java.lang.Object
org.cicirello.search.evo.ExponentialRankSelection
 All Implemented Interfaces:
Splittable<SelectionOperator>
,SelectionOperator
This class implements exponential rank selection. Exponential rank selection begins be
determining the rank of each population member, where the least fit member of the population has
rank 1, and the most fit member of the population has rank N, where the population size is N.
During selection, the population member with rank r is chosen randomly with probability
proportional to: c^{Nr}. The c is a realvalued parameter that must be in the interval
(0, 1). The mostfit member of the population will be chosen with probability proportional to 1,
while the leastfit will be chosen with probability proportional to c^{N1}. In the limit
as c approaches 1, exponential selection converges to a uniform random selection method. Whereas
in the limit as c approaches 0, exponential selection converges upon a degenerate selection
method that chooses an entire population of copies of the single mostfit population member. The
lower the value of c, the faster the degree of exponential decline in weight given to lower
ranked population members.
The runtime to select M population members from a population of size N is O(N lg N + M lg N).

Constructor Summary
ConstructorDescriptionExponentialRankSelection
(double c) Construct an exponential rank selection operator. 
Method Summary
Modifier and TypeMethodDescriptionfinal void
select
(PopulationFitnessVector.Double fitnesses, int[] selected) Selects a set of members of the population based on fitness.final void
select
(PopulationFitnessVector.Integer fitnesses, int[] selected) Selects a set of members of the population based on fitness.split()
Generates a functionally identical copy of this object, for use in multithreaded implementations of search algorithms.Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
Methods inherited from interface org.cicirello.search.evo.SelectionOperator
init

Constructor Details

ExponentialRankSelection
public ExponentialRankSelection(double c) Construct an exponential rank selection operator. Parameters:
c
 The base of the exponential, such that c is in the interval (0.0, 1.0). The closer c is to 0, the faster the selection weights decline from mostfit population member to leastfit, and the closer c is to 1, the closer the selection method is to a uniform random process. Throws:
IllegalArgumentException
 if c is less than or equal to 0 or greater than or equal to 1.


Method Details

split
Description copied from interface:Splittable
Generates a functionally identical copy of this object, for use in multithreaded implementations of search algorithms. The state of the object that is returned may or may not be identical to that of the original. Thus, this is a distinct concept from the functionality of theCopyable
interface. Classes that implement this interface must ensure that the object returned performs the same functionality, and that it does not share any state data that would be either unsafe or inefficient for concurrent access by multiple threads. The split method is allowed to simply return the this reference, provided that it is both safe and efficient for multiple threads to share a single copy of the Splittable object. The intention is to provide a multithreaded search with the capability to provide spawned threads with their own distinct search operators. Such multithreaded algorithms can call the split method for each thread it spawns to generate a functionally identical copy of the operator, but with independent state. Returns:
 A functionally identical copy of the object, or a reference to this if it is both safe and efficient for multiple threads to share a single instance of this Splittable object.

select
Description copied from interface:SelectionOperator
Selects a set of members of the population based on fitness. Implementations should ensure that the array of indexes of population members is in a random order. For some selection operators, this required behavior is met by definition (e.g., the common fitness proportionate selection will have this behavior as is). But other selection operators may require randomizing the array of indexes after selection. For example, the obvious implementation of stochastic universal sampling will likely have all copies of an individual population member ordered together, and thus will require a shuffling of the array before returning. Specified by:
select
in interfaceSelectionOperator
 Parameters:
fitnesses
 A vector of fitnesses of the members of the population.selected
 An array for the result. The selection operator should select selected.length members of the population based on fitnesses, populating selected with the indexes of the chosen members. Note that selected.length may be different than the fitnesses.size().

select
Description copied from interface:SelectionOperator
Selects a set of members of the population based on fitness. Implementations should ensure that the array of indexes of population members is in a random order. For some selection operators, this required behavior is met by definition (e.g., the common fitness proportionate selection will have this behavior as is). But other selection operators may require randomizing the array of indexes after selection. For example, the obvious implementation of stochastic universal sampling will likely have all copies of an individual population member ordered together, and thus will require a shuffling of the array before returning. Specified by:
select
in interfaceSelectionOperator
 Parameters:
fitnesses
 A vector of fitnesses of the members of the population.selected
 An array for the result. The selection operator should select selected.length members of the population based on fitnesses, populating selected with the indexes of the chosen members. Note that selected.length may be different than the fitnesses.size().
