Black Box Optimization, Machine Learning, and No-Free Lunch Theorems - Springer Optimization and Its Applications
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(FIVE YEARS 13)

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Published By Springer International Publishing

9783030665142, 9783030665159

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Variable Neighborhood Programming as a Tool of Machine Learning

Black Box Optimization, Machine Learning, and No-Free Lunch Theorems - Springer Optimization and Its Applications ◽

10.1007/978-3-030-66515-9_9 ◽

2021 ◽

pp. 221-271

Author(s):

Nenad Mladenovic ◽

Bassem Jarboui ◽

Souhir Elleuch ◽

Rustam Mussabayev ◽

Olga Rusetskaya

Keyword(s):

Machine Learning

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

Black Box Optimization, Machine Learning, and No-Free Lunch Theorems - Springer Optimization and Its Applications ◽

10.1007/978-3-030-66515-9_3 ◽

2021 ◽

pp. 67-108

Author(s):

Thomas Bartz-Beielstein ◽

Frederik Rehbach ◽

Margarita Rebolledo

Keyword(s):

State Of The Art ◽

Black Box ◽

Future Perspectives

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

Black Box Optimization, Machine Learning, and No-Free Lunch Theorems - Springer Optimization and Its Applications ◽

10.1007/978-3-030-66515-9_12 ◽

2021 ◽

pp. 357-372

Author(s):

Loris Serafino

Keyword(s):

Free Lunch ◽

No Free Lunch Theorem ◽

No Free Lunch

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What Is Important About the No Free Lunch Theorems?

Black Box Optimization, Machine Learning, and No-Free Lunch Theorems - Springer Optimization and Its Applications ◽

10.1007/978-3-030-66515-9_13 ◽

2021 ◽

pp. 373-388

Author(s):

David H. Wolpert

Keyword(s):

Free Lunch ◽

No Free Lunch Theorems ◽

No Free Lunch

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Quality-Diversity Optimization: A Novel Branch of Stochastic Optimization

Black Box Optimization, Machine Learning, and No-Free Lunch Theorems - Springer Optimization and Its Applications ◽

10.1007/978-3-030-66515-9_4 ◽

2021 ◽

pp. 109-135

Author(s):

Konstantinos Chatzilygeroudis ◽

Antoine Cully ◽

Vassilis Vassiliades ◽

Jean-Baptiste Mouret

Keyword(s):

Stochastic Optimization

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Finding Effective SAT Partitionings Via Black-Box Optimization

Black Box Optimization, Machine Learning, and No-Free Lunch Theorems - Springer Optimization and Its Applications ◽

10.1007/978-3-030-66515-9_11 ◽

2021 ◽

pp. 319-355

Author(s):

Alexander Semenov ◽

Oleg Zaikin ◽

Stepan Kochemazov

Keyword(s):

Black Box

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Black-Box Optimization: Methods and Applications

Black Box Optimization, Machine Learning, and No-Free Lunch Theorems - Springer Optimization and Its Applications ◽

10.1007/978-3-030-66515-9_2 ◽

2021 ◽

pp. 35-65

Author(s):

Ishan Bajaj ◽

Akhil Arora ◽

M. M. Faruque Hasan

Keyword(s):

Optimization Methods ◽

Black Box

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

Black Box Optimization, Machine Learning, and No-Free Lunch Theorems - Springer Optimization and Its Applications ◽

10.1007/978-3-030-66515-9_5 ◽

2021 ◽

pp. 137-162

Author(s):

Carlos A. Coello Coello ◽

Silvia González Brambila ◽

Josué Figueroa Gamboa ◽

Ma. Guadalupe Castillo Tapia

Keyword(s):

Evolutionary Algorithms ◽

Multi Objective

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Black-Box and Data-Driven Computation

Black Box Optimization, Machine Learning, and No-Free Lunch Theorems - Springer Optimization and Its Applications ◽

10.1007/978-3-030-66515-9_6 ◽

2021 ◽

pp. 163-168

Author(s):

Rong Jin ◽

Weili Wu ◽

My T. Thai ◽

Ding-Zhu Du

Keyword(s):

Black Box ◽

Data Driven

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Learning Enabled Constrained Black-Box Optimization

Black Box Optimization, Machine Learning, and No-Free Lunch Theorems - Springer Optimization and Its Applications ◽

10.1007/978-3-030-66515-9_1 ◽

2021 ◽

pp. 1-33

Author(s):

F. Archetti ◽

A. Candelieri ◽

B. G. Galuzzi ◽

R. Perego

Keyword(s):

Black Box

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Black Box Optimization, Machine Learning, and No-Free Lunch Theorems - Springer Optimization and Its ApplicationsLatest Publications

TOTAL DOCUMENTS

H-INDEX

Published By Springer International Publishing

Variable Neighborhood Programming as a Tool of Machine Learning

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

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

What Is Important About the No Free Lunch Theorems?

Quality-Diversity Optimization: A Novel Branch of Stochastic Optimization

Finding Effective SAT Partitionings Via Black-Box Optimization

Black-Box Optimization: Methods and Applications

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

Black-Box and Data-Driven Computation

Learning Enabled Constrained Black-Box Optimization

Black Box Optimization, Machine Learning, and No-Free Lunch Theorems - Springer Optimization and Its Applications
Latest Publications