An Extension of Geiringer's Theorem for a Wide Class of Evolutionary Search Algorithms
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- @Article{MR:EC:06,
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title = "An Extension of Geiringer's Theorem for a Wide Class
of Evolutionary Search Algorithms",
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author = "Boris Mitavskiy and Jonathan Rowe",
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journal = "Evolutionary Computation",
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year = "2006",
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volume = "14",
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number = "1",
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pages = "87--118",
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month = "Spring",
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keywords = "genetic algorithms, genetic programming, crossover,
schemata, Geiringer theorem, Markov process, stationary
distribution, random walk on a group, mutation",
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URL = "http://www.mitpressjournals.org/doi/abs/10.1162/evco.2006.14.1.87",
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DOI = "doi:10.1162/evco.2006.14.1.87",
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size = "32 pages",
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abstract = "he frequency with which various elements of the search
space of a given evolutionary algorithm are sampled is
affected by the family of recombination (reproduction)
operators. The original Geiringer theorem tells us the
limiting frequency of occurrence of a given individual
under repeated application of crossover alone for the
classical genetic algorithm. Recently, Geiringer's
theorem has been generalised to include the case of
linear GP with homologous crossover (which can also be
thought of as a variable length GA). In the current
paper we prove a general theorem which tells us that
under rather mild conditions on a given evolutionary
algorithm, call it A, the stationary distribution of a
certain Markov chain of populations in the absence of
selection is unique and uniform. This theorem not only
implies the already existing versions of Geiringer's
theorem, but also provides a recipe of how to obtain
similar facts for a rather wide class of evolutionary
algorithms. The techniques which are used to prove this
theorem involve a classical fact about random walks on
a group and may allow us to compute and/or estimate the
eigenvalues of the corresponding Markov transition
matrix which is directly related to the rate of
convergence towards the unique limiting distribution.",
- }
Genetic Programming entries for
Boris Mitavskiy
Jonathan E Rowe
Citations