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

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 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.

References

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

Examples

Run this code

# NOT RUN {
   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 {
   
# }
# NOT RUN {
plot(bb)
# }
# NOT RUN {
   
# }
# NOT RUN {
blast(bb, 1)
# }
# NOT RUN {
   
# }
# NOT RUN {
plot(bb)
# }
# NOT RUN {
 
# }