| evolve.Galgo {galgo} | R Documentation |
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.
## S3 method for class 'Galgo': evolve(.O, parent=.O, ...)
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. |
Returns nothing. The results are saved in the Galgo object.
Victor Trevino. Francesco Falciani Group. University of Birmingham, U.K. http://www.bip.bham.ac.uk/bioinf
Goldberg, David E. 1989 Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Pub. Co. ISBN: 0201157675
For more information see Galgo.
wo <- World(niches=newRandomCollection(Niche(chromosomes=newRandomCollection(
Chromosome(genes=newCollection(Gene(shape1=1, shape2=1000),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)