The proposed text offers a solid introduction to evolutionary computation for use in applied statistics research. The authors draw from a vast base of knowledge about the current literature in both the design of evolutionary algorithms and statistical techniques. Modern statistical research is in the process of solving increasingly complex problems in high dimensions and its methodology is now being generalized to parameters whose estimators do not follow mathematically simple distributions. Many of these challenges involve optimizing functions for which analytic solutions are not feasible. Evolutionary algorithms present a powerful and easily understood means of approximating the optimum value in a variety of settings. The proposed text seeks to guide readers through the crucial issues of optimization problems in statistical settings and the implementation of tailored methods (including both stand-alone evolutionary algorithms and hybrid methods).
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)
"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)