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
Memetic Computation = The Mainspring...
~
SpringerLink (Online service)
Memetic Computation = The Mainspring of Knowledge Transfer in a Data-Driven Optimization Era /
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
Memetic Computation/ by Abhishek Gupta, Yew-Soon Ong.
其他題名:
The Mainspring of Knowledge Transfer in a Data-Driven Optimization Era /
作者:
Gupta, Abhishek.
其他作者:
Ong, Yew-Soon.
面頁冊數:
XI, 104 p.online resource. :
Contained By:
Springer Nature eBook
標題:
Computational intelligence. -
電子資源:
https://doi.org/10.1007/978-3-030-02729-2
ISBN:
9783030027292
Memetic Computation = The Mainspring of Knowledge Transfer in a Data-Driven Optimization Era /
Gupta, Abhishek.
Memetic Computation
The Mainspring of Knowledge Transfer in a Data-Driven Optimization Era /[electronic resource] :by Abhishek Gupta, Yew-Soon Ong. - 1st ed. 2019. - XI, 104 p.online resource. - Adaptation, Learning, and Optimization,211867-4534 ;. - Adaptation, Learning, and Optimization,18.
Introduction: Rise of Memetics in Computing -- Canonical Memetic Algorithms -- Data-Driven Adaptation in Memetic Algorithms -- The Memetic Automaton -- Sequential Knowledge Transfer across Problems -- Multitask Knowledge Transfer across Problems -- Future Direction: Meme Space Evolutions.
This book bridges the widening gap between two crucial constituents of computational intelligence: the rapidly advancing technologies of machine learning in the digital information age, and the relatively slow-moving field of general-purpose search and optimization algorithms. With this in mind, the book serves to offer a data-driven view of optimization, through the framework of memetic computation (MC). The authors provide a summary of the complete timeline of research activities in MC – beginning with the initiation of memes as local search heuristics hybridized with evolutionary algorithms, to their modern interpretation as computationally encoded building blocks of problem-solving knowledge that can be learned from one task and adaptively transmitted to another. In the light of recent research advances, the authors emphasize the further development of MC as a simultaneous problem learning and optimization paradigm with the potential to showcase human-like problem-solving prowess; that is, by equipping optimization engines to acquire increasing levels of intelligence over time through embedded memes learned independently or via interactions. In other words, the adaptive utilization of available knowledge memes makes it possible for optimization engines to tailor custom search behaviors on the fly – thereby paving the way to general-purpose problem-solving ability (or artificial general intelligence). In this regard, the book explores some of the latest concepts from the optimization literature, including, the sequential transfer of knowledge across problems, multitasking, and large-scale (high dimensional) search, systematically discussing associated algorithmic developments that align with the general theme of memetics. The presented ideas are intended to be accessible to a wide audience of scientific researchers, engineers, students, and optimization practitioners who are familiar with the commonly used terminologies of evolutionary computation. A full appreciation of the mathematical formalizations and algorithmic contributions requires an elementary background in probability, statistics, and the concepts of machine learning. A prior knowledge of surrogate-assisted/Bayesian optimization techniques is useful, but not essential.
ISBN: 9783030027292
Standard No.: 10.1007/978-3-030-02729-2doiSubjects--Topical Terms:
568984
Computational intelligence.
LC Class. No.: Q342
Dewey Class. No.: 006.3
Memetic Computation = The Mainspring of Knowledge Transfer in a Data-Driven Optimization Era /
LDR
:03991nam a22003975i 4500
001
1010410
003
DE-He213
005
20200705004435.0
007
cr nn 008mamaa
008
210106s2019 gw | s |||| 0|eng d
020
$a
9783030027292
$9
978-3-030-02729-2
024
7
$a
10.1007/978-3-030-02729-2
$2
doi
035
$a
978-3-030-02729-2
050
4
$a
Q342
072
7
$a
UYQ
$2
bicssc
072
7
$a
TEC009000
$2
bisacsh
072
7
$a
UYQ
$2
thema
082
0 4
$a
006.3
$2
23
100
1
$a
Gupta, Abhishek.
$4
aut
$4
http://id.loc.gov/vocabulary/relators/aut
$3
1246857
245
1 0
$a
Memetic Computation
$h
[electronic resource] :
$b
The Mainspring of Knowledge Transfer in a Data-Driven Optimization Era /
$c
by Abhishek Gupta, Yew-Soon Ong.
250
$a
1st ed. 2019.
264
1
$a
Cham :
$b
Springer International Publishing :
$b
Imprint: Springer,
$c
2019.
300
$a
XI, 104 p.
$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
Adaptation, Learning, and Optimization,
$x
1867-4534 ;
$v
21
505
0
$a
Introduction: Rise of Memetics in Computing -- Canonical Memetic Algorithms -- Data-Driven Adaptation in Memetic Algorithms -- The Memetic Automaton -- Sequential Knowledge Transfer across Problems -- Multitask Knowledge Transfer across Problems -- Future Direction: Meme Space Evolutions.
520
$a
This book bridges the widening gap between two crucial constituents of computational intelligence: the rapidly advancing technologies of machine learning in the digital information age, and the relatively slow-moving field of general-purpose search and optimization algorithms. With this in mind, the book serves to offer a data-driven view of optimization, through the framework of memetic computation (MC). The authors provide a summary of the complete timeline of research activities in MC – beginning with the initiation of memes as local search heuristics hybridized with evolutionary algorithms, to their modern interpretation as computationally encoded building blocks of problem-solving knowledge that can be learned from one task and adaptively transmitted to another. In the light of recent research advances, the authors emphasize the further development of MC as a simultaneous problem learning and optimization paradigm with the potential to showcase human-like problem-solving prowess; that is, by equipping optimization engines to acquire increasing levels of intelligence over time through embedded memes learned independently or via interactions. In other words, the adaptive utilization of available knowledge memes makes it possible for optimization engines to tailor custom search behaviors on the fly – thereby paving the way to general-purpose problem-solving ability (or artificial general intelligence). In this regard, the book explores some of the latest concepts from the optimization literature, including, the sequential transfer of knowledge across problems, multitasking, and large-scale (high dimensional) search, systematically discussing associated algorithmic developments that align with the general theme of memetics. The presented ideas are intended to be accessible to a wide audience of scientific researchers, engineers, students, and optimization practitioners who are familiar with the commonly used terminologies of evolutionary computation. A full appreciation of the mathematical formalizations and algorithmic contributions requires an elementary background in probability, statistics, and the concepts of machine learning. A prior knowledge of surrogate-assisted/Bayesian optimization techniques is useful, but not essential.
650
0
$a
Computational intelligence.
$3
568984
650
0
$a
Artificial intelligence.
$3
559380
650
0
$a
Mathematical optimization.
$3
527675
650
1 4
$a
Computational Intelligence.
$3
768837
650
2 4
$a
Artificial Intelligence.
$3
646849
650
2 4
$a
Optimization.
$3
669174
700
1
$a
Ong, Yew-Soon.
$4
aut
$4
http://id.loc.gov/vocabulary/relators/aut
$3
896569
710
2
$a
SpringerLink (Online service)
$3
593884
773
0
$t
Springer Nature eBook
776
0 8
$i
Printed edition:
$z
9783030027285
776
0 8
$i
Printed edition:
$z
9783030027308
830
0
$a
Adaptation, Learning, and Optimization,
$x
1867-4534 ;
$v
18
$3
1261891
856
4 0
$u
https://doi.org/10.1007/978-3-030-02729-2
912
$a
ZDB-2-INR
912
$a
ZDB-2-SXIT
950
$a
Intelligent Technologies and Robotics (SpringerNature-42732)
950
$a
Intelligent Technologies and Robotics (R0) (SpringerNature-43728)
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