evaluate.World: Evaluate all niches with a fitness function

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

Description

Evaluate all niches with a fitness function. The result of this evaluation is treated as the “fitness” value as defined by Goldberg (see references). The Galgo object call this method and store the resulted value in order to decide which chromosomes are better choices to be part of the next generation. The “fitness function” should returns a numeric value scaled from 0 to 1. As close to 1 as better chance it have to be part of the next generation.

Usage

## S3 method for class 'World'
evaluate(.O, fn, parent, ...)

Arguments

fn	The “fitness” function to be called to evaluate all niches. It should follow the format function(obj, parent) { ... }
parent	The original object calling for the evaluation. This is passed when the function is sensitive to data stored in parent object. Commonly it is a BigBang object (perhaps Galgo instead).

Value

Returns nothing.

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 World.

Examples

   cr <- Chromosome(genes=newCollection(Gene(shape1=1, shape2=100),5))
   cr
   ni <- Niche(chromosomes = newRandomCollection(cr, 10))
   ni
   fn <- function(chr, parent) { sd(as.double(chr))/mean(as.double(chr)) }
   evaluate(ni, fn, parent)
   getFitness(ni) ## see results
   summary(ni)
   wo <- World(niches=newRandomCollection(ni,2))
   evaluate(wo, fn, parent)
 

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