Package org.cicirello.search.evo


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).
  • Class
    Description
    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 fitness proportional selection that uses shifted fitness values and applies a bias function to further transform the shifted fitness values.
    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.
    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 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.
    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, 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 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 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 provides a convenient mechanism for transforming optimization cost values to fitness values.
    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 provides a convenient mechanism for transforming optimization cost values to fitness values.
    This class provides a convenient mechanism for transforming optimization cost values to fitness values.
    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.
    This class implements a variation of fitness proportional selection that uses shifted fitness values.
    This class implements a variation of Stochastic Universal Sampling (SUS) that uses shifted fitness values.
    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.