evolve.Galgo: Evolves the chromosomes populations of a Galgo (Genetic Algorithm)

Description

A generation consist of the evaluation of the fitness function to all chomosome populations and the determination of the maximum and best chromosomes. If a stoping rule has not been met, progeny is called to generate an ``evolved'' population and the process start again. The stoping rules are maxGenerations has been met, goalFitness has been reach or user-cancelled via callBackFunc. As any other program in R the process can be broken using Ctrl-C keys (Esc in Windows). Theoretically, if the process is cancelled via Ctrl-C, the process may be continued calling evolve method again; however it is never recommended.

Usage

# S3 method for Galgo
evolve(.O, parent=.O, ...)

Arguments

parent

The original object calling for the evaluation. This is passed to the fitness function in order to evaluate the function inside a context. Commonly it is a BigBang object.

Value

Returns nothing. The results are saved in the Galgo 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 {
  wo <- World(niches=newRandomCollection(Niche(chromosomes=newRandomCollection(
  Chromosome(genes=newCollection(Gene(shape1=1, shape2=100),5)), 10),2),2))
  ga <- Galgo(populations=newRandomCollection(wo,1), goalFitness = 0.75,
              callBackFunc=plot,
              fitnessFunc=function(chr, parent) 5/sd(as.numeric(chr)))
  evolve(ga) 
  best(ga)
  bestFitness(ga)
# }