語系:
繁體中文
English
說明(常見問題)
回圖書館首頁
手機版館藏查詢
登入
回首頁
到查詢結果
[ subject:"Stochastic Analysis." ]
切換:
標籤
|
MARC模式
|
ISBD
Deep statistical comparison for meta...
~
Eftimov, Tome.
FindBook
Google Book
Amazon
博客來
Deep statistical comparison for meta-heuristic stochastic optimization algorithms
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Deep statistical comparison for meta-heuristic stochastic optimization algorithms/ by Tome Eftimov, Peter Korosec.
作者:
Eftimov, Tome.
其他作者:
Korosec, Peter.
出版者:
Cham :Springer International Publishing : : 2022.,
面頁冊數:
xvii, 133 p. :ill. (some col.), digital ;24 cm.
內容註:
Introduction -- Metaheuristic Stochastic Optimization -- Benchmarking Theory -- Introduction to Statistical Analysis -- Approaches to Statistical Comparisons -- Deep Statistical Comparison in Single-Objective Optimization -- Deep Statistical Comparison in Multiobjective Optimization -- DSCTool: A Web-Service-Based E-Learning Tool -- Summary.
Contained By:
Springer Nature eBook
標題:
Metaheuristics. -
電子資源:
https://doi.org/10.1007/978-3-030-96917-2
ISBN:
9783030969172
Deep statistical comparison for meta-heuristic stochastic optimization algorithms
Eftimov, Tome.
Deep statistical comparison for meta-heuristic stochastic optimization algorithms
[electronic resource] /by Tome Eftimov, Peter Korosec. - Cham :Springer International Publishing :2022. - xvii, 133 p. :ill. (some col.), digital ;24 cm. - Natural computing series. - Natural computing series..
Introduction -- Metaheuristic Stochastic Optimization -- Benchmarking Theory -- Introduction to Statistical Analysis -- Approaches to Statistical Comparisons -- Deep Statistical Comparison in Single-Objective Optimization -- Deep Statistical Comparison in Multiobjective Optimization -- DSCTool: A Web-Service-Based E-Learning Tool -- Summary.
Focusing on comprehensive comparisons of the performance of stochastic optimization algorithms, this book provides an overview of the current approaches used to analyze algorithm performance in a range of common scenarios, while also addressing issues that are often overlooked. In turn, it shows how these issues can be easily avoided by applying the principles that have produced Deep Statistical Comparison and its variants. The focus is on statistical analyses performed using single-objective and multi-objective optimization data. At the end of the book, examples from a recently developed web-service-based e-learning tool (DSCTool) are presented. The tool provides users with all the functionalities needed to make robust statistical comparison analyses in various statistical scenarios. The book is intended for newcomers to the field and experienced researchers alike. For newcomers, it covers the basics of optimization and statistical analysis, familiarizing them with the subject matter before introducing the Deep Statistical Comparison approach. Experienced researchers can quickly move on to the content on new statistical approaches. The book is divided into three parts: Part I: Introduction to optimization, benchmarking, and statistical analysis - Chapters 2-4. Part II: Deep Statistical Comparison of meta-heuristic stochastic optimization algorithms - Chapters 5-7. Part III: Implementation and application of Deep Statistical Comparison - Chapter 8.
ISBN: 9783030969172
Standard No.: 10.1007/978-3-030-96917-2doiSubjects--Topical Terms:
2206834
Metaheuristics.
LC Class. No.: QA76.9.A43 / E47 2022
Dewey Class. No.: 005.13
Deep statistical comparison for meta-heuristic stochastic optimization algorithms
LDR
:02933nmm a2200349 a 4500
001
2301797
003
DE-He213
005
20220611153246.0
006
m d
007
cr nn 008maaau
008
230409s2022 sz s 0 eng d
020
$a
9783030969172
$q
(electronic bk.)
020
$a
9783030969165
$q
(paper)
024
7
$a
10.1007/978-3-030-96917-2
$2
doi
035
$a
978-3-030-96917-2
040
$a
GP
$c
GP
041
0
$a
eng
050
4
$a
QA76.9.A43
$b
E47 2022
072
7
$a
UYQ
$2
bicssc
072
7
$a
COM004000
$2
bisacsh
072
7
$a
UYQ
$2
thema
082
0 4
$a
005.13
$2
23
090
$a
QA76.9.A43
$b
E27 2022
100
1
$a
Eftimov, Tome.
$3
3601530
245
1 0
$a
Deep statistical comparison for meta-heuristic stochastic optimization algorithms
$h
[electronic resource] /
$c
by Tome Eftimov, Peter Korosec.
260
$a
Cham :
$b
Springer International Publishing :
$b
Imprint: Springer,
$c
2022.
300
$a
xvii, 133 p. :
$b
ill. (some col.), digital ;
$c
24 cm.
338
$a
online resource
$b
cr
$2
rdacarrier
490
1
$a
Natural computing series
505
0
$a
Introduction -- Metaheuristic Stochastic Optimization -- Benchmarking Theory -- Introduction to Statistical Analysis -- Approaches to Statistical Comparisons -- Deep Statistical Comparison in Single-Objective Optimization -- Deep Statistical Comparison in Multiobjective Optimization -- DSCTool: A Web-Service-Based E-Learning Tool -- Summary.
520
$a
Focusing on comprehensive comparisons of the performance of stochastic optimization algorithms, this book provides an overview of the current approaches used to analyze algorithm performance in a range of common scenarios, while also addressing issues that are often overlooked. In turn, it shows how these issues can be easily avoided by applying the principles that have produced Deep Statistical Comparison and its variants. The focus is on statistical analyses performed using single-objective and multi-objective optimization data. At the end of the book, examples from a recently developed web-service-based e-learning tool (DSCTool) are presented. The tool provides users with all the functionalities needed to make robust statistical comparison analyses in various statistical scenarios. The book is intended for newcomers to the field and experienced researchers alike. For newcomers, it covers the basics of optimization and statistical analysis, familiarizing them with the subject matter before introducing the Deep Statistical Comparison approach. Experienced researchers can quickly move on to the content on new statistical approaches. The book is divided into three parts: Part I: Introduction to optimization, benchmarking, and statistical analysis - Chapters 2-4. Part II: Deep Statistical Comparison of meta-heuristic stochastic optimization algorithms - Chapters 5-7. Part III: Implementation and application of Deep Statistical Comparison - Chapter 8.
650
0
$a
Metaheuristics.
$3
2206834
650
0
$a
Mathematical optimization.
$3
517763
650
1 4
$a
Artificial Intelligence.
$3
769149
650
2 4
$a
Stochastic Analysis.
$3
3599427
650
2 4
$a
Statistics.
$3
517247
700
1
$a
Korosec, Peter.
$3
3331923
710
2
$a
SpringerLink (Online service)
$3
836513
773
0
$t
Springer Nature eBook
830
0
$a
Natural computing series.
$3
2057566
856
4 0
$u
https://doi.org/10.1007/978-3-030-96917-2
950
$a
Computer Science (SpringerNature-11645)
筆 0 讀者評論
館藏地:
全部
電子資源
出版年:
卷號:
館藏
1 筆 • 頁數 1 •
1
條碼號
典藏地名稱
館藏流通類別
資料類型
索書號
使用類型
借閱狀態
預約狀態
備註欄
附件
W9443346
電子資源
11.線上閱覽_V
電子書
EB QA76.9.A43 E47 2022
一般使用(Normal)
在架
0
1 筆 • 頁數 1 •
1
多媒體
評論
新增評論
分享你的心得
Export
取書館
處理中
...
變更密碼
登入