Uses of Package
org.cicirello.search.evo

Packages that use org.cicirello.search.evo
Package
Description
This package includes classes and interfaces directly related to implementing evolutionary algorithms.
  • Class
    Description
    This class implements an evolutionary algorithm with adaptive control parameters (i.e., crossover rates and mutation rates that evolve during the search).
    This class implements a variation of fitness proportional selection that applies a bias function to transform the fitness values.
    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.
    This class implements Boltzmann selection.
    This class implements Boltzmann selection using Stochastic Universal Sampling (SUS).
    This class implements exponential rank selection.
    This class implements exponential rank selection using Stochastic Universal Sampling (SUS).
    This functional interface is used to provide a bias function to the BiasedFitnessProportionalSelection operator as well as the BiasedStochasticUniversalSampling operator.
    Fitness function interfaces.
    Fitness function interface for double-valued fitnesses.
    Fitness function interface for int-valued fitnesses.
    This class implements fitness proportional selection, sometimes referred to as weighted roulette wheel, for evolutionary algorithms.
    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.
    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.
    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.
    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.
    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.
    This class implements linear rank selection.
    This class implements linear rank selection using Stochastic Universal Sampling (SUS).
    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.
    This class implements a (1+1)-EA.
    This class implements a (1+1)-GA, a special case of a (1+1)-EA, where solutions are represented with a vector of bits.
    An interface to a vector of fitnesses of a population.
    An interface to a vector of fitnesses, each a double, of a population.
    An interface to a vector of fitnesses, each an int, of a population.
    This class implements a simple random selection operator that selects members of the population uniformly at random, independent of fitness values.
    Implement this interface to provide a selection operator for use by genetic algorithms and other forms of evolutionary computation.
    Implements sigma scaling by wrapping your chosen selection operator.
    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.
    This class implements Stochastic Universal Sampling (SUS), a selection operator for evolutionary algorithms.
    This class implements tournament selection for evolutionary algorithms.
    This class implements truncation selection for evolutionary algorithms.