blast.BigBang: Evolves Galgo objects saving the results for further analysis

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

Description

The basic process is as follows.\n \tab1. Clone Galgo and generate random chromosomes\n \tab2. Call evolve method\n \tab3. Save results in BigBang object\n \tab4. Verify stop rules\n \tab5. Goto 1\n

Usage

## S3 method for class 'BigBang'
blast(.bb, add=0, ...)

Arguments

add	Force to add a number to maxBigBangs and maxSolutions in order to search for more solutions.

Value

Returns nothing. The results are saved in the the BigBang object.

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. evolve.Galgo().

Examples

   cr <- Chromosome(genes=newCollection(Gene(shape1=1, shape2=100),5))
   ni <- Niche(chromosomes=newRandomCollection(cr, 10))
   wo <- World(niches=newRandomCollection(ni,2))
   ga <- Galgo(populations=newRandomCollection(wo,1), goalFitness = 0.75,
				callBackFunc=plot,
               fitnessFunc=function(chr, parent) 5/sd(as.numeric(chr)))
 
   #evolve(ga) ## not needed here

   bb <- BigBang(galgo=ga, maxSolutions=10, maxBigBangs=10, saveGeneBreaks=1:100)
   ## Not run: blast(bb)
   ## Not run: plot(bb)
   ## Not run: blast(bb, 1)
   ## Not run: plot(bb)
 

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