Hierarchical Bayesian optimization algorithm : toward a new generation of evolutionary algorithms This book provides a framework for the design of competent optimization techniques by combining advanced evolutionary algorithms with state of the art machine learning techniques. The primary focus of the book is on two algorithms that replace traditional variation operators of evolutionary algorithms by learning and sampling Bayesian networks: the Bayesian optimization algorithm (BOA) and the hierarchical BOA (hBOA) . They provide a scalable solution to a broad class of problems. The book provides an overview of evolutionary algorithms that use probabilistic models to guide their search motivates and describes BOA and hBOA in a way accessible to a wide audience and presents numerous results confirming that they are revolutionary approaches to black box optimization. TOC:From Genetic Variation to Probabilistic Modeling. Probabilistic Model Building Genetic Algorithms. Bayesian Optimization Algorithm. Scalability Analysis. The Challenge of Hierarchical Difficulty. Hierarchical Bayesian Optimization Algorithm. Hierarchical BOA in the Real World