Roulette Selection in Genetic Algorithms

Lots of correct solutions already, but I think this code is clearer.

def select(fs):
    p = random.uniform(0, sum(fs))
    for i, f in enumerate(fs):
        if p <= 0:
            break
        p -= f
    return i

In addition, if you accumulate the fs, you can produce a more efficient solution.

cfs = [sum(fs[:i+1]) for i in xrange(len(fs))]

def select(cfs):
    return bisect.bisect_left(cfs, random.uniform(0, cfs[-1]))

This is both faster and it's extremely concise code. STL in C++ has a similar bisection algorithm available if that's the language you're using.

It's been a few years since i've done this myself, however the following pseudo code was found easily enough on google.

for all members of population
    sum += fitness of this individual
end for

for all members of population
    probability = sum of probabilities + (fitness / sum)
    sum of probabilities += probability
end for

loop until new population is full
    do this twice
        number = Random between 0 and 1
        for all members of population
            if number > probability but less than next probability 
                then you have been selected
        end for
    end
    create offspring
end loop

The site where this came from can be found here if you need further details.