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Black Box Optimization, Machine Learning, and No-Free Lunch Theorems
Editor
- Panos M. Pardalos(University of Florida)Varvara Rasskazova(Moscow Aviation Institute)Michael N. Vrahatis(University of Patras)
Abstract
No abstract is available for this item.Individual chapters are listed in the "Chapters" tab
Suggested Citation
- Panos M. Pardalos & Varvara Rasskazova & Michael N. Vrahatis (ed.), 2021. "Black Box Optimization, Machine Learning, and No-Free Lunch Theorems," Springer Optimization and Its Applications, Springer, number 978-3-030-66515-9, June.
Handle: RePEc:spr:spopap:978-3-030-66515-9
DOI: 10.1007/978-3-030-66515-9
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Book Chapters
The following chapters of this book are listed in IDEAS- F. Archetti & A. Candelieri & B. G. Galuzzi & R. Perego, 2021. "Learning Enabled Constrained Black-Box Optimization," 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 1-33, Springer.
- 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.
- Thomas Bartz-Beielstein & Frederik Rehbach & Margarita Rebolledo, 2021. "Tuning Algorithms for Stochastic Black-Box Optimization: State of the Art and Future Perspectives," 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 67-108, Springer.
- Konstantinos Chatzilygeroudis & Antoine Cully & Vassilis Vassiliades & Jean-Baptiste Mouret, 2021. "Quality-Diversity Optimization: A Novel Branch of Stochastic Optimization," 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 109-135, Springer.
- Carlos A. Coello Coello & Silvia González Brambila & Josué Figueroa Gamboa & Ma. Guadalupe Castillo Tapia, 2021. "Multi-Objective Evolutionary Algorithms: Past, Present, and Future," 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 137-162, Springer.
- Rong Jin & Weili Wu & My T. Thai & Ding-Zhu Du, 2021. "Black-Box and Data-Driven Computation," 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 163-168, Springer.
- Ralph Baker Kearfott, 2021. "Mathematically Rigorous Global Optimization and Fuzzy Optimization," 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 169-194, Springer.
- Vladik Kreinovich & Olga Kosheleva, 2021. "Optimization Under Uncertainty Explains Empirical Success of Deep Learning Heuristics," 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 195-220, Springer.
- Nenad Mladenovic & Bassem Jarboui & Souhir Elleuch & Rustam Mussabayev & Olga Rusetskaya, 2021. "Variable Neighborhood Programming as a Tool of Machine Learning," 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 221-271, Springer.
- Jack Noonan & Anatoly Zhigljavsky, 2021. "Non-lattice Covering and Quantization of High Dimensional Sets," 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 273-318, Springer.
- Alexander Semenov & Oleg Zaikin & Stepan Kochemazov, 2021. "Finding Effective SAT Partitionings Via Black-Box Optimization," 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 319-355, Springer.
- Loris Serafino, 2021. "The No Free Lunch Theorem: What Are its Main Implications for the Optimization Practice?," 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 357-372, Springer.
- David H. Wolpert, 2021. "What Is Important About the No Free Lunch Theorems?," 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 373-388, Springer.
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