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Each trial solution (individual) is assigned a figure of merit that represents
how good a solution it is according to the fitness function (cost function or
objective function) of the problem. The most fit individuals (lowest
cost-values for minimization problems) are given a higher probability of mating
in order to produce the next generation. There are several ways to select the
``parents'' for mating, such as random pairing, roulette wheel, rank weighting
and tournament selection Haupt and Haupt (1998). Whatever the selection method, the net
effect is to skew the next generation towards the most fit individuals, that
is, towards the most promising regions of the search space. It is still
possible and desirable to allow less fit individuals to mate, albeit with a
lower probability.
This increases the exploration of the search space and helps prevent premature
convergence, i.e. convergence to a local minimum.

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Stanford Exploration Project

11/11/2002