meanFitness.BigBang: Computes the "mean" fitness from several solutions

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

Description

Computes the “mean” fitness from several solutions.

Usage

## S3 method for class 'BigBang'
meanFitness(o, filter="none", subset=TRUE, ...)

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.

Details

The mean is built considering all solutions. For solutions that have finished earlier, the final fitness is used for futher genertions.

Value

A vector with the mean fitness in each 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.

Examples

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

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