SelectLinearRankTSR: Linear rank selection with interpolated target sampling...

In xegaSelectGene: Selection of Genes and Gene Representation Independent Functions

Linear rank selection with interpolated target sampling rates.

Description

SelectLinearRankTSR() implements selection with interpolated target sampling rates.

Usage

SelectLinearRankTSR(fit, lF, size = 1)

Arguments

fit	Fitness vector.
lF	Local configuration.
size	Size of return vector (default: 1).

Details

The target sampling rate is a linear interpolation between lF$MaxTSR() and Min<-2-lF$MaxTSR(), because the sum of the target sampling rates is $n$. The target sampling rates are computed and used as a fitness vector for stochastic universal sampling algorithm implemented by SelectSUS(). lF$MaxTSR() should be in [1.0, 2.0].

TODO: More efficient implementation. We use two sorts!

Value

The index vector of selected genes.

References

Grefenstette, John J. and Baker, James E. (1989): How Genetic Algorithms Work: A Critical Look at Implicit Parallelism In Schaffer, J. David (Ed.) Proceedings of the Third International Conference on Genetic Algorithms on Genetic Algorithms, pp. 20-27. (ISBN:1-55860-066-3)

See Also

Other Selection Functions: SelectDuel(), SelectLRSelective(), SelectPropFit(), SelectPropFitDiff(), SelectPropFitDiffM(), SelectPropFitDiffOnln(), SelectPropFitM(), SelectPropFitOnln(), SelectSTournament(), SelectSUS(), SelectTournament(), SelectUniform(), SelectUniformP()

Examples

fit<-sample(10, 15, replace=TRUE)
SelectLinearRankTSR(fit, NewlFselectGenes()) 
SelectLinearRankTSR(fit, NewlFselectGenes(), length(fit)) 

xegaSelectGene documentation built on April 16, 2025, 5:12 p.m.