Abstract
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.
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The research has been supported by grant of the Romanian National Authority for Scientific Research, CNCS-UEFISCDI, project number PNII-ID-PCCE-2011-0015, and by a COBASE grant from the National Science Foundation, USA.
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Agapie, A., Wright, A.H. Theoretical analysis of steady state genetic algorithms. Appl Math 59, 509–525 (2014). https://doi.org/10.1007/s10492-014-0069-z
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DOI: https://doi.org/10.1007/s10492-014-0069-z