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On the Foundations and the Applications of Evolutionary Computing

  • Pierre Del Moral
  • Alexandru-Adrian Tantar
  • Emilia Tantar
Part of the Studies in Computational Intelligence book series (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.

Keywords

Genetic Algorithm Markov Chain Monte Carlo Evolutionary Computation Approximate Bayesian Computation Evolutionary Computing 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Berlin Heidelberg 2013

Authors and Affiliations

  • Pierre Del Moral
    • 1
    • 2
  • Alexandru-Adrian Tantar
    • 3
  • Emilia Tantar
    • 3
  1. 1.Centre INRIA Bordeaux Sud-Ouest, Institut de Mathématiques de BordeauxUniversité de Bordeaux ITalence cedexFrance
  2. 2.Applied Mathematics DepartmentCMAP École PolyetchniqueParisFrance
  3. 3.Computer Science and Communications Research UnitUniversity of LuxembourgLuxembourgLuxembourg

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