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

Archiving strategies for evolutionar...

Schutze, Oliver.

FindBook

Google Book

Amazon

博客來

Archiving strategies for evolutionary multi-objective optimization algorithms

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Archiving strategies for evolutionary multi-objective optimization algorithms/ by Oliver Schutze, Carlos Hernandez.
作者:	Schutze, Oliver.
其他作者:	Hernandez, Carlos.
出版者:	Cham :Springer International Publishing : : 2021.,
面頁冊數:	xiii, 234 p. :ill., digital ;24 cm.
內容註:	Introduction -- Multi-objective Optimization -- The Framework -- Computing the Entire Pareto Front -- Computing Gap Free Pareto Fronts -- Using Archivers within MOEAs -- Test Problems.
Contained By:	Springer Nature eBook
標題:	Computer algorithms. -
電子資源:	https://doi.org/10.1007/978-3-030-63773-6
ISBN:	9783030637736

Archiving strategies for evolutionary multi-objective optimization algorithms
Schutze, Oliver.

Archiving strategies for evolutionary multi-objective optimization algorithms[electronic resource] /by Oliver Schutze, Carlos Hernandez. - Cham :Springer International Publishing :2021. - xiii, 234 p. :ill., digital ;24 cm. - Studies in computational intelligence,v.9381860-949X ;. - Studies in computational intelligence ;v.938..

Introduction -- Multi-objective Optimization -- The Framework -- Computing the Entire Pareto Front -- Computing Gap Free Pareto Fronts -- Using Archivers within MOEAs -- Test Problems.

This book presents an overview of archiving strategies developed over the last years by the authors that deal with suitable approximations of the sets of optimal and nearly optimal solutions of multi-objective optimization problems by means of stochastic search algorithms. All presented archivers are analyzed with respect to the approximation qualities of the limit archives that they generate and the upper bounds of the archive sizes. The convergence analysis will be done using a very broad framework that involves all existing stochastic search algorithms and that will only use minimal assumptions on the process to generate new candidate solutions. All of the presented archivers can effortlessly be coupled with any set-based multi-objective search algorithm such as multi-objective evolutionary algorithms, and the resulting hybrid method takes over the convergence properties of the chosen archiver. This book hence targets at all algorithm designers and practitioners in the field of multi-objective optimization.

ISBN: 9783030637736

Standard No.: 10.1007/978-3-030-63773-6doiSubjects--Topical Terms:

523872
Computer algorithms.

LC Class. No.: QA76.9.A43 / S388 2021

Dewey Class. No.: 005.13

Archiving strategies for evolutionary multi-objective optimization algorithms
LDR:02325nmm a2200337 a 4500 001 2237557
003 DE-He213
005 20210629095512.0
006 m d
007 cr nn 008maaau
008 211111s2021 sz s 0 eng d
020 $a 9783030637736 $q (electronic bk.)
020 $a 9783030637729 $q (paper)
024 7 $a 10.1007/978-3-030-63773-6 $2 doi
035 $a 978-3-030-63773-6
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.9.A43 $b S388 2021
072 7 $a UYQ $2 bicssc
072 7 $a TEC009000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 005.13 $2 23
090 $a QA76.9.A43 $b S396 2021
100 1 $a Schutze, Oliver. $3 3409938
245 1 0 $a Archiving strategies for evolutionary multi-objective optimization algorithms $h [electronic resource] / $c by Oliver Schutze, Carlos Hernandez.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2021.
300 $a xiii, 234 p. : $b ill., digital ; $c 24 cm.
490 1 $a Studies in computational intelligence, $x 1860-949X ; $v v.938
505 0 $a Introduction -- Multi-objective Optimization -- The Framework -- Computing the Entire Pareto Front -- Computing Gap Free Pareto Fronts -- Using Archivers within MOEAs -- Test Problems.
520 $a This book presents an overview of archiving strategies developed over the last years by the authors that deal with suitable approximations of the sets of optimal and nearly optimal solutions of multi-objective optimization problems by means of stochastic search algorithms. All presented archivers are analyzed with respect to the approximation qualities of the limit archives that they generate and the upper bounds of the archive sizes. The convergence analysis will be done using a very broad framework that involves all existing stochastic search algorithms and that will only use minimal assumptions on the process to generate new candidate solutions. All of the presented archivers can effortlessly be coupled with any set-based multi-objective search algorithm such as multi-objective evolutionary algorithms, and the resulting hybrid method takes over the convergence properties of the chosen archiver. This book hence targets at all algorithm designers and practitioners in the field of multi-objective optimization.
650 0 $a Computer algorithms. $3 523872
650 0 $a Computational intelligence. $3 595739
650 0 $a Artificial intelligence. $3 516317
650 1 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Artificial Intelligence. $3 769149
700 1 $a Hernandez, Carlos. $3 3489888
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Studies in computational intelligence ; $v v.938. $3 3489889
856 4 0 $u https://doi.org/10.1007/978-3-030-63773-6
950 $a Intelligent Technologies and Robotics (SpringerNature-42732)