Evaluates all chromosomes with a fitness function. The result of this evaluation is treated as the “fitness” value (as defined by Goldberg, see references).
Galgo object call this method and store the returned value asociated with each chromosome in order to decide which chromosomes are the best choices to be part of the next generation.
The “fitness function” commonly returns a numeric value scaled from 0 to 1 (but not always, For more information see
Galgo.). As close to 1 as better chance it could be part of the next generation.
The “fitness” function to be called to evaluate all chromosomes. It should follow the format
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
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
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cr <- Chromosome(genes=newCollection(Gene(shape1=1, shape2=100),5)) ni <- Niche(chromosomes = newRandomCollection(cr, 10)) wo <- World(niches=newRandomCollection(ni,2)) ga <- Galgo(populations=newRandomCollection(wo,1), goalFitness = 0.75, callBackFunc=plot, fitnessFunc=function(chr, parent) 5/sd(as.numeric(chr))) ga summary(ga) evaluate(ga) # manual evaluation ga$generation <- ga$generation + 1 ga refreshStats(ga) ga # updated values summary(ga) # but chromosomes have not been "evolved" evolve(ga) # the usual evaluation of fitness function is inside evolve method
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