Evolutionary Statistical Procedures / Statistics and Computing (PDF)

Roberto Baragona received the 'laurea' in Mathematics from Sapienza University of Rome, Italy, in 1972. He is currently a professor of Data Analysis at Sapienza University. His main research interests are in time series analysis and multivariate statistics with a special emphasis on meta-heuristic methods. Referee for several international journals and associate editor of Statistical Methods and Applications.

Francesco Battaglia is a professor of Statistical Forecasting at Sapienza University of Rome. He has taught at the University of Cagliari and at the Italian Public Administration School, and visited several European universities. He is a former head of Sapienza University's Department of Statistics, and head of the Time Series Analysis Group of the Italian Statistical Society. Editor-in-chief of Statistical Methods and Applications.

Irene Poli is Professor of Statistics at Ca' Foscari University of Venice, and Director of the European Centre for Living Technology (ECLT, www.ecltech.org). Her current research involves developing statistical procedures for high dimensional data and deriving evolutionary experimental designs and multiobjective optimizations mainly for biochemical problems. She is a Fellow of the New York Academy of Science, of the Royal Statistical Society, the Bernoulli Society, and a member of the Italian Statistical Society.

From the book reviews:

“After the introductory Chapter 1, in Chapter 2, a detailed review of evolutionary computation is presented. Chapters 3–7 then discuss applications in regression, time series, design of experiments, outlier detection, and cluster analysis. Evolutionary techniques have been successfully used to solve optimization problems in several challenging applications, consequently this book may be useful to many applied statisticians.” (Snigdhansu Chatterjee, Technometrics, Vol. 54 (4), November, 2012)

“The monograph under review is … to provide a sort of guide through the world of evolutionary computation for optimization problems in statistics and related implementation issues in a variety of applications. It represents a comprehensive reference work for advanced graduate students and researchers working in the rich field at the intersection between statistics, evolutionary computation, and computer science. … It is self-contained, each chapter is nicely introduced by a summary of the main contents and a comprehensive list of references is provided.” (Marcello Sanguineti, Mathematical Reviews, Issue 2012 d)