Performs offspring, crossover, mutation, and elitism mechanism to generate the “evolved” niche

Description

Performs offspring, crossover, mutation, and elitism mechanism to generate the ``evolved'' niche.

Usage

## S3 method for class 'Niche':
progeny(ni, immigration=NULL, ...)

Arguments

Details

The basic idea to generate a progeny is a random selection biased toward the best chromosomes (see Goldberg). We implented this idea as a weighted probability for a chromosome to be selected using the formula:

p = scale * max(0,fitness - mean * mean(fitness))^ power

where scale, mean and power are the properties of the niche (offspringScaleFactor, offspringMeanFactor and offspringPowerFactor respectively). The default values were selected to be reasonably bias when the variance in the fitness are both high (at early generations) and low (in late generatios).

offspring is part of progeny method.

For related details For more information see Niche.

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 Niche. *offspring(), *crossover().

Examples

   cr <- Chromosome(genes=newCollection(Gene(shape1=1, shape2=1000),5))
   cr
   ni <- Niche(chromosomes = newRandomCollection(cr, 10))
   ni
   ni$fitness <- 1:10/10 # tricky fitness, only for showing purposes
   progeny(ni)
   ni