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Title: Theoretical analysis of steady state genetic algorithms (English)
Author: Agapie, Alexandru
Author: Wright, Alden H.
Language: English
Journal: Applications of Mathematics
ISSN: 0862-7940 (print)
ISSN: 1572-9109 (online)
Volume: 59
Issue: 5
Year: 2014
Pages: 509-525
Summary lang: English
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Category: math
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Summary: Evolutionary Algorithms, also known as Genetic Algorithms in a former terminology, are probabilistic algorithms for optimization, which mimic operators from natural selection and genetics. The paper analyses the convergence of the heuristic associated to a special type of Genetic Algorithm, namely the Steady State Genetic Algorithm (SSGA), considered as a discrete-time dynamical system non-generational model. Inspired by the Markov chain results in finite Evolutionary Algorithms, conditions are given under which the SSGA heuristic converges to the population consisting of copies of the best chromosome. (English)
Keyword: genetic algorithm
Keyword: Markov chain
Keyword: random heuristic search
MSC: 60J10
MSC: 60J20
MSC: 68T05
MSC: 68W20
MSC: 90C59
idZBL: Zbl 06391448
idMR: MR3255793
DOI: 10.1007/s10492-014-0069-z
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Date available: 2014-09-29T08:57:31Z
Last updated: 2020-07-02
Stable URL: http://hdl.handle.net/10338.dmlcz/143928
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