galgo (version 1.4)

meanFitness.BigBang: Computes the ``mean'' fitness from several solutions

Description

Computes the ``mean'' fitness from several solutions.

Usage

# S3 method for BigBang
meanFitness(o, filter="none", subset=TRUE, ...)

Arguments

filter

The BigBang object can save information about solutions that did not reach the goalFitness. filter=="solutions" ensures that only chromosomes that reach the goalFitness are considered. fitlter=="none" take all chromosomes. filter=="nosolutions" consider only no-solutions (for comparative purposes).

subset

Second level of filter. subset can be a vector specifying which filtered chromosomes are used. It can be a logical vector or a numeric vector (indexes in order given by $bestChromosomes in BigBang object variable). If it is a numeric vector length one, a positive value means take those top chromosomes sorted by fitness, a negative value take those at bottom.

Value

A vector with the mean fitness in each generation.

Details

The mean is built considering all solutions. For solutions that have finished earlier, the final fitness is used for futher genertions.

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 BigBang.

Examples

# NOT RUN {
   #bb is a BigBang object
   geneRankStability(bb)
   
# }
# NOT RUN {
 
# }