On the foundations and the applications of evolutionary computing. (English) Zbl 1251.68199
Tantar, Emilia (ed.) et al., EVOLVE – a bridge between probability, set oriented numerics and evolutionary computation. Selected papers based on the presentations at the workshop 2011, Bourglinster Castle, Luxembourg, May 25–27, 2011. Berlin: Springer (ISBN 978-3-642-32725-4/hbk; 978-3-642-32726-1/ebook). Studies in Computational Intelligence 447, 3-89 (2013).
Summary: 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.
For the entire collection see [Zbl 1250.68052].
For the entire collection see [Zbl 1250.68052].
MSC:
| 68T20 | Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) |
| 60G35 | Signal detection and filtering (aspects of stochastic processes) |
| 60J85 | Applications of branching processes |
| 60J22 | Computational methods in Markov chains |
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