Selection Functions

[Michael Stillwell]

I'm working on a GA-like system in which the probability that a bug is
chosen to breed is related to its fitness.  It appears that the
traditional way to do this is to use "roulette wheel selection"--each
bug gets a slice of the wheel in proportion to its fitness, the wheel
is spun, and the bug associated with the point where the wheel ended
up is gets to breed.

Do other "selection functions" exist?  My problem with roulette wheel
selection is that it offers no real control over the distribution of
the expected number of offspring.  For example, if all the bugs (or
"solutions", etc.) are *equally* *fit*, then there is an e^-1 chance
(approx 36%) that a bug will not be selected at all.  This doesn't
seem to mirror what happens in nature, where one might expect each bug
to have one child (or close to it).

(I am aware of one other "selection function"--Goldberg's "Genetic
Algorithms in Search, Optimisation and Machine Learning" describes an
"expected value model" that effectively reduces the size of the slice
granted to a bug every time it is selected for reproduction, but I
would be interested to hear of others.)






Michael


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