語系:
繁體中文
English
說明(常見問題)
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
Mathematical Foundations of Nature-I...
~
Yang, Xin-She.
Mathematical Foundations of Nature-Inspired Algorithms
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
Mathematical Foundations of Nature-Inspired Algorithms/ by Xin-She Yang, Xing-Shi He.
作者:
Yang, Xin-She.
其他作者:
He, Xing-Shi.
面頁冊數:
XI, 107 p. 4 illus., 2 illus. in color.online resource. :
Contained By:
Springer Nature eBook
標題:
Mathematical optimization. -
電子資源:
https://doi.org/10.1007/978-3-030-16936-7
ISBN:
9783030169367
Mathematical Foundations of Nature-Inspired Algorithms
Yang, Xin-She.
Mathematical Foundations of Nature-Inspired Algorithms
[electronic resource] /by Xin-She Yang, Xing-Shi He. - 1st ed. 2019. - XI, 107 p. 4 illus., 2 illus. in color.online resource. - SpringerBriefs in Optimization,2190-8354. - SpringerBriefs in Optimization,.
1 Introduction to Optimization -- 2 Nature-Inspired Algorithms -- 3 Mathematical Foundations -- 4 Mathematical Analysis I -- 5 Mathematical Analysis II.
This book presents a systematic approach to analyze nature-inspired algorithms. Beginning with an introduction to optimization methods and algorithms, this book moves on to provide a unified framework of mathematical analysis for convergence and stability. Specific nature-inspired algorithms include: swarm intelligence, ant colony optimization, particle swarm optimization, bee-inspired algorithms, bat algorithm, firefly algorithm, and cuckoo search. Algorithms are analyzed from a wide spectrum of theories and frameworks to offer insight to the main characteristics of algorithms and understand how and why they work for solving optimization problems. In-depth mathematical analyses are carried out for different perspectives, including complexity theory, fixed point theory, dynamical systems, self-organization, Bayesian framework, Markov chain framework, filter theory, statistical learning, and statistical measures. Students and researchers in optimization, operations research, artificial intelligence, data mining, machine learning, computer science, and management sciences will see the pros and cons of a variety of algorithms through detailed examples and a comparison of algorithms.
ISBN: 9783030169367
Standard No.: 10.1007/978-3-030-16936-7doiSubjects--Topical Terms:
527675
Mathematical optimization.
LC Class. No.: QA402.5-402.6
Dewey Class. No.: 519.6
Mathematical Foundations of Nature-Inspired Algorithms
LDR
:02735nam a22003975i 4500
001
1006320
003
DE-He213
005
20200630112711.0
007
cr nn 008mamaa
008
210106s2019 gw | s |||| 0|eng d
020
$a
9783030169367
$9
978-3-030-16936-7
024
7
$a
10.1007/978-3-030-16936-7
$2
doi
035
$a
978-3-030-16936-7
050
4
$a
QA402.5-402.6
072
7
$a
PBU
$2
bicssc
072
7
$a
MAT003000
$2
bisacsh
072
7
$a
PBU
$2
thema
082
0 4
$a
519.6
$2
23
100
1
$a
Yang, Xin-She.
$4
aut
$4
http://id.loc.gov/vocabulary/relators/aut
$3
786470
245
1 0
$a
Mathematical Foundations of Nature-Inspired Algorithms
$h
[electronic resource] /
$c
by Xin-She Yang, Xing-Shi He.
250
$a
1st ed. 2019.
264
1
$a
Cham :
$b
Springer International Publishing :
$b
Imprint: Springer,
$c
2019.
300
$a
XI, 107 p. 4 illus., 2 illus. in color.
$b
online resource.
336
$a
text
$b
txt
$2
rdacontent
337
$a
computer
$b
c
$2
rdamedia
338
$a
online resource
$b
cr
$2
rdacarrier
347
$a
text file
$b
PDF
$2
rda
490
1
$a
SpringerBriefs in Optimization,
$x
2190-8354
505
0
$a
1 Introduction to Optimization -- 2 Nature-Inspired Algorithms -- 3 Mathematical Foundations -- 4 Mathematical Analysis I -- 5 Mathematical Analysis II.
520
$a
This book presents a systematic approach to analyze nature-inspired algorithms. Beginning with an introduction to optimization methods and algorithms, this book moves on to provide a unified framework of mathematical analysis for convergence and stability. Specific nature-inspired algorithms include: swarm intelligence, ant colony optimization, particle swarm optimization, bee-inspired algorithms, bat algorithm, firefly algorithm, and cuckoo search. Algorithms are analyzed from a wide spectrum of theories and frameworks to offer insight to the main characteristics of algorithms and understand how and why they work for solving optimization problems. In-depth mathematical analyses are carried out for different perspectives, including complexity theory, fixed point theory, dynamical systems, self-organization, Bayesian framework, Markov chain framework, filter theory, statistical learning, and statistical measures. Students and researchers in optimization, operations research, artificial intelligence, data mining, machine learning, computer science, and management sciences will see the pros and cons of a variety of algorithms through detailed examples and a comparison of algorithms.
650
0
$a
Mathematical optimization.
$3
527675
650
0
$a
Numerical analysis.
$3
527939
650
0
$a
Markov processes.
$3
527825
650
0
$a
Algorithms.
$3
527865
650
1 4
$a
Optimization.
$3
669174
650
2 4
$a
Numerical Analysis.
$3
671433
650
2 4
$a
Markov model.
$3
1227422
700
1
$a
He, Xing-Shi.
$4
aut
$4
http://id.loc.gov/vocabulary/relators/aut
$3
1227420
710
2
$a
SpringerLink (Online service)
$3
593884
773
0
$t
Springer Nature eBook
776
0 8
$i
Printed edition:
$z
9783030169350
776
0 8
$i
Printed edition:
$z
9783030169374
830
0
$a
SpringerBriefs in Optimization,
$x
2190-8354
$3
1254063
856
4 0
$u
https://doi.org/10.1007/978-3-030-16936-7
912
$a
ZDB-2-SMA
912
$a
ZDB-2-SXMS
950
$a
Mathematics and Statistics (SpringerNature-11649)
950
$a
Mathematics and Statistics (R0) (SpringerNature-43713)
筆 0 讀者評論
多媒體
評論
新增評論
分享你的心得
Export
取書館別
處理中
...
變更密碼[密碼必須為2種組合(英文和數字)及長度為10碼以上]
登入