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EVOLVE- A Bridge between Probability, Set Oriented Numerics and Evolutionary Computation pp 3–89Cite as

On the Foundations and the Applications of Evolutionary Computing

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 447))

Abstract

Genetic type particle methods are increasingly used to sample from complex high-dimensional distributions. They have found a wide range of applications in applied probability, Bayesian statistics, information theory, and engineering sciences. Understanding rigorously these new Monte Carlo simulation tools leads to fascinating mathematics related to Feynman-Kac path integral theory and their interacting particle interpretations. In this chapter, we provide an introduction to the stochastic modeling and the theoretical analysis of these particle algorithms. We also illustrate these methods through several applications.

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  1. Centre INRIA Bordeaux Sud-Ouest, Institut de Mathématiques de Bordeaux, Université de Bordeaux I, 351 cours de la Libération, 33405, Talence cedex, France

    Pierre Del Moral

  2. Applied Mathematics Department, CMAP École Polyetchnique, Paris, France

    Pierre Del Moral

  3. Computer Science and Communications Research Unit, University of Luxembourg, Luxembourg, Luxembourg

    Alexandru-Adrian Tantar & Emilia Tantar

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    Emilia Tantar

  2. , Computer Science and Communications, University of Luxembourg, rue Richard Coudenhove-Kalergi 6, Luxembourg, 1359, Luxembourg

    Alexandru-Adrian Tantar

  3. Computer Science, and Communications, University of Luxembourg, rue Richard Coudenhove-Kalergi 6, Luxembourg, L-1359, Luxembourg

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  4. Bordeaux Mathematical Institute, INRIA Bordeaux-Sud Ouest, Université Bordeaux I, cours de la Libération 351, Talence Cedex, 33405, France

    Pierre Del Moral

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Del Moral, P., Tantar, AA., Tantar, E. (2013). On the Foundations and the Applications of Evolutionary Computing. In: Tantar, E., et al. EVOLVE- A Bridge between Probability, Set Oriented Numerics and Evolutionary Computation. Studies in Computational Intelligence, vol 447. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32726-1_1

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