Steady State Selection

Most GAs described in the literature have been "generational"—at each generation the new population consists entirely of offspring formed by parents in the previous generation (though some of these offspring may be identical to their parents). In some schemes, such as the elitist schemes described above, successive generations overlap to some degree—some portion of the previous generation is retained in the new population. The fraction of new individuals at each generation has been called the "generation gap" (De Jong 1975). In steady-state selection, only a few individuals are replaced in each generation: usually a small number of the least fit individuals are replaced by offspring resulting from crossover and mutation of the fittest individuals. Steady-state GAs are often used in evolving rule-based systems (e.g., classifier systems; see Holland 1986) in which incremental learning (and remembering what has already been learned) is important and in which members of the population collectively (rather than individually) solve the problem at hand. Steady-state selection has been analyzed by Syswerda (1989, 1991), by Whitley (1989), and by De Jong and Sarma (1993).

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