GeneticAlgorithmT Properties |
The GeneticAlgorithmT generic type exposes the following members.
Properties| Name | Description | |
|---|---|---|
 | BestIndividual |
The best individual from current population.
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| BestObjective |
Objective of the best individual from current population.
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| CreationDelegate |
Creation a new individual delegate.
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| CrossoverOneChildDelegate |
Crossovers two individuals and produces one child delegate.
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| CrossoverTwoChildrenDelegate |
Crossovers two individuals and produces one child delegate.
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| DominationPercent | Obsolete.
Percent of population that will be left intact.
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| DominationRate |
The part of population that will be left intact [0,1].
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| EliteDelegate |
Elite determination delegate.
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| EliteSize |
Size of elite of current generation.
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| ElitismPercent | Obsolete.
Percent of population selected for crossover [0,100].
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| ElitismRate |
The part of population selected for crossover [0,1].
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| ExitDelegate |
Exit condition determination delegate.
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| FitnessScaling |
Fitness scaling type
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| Generation |
Current generation number.
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| GenerationsCount |
Number of generations algorithm will made
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| MutationDelegate |
Mutates individual delegate.
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| MutationPercent | Obsolete.
Percent of next population that will be mutated [0,100].
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| MutationRate |
The part of next population that will be mutated [0,1].
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| ObjectInitializationDelegate |
The new empty object creation delegate.
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| ObjectiveDelegate |
Objective estimation delegate. Minimal value is searched.
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| ObjectiveValues |
The objective values array.
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| Population |
Current population.
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| PopulationSize |
Population size of each generation.
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| Random |
Random generator.
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| SelectionAlgorithm |
Selection algorithm.
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| SelectionFunction |
Selection probability function type
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| TournamentSize |
Tournament size for tournament selection.
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See Also