國立高雄大學圖資館 |

Language: English

Back

Genetic programming for production s...

SpringerLink (Online service)

Genetic programming for production schedulingan evolutionary learning approach /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Genetic programming for production schedulingby Fangfang Zhang ... [et al.].
Reminder of title:	an evolutionary learning approach /
Author:	Zhang, Fangfang.
Published:	Singapore :Springer Singapore :2021.
Description:	xxxiii, 336 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Computer scheduling.
Online resource:	https://doi.org/10.1007/978-981-16-4859-5
ISBN:	9789811648595$q(electronic bk.)

Genetic programming for production schedulingan evolutionary learning approach /
Zhang, Fangfang.

Genetic programming for production schedulingan evolutionary learning approach /[electronic resource] :by Fangfang Zhang ... [et al.]. - Singapore :Springer Singapore :2021. - xxxiii, 336 p. :ill. (some col.), digital ;24 cm. - Machine learning: foundations, methodologies, and applications,2730-9916. - Machine learning: foundations, methodologies, and applications..

This book introduces readers to an evolutionary learning approach, specifically genetic programming (GP), for production scheduling. The book is divided into six parts. In Part I, it provides an introduction to production scheduling, existing solution methods, and the GP approach to production scheduling. Characteristics of production environments, problem formulations, an abstract GP framework for production scheduling, and evaluation criteria are also presented. Part II shows various ways that GP can be employed to solve static production scheduling problems and their connections with conventional operation research methods. In turn, Part III shows how to design GP algorithms for dynamic production scheduling problems and describes advanced techniques for enhancing GP's performance, including feature selection, surrogate modeling, and specialized genetic operators. In Part IV, the book addresses how to use heuristics to deal with multiple, potentially conflicting objectives in production scheduling problems, and presents an advanced multi-objective approach with cooperative coevolution techniques or multi-tree representations. Part V demonstrates how to use multitask learning techniques in the hyper-heuristics space for production scheduling. It also shows how surrogate techniques and assisted task selection strategies can benefit multitask learning with GP for learning heuristics in the context of production scheduling. Part VI rounds out the text with an outlook on the future. Given its scope, the book benefits scientists, engineers, researchers, practitioners, postgraduates, and undergraduates in the areas of machine learning, artificial intelligence, evolutionary computation, operations research, and industrial engineering.

ISBN: 9789811648595$q(electronic bk.)

Standard No.: 10.1007/978-981-16-4859-5doiSubjects--Topical Terms:

264980
Computer scheduling.

LC Class. No.: TS157.5

Dewey Class. No.: 004.35

Genetic programming for production schedulingan evolutionary learning approach /
LDR:02858nmm 22003255a 4500 001 613838
003 DE-He213
005 20211112150616.0
006 m d
007 cr nn 008maaau
008 220627s2021 si s 0 eng d
020 $a 9789811648595$q(electronic bk.)
020 $a 9789811648588$q(paper)
024 7 $a 10.1007/978-981-16-4859-5 $2 doi
035 $a 978-981-16-4859-5
040 $a GP $c GP
041 0 $a eng
050 4 $a TS157.5
072 7 $a UYQM $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQM $2 thema
082 0 4 $a 004.35 $2 23
090 $a TS157.5 $b .G328 2021
100 1 $a Zhang, Fangfang. $3 911918
245 0 0 $a Genetic programming for production scheduling $h [electronic resource] : $b an evolutionary learning approach / $c by Fangfang Zhang ... [et al.].
260 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2021.
300 $a xxxiii, 336 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Machine learning: foundations, methodologies, and applications, $x 2730-9916
520 $a This book introduces readers to an evolutionary learning approach, specifically genetic programming (GP), for production scheduling. The book is divided into six parts. In Part I, it provides an introduction to production scheduling, existing solution methods, and the GP approach to production scheduling. Characteristics of production environments, problem formulations, an abstract GP framework for production scheduling, and evaluation criteria are also presented. Part II shows various ways that GP can be employed to solve static production scheduling problems and their connections with conventional operation research methods. In turn, Part III shows how to design GP algorithms for dynamic production scheduling problems and describes advanced techniques for enhancing GP's performance, including feature selection, surrogate modeling, and specialized genetic operators. In Part IV, the book addresses how to use heuristics to deal with multiple, potentially conflicting objectives in production scheduling problems, and presents an advanced multi-objective approach with cooperative coevolution techniques or multi-tree representations. Part V demonstrates how to use multitask learning techniques in the hyper-heuristics space for production scheduling. It also shows how surrogate techniques and assisted task selection strategies can benefit multitask learning with GP for learning heuristics in the context of production scheduling. Part VI rounds out the text with an outlook on the future. Given its scope, the book benefits scientists, engineers, researchers, practitioners, postgraduates, and undergraduates in the areas of machine learning, artificial intelligence, evolutionary computation, operations research, and industrial engineering.
650 0 $a Computer scheduling. $3 264980
650 0 $a Genetic programming (Computer science) $3 206263
650 0 $a Machine learning. $3 188639
650 1 4 $a Machine Learning. $3 833608
650 2 4 $a Knowledge based Systems. $3 835019
650 2 4 $a Industrial and Production Engineering. $3 273753
650 2 4 $a Operations Research/Decision Theory. $3 273963
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
830 0 $a Machine learning: foundations, methodologies, and applications. $3 911919
856 4 0 $u https://doi.org/10.1007/978-981-16-4859-5
950 $a Computer Science (SpringerNature-11645)