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

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

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 class '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.

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 Galgo.

Examples

  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)

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