Language:
English
繁體中文
Help
回圖書館首頁
手機版館藏查詢
Login
Back
Switch To:
Labeled
|
MARC Mode
|
ISBD
Black box optimization, machine lear...
~
Pardalos, Panos M.
Linked to FindBook
Google Book
Amazon
博客來
Black box optimization, machine learning, and no-free lunch theorems
Record Type:
Electronic resources : Monograph/item
Title/Author:
Black box optimization, machine learning, and no-free lunch theorems/ edited by Panos M. Pardalos, Varvara Rasskazova, Michael N. Vrahatis.
other author:
Pardalos, Panos M.
Published:
Cham :Springer International Publishing : : 2021.,
Description:
x, 388 p. :ill., digital ;24 cm.
[NT 15003449]:
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)
Contained By:
Springer Nature eBook
Subject:
Machine learning - Mathematics. -
Online resource:
https://doi.org/10.1007/978-3-030-66515-9
ISBN:
9783030665159
Black box optimization, machine learning, and no-free lunch theorems
Black box optimization, machine learning, and no-free lunch theorems
[electronic resource] /edited by Panos M. Pardalos, Varvara Rasskazova, Michael N. Vrahatis. - Cham :Springer International Publishing :2021. - x, 388 p. :ill., digital ;24 cm. - Springer optimization and its applications,v.1701931-6828 ;. - Springer optimization and its applications ;v.170..
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)
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.
ISBN: 9783030665159
Standard No.: 10.1007/978-3-030-66515-9doiSubjects--Topical Terms:
3442737
Machine learning
--Mathematics.
LC Class. No.: Q325.5 / .B53 2021
Dewey Class. No.: 006.310151
Black box optimization, machine learning, and no-free lunch theorems
LDR
:03257nmm a2200337 a 4500
001
2240851
003
DE-He213
005
20210527195616.0
006
m d
007
cr nn 008maaau
008
211111s2021 sz s 0 eng d
020
$a
9783030665159
$q
(electronic bk.)
020
$a
9783030665142
$q
(paper)
024
7
$a
10.1007/978-3-030-66515-9
$2
doi
035
$a
978-3-030-66515-9
040
$a
GP
$c
GP
041
0
$a
eng
050
4
$a
Q325.5
$b
.B53 2021
072
7
$a
PBU
$2
bicssc
072
7
$a
MAT003000
$2
bisacsh
072
7
$a
PBU
$2
thema
082
0 4
$a
006.310151
$2
23
090
$a
Q325.5
$b
.B627 2021
245
0 0
$a
Black box optimization, machine learning, and no-free lunch theorems
$h
[electronic resource] /
$c
edited by Panos M. Pardalos, Varvara Rasskazova, Michael N. Vrahatis.
260
$a
Cham :
$b
Springer International Publishing :
$b
Imprint: Springer,
$c
2021.
300
$a
x, 388 p. :
$b
ill., digital ;
$c
24 cm.
490
1
$a
Springer optimization and its applications,
$x
1931-6828 ;
$v
v.170
505
0
$a
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)
520
$a
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.
650
0
$a
Machine learning
$x
Mathematics.
$3
3442737
650
0
$a
Mathematical optimization.
$3
517763
650
0
$a
Computer algorithms.
$3
523872
650
1 4
$a
Optimization.
$3
891104
650
2 4
$a
Machine Learning.
$3
3382522
700
1
$a
Pardalos, Panos M.
$3
670825
700
1
$a
Rasskazova, Varvara.
$3
3496171
700
1
$a
Vrahatis, Michael N.
$3
3496172
710
2
$a
SpringerLink (Online service)
$3
836513
773
0
$t
Springer Nature eBook
830
0
$a
Springer optimization and its applications ;
$v
v.170.
$3
3496173
856
4 0
$u
https://doi.org/10.1007/978-3-030-66515-9
950
$a
Mathematics and Statistics (SpringerNature-11649)
based on 0 review(s)
Location:
ALL
電子資源
Year:
Volume Number:
Items
1 records • Pages 1 •
1
Inventory Number
Location Name
Item Class
Material type
Call number
Usage Class
Loan Status
No. of reservations
Opac note
Attachments
W9402736
電子資源
11.線上閱覽_V
電子書
EB Q325.5 .B53 2021
一般使用(Normal)
On shelf
0
1 records • Pages 1 •
1
Multimedia
Reviews
Add a review
and share your thoughts with other readers
Export
pickup library
Processing
...
Change password
Login