Module org.cicirello.chips_n_salsa
Package org.cicirello.search.evo
This package includes classes and interfaces directly related to implementing
evolutionary algorithms. This includes implementations of genetic algorithms
and other forms of evolutionary computation, as well as classes to support
such implementation (e.g., classes for maintaining populations).

Interface Summary Interface Description FitnessBiasFunction This functional interface is used to provide a bias function to theBiasedFitnessProportionalSelection
operator as well as theBiasedStochasticUniversalSampling
operator.FitnessFunction<T extends Copyable<T>> Fitness function interfaces.FitnessFunction.Double<T extends Copyable<T>> Fitness function interface for doublevalued fitnesses.FitnessFunction.Integer<T extends Copyable<T>> Fitness function interface for intvalued fitnesses.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.SelectionOperator Implement this interface to provide a selection operator for use by genetic algorithms and other forms of evolutionary computation. 
Class Summary Class Description 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.FitnessProportionalSelection This class implements fitness proportional selection, sometimes referred to as weighted roulette wheel, for evolutionary algorithms.GenerationalEvolutionaryAlgorithm<T extends Copyable<T>> 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<T extends Copyable<T>> 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<T extends Copyable<T>> 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.InverseCostFitnessFunction<T extends Copyable<T>> This class provides a convenient mechanism for transforming optimization cost values to fitness values.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.NegativeCostFitnessFunction<T extends Copyable<T>> This class provides a convenient mechanism for transforming optimization cost values to fitness values.NegativeIntegerCostFitnessFunction<T extends Copyable<T>> This class provides a convenient mechanism for transforming optimization cost values to fitness values.RandomSelection This class implements a simple random selection operator that selects members of the population uniformly at random, independent of fitness values.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.