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Evolutionary Algorithms

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Abstract

The biological evolution generated extremely complex autonomous living beings which can solve extraordinarily difficult problems, such as the continuous adaptation to complex, uncertain environments and in perpetual transformation.

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Notes

  1. 1.

    https://scholar.google.com/scholar?q=genetic+algorithms.

  2. 2.

    The Hamming distance is the number of different bits between two bit strings of the same length.

  3. 3.

    In (1 + 1)-\(\mathrm{{ES}}\): the population is composed of only one parent individual, and this generates only one offspring; the best of both is preserved for the next generation.

  4. 4.

    Possibly with a column permutation of the matrix \(\mathbf R\) and the corresponding diagonal coefficients in \(\mathbf S\).

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Authors and Affiliations

  1. Telecom SudParis, 91000, Evry, France

    Alain Petrowski

  2. Université Paris Ouest, 92000, Nanterre, France

    Sana Ben Hamida

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  1. Alain Petrowski
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  1. Laboratory LiSSi (EA 3956), Université Paris-Est Créteil, Créteil, France

    Patrick Siarry

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Petrowski, A., Ben Hamida, S. (2016). Evolutionary Algorithms. In: Siarry, P. (eds) Metaheuristics. Springer, Cham. https://doi.org/10.1007/978-3-319-45403-0_6

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