東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Genetic algorithm essentials

SpringerLink (Online service)

FindBook

Google Book

Amazon

博客來

Genetic algorithm essentials

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Genetic algorithm essentials/ by Oliver Kramer.
作者:	Kramer, Oliver.
出版者:	Cham :Springer International Publishing : : 2017.,
面頁冊數:	ix, 92 p. :ill., digital ;24 cm.
內容註:	Part I: Foundations -- Introduction -- Genetic Algorithms -- Parameters -- Part II: Solution Spaces -- Multimodality -- Constraints -- Multiple Objectives -- Part III: Advanced Concepts -- Theory -- Machine Learning -- Applications -- Part IV: Ending -- Summary and Outlook -- Index -- References.
Contained By:	Springer eBooks
標題:	Genetic algorithms. -
電子資源:	http://dx.doi.org/10.1007/978-3-319-52156-5
ISBN:	9783319521565

Genetic algorithm essentials
Kramer, Oliver.

Genetic algorithm essentials[electronic resource] /by Oliver Kramer. - Cham :Springer International Publishing :2017. - ix, 92 p. :ill., digital ;24 cm. - Studies in computational intelligence,v.6791860-949X ;. - Studies in computational intelligence ;v.679..

Part I: Foundations -- Introduction -- Genetic Algorithms -- Parameters -- Part II: Solution Spaces -- Multimodality -- Constraints -- Multiple Objectives -- Part III: Advanced Concepts -- Theory -- Machine Learning -- Applications -- Part IV: Ending -- Summary and Outlook -- Index -- References.

This book introduces readers to genetic algorithms (GAs) with an emphasis on making the concepts, algorithms, and applications discussed as easy to understand as possible. Further, it avoids a great deal of formalisms and thus opens the subject to a broader audience in comparison to manuscripts overloaded by notations and equations. The book is divided into three parts, the first of which provides an introduction to GAs, starting with basic concepts like evolutionary operators and continuing with an overview of strategies for tuning and controlling parameters. In turn, the second part focuses on solution space variants like multimodal, constrained, and multi-objective solution spaces. Lastly, the third part briefly introduces theoretical tools for GAs, the intersections and hybridizations with machine learning, and highlights selected promising applications.

ISBN: 9783319521565

Standard No.: 10.1007/978-3-319-52156-5doiSubjects--Topical Terms:

533907
Genetic algorithms.

LC Class. No.: QA402.5

Dewey Class. No.: 519.625

Genetic algorithm essentials
LDR:02133nmm a2200325 a 4500 001 2089725
003 DE-He213
005 20170810131730.0
006 m d
007 cr nn 008maaau
008 171013s2017 gw s 0 eng d
020 $a 9783319521565 $q (electronic bk.)
020 $a 9783319521558 $q (paper)
024 7 $a 10.1007/978-3-319-52156-5 $2 doi
035 $a 978-3-319-52156-5
040 $a GP $c GP
041 0 $a eng
050 4 $a QA402.5
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
082 0 4 $a 519.625 $2 23
090 $a QA402.5 $b .K89 2017
100 1 $a Kramer, Oliver. $3 924176
245 1 0 $a Genetic algorithm essentials $h [electronic resource] / $c by Oliver Kramer.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2017.
300 $a ix, 92 p. : $b ill., digital ; $c 24 cm.
490 1 $a Studies in computational intelligence, $x 1860-949X ; $v v.679
505 0 $a Part I: Foundations -- Introduction -- Genetic Algorithms -- Parameters -- Part II: Solution Spaces -- Multimodality -- Constraints -- Multiple Objectives -- Part III: Advanced Concepts -- Theory -- Machine Learning -- Applications -- Part IV: Ending -- Summary and Outlook -- Index -- References.
520 $a This book introduces readers to genetic algorithms (GAs) with an emphasis on making the concepts, algorithms, and applications discussed as easy to understand as possible. Further, it avoids a great deal of formalisms and thus opens the subject to a broader audience in comparison to manuscripts overloaded by notations and equations. The book is divided into three parts, the first of which provides an introduction to GAs, starting with basic concepts like evolutionary operators and continuing with an overview of strategies for tuning and controlling parameters. In turn, the second part focuses on solution space variants like multimodal, constrained, and multi-objective solution spaces. Lastly, the third part briefly introduces theoretical tools for GAs, the intersections and hybridizations with machine learning, and highlights selected promising applications.
650 0 $a Genetic algorithms. $3 533907
650 1 4 $a Engineering. $3 586835
650 2 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Artificial Intelligence (incl. Robotics) $3 890894
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
830 0 $a Studies in computational intelligence ; $v v.679. $3 3220602
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-52156-5
950 $a Engineering (Springer-11647)