meanGeneration.BigBang: Computes the mean number of generations requiered to reach a...

In galgo: Genetic Algorithms for Multivariate Statistical Models from Large-Scale Functional Genomics Data

Description

Computes the mean number of generations requiered to reach a given fitness value. We have seen that this value is actually closer to the median of the final generation.

Usage

## S3 method for class 'BigBang'
meanGeneration(o, filter="none", subset=TRUE, fitness=o$galgo$goalFitness, ...)

Arguments

filter	The BigBang object can save information about solutions that did not reach the goalFitness. filter=="solutions" ensures that only chromosomes that reach the goalFitness are considered. fitlter=="none" take all chromosomes. filter=="nosolutions" consider only no-solutions (for comparative purposes).
subset	Second level of filter. subset can be a vector specifying which filtered chromosomes are used. It can be a logical vector or a numeric vector (indexes in order given by $bestChromosomes in BigBang object variable). If it is a numeric vector length one, a positive value means take those top chromosomes sorted by fitness, a negative value take those at bottom.
fitness	The fitness value desired. The default is $galgo$goalFitness.

Details

This function use meanFitness to compute the mean number of generations from solutions, then it finds the generation whose fitness mean value is not below the specified fitness.

Value

Return the expected mean generation.

Author(s)

Victor Trevino. Francesco Falciani Group. University of Birmingham, U.K. http://www.bip.bham.ac.uk/bioinf

References

Goldberg, David E. 1989 Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Pub. Co. ISBN: 0201157675

See Also

For more information see BigBang. *meanFitness()

Examples

## Not run: 
   #bb is a BigBang object
   meanGeneration(bb)
   
## End(Not run)
 

galgo documentation built on May 2, 2019, 4:20 a.m.