This book is focused like a laser beam in one of the hottest topics in evolutionary computation during the last decade: estimation of distribution algorithms (EDAs). Buy it, read it, and take lessons to heart. David E Goldberg, University of Illinois at Urbana-Champaign This book is an excellent compilation of selected topics in estimation of distribution algorithms --- search algorithm that combines ideas from evolutionary algorithms and machine learning. This book covers a wide spectrum of important subjects ranging from design robust and scalable optimization algorithms to improve efficiency and application of this algorithm. This book should appeal to theorists and practitioners together, and must-have resource for those interested in stochastic optimization in general, and genetic algorithms and evolution in particular. John R. Koza, Stanford University The book was edited describe population-based optimization algorithms and applications, covering the entire gamut of optimization problems have a single calculation and a number of purposes, discrete and continuous variables, serial and parallel, and the model simple and complex functions. This book opened my eyes and a must-read text, includes easy to read yet scholarly articles on EDA methodology was formed and emerged from the real experts in the field. Kalyanmoy Deb, Indian Institute of Technology Kanpur This book is a comprehensive source of excellent in the estimation of distribution algorithms. This algorithm combines the strategic advantages of genetic and evolutionary computation with advanced statistics, machine learning techniques to build models. However, probability-based optimization methods can have a big impact now multiscale scientific and engineering design problems, especially true with efficient and competent genetic algorithms (GA) which is the basis of this book.