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Evolutionary Computation

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Evolutionary Statistical Procedures

Part of the book series: Statistics and Computing ((SCO))

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Abstract

The evolutionary computation methods are introduced by discussing their origin inside the artificial intelligence framework, and the contributions of Darwin’s theory of natural evolution and Genetics. We attempt to highlight the main features of an evolutionary computation method, and describe briefly some of them: evolutionary programming, evolution strategies, genetic algorithm, estimation of distribution algorithms, differential evolution. The remainder of the chapter is devoted to a closer illustration of genetic algorithms and more recent advancements, to the problem of convergence and to the practical use of them. A final section on the relationship between genetic algorithms and random sampling techniques is included.

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Notes

  1. 1.

    Formally, convergence to zero could also arise from \(\hat{\theta}(x)-\theta^*(x)\) tending to assume the same values of \(\theta-\theta^*(x)\), but this seems very artificial and unreasonable, thus we shall not admit this possibility

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Authors and Affiliations

  1. Department of Communication and Social Research, Sapienza University of Rome, Via Salaria 113, 00198, Rome, Italy

    Prof. Roberto Baragona

  2. Department of Statistical Sciences, Sapienza University of Rome, Piazzale Aldo Moro 5, 00100, Roma, Italy

    Prof. Francesco Battaglia

  3. Department of Statistics, Ca’ Foscari University of Venice, Cannaregio 873, 30121, Venice, Italy

    Prof. Irene Poli

Authors
  1. Prof. Roberto Baragona
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  2. Prof. Francesco Battaglia
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  3. Prof. Irene Poli
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Correspondence to Roberto Baragona .

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© 2011 Springer-Verlag Berlin Heidelberg

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Baragona, R., Battaglia, F., Poli, I. (2011). Evolutionary Computation. In: Evolutionary Statistical Procedures. Statistics and Computing. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16218-3_2

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