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Uses of Stochastic in FitnessEvaluation.BinomialProblem |
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Classes in FitnessEvaluation.BinomialProblem that implement Stochastic | |
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BinomialJScheme
Evaluates the fitness for the BinomialProblem class. |
Uses of Stochastic in FitnessEvaluation.externalInterpreters.SymbolicRegression |
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Classes in FitnessEvaluation.externalInterpreters.SymbolicRegression that implement Stochastic | |
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SymbolicRegressionJScheme
Evaluates the fitness for the SymbolicRegressionExperiment class. |
Uses of Stochastic in FitnessEvaluation.MaxProblem |
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Classes in FitnessEvaluation.MaxProblem that implement Stochastic | |
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class |
MaxProblemInterpreter
This problem tries to generate the largest number possible from with a given terminal and functional set and with a depth limit. |
Uses of Stochastic in FitnessEvaluation.SymbolicRegression |
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Classes in FitnessEvaluation.SymbolicRegression that implement Stochastic | |
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SymbolicRegressionInterpreter
Interpreter for symbolic regression. |
Uses of Stochastic in Operator |
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Classes in Operator that implement Stochastic | |
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class |
CrossoverModule
CrossoverModule performs crossover on an entire population. |
class |
EliteReplacementOperator
EliteReplacementOperator has a EliteReplacementOperation. |
class |
FitnessEvaluator
FitnessEvaluator is a module that is used to evaluate an entire populations fitness. |
class |
Initialiser
Initialiser has a CreationOperation and is used to create a population. |
class |
JoinOperator
Abstract class for joining two populations. |
class |
MeritReplacementStrategy
MeritReplacementStrategy joins to populations. |
class |
MutationOperator
MutationOperator has a MutationOperation. |
class |
OperatorModule
Abstract class for Modules with operations. |
class |
RampedHalfAndHalfInitialiser
Class for performing ramped full and grow initialization. |
class |
SelectionScheme
SelectionScheme has a SelectionOperation This opertor is used for selecting from the population |
class |
SimpleReplacementStrategy
SimpleReplacementStrategy joins two populations. |
class |
SourceModule
Abstract class used for a source module. |
class |
SplitOperator
Abstract class for splitting a population. |
Uses of Stochastic in Operator.Operations |
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Classes in Operator.Operations that implement Stochastic | |
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class |
CrossoverOperation
Abstract class for CrossoverOperation |
class |
FullInitialiser
Create a genotype by growing a tree to maxDepth for all leaves |
class |
GrowInitialiser
Class for growing individuals to the maximum derrivationTree size of maxDepth |
class |
IntFlipByteMutation
IntFlipMutation does integer mutation. |
class |
IntFlipFitness
IntFlipMutation does integer mutation. |
class |
IntFlipMutation
IntFlipMutation does integer mutation |
class |
LSystemSelect
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class |
MutationOperation
Abstract class for mutaion operation. |
class |
ProportionalRouletteWheel
Roulette wheel selection based on fitness proprtional selection. |
class |
RandomInitialiser
Create an Individual with a randomly generated chromosome |
class |
RouletteWheel
This is an abstract class for creating Roulette wheel selection methods with different distributions |
class |
ScaledRouletteWheel
Scaled Roulette wheel selection based on weighted selection, The likelyhood of an individual getting picked is: total sum of the fitness - fitness of the individual |
class |
SinglePointCrossover
Single point crossover. |
class |
TournamentSelect
The operation of this class is tournament selection. |
Uses of Stochastic in Operator.Operations.ContextSensitiveOperations |
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Classes in Operator.Operations.ContextSensitiveOperations that implement Stochastic | |
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class |
NodalMutation
This will find if the chromosome contains a expandable leaf node and will mutate it depending on the mutation probability |
class |
StructuralMutation
This will find if the chromosome contains a expandable structural node and will mutate it depending on the mutation probability |
class |
SubtreeCrossover
Preform a crossover where the branch of one tree crosses with the branch of another. |
class |
SubtreeMutation
Pick a single point to mutate, but having mutated, remove all the codons that used to define the old branch, and then follow the new branch, randomly generating values to pick productions, until a complete branch is built or the maximum depth is reached. |
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