Uses of Package
org.cicirello.search.evo

Packages that use org.cicirello.search.evo

Package	Description
org.cicirello.search.evo	This package includes classes and interfaces directly related to implementing evolutionary algorithms.

Classes in org.cicirello.search.evo used by org.cicirello.search.evo

Class	Description
AdaptiveEvolutionaryAlgorithm	This class implements an evolutionary algorithm with adaptive control parameters (i.e., crossover rates and mutation rates that evolve during the search).
AdaptiveMutationOnlyEvolutionaryAlgorithm	This class implements an mutation-only evolutionary algorithm with an adaptive mutation rate that evolves during the search.
BiasedFitnessProportionalSelection	This class implements a variation of fitness proportional selection that applies a bias function to transform the fitness values.
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.
BoltzmannSelection	This class implements Boltzmann selection.
BoltzmannStochasticUniversalSampling	This class implements Boltzmann selection using Stochastic Universal Sampling (SUS).
ExponentialRankSelection	This class implements exponential rank selection.
ExponentialRankStochasticUniversalSampling	This class implements exponential rank selection using Stochastic Universal Sampling (SUS).
FitnessBiasFunction	This functional interface is used to provide a bias function to the BiasedFitnessProportionalSelection operator as well as the BiasedStochasticUniversalSampling operator.
FitnessFunction	Fitness function interfaces.
FitnessFunction.Double	Fitness function interface for double-valued fitnesses.
FitnessFunction.Integer	Fitness function interface for int-valued fitnesses.
FitnessProportionalSelection	This class implements fitness proportional selection, sometimes referred to as weighted roulette wheel, for evolutionary algorithms.
FitnessShifter	FitnessShifter wraps another SelectionOperator, shifting all fitness values by the minimum fitness minus one, such that the least fit population member's transformed fitness is equal to 1, with the wrapped SelectionOperator than performing selection using the transformed fitnesses.
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.
GenerationalEvolutionaryAlgorithmMutuallyExclusiveOperators	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.
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.
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.
LinearRankSelection	This class implements linear rank selection.
LinearRankStochasticUniversalSampling	This class implements linear rank selection using Stochastic Universal Sampling (SUS).
MutationOnlyGeneticAlgorithm	This class is an implementation of a mutation-only 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.
NaiveGenerationalEvolutionaryAlgorithm	Deprecated. IMPORTANT: This class is being introduced temporarily in support of research experiments.
OnePlusOneEvolutionaryAlgorithm	This class implements a (1+1)-EA.
OnePlusOneGeneticAlgorithm	This class implements a (1+1)-GA, a special case of a (1+1)-EA, where solutions are represented with a vector of bits.
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.
SigmaScaling	Implements sigma scaling by wrapping your chosen selection operator.
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.