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RankSelection Class
Rank selection method.
Inheritance Hierarchy
SystemObject
  Accord.GeneticRankSelection

Namespace:  Accord.Genetic
Assembly:  Accord.Genetic (in Accord.Genetic.dll) Version: 3.8.0
Syntax
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public class RankSelection : ISelectionMethod
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The RankSelection type exposes the following members.

Constructors
  NameDescription
Public methodRankSelection
Initializes a new instance of the RankSelection class.
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Methods
  NameDescription
Public methodApplySelection
Apply selection to the specified population.
Public methodEquals
Determines whether the specified object is equal to the current object.
(Inherited from Object.)
Protected methodFinalize
Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection.
(Inherited from Object.)
Public methodGetHashCode
Serves as the default hash function.
(Inherited from Object.)
Public methodGetType
Gets the Type of the current instance.
(Inherited from Object.)
Protected methodMemberwiseClone
Creates a shallow copy of the current Object.
(Inherited from Object.)
Public methodToString
Returns a string that represents the current object.
(Inherited from Object.)
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Extension Methods
  NameDescription
Public Extension MethodHasMethod
Checks whether an object implements a method with the given name.
(Defined by ExtensionMethods.)
Public Extension MethodIsEqual
Compares two objects for equality, performing an elementwise comparison if the elements are vectors or matrices.
(Defined by Matrix.)
Public Extension MethodTo(Type)Overloaded.
Converts an object into another type, irrespective of whether the conversion can be done at compile time or not. This can be used to convert generic types to numeric types during runtime.
(Defined by ExtensionMethods.)
Public Extension MethodToTOverloaded.
Converts an object into another type, irrespective of whether the conversion can be done at compile time or not. This can be used to convert generic types to numeric types during runtime.
(Defined by ExtensionMethods.)
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Remarks

The algorithm selects chromosomes to the new generation depending on their fitness values - the better fitness value chromosome has, the more chances it has to become member of the new generation. Each chromosome can be selected several times to the new generation.

This algorithm is similar to Roulette Wheel Selection algorithm, but the difference is in "wheel" and its sectors' size calculation method. The size of the wheel equals to size * ( size + 1 ) / 2, where size is the current size of population. The worst chromosome has its sector's size equal to 1, the next chromosome has its sector's size equal to 2, etc.

See Also