for DoubleChromosome objects.
Writers.Genotype	This class contains static writer methods for Genotype objects.
Writers.Genotypes	This class contains static writer methods for Genotype objects.
Writers.IntegerChromosome	This class contains static writer methods for IntegerChromosome objects.
Writers.LongChromosome	This class contains static writer methods for LongChromosome objects.
Writers.PermutationChromosome	This class contains static writer methods for PermutationChromosome objects.
XML	This class contains helper methods for creating XMLStreamReader and XMLStreamWriter objects.