Go to main content

Black box optimization, machine learning, and no-free lunch theorems [electronic resource] / Panos M. Pardalos, Varvara Rasskazova, Michael N. Vrahatis, editors.

Pardalos, P. M. (Panos M.), 1954- editor.; Rasskazova, Varvara, editor.; Vrahatis, Michael N., 1955- editor.
2021
Q325.5
Available Online
Formats
Format
BibTeX
MARCXML
TextMARC
MARC
DublinCore
EndNote
NLM
RefWorks
RIS
Add to Basket
Cite
Citation

Linked e-resources

Linked Resource
Online Access
Concurrent users
Unlimited
Authorized users
Authorized users
Document Delivery Supplied
Can lend chapters, not whole ebooks

Details

Title
Black box optimization, machine learning, and no-free lunch theorems [electronic resource] / Panos M. Pardalos, Varvara Rasskazova, Michael N. Vrahatis, editors.
ISBN
9783030665159 (electronic bk.)
3030665151 (electronic bk.)
9783030665166 (print)
303066516X
9783030665173 (print)
3030665178
3030665143
9783030665142
DOI
https://doi.org/10.1007/978-3-030-66515-9
Published
Cham : Springer, 2021.
Language
English
Description
1 online resource.
Item Number
10.1007/978-3-030-66515-9 doi
Call Number
Q325.5
Dewey Decimal Classification
006.3/10151
Summary
This edited volume illustrates the connections between machine learning techniques, black box optimization, and no-free lunch theorems. Each of the thirteen contributions focuses on the commonality and interdisciplinary concepts as well as the fundamentals needed to fully comprehend the impact of individual applications and problems. Current theoretical, algorithmic, and practical methods used are provided to stimulate a new effort towards innovative and efficient solutions. The book is intended for beginners who wish to achieve a broad overview of optimization methods and also for more experienced researchers as well as researchers in mathematics, optimization, operations research, quantitative logistics, data analysis, and statistics, who will benefit from access to a quick reference to key topics and methods. The coverage ranges from mathematically rigorous methods to heuristic and evolutionary approaches in an attempt to equip the reader with different viewpoints of the same problem.
Access Note
Access limited to authorized users.
Digital File Characteristics
text file
PDF
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed June 3, 2021).
Added Author
Pardalos, P. M. (Panos M.), 1954- editor.
Rasskazova, Varvara, editor.
Vrahatis, Michael N., 1955- editor.
Series
Springer optimization and its applications ; v. 170.
Available in Other Form
Print version: 9783030665142
Linked Resources
Online Access
Record Appears in
Online Resources > Ebooks
All Resources
Learning enabled constrained black box optimization (Archetti)
Black-box optimization: Methods and applications (Hasan)
Tuning algorithms for stochastic black-box optimization: State of the art and future perspectives (Bartz-Beielstein)
Quality diversity optimization: A novel branch of stochastic optimization (Chatzilygeroudis)
Multi-objective evolutionary algorithms: Past, present and future (Coello C.A)
Black-box and data driven computation (Du)
Mathematically rigorous global optimization and fuzzy optimization: A brief comparison of paradigms, methods, similarities and differences (Kearfott)
Optimization under Uncertainty Explains Empirical Success of Deep Learning Heuristics (Kreinovich)
Variable neighborhood programming as a tool of machine learning (Mladenovic)
Non-lattice covering and quanitization of high dimensional sets (Zhigljavsky)
Finding effective SAT partitionings via black-box optimization (Semenov)
The No Free Lunch Theorem: What are its main implications for the optimization practice? (Serafino)
What is important about the No Free Lunch theorems? (Wolpert).

Browse Subjects

Show more subjects...

Statistics

from
to
Export