A B C F G I L N O R S T U
All Classes All Packages

A

AbstractCrossover<ST,PT> - Class in science.aist.machinelearning.algorithm.ga.crossover

AbstractCrossover() - Constructor for class science.aist.machinelearning.algorithm.ga.crossover.AbstractCrossover

addIndividual(Solution<GT, PT>) - Method in class science.aist.machinelearning.algorithm.ga.GeneticAlgorithm

Adds an inidivual to the population only if the population size was not yet exceeded

B

bestQuality(Solution<GT, PT>, Solution<GT, PT>) - Method in class science.aist.machinelearning.algorithm.ga.GeneticAlgorithm

Calculate the bestQuality of the two solutions.

bestSolution - Variable in class science.aist.machinelearning.algorithm.ga.GeneticAlgorithm

Best currently known solution

breed(List<Solution<ST, PT>>, Selector<ST, PT>) - Method in class science.aist.machinelearning.algorithm.ga.crossover.AbstractCrossover

breed(List<Solution<ST, PT>>, Selector<ST, PT>) - Method in interface science.aist.machinelearning.algorithm.ga.Crossover

breedTwo(Solution<ST, PT>, Solution<ST, PT>) - Method in class science.aist.machinelearning.algorithm.ga.crossover.AbstractCrossover

breedTwo(Solution<ST, PT>, Solution<ST, PT>) - Method in class science.aist.machinelearning.algorithm.ga.crossover.OnePointCrossover

breedTwo(Solution<ST, PT>, Solution<ST, PT>) - Method in class science.aist.machinelearning.algorithm.ga.crossover.UniformCrossover

C

Crossover<ST,PT> - Interface in science.aist.machinelearning.algorithm.ga

crossoverRate - Variable in class science.aist.machinelearning.algorithm.ga.crossover.UniformCrossover

F

finalizeLog(Problem<PT>) - Method in class science.aist.machinelearning.algorithm.ga.GeneticAlgorithm

Helper function that is called after the last generation was created

G

GeneticAlgorithm<GT,PT> - Class in science.aist.machinelearning.algorithm.ga

Standard genetic algorithm implementation

GeneticAlgorithm() - Constructor for class science.aist.machinelearning.algorithm.ga.GeneticAlgorithm

getCrossover() - Method in class science.aist.machinelearning.algorithm.ga.GeneticAlgorithm

getCrossoverPoint() - Method in class science.aist.machinelearning.algorithm.ga.crossover.OnePointCrossover

getCrossoverRate() - Method in class science.aist.machinelearning.algorithm.ga.crossover.UniformCrossover

getElites() - Method in class science.aist.machinelearning.algorithm.ga.GeneticAlgorithm

getMaximumGenerations() - Method in class science.aist.machinelearning.algorithm.ga.GeneticAlgorithm

getMutationProbability() - Method in class science.aist.machinelearning.algorithm.ga.GeneticAlgorithm

getMutator() - Method in class science.aist.machinelearning.algorithm.ga.GeneticAlgorithm

getOptions() - Method in class science.aist.machinelearning.algorithm.ga.crossover.OnePointCrossover

getOptions() - Method in class science.aist.machinelearning.algorithm.ga.crossover.UniformCrossover

getOptions() - Method in class science.aist.machinelearning.algorithm.ga.selector.TournamentSelector

getPopulation() - Method in class science.aist.machinelearning.algorithm.ga.GeneticAlgorithm

Returns a COPY of the current population.

getPopulationSize() - Method in class science.aist.machinelearning.algorithm.ga.GeneticAlgorithm

getSelector() - Method in class science.aist.machinelearning.algorithm.ga.GeneticAlgorithm

getSpecificOptions() - Method in class science.aist.machinelearning.algorithm.ga.GeneticAlgorithm

getTournamentSize() - Method in class science.aist.machinelearning.algorithm.ga.selector.TournamentSelector

I

initializeLog(Problem<PT>) - Method in class science.aist.machinelearning.algorithm.ga.GeneticAlgorithm

Creates the log headers, and logs the current algorithm configuration

L

logSolution(Solution<GT, PT>) - Method in class science.aist.machinelearning.algorithm.ga.GeneticAlgorithm

Log the given mapping with analytics.

N

nextGeneration(Problem<PT>) - Method in class science.aist.machinelearning.algorithm.ga.GeneticAlgorithm

Calculates one single generation until all generations are full If all generations have been calculated, it returns null

O

OnePointCrossover<ST,PT> - Class in science.aist.machinelearning.algorithm.ga.crossover

GenericCrossover that splits both individuals by half.

OnePointCrossover() - Constructor for class science.aist.machinelearning.algorithm.ga.crossover.OnePointCrossover

R

removeIndividual(Solution<GT, PT>) - Method in class science.aist.machinelearning.algorithm.ga.GeneticAlgorithm

Removes an individual from the population.

reset() - Method in class science.aist.machinelearning.algorithm.ga.GeneticAlgorithm

Helper function that resets the state of the genetic algorithm as if it never did anything NOTE: This does not currently reset the analytics.

runs - Static variable in class science.aist.machinelearning.algorithm.ga.crossover.OnePointCrossover

S

science.aist.machinelearning.algorithm.ga - package science.aist.machinelearning.algorithm.ga

science.aist.machinelearning.algorithm.ga.crossover - package science.aist.machinelearning.algorithm.ga.crossover

science.aist.machinelearning.algorithm.ga.selector - package science.aist.machinelearning.algorithm.ga.selector

select(List<Solution<GT, PT>>) - Method in class science.aist.machinelearning.algorithm.ga.selector.TournamentSelector

select(List<Solution<ST, PT>>) - Method in interface science.aist.machinelearning.algorithm.ga.Selector

Selector<ST,PT> - Interface in science.aist.machinelearning.algorithm.ga

setCrossover(Crossover<GT, PT>) - Method in class science.aist.machinelearning.algorithm.ga.GeneticAlgorithm

Setter for dependency injection

setCrossoverPoint(double) - Method in class science.aist.machinelearning.algorithm.ga.crossover.OnePointCrossover

setCrossoverRate(double) - Method in class science.aist.machinelearning.algorithm.ga.crossover.UniformCrossover

setElites(int) - Method in class science.aist.machinelearning.algorithm.ga.GeneticAlgorithm

Setter for configuration

setGenMutator(Mutator<GT, PT>) - Method in class science.aist.machinelearning.algorithm.ga.GeneticAlgorithm

Setter for dependency injection

setMaximumGenerations(int) - Method in class science.aist.machinelearning.algorithm.ga.GeneticAlgorithm

Setter for configuration

setMutationProbability(double) - Method in class science.aist.machinelearning.algorithm.ga.GeneticAlgorithm

Setter for configuration

setOption(String, Descriptor) - Method in class science.aist.machinelearning.algorithm.ga.crossover.OnePointCrossover

setOption(String, Descriptor) - Method in class science.aist.machinelearning.algorithm.ga.crossover.UniformCrossover

setOption(String, Descriptor) - Method in class science.aist.machinelearning.algorithm.ga.selector.TournamentSelector

setPopulationSize(int) - Method in class science.aist.machinelearning.algorithm.ga.GeneticAlgorithm

Setter for configuration

setSelector(Selector<GT, PT>) - Method in class science.aist.machinelearning.algorithm.ga.GeneticAlgorithm

Setter for dependency injection

setSpecificOption(String, Descriptor) - Method in class science.aist.machinelearning.algorithm.ga.GeneticAlgorithm

setTournamentSize(int) - Method in class science.aist.machinelearning.algorithm.ga.selector.TournamentSelector

solve(Problem<PT>) - Method in class science.aist.machinelearning.algorithm.ga.GeneticAlgorithm

solve(Problem<PT>, Solution<GT, PT>) - Method in class science.aist.machinelearning.algorithm.ga.GeneticAlgorithm

T

TournamentSelector<GT,PT> - Class in science.aist.machinelearning.algorithm.ga.selector

The tournament selector "holds a tournament" by randomly selecting solutions from the population.

TournamentSelector() - Constructor for class science.aist.machinelearning.algorithm.ga.selector.TournamentSelector

U

UniformCrossover<ST,PT> - Class in science.aist.machinelearning.algorithm.ga.crossover

UniformCrossover() - Constructor for class science.aist.machinelearning.algorithm.ga.crossover.UniformCrossover

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