Titlebook: Data-Driven Evolutionary Optimization; Integrating Evolutio Yaochu Jin,Handing Wang,Chaoli Sun Book 2021 The Editor(s) (if applicable) and

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Anthony Chun,Jeffrey D. Hoffmannline data-driven optimization, are introduced. A variety of heuristic population and individual based surrogate management strategies for surrogate assisted evolutionary optimization are presented, and mathematically more established model management strategies such as the trust region method and a

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Segmental Duration and Speech Timinglexity in the structure of the Pareto front, the increased number of solutions needed to represent the Pareto front, and the selection of solutions. Many-objective optimization becomes even more challenging when they are expensive and must be solved with the assistance of surrogates. This chapter in

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Introduction and Chronological Perspectiveon problems where only a limited number of samples can be afforded. This chapter focuses on addressing high-dimensional expensive problems that have over 30 and up to some 200 decision variables. The main techniques include the use of more exploratory search, co-operative search between multiple pop

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Data-Driven Evolutionary Optimization978-3-030-74640-7Series ISSN 1860-949X Series E-ISSN 1860-9503