Assigns a weight for every chromosome to be selected for the next generation.
The basic idea to generate a progeny is a 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).
scaling is part of
To replace this behaviour, overwrite the method with your preference or create a new class overwritting this method.
For related details For more information see
Returns a vector with the weights.
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
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