Geometric convergence of genetic algorithms under tempered random restart
F. Mendivil, R. SHONKWILER, and M. C. SPRUILL
Source: J. Appl. Probab. Volume 46, Number 4
(2009), 960-974.
Abstract
Geometric convergence to 0 of the probability that the goal has
not been encountered by the $n$th generation is established for a
class of genetic algorithms. These algorithms employ a quickly
decreasing mutation rate and a crossover which restarts the
algorithm in a controlled way depending on the current population
and restricts execution of this crossover to occasions when
progress of the algorithm is too slow. It is shown that without
the crossover studied here, which amounts to a tempered restart of
the algorithm, the asserted geometric convergence need not hold.
Secondary Subjects:
65C05
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Links and Identifiers
Permanent link to this document: http://projecteuclid.org/euclid.jap/1261670682
Digital Object Identifier: doi:10.1239/jap/1261670682
Zentralblatt MATH identifier: 05665449
Mathematical Reviews number (MathSciNet): MR2582700
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