【新书推荐】【2019.09】基于高性能仿真的优化
本书介绍了设计高性能算法的最新进展，该算法将模拟和优化结合起来，以解决科学和工业中复杂的优化问题，这些问题涉及耗时的模拟和昂贵的多目标函数评估。
This book presents the state of the art in designing high-performance algorithms that combine simulation and optimization in order to solve complex optimization problems in science and industry, problems that involve time-consuming simulations and expensive multi-objective function evaluations.
由于传统的优化方法本身不适用，所以将计算智能、机器学习和高性能计算方法结合起来是很流行的解决方案。
As traditional optimization approaches are not applicable per se, combinations of computational intelligence, machine learning, and high-performance computing methods are popular solutions.
但是找到一个合适的方法是一个挑战性的任务，因为在这个高度动态的研究领域已经提出了许多方法。
But finding a suitable method is a challenging task, because numerous approaches have been proposed in this highly dynamic field of research.
这就是本书的用武之地：它涵盖了理论和实践，借鉴了作者们从现实世界中获得的见解，这些作者都是顶尖的研究人员。
That’s where this book comes in: It covers both theory and practice, drawing on the real-world insights gained by the contributing authors, all of whom are leading researchers.
鉴于其阐述范围，本书为研究人员、实践者和对使用计算智能和机器学习解决昂贵优化问题感兴趣的高年级学生提供了一个全面的参考指南。
Given its scope, if offers a comprehensive reference guide for researchers, practitioners, and advanced-level students interested in using computational intelligence and machine learning to solve expensive optimization problems.
Infill Criteria for Multiobjective Bayesian Optimization
Pages 3-16
Emmerich, Michael T. M. (et al.)
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Many-Objective Optimization with Limited Computing Budget
Pages 17-46
Bhattacharjee, Kalyan Shankar (et al.)
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Multi-objective Bayesian Optimization for Engineering Simulation
Pages 47-68
van der Herten, Joachim (et al.)
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Automatic Configuration of Multi-objective Optimizers and Multi-objective Configuration
Pages 69-92
Bezerra, Leonardo C. T. (et al.)
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Optimization and Visualization in Many-Objective Space Trajectory Design
Pages 93-112
Aguirre, Hernán (et al.)
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