An Introduction to Genetic Algorithms - Boente

Repeat N times:
Choose a random integer r between 0 and T.
Loop through the individuals in the population, summing the expected values, until the sum is greater than or
equal to r. The individual whose expected value puts the sum over this limit is the one selected.
This stochastic method statistically results in the expected number of offspring for each individual. However,
with the relatively small populations typically used in GAs, the actual number of offspring allocated to an
individual is often far from its expected value (an extremely unlikely series of spins of the roulette wheel
could even allocate all offspring to the worst individual in the population). James Baker (1987) proposed a
different sampling method—"stochastic universal sampling" (SUS)—to minimize this "spread" (the range of
possible actual values, given an expected value). Rather than spin the roulette wheel N times to select N
parents, SUS spins the wheel once—but with N equally spaced pointers, which are used to selected the N
parents. Baker (1987) gives the following code fragment for SUS (in C):
ptr = Rand(); /* Returns random number uniformly distributed in [0,1] */
for (sum = i = 0; i < N; i++)
for (sum += ExpVal(i,t); sum > ptr; ptr++)
Select(i);
where i is an index over population members and where ExpVal(i,t) gives the expected value of individual i at
time t. Under this method, each individual i is guaranteed to reproduce at least ExpVal(i,t) times but no
more than ExpVal(i,t) times. (The proof of this is left as an exercise.)
SUS does not solve the major problems with fitness−proportionate selection. Typically, early in the search the
fitness variance in the population is high and a small number of individuals are much fitter than the others.
Under fitness−proportionate selection, they and their descendents will multiply quickly in the population, in
effect preventing the GA from doing any further exploration. This is known as "premature convergence." In
other words, fitness−proportionate selection early on often puts too much emphasis on "exploitation" of
highly fit strings at the expense of exploration of other regions of the search space. Later in the search, when
all individuals in the population are very similar (the fitness variance is low), there are no real fitness
differences for selection to exploit, and evolution grinds to a near halt. Thus, the rate of evolution depends on
the variance of fitnesses in the population.
Sigma Scaling
Chapter 4: Theoretical Foundations of Genetic Algorithms
To address such problems, GA researchers have experimented with several "scaling" methods—methods for
mapping "raw" fitness values to expected values so as to make the GA less susceptible to premature
convergence. One example is "sigma scaling" (Forrest 1985; it was called "sigma truncation" in Goldberg
1989a), which keeps the selection pressure (i.e., the degree to which highly fit individuals are allowed many
offspring) relatively constant over the course of the run rather than depending on the fitness variances in the
population. Under sigma scaling, an individual's expected value is a function of its fitness, the population
mean, and the population standard deviation. A example of sigma scaling would be
where ExpVal(i,t) is the expected value of individual i at time t, f(i) is the fitness of i,f(t) is the mean fitness of
the population at time t, and Ã(t) is the standard deviation of the population fitnesses at time t. This function,
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