GeneticSharp.Domain.Selections Namespace

GeneticSharp

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GeneticSharp.Domain.Selections Namespace GeneticSharp

[Missing <summary> documentation for "N:GeneticSharp.Domain.Selections"]

Classes
  Class Description
Public class EliteSelection
Selects the chromosomes with the best fitness.
Public class RouletteWheelSelection
Roulette Wheel Selection
Remarks
Is a kind of Fitness Proportionate Selection. Fitness-Proportionate Selection In the Roulette wheel selection method [Holland, 1992], the first step is to calculate the cumulative fitness of the whole population through the sum of the fitness of all individuals. After that, the probability of selection is calculated for each individual. Then, an array is built containing cumulative probabilities of the individuals. So, n random numbers are generated in the range 0 to fitness sum. and for each random number an array element which can have higher value is searched for. Therefore, individuals are selected according to their probabilities of selection. OnlineWikipedia
Public class SelectionBase
A base class for selection.
Public class SelectionException
Exception throw when an error occurs during the execution of selection.
Public class SelectionService
Selection service.
Public class StochasticUniversalSamplingSelection
Stochastic Universal Sampling.
Remarks
Is a kind of Fitness Proportionate Selection. Fitness-Proportionate Selection Stochastic Universal Sampling is an elaborately-named variation of roulette wheel selection. Stochastic Universal Sampling ensures that the observed selection frequencies of each individual are in line with the expected frequencies. So if we have an individual that occupies 4.5% of the wheel and we select 100 individuals, we would expect on average for that individual to be selected between four and five times. Stochastic Universal Sampling guarantees this. The individual will be selected either four times or five times, not three times, not zero times and not 100 times. Standard roulette wheel selection does not make this guarantee. OnlineWikipedia
Public class TournamentSelection
Tournament selection involves running several "tournaments" among a few individuals chosen at random from the population. The winner of each tournament (the one with the best fitness) is selected for crossover.
Remarks
Selection pressure is easily adjusted by changing the tournament size. If the tournament size is larger, weak individuals have a smaller chance to be selected.
Interfaces
  Interface Description
Public interface ISelection
Defines a interface for selection. Selection is the stage of a genetic algorithm in which individual genomes are chosen from a population for later breeding (recombination or crossover). OnlineWikipediaA Review of Selection Methods in Genetic Algorithm
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