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.
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