Package edu.udo.cs.yale.tools.math.optimization.ec.es

Evolutionary Strategies Optimization for real valued optimization problems.

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Description

Interface Summary

PopulationOperator

A population operator which can be applied on the population.

Class Summary

BoltzmannSelection	Like RouletteWheel this population operator selects a given fixed number of individuals by subdividing a roulette wheel in sections of size proportional to the individuals' fitness values.
Crossover	Crossover operator for the values of an evolution strategies optimization.
CutSelection	Creates a new population by a deterministical selection of the best individuals.
ElitistSelection	Performs a very elitist selection by just adding the best individual for k times.
ESOptimization	Evolutionary Strategy approach for SVM optimization.
GaussianMutation	Changes the values by adding a gaussian distribution multiplied with the current variance.
Individual	Individuals store information about the value vectors and the fitness.
NonDominatedSortingSelection	Performs the non-dominated sorting selection from NSGA-II.
NonDominatedSortingSelection.CriteriaComparator	The comparator for aggregation individuals using the fitness values of the m-th criterion.
NonDominatedSortingSelection.CrowdingComparator	The comparator for aggregation individuals using the crowding distance.
Population	A set of individuals.
PopulationPlotter	Plots the current generation's Pareto set.
RankSelection	Selects a given fixed number of individuals by subdividing a roulette wheel in sections of size proportional to the individuals' rank based on their fitness values.
RouletteWheel	Selects a given fixed number of individuals by subdividing a roulette wheel in sections of size proportional to the individuals' fitness values.
SparsityMutation	Checks for each value if it should mutated.
StochasticUniversalSampling	Similar to a the roulette wheel selection the fitness values of all individuals build a partition of the 360 degrees of a wheel.
SwitchingMutation	Checks for each value if it should mutated.
TournamentSelection	Performs tournaments with k participants.
UniformSelection	Selects a given fixed number of individuals by uniformly sampling from the current population until the desired population size is reached.
VarianceAdaption	Implements the 1/5-Rule for dynamic parameter adaption of the variance of a GaussianMutation.

Package edu.udo.cs.yale.tools.math.optimization.ec.es Description

Evolutionary Strategies Optimization for real valued optimization problems.