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Black-Box Optimization: Methods and Applications

In: Black Box Optimization, Machine Learning, and No-Free Lunch Theorems

Author

Listed:
  • Ishan Bajaj

    (University of Wisconsin-Madison)

  • Akhil Arora

    (Texas A&M University)

  • M. M. Faruque Hasan

    (Texas A&M University)

Abstract

Black-box optimization (BBO) is a rapidly growing field of optimization and a topic of critical importance in many areas including complex systems engineering, energy and the environment, materials design, drug discovery, chemical process synthesis, and computational biology. In this chapter, we present an overview of theoretical advancements, algorithmic developments, implementations, and several applications of BBO. Lastly, we point to open problems and provide future research directions.

Suggested Citation

  • Ishan Bajaj & Akhil Arora & M. M. Faruque Hasan, 2021. "Black-Box Optimization: Methods and Applications," Springer Optimization and Its Applications, in: Panos M. Pardalos & Varvara Rasskazova & Michael N. Vrahatis (ed.), Black Box Optimization, Machine Learning, and No-Free Lunch Theorems, pages 35-65, Springer.
    • Handle: RePEc:spr:spochp:978-3-030-66515-9_2
    DOI: 10.1007/978-3-030-66515-9_2
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    Cited by:

    1. Chen, Zhenyu & Lin, Zhongwei & Ren, Xin & Chen, Kaixuan & Zhang, Guangming & Xie, Zhen & Wang, Chuanxi & She, Chao, 2023. "Amplitude-optimized Koopman-linear flow estimator for wind turbine wake dynamics: Approximation, prediction and reconstruction," Energy, Elsevier, vol. 263(PE).
    2. Gandhi, Akhilesh & Zantye, Manali S. & Faruque Hasan, M.M., 2022. "Cryogenic energy storage: Standalone design, rigorous optimization and techno-economic analysis," Applied Energy, Elsevier, vol. 322(C).

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