Description Usage Arguments Value Author(s) References See Also Examples
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
1 2 |
add |
Force to add a number to maxBigBangs and maxSolutions in order to search for more solutions. |
Returns nothing. The results are saved in the the BigBang
object.
Victor Trevino. Francesco Falciani Group. University of Birmingham, U.K. http://www.bip.bham.ac.uk/bioinf
Goldberg, David E. 1989 Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Pub. Co. ISBN: 0201157675
For more information see BigBang
.
evolve.Galgo
().
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | 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)
|
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