BiasedFitnessProportionalSelection 
This class implements a variation of fitness proportional selection that applies
a bias function to transform the fitness values.

BiasedShiftedFitnessProportionalSelection 
This class implements a variation of fitness proportional selection that
uses shifted fitness values and applies
a bias function to further transform the shifted fitness values.

BiasedShiftedStochasticUniversalSampling 
This class implements a variation of Stochastic Universal Sampling (SUS) that we call
Biased Shifted Stochastic Universal Sampling (Biased Shifted SUS), which uses shifted fitness values
and integrates the use of a bias function
with SUS to enable transforming the shifted fitness values prior to the stochastic selection decisions.

BiasedStochasticUniversalSampling 
This class implements a variation of Stochastic Universal Sampling (SUS) that we call
Biased Stochastic Universal Sampling (Biased SUS), which integrates the use of a bias function
with SUS to enable transforming fitness values prior to the stochastic selection decisions.

FitnessBiasFunction 

FitnessFunction 
Fitness function interfaces.

FitnessFunction.Double 
Fitness function interface for doublevalued fitnesses.

FitnessFunction.Integer 
Fitness function interface for intvalued fitnesses.

FitnessProportionalSelection 
This class implements fitness proportional selection, sometimes referred to as weighted
roulette wheel, for evolutionary algorithms.

GenerationalEvolutionaryAlgorithm 
This class implements an evolutionary algorithm with a generational
model, such as is commonly used in genetic algorithms, where a
population of children are formed by applying genetic operators to
members of the parent population, and where the children replace the
parents in the next generation.

GenerationalMutationOnlyEvolutionaryAlgorithm 
This class implements an evolutionary algorithm (EA) with a generational
model, such as is commonly used in genetic algorithms, where a
population of children are formed by applying mutation to
members of the parent population, and where the children replace the
parents in the next generation.

GenerationalNANDOperatorsEvolutionaryAlgorithm 
This class implements an evolutionary algorithm (EA) with a generational
model, where a population of children are formed by applying genetic operators to
members of the parent population, and where the children replace the
parents in the next generation.

GeneticAlgorithm 
This class is an implementation of a genetic algorithm (GA) with the common
bit vector representation of solutions to optimization problems, and the
generational model where children replace their parents each generation.

MutationOnlyGeneticAlgorithm 
This class is an implementation of a mutationonly genetic algorithm (GA) with the common
bit vector representation of solutions to optimization problems, and the
generational model where children replace their parents each generation.

PopulationFitnessVector 
An interface to a vector of fitnesses of a population.

PopulationFitnessVector.Double 
An interface to a vector of fitnesses, each a double, of a population.

PopulationFitnessVector.Integer 
An interface to a vector of fitnesses, each an int, of a population.

RandomSelection 
This class implements a simple random selection operator that selects
members of the population uniformly at random, independent of fitness
values.

SelectionOperator 
Implement this interface to provide a selection operator
for use by genetic algorithms and other forms of
evolutionary computation.

ShiftedFitnessProportionalSelection 
This class implements a variation of fitness proportional selection that uses
shifted fitness values.

ShiftedStochasticUniversalSampling 
This class implements a variation of Stochastic Universal Sampling (SUS) that uses
shifted fitness values.

SimpleGeneticAlgorithm 
This class is an implementation of the simple genetic algorithm (Simple GA) with the common
bit vector representation of solutions to optimization problems, and the
generational model where children replace their parents each generation.

StochasticUniversalSampling 
This class implements Stochastic Universal Sampling (SUS), a selection operator
for evolutionary algorithms.

TournamentSelection 
This class implements tournament selection for evolutionary algorithms.

TruncationSelection 
This class implements truncation selection for evolutionary algorithms.
