Titlebook: Evolutionary Statistical Procedures; An Evolutionary Comp Roberto Baragona,Francesco Battaglia,Irene Poli Book 2011 Springer-Verlag Berlin

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Evolutionary Computation, 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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Evolving Regression Models,lihood, therefore maximum likelihood estimation requires optimization of complicated functions. In this chapter evolutionary computation methods are presented that have been developed to either support or surrogate analytic tools if the problem size and complexity limit their efficiency.

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Introduction,e present book as far as the impact of the new research technologies on statistics and strictly related subjects is concerned is given with special emphasis on methods inspired by natural biological evolution.

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The Exports of China: An Overview,riments is important also to tackle the uncertainty in the experimental results derived from systematic and random errors that frequently obscure the effects under investigation. In this chapter we will first present the essentials of designing experiments and then describe the evolutionary approach to design in high dimensional settings.

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Time Series Linear and Nonlinear Models,d non Gaussian time series that require special assumptions and procedures to ensure that identification and estimation may be performed, and special statistics for diagnostic checking. Several devices are available that allow such time series to be handled and remain within the domain of linear mod