Package | Description |
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jcgp.backend.modules.es | |
jcgp.backend.modules.mutator | |
jcgp.backend.modules.problem |
Modifier and Type | Class and Description |
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class |
EvolutionaryStrategy
This class specifies the characteristics of an evolutionary strategy.
|
class |
MuPlusLambda
(μ + λ)-ES
This strategy selects the μ fittest chromosomes from the population. |
class |
TournamentSelection
Tournament selection
This strategy generates a new population by selecting a specified number of chromosomes from the original population and selecting the fittest out of the isolated subset (the tournament). |
Modifier and Type | Class and Description |
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class |
FixedPointMutator
Fixed point mutator
This operator uses the point mutator algorithm to mutate a user-defined fixed number of genes. |
class |
Mutator
This class specifies the basic characteristics of a mutation operator.
|
class |
PercentPointMutator
Percent point mutator
This operator calculates how many genes to mutate based on the mutation rate parameter. |
class |
PointMutator
Point mutator
In point mutation, a number of random genes is picked and mutated until all required mutations have been performed. |
class |
ProbabilisticMutator
Probabilistic mutator
This operator iterates through every mutable gene in the chromosome and decides whether to mutate each of them individually. |
Modifier and Type | Class and Description |
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class |
DigitalCircuitProblem
Digital circuit problem
Using this problem type, digital logic circuits can be evolved. |
class |
Problem
Defines the general behaviour of a CGP problem.
|
class |
SymbolicRegressionProblem
Symbolic regression functions
Using this problem type, regression problems can be solved. |
class |
TestCaseProblem<T>
Abstract model for a problem that uses test cases.
|