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Home > Black Box Optimization, Machine Learning, and No-Free Lunch Theorems > Black-Box and Data-Driven Computation
Black Box Optimization, Machine Learning, and No-Free Lunch Theorems |
Panos M. Pardalos; Varvara Rasskazova; Michael N. Vrahatis
Springer International Publishing
Black Box Optimization, Machine Learning, and No-Free Lunch Theorems
Black-Box and Data-Driven Computation
Authors

Rong Jin; Weili Wu; My T. Thai; Ding-Zhu Du

ARI Id

1665320057435_5906287

Access

Not Available Free

Pages

163-168

DOI

10.1007/978-3-030-66515-9_6

Chapter URL

https://rd.springer.com/chapter/10.1007/978-3-030-66515-9_6

Table of Contents of Book
Showing 1 to 14 of 14 entries
Chapters/HeadingsAuthor(s)PagesInfo
Front Matter
i-x
Learning Enabled Constrained Black-Box Optimization
F. Archetti; A. Candelieri; B. G. Galuzzi; R. Perego
1-33
Black-Box Optimization: Methods and Applications
Ishan Bajaj; Akhil Arora; M. M. Faruque Hasan
35-65
Tuning Algorithms for Stochastic Black-Box Optimization: State of the Art and Future Perspectives
Thomas Bartz-Beielstein; Frederik Rehbach; Margarita Rebolledo
67-108
Quality-Diversity Optimization: A Novel Branch of Stochastic Optimization
Konstantinos Chatzilygeroudis; Antoine Cully; Vassilis Vassiliades; Jean-Baptiste Mouret
109-135
Multi-Objective Evolutionary Algorithms: Past, Present, and Future
Carlos A. Coello Coello; Silvia González Brambila; Josué Figueroa Gamboa; Ma. Guadalupe Castillo Tapia
137-162
Black-Box and Data-Driven Computation
Rong Jin; Weili Wu; My T. Thai; Ding-Zhu Du
163-168
Mathematically Rigorous Global Optimization and Fuzzy Optimization
Ralph Baker Kearfott
169-194
Optimization Under Uncertainty Explains Empirical Success of Deep Learning Heuristics
Vladik Kreinovich; Olga Kosheleva
195-220
Variable Neighborhood Programming as a Tool of Machine Learning
Nenad Mladenovic; Bassem Jarboui; Souhir Elleuch; Rustam Mussabayev; Olga Rusetskaya
221-271
Non-lattice Covering and Quantization of High Dimensional Sets
Jack Noonan; Anatoly Zhigljavsky
273-318
Finding Effective SAT Partitionings Via Black-Box Optimization
Alexander Semenov; Oleg Zaikin; Stepan Kochemazov
319-355
The No Free Lunch Theorem: What Are its Main Implications for the Optimization Practice?
Loris Serafino
357-372
What Is Important About the No Free Lunch Theorems?
David H. Wolpert
373-388
Chapters/HeadingsAuthor(s)PagesInfo
Showing 1 to 14 of 14 entries
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