|
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 all real-valued optimization tasks. |
| 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. |
| 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. |