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

Edited by Panos M. Pardalos (), Varvara Rasskazova () and Michael N. Vrahatis ()

in Springer Optimization and Its Applications from Springer, currently edited by Pardalos, Panos, Thai, My T. and Du, Ding-Zhu

Date: 2021
ISBN: 978-3-030-66515-9
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Chapters in this book:

Learning Enabled Constrained Black-Box Optimization

F. Archetti, A. Candelieri, B. G. Galuzzi and R. Perego

Black-Box Optimization: Methods and Applications

Ishan Bajaj, Akhil Arora and M. M. Faruque Hasan

Tuning Algorithms for Stochastic Black-Box Optimization: State of the Art and Future Perspectives

Thomas Bartz-Beielstein, Frederik Rehbach and Margarita Rebolledo

Quality-Diversity Optimization: A Novel Branch of Stochastic Optimization

Konstantinos Chatzilygeroudis, Antoine Cully, Vassilis Vassiliades and Jean-Baptiste Mouret

Multi-Objective Evolutionary Algorithms: Past, Present, and Future

Carlos A. Coello Coello, Silvia González Brambila, Josué Figueroa Gamboa and Ma. Guadalupe Castillo Tapia

Black-Box and Data-Driven Computation

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

Mathematically Rigorous Global Optimization and Fuzzy Optimization

Ralph Baker Kearfott

Optimization Under Uncertainty Explains Empirical Success of Deep Learning Heuristics

Vladik Kreinovich and Olga Kosheleva

Variable Neighborhood Programming as a Tool of Machine Learning

Nenad Mladenovic, Bassem Jarboui, Souhir Elleuch, Rustam Mussabayev and Olga Rusetskaya

Non-lattice Covering and Quantization of High Dimensional Sets

Jack Noonan and Anatoly Zhigljavsky

Finding Effective SAT Partitionings Via Black-Box Optimization

Alexander Semenov, Oleg Zaikin and Stepan Kochemazov

The No Free Lunch Theorem: What Are its Main Implications for the Optimization Practice?

Loris Serafino

What Is Important About the No Free Lunch Theorems?

David H. Wolpert

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DOI: 10.1007/978-3-030-66515-9

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